Wireless communication method and device

Abstract

According to the wireless communication method and device provided by the embodiment of the invention, the first device and the second device can flexibly interact AI / ML-related capability information, so that the communication device can conveniently and finely apply the AI / ML function.

Description

Wireless communication method and device Technical Field

[0001] The present invention relates to the field of communications, and more specifically, to a method and device for wireless communications. Background Art

[0002] With the continuous development of artificial intelligence (AI) and machine learning (ML) technologies, the integration of communication technologies with AI / ML technologies is one of the future trends in communications. However, the existing capability definitions of communication systems do not take AI / ML features into account. Therefore, the capabilities used for AI / ML features need to be reorganized and redefined.

[0003] Summary of the Invention

[0004] Embodiments of the present application provide a wireless communication method and device, which enable flexible exchange of AI / ML-related capability information between a first device and a second device, thereby facilitating the refined application of AI / ML functions by communication devices.

[0005] In a first aspect, a wireless communication method is provided, the method comprising:

[0006] The first device sends a first message to the second device;

[0007] The first message includes first AI / ML-related capability information, the first AI / ML-related capability information is associated with the first device, and the first AI / ML-related capability information includes M pieces of information, where M is a positive integer;

[0008] Among them, the M information is used to indicate at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capabilities for AI / ML purposes, whether the first device supports data reporting capabilities for AI / ML purposes, whether the first device supports data measurement capabilities for AI / ML purposes, whether the first device supports offline AI / ML model training capabilities, whether the first device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the first device, whether the first device supports AI / ML model reasoning capabilities, whether the first device supports AI / ML model switching capabilities, whether the first device supports AI / ML model activation or deactivation capabilities, whether the first device supports AI / ML model performance monitoring capabilities, whether the first device supports AI / ML model transmission capabilities, and whether the first device supports AI / ML model update capabilities.

[0009] In a second aspect, a wireless communication method is provided, the method comprising:

[0010] The second device receives the first message sent by the first device;

[0011] The first message includes first AI / ML-related capability information, the first AI / ML-related capability information is associated with the first device, and the first AI / ML-related capability information includes M pieces of information, where M is a positive integer;

[0012] Among them, the M information is used to indicate at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capabilities for AI / ML purposes, whether the first device supports data reporting capabilities for AI / ML purposes, whether the first device supports data measurement capabilities for AI / ML purposes, whether the first device supports offline AI / ML model training capabilities, whether the first device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the first device, whether the first device supports AI / ML model reasoning capabilities, whether the first device supports AI / ML model switching capabilities, whether the first device supports AI / ML model activation or deactivation capabilities, whether the first device supports AI / ML model performance monitoring capabilities, whether the first device supports AI / ML model transmission capabilities, and whether the first device supports AI / ML model update capabilities.

[0013] In a third aspect, a communication device is provided for executing the method in the first aspect.

[0014] Specifically, the communication device includes a functional module for executing the method in the above-mentioned first aspect.

[0015] In a fourth aspect, a communication device is provided for executing the method in the second aspect.

[0016] Specifically, the communication device includes a functional module for executing the method in the above-mentioned second aspect.

[0017] In a fifth aspect, a communication device is provided, comprising a processor and a memory; the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, so that the communication device executes the method in the above-mentioned first aspect.

[0018] In a sixth aspect, a communication device is provided, comprising a processor and a memory; the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, so that the communication device executes the method in the above-mentioned second aspect.

[0019] In a seventh aspect, a device is provided for implementing the method in any one of the first to second aspects above.

[0020] Specifically, the apparatus includes: a processor, configured to call and run a computer program from a memory, so that a device equipped with the apparatus executes the method in any one of the first to second aspects described above.

[0021] In an eighth aspect, a computer-readable storage medium is provided for storing a computer program, wherein the computer program enables a computer to execute the method in any one of the first to second aspects above.

[0022] In a ninth aspect, a computer program product is provided, comprising computer program instructions, wherein the computer program instructions enable a computer to execute the method in any one of the first to second aspects above.

[0023] In a tenth aspect, a computer program is provided, which, when executed on a computer, enables the computer to execute the method in any one of the first to second aspects above.

[0024] Through the above technical solution, the first device can send first AI / ML-related capability information to the second device, where the M information included in the first AI / ML-related capability information is used to indicate at least one of the following: whether the first device is deployed with a functional entity that processes AI / ML-related operations, whether the first device supports the capability of configuring the functional entity that processes AI / ML-related operations on demand, whether the first device supports data collection capability for AI / ML purposes, whether the first device supports data reporting capability for AI / ML purposes, whether the first device supports data measurement capability for AI / ML purposes, whether the first device supports offline AI / ML model training capability, whether the first device supports online AI / ML model training capability, AI / ML model running or compilation format supported by the first device, whether the first device supports AI / ML model reasoning capability, whether the first device supports AI / ML model switching capability, whether the first device supports AI / ML model activation or deactivation capability, whether the first device supports AI / ML model performance monitoring capability, whether the first device supports AI / ML model transmission capability, and whether the first device supports AI / ML model update capability. That is, through the above technical solution, the first device and the second device can flexibly exchange AI / ML-related capability information, which facilitates the communication device to apply AI / ML functions in a refined manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG1 is a schematic diagram of a communication system architecture applied in an embodiment of the present application.

[0026] FIG2 is a schematic flowchart of a wireless communication method provided according to an embodiment of the present application.

[0027] FIG3 is a schematic block diagram of a wireless communication device provided according to an embodiment of the present application.

[0028] FIG4 is a schematic block diagram of a wireless communication device provided according to an embodiment of the present application.

[0029] FIG5 is a schematic block diagram of a communication device provided according to an embodiment of the present application.

[0030] FIG6 is a schematic block diagram of a device provided according to an embodiment of the present application.

[0031] FIG7 is a schematic block diagram of a communication system provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] The following will describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. With respect to the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0033] The technical solutions of the embodiments of the present application can be applied to various communication systems, such as: Global System of Mobile communication (GSM) system, Code Division Multiple Access (CDMA) system, Wideband Code Division Multiple Access (WCDMA) system, General Packet Radio Service (GPRS), Long Term Evolution (LTE) system, Advanced Long Term Evolution (LTE-A) system, New Radio (NR) system, NR system evolution system, LTE-based access to unlicensed spectrum (LTE-U) system on unlicensed spectrum, NR-based access to unlicensed spectrum (NR-U) system on unlicensed spectrum, Non-Terrestrial Networks (NTN) system, Universal Mobile Telecommunication System (UMTS), Wireless Local Area Networks (WLAN), Internet of Things (IoT), Wireless Fidelity (WFI) system. Fidelity, WiFi), fifth-generation communication (5th-Generation, 5G) system, sixth-generation communication (6G) system or other communication systems.

[0034] Generally speaking, traditional communication systems support a limited number of connections and are easy to implement. However, with the development of communication technology, mobile communication systems will not only support traditional communications, but will also support, for example, device-to-device (D2D) communication, machine-to-machine (M2M) communication, machine type communication (MTC), vehicle-to-vehicle (V2V) communication, sidelink (SL) communication, vehicle-to-everything (V2X) communication, etc. The embodiments of the present application can also be applied to these communication systems.

[0035] In some embodiments, the communication system in the embodiments of the present application can be applied to a carrier aggregation (CA) scenario, a dual connectivity (DC) scenario, an independent (SA) networking scenario, or a non-standalone (NSA) networking scenario.

[0036] In some embodiments, the communication system in the embodiments of the present application can be applied to an unlicensed spectrum, where the unlicensed spectrum can also be considered as a shared spectrum; or, the communication system in the embodiments of the present application can also be applied to an authorized spectrum, where the authorized spectrum can also be considered as an unshared spectrum.

[0037] In some embodiments, the communication system in the embodiments of the present application can be applied to the FR1 frequency band (corresponding to the frequency band range of 410MHz to 7.125GHz), can also be applied to the FR2 frequency band (corresponding to the frequency band range of 24.25GHz to 52.6GHz), and can also be applied to new frequency bands such as high-frequency bands corresponding to the frequency band range of 52.6GHz to 71GHz or the frequency band range of 71GHz to 114.25GHz.

[0038] The embodiments of the present application describe various embodiments in conjunction with network devices and terminal devices, wherein the terminal device may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device, etc.

[0039] The terminal device can be a station (ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a next-generation communication system such as an NR network, or a terminal device in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0040] In an embodiment of the present application, the terminal device can be deployed on land, including indoors or outdoors, handheld, wearable or vehicle-mounted; it can also be deployed on the water surface (such as ships, etc.); it can also be deployed in the air (such as airplanes, balloons and satellites, etc.).

[0041] In an embodiment of the present application, the terminal device may be a mobile phone, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city or a wireless terminal device in a smart home, an in-vehicle communication device, a wireless communication chip / application specific integrated circuit (ASIC) / system on chip (SoC), etc.

[0042] As an example and not a limitation, in the embodiment of the present application, the terminal device may also be a wearable device. Wearable devices may also be called wearable smart devices, which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0043] In an embodiment of the present application, the network device may be a device for communicating with a mobile device. The network device may be an access point (AP) in WLAN, a base station (BTS) in GSM or CDMA, a base station (NodeB, NB) in WCDMA, an evolved base station (eNB or eNodeB) in LTE, or a relay station or access point, or a network device or base station (gNB) or a transmission reception point (TRP) in a vehicle-mounted device, a wearable device, and an NR network, or a network device in a future evolved PLMN network or a network device in an NTN network, etc.

[0044] As an example and not a limitation, in an embodiment of the present application, the network device may have a mobile feature, for example, the network device may be a mobile device. In some embodiments, the network device may be a satellite or a balloon station. For example, the satellite may be a low earth orbit (LEO) satellite, a medium earth orbit (MEO) satellite, a geostationary earth orbit (GEO) satellite, a high elliptical orbit (HEO) satellite, etc. In some embodiments, the network device may also be a base station set up in a location such as land or water.

[0045] In an embodiment of the present application, the network device can provide services for a cell, and the terminal device communicates with the network device through the transmission resources used by the cell (for example, frequency domain resources, or spectrum resources). The cell can be a cell corresponding to the network device (for example, a base station). The cell can belong to a macro base station or a base station corresponding to a small cell. The small cells here may include: metro cells, micro cells, pico cells, femto cells, etc. These small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data transmission services.

[0046] For example, a communication system 100 used in an embodiment of the present application is shown in FIG1 . The communication system 100 may include a network device 110, which may be a device that communicates with a terminal device 120 (or a communication terminal or terminal). The network device 110 may provide communication coverage for a specific geographic area and may communicate with terminal devices within the coverage area.

[0047] FIG1 exemplarily shows a network device and two terminal devices. In some embodiments, the communication system 100 may include multiple network devices and each network device may include another number of terminal devices within its coverage area, which is not limited in the embodiments of the present application.

[0048] In some embodiments, the communication system 100 may further include other network entities such as a network controller and a mobility management entity, which is not limited in the embodiments of the present application.

[0049] It should be understood that in the embodiments of the present application, a device having a communication function in a network / system may be referred to as a communication device. Taking the communication system 100 shown in FIG1 as an example, the communication device may include a network device 110 and a terminal device 120 having a communication function. The network device 110 and the terminal device 120 may be the specific devices described above and will not be described in detail here. The communication device may also include other devices in the communication system 100, such as a network controller, a mobility management entity, and other network entities, which are not limited in the embodiments of the present application.

[0050] It should be understood that the terms "system" and "network" are often used interchangeably herein. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates that the related objects are in an "or" relationship.

[0051] It should be understood that this article involves terminal devices and network devices, among which terminal devices include mobile phones, machine facilities, customer premises equipment (CPE), industrial equipment, vehicles, etc.; network devices can be access network devices (such as gNB), core network devices, etc.

[0052] The terms used in the embodiments of this application are intended only to explain the specific embodiments of this application and are not intended to limit this application. The terms "first," "second," "third," and "fourth," etc. in the specification and claims of this application and the accompanying drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions.

[0053] It should be understood that the "indication" mentioned in the embodiments of this application can be a direct indication, an indirect indication, or an indication of an association. For example, "A indicates B" can mean that A directly indicates B, for example, B can be obtained through A; it can also mean that A indirectly indicates B, for example, A indicates C, and B can be obtained through C; it can also mean that there is an association between A and B.

[0054] In the description of the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship between indication and being indicated, configuration and being configured, etc.

[0055] In the embodiments of the present application, "pre-definition" or "pre-configuration" may be implemented by pre-storing corresponding codes, tables, or other methods that can be used to indicate relevant information in a device (e.g., a terminal device and a network device). The present application does not limit the specific implementation method. For example, pre-definition may refer to information defined in a protocol.

[0056] In the embodiments of the present application, the “protocol” may refer to a standard protocol in the communications field, for example, it may be an evolution of an existing LTE protocol, NR protocol, Wi-Fi protocol, or a protocol related to other communications systems. The present application does not limit the protocol type.

[0057] To facilitate understanding of the technical solutions of the embodiments of the present application, the technical solutions of the present application are described in detail below through specific embodiments. The following related technologies can be combined with the technical solutions of the embodiments of the present application as optional solutions, and they all fall within the scope of protection of the embodiments of the present application. The embodiments of the present application include at least part of the following contents.

[0058] To facilitate a better understanding of the embodiments of the present application, the terminal capabilities related to the present application are described.

[0059] The capabilities of a terminal device are generally used to indicate which functions the terminal device can support and / or which functions it cannot support. The network device can configure or activate corresponding functions for the terminal device based on the acquired terminal device capabilities.

[0060] In order to facilitate a better understanding of the embodiments of the present application, the problems solved by the present application are explained.

[0061] With the continuous development of artificial intelligence (AI) and machine learning (ML) technologies, the integration of communication technologies with AI / ML technologies is one of the future trends in communications. However, the existing capability definitions of communication systems do not take AI / ML features into account. Therefore, the capabilities used for AI / ML features need to be reorganized and redefined.

[0062] Based on the above problems, this application proposes a solution for reporting AI / ML-related capability information, which can flexibly exchange AI / ML-related capability information between the first device and the second device, facilitating the communication device to apply AI / ML functions in a refined manner.

[0063] The technical solution of this application is described in detail below through specific embodiments.

[0064] FIG2 is a schematic flowchart of a wireless communication method 200 according to an embodiment of the present application. As shown in FIG2 , the wireless communication method 200 may include at least part of the following contents:

[0065] S210. The first device sends a first message to the second device; wherein the first message includes first AI / ML-related capability information, the first AI / ML-related capability information is associated with the first device, and the first AI / ML-related capability information includes M pieces of information, where M is a positive integer; wherein the M pieces of information are used to indicate at least one of the following: whether the first device is deployed with a functional entity that processes AI / ML-related operations, whether the first device supports on-demand configuration of a functional entity that processes AI / ML-related operations, whether the first device supports a data collection capability for AI / ML purposes, and whether the first device supports a data reporting capability for AI / ML purposes. whether the first device supports data measurement capabilities for AI / ML purposes, whether the first device supports offline AI / ML model training capabilities, whether the first device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the first device, whether the first device supports AI / ML model inference capabilities, whether the first device supports AI / ML model switching capabilities, whether the first device supports AI / ML model activation or deactivation capabilities, whether the first device supports AI / ML model performance monitoring capabilities, whether the first device supports AI / ML model transmission capabilities, and whether the first device supports AI / ML model update capabilities;

[0066] S220: The second device receives the first message.

[0067] In the embodiment of the present application, the first device and the second device can flexibly exchange AI / ML-related capability information, which facilitates the communication device to fine-tune the application of AI / ML functions.

[0068] In some embodiments, the first device is a terminal device, and the second device is a network device. The network device can be an access network device or a core network device. That is, the terminal device can report its associated AI / ML-related capability information, so that the network device can deploy an AI / ML model or apply an AI / ML function based on the AI / ML-related capability information associated with the terminal device.

[0069] In some embodiments, the first device is a network device, and the second device is a terminal device. The network device may be an access network device or a core network device. That is, the network device may transmit its associated AI / ML-related capability information, so that the terminal device may deploy an AI / ML model or apply an AI / ML function based on the AI / ML-related capability information associated with the network device.

[0070] In some embodiments, the first device is a terminal device, and the second device is another terminal device. That is, one terminal device can send its associated AI / ML-related capability information, so that the other terminal device can deploy an AI / ML model or apply an AI / ML function based on the AI / ML-related capability information.

[0071] In some embodiments, the first device is a network device, and the second device is another network device. For example, the first device is an access network device, and the second device is a core network device or another access network device. In another example, the first device is a core network device, and the second device is an access network device. In another example, the first device is a core network element, and the second device is another core network element. That is, one network device can transmit its associated AI / ML-related capability information, so that the other network device can deploy an AI / ML model or apply AI / ML functions based on the AI / ML-related capability information.

[0072] In some embodiments, the first message is one of the following: a non-access stratum (NAS) message, an access stratum (AS) message, an interface message, or an AL / ML dedicated message. Optionally, the AS message may be an RRC message, an L2 message, or an L1 message, and the interface message may be an NG message, an Xn message, an F1 message, or an E1 message.

[0073] In some embodiments, the M pieces of information include first information, which is used to indicate whether the first device is deployed with a functional entity that processes AI / ML related operations.

[0074] In some embodiments, the first information includes a first bit; wherein the first bit is used to indicate whether the first device as a whole deploys a functional entity for processing AI / ML-related operations, and some or all functional units within the first device share the capability indicated by the first bit. In other words, AI / ML-related operations involved in each functional unit within the first device can be processed by the functional entity for processing AI / ML-related operations.

[0075] In some embodiments, the first information is associated with AI / ML functional entity type indication information, where the AI / ML functional entity type indication information is used to indicate the type of functional entity that processes AI / ML-related operations. Optionally, the AI / ML functional entity type indication information associated with the first information is configured by default in the protocol or explicitly configured via an additional information field included in the first information.

[0076] In some embodiments, the first information includes n1 groups of sub-information, each group of sub-information in the n1 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n1 is a positive integer;

[0077] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information deploys a functional entity for processing AI / ML-related operations; or

[0078] When each group of sub-information includes at least two bits, each group of sub-information includes a first information field and a second information field, the first information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information, and the second information field included in the i-th group of sub-information is used to indicate the type of the functional entity for processing AI / ML-related operations deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information; or

[0079] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a third information field, and the third information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in one or a group of functional units within the first device associated with the i-th group of sub-information, and to indicate the type of the functional entity for processing AI / ML-related operations deployed in the one or a group of functional units within the first device associated with the i-th group of sub-information; or

[0080] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity deployed in one or a group of functional units within the first device associated with the i-th group of sub-information and for processing AI / ML-related operations;

[0081] Wherein, i is a positive integer, and 1≤i≤n1.

[0082] In some embodiments, the first information field includes one bit, and / or the second information field includes at least one bit.

[0083] In some embodiments, each group of sub-information includes a fourth information field, and the fourth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0084] In some embodiments, the functional unit or units within the first device associated with each set of sub-information in the n1 sets of sub-information are agreed upon by a protocol.

[0085] In some embodiments, the first information includes information about the type of a functional entity deployed on the first device that processes an AI / ML-related operation. In some embodiments, the type of the functional entity for processing the AI / ML-related operation is divided based on the source of the processable AI / ML-related operation, or the type of the functional entity for processing the AI / ML-related operation is divided based on the type of the processable AI / ML-related operation.

[0086] In some embodiments, when the types of functional entities used to process AI / ML-related operations are divided based on the source of the processable AI / ML-related operations, the types of functional entities used to process AI / ML-related operations include at least one of the following:

[0087] An AI / ML functional entity that processes AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity, an AI / ML functional entity that processes AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity, and an AI / ML functional entity that can process AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity and can also process AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity.

[0088] In some embodiments, when the types of functional entities used to process AI / ML-related operations are divided based on the types of AI / ML-related operations that can be processed, different types of AI / ML functional entities support different sets of AI / ML-related operation tasks.

[0089] In some embodiments, the AI / ML-related operations include at least one of the following operational tasks: data management tasks, storage management tasks, computing power management tasks, and model management tasks. Optionally, the data management tasks include, but are not limited to, at least one of the following: data collection, data storage, data modification, data update, data deletion, data replication, and data forwarding. Optionally, the storage management tasks include, but are not limited to, at least one of the following: remaining storage size indication, storage reservation, storage allocation, storage sharing, storage recycling, and storage formatting. Optionally, the computing power management tasks include, but are not limited to, at least one of the following: remaining computing power indication, computing power reservation, computing power allocation, computing power sharing, and computing power recycling. Optionally, the model management tasks include, but are not limited to, at least one of the following: model training, model verification, model testing, model deployment, model replication, model forwarding, model inference, model monitoring, model update, model activation, model deactivation, model deletion, and model switching.

[0090] For example, the first information contains 3 bits, and the first information contains three groups of sub-information, each group of sub-information contains one bit, and each group of sub-information is associated with one or a group of functional units inside the first device, that is, each group of sub-information is used to indicate whether the functional unit inside the one or a group of first devices associated with the group of sub-information is deployed with a functional entity that processes AI / ML related operations. For example, the first group of sub-information of the first information (that is, the first bit of the first information) is associated with the physical layer (PHY) functional unit inside the first device, the second group of sub-information of the first information is associated with the media access control (MAC) and radio link control (RLC) functional units inside the first device, and the third group of sub-information of the first information is associated with the radio resource control (RRC) functional unit inside the first device.

[0091] For another specific example, the first information contains 9 bits, and the first information contains three groups of sub-information, each group of sub-information contains 3 bits and correspondingly contains a first information field and a second information field, the first information field contains one bit, and the second information field contains 2 bits. The second information field can indicate up to 4 types of functional entities used to process AI / ML related operations.

[0092] For another specific example, the first information contains 6 bits, and the first information contains three groups of sub-information, each group of sub-information contains 2 bits, each group of sub-information is associated with one or a group of functional units inside the first device, and each value of each group of sub-information corresponds to a type of AI / ML functional entity (here the two bits of each group of sub-information have four values ​​'00', '01', '10', and '11', and some values ​​are allowed to have no specific meaning defined), wherein the value '00' indicates that the functional unit inside the one or a group of first devices associated with this group of sub-information is not deployed with a functional entity for processing AI / ML related operations, and the values ​​'01', '10', and '11' indicate that the functional unit inside the one or a group of first devices associated with this group of sub-information is deployed with a functional entity for processing AI / ML related operations, and each value of '01', '10', and '11' corresponds to a type of functional entity for processing AI / ML related operations.

[0093] In some embodiments, the M pieces of information include second information, where the second information is used to indicate whether the first device supports the ability to configure on-demand functional entities for processing AI / ML related operations.

[0094] In some embodiments, the second information includes a second bit; wherein the second bit is used to indicate whether the first device supports the capability of configuring a functional entity for processing AI / ML-related operations on demand, and some or all functional units within the first device share the capability indicated by the second bit. That is, the functional units within the first device share this capability (of course, the set of functional units sharing this capability may also be a subset of the set of all functional units supported within the first device), that is, the capability either indicates that all functional units within the first device support the capability of configuring a functional entity for processing AI / ML-related operations on demand, or indicates that none of the functional units within the first device support the capability of configuring a functional entity for processing AI / ML-related operations on demand.

[0095] In some embodiments, the second information is associated with information indicating an AI / ML functional entity type that can be configured on demand, wherein the information indicating the AI / ML functional entity type that can be configured on demand is used to indicate the type of the AI / ML functional entity that can be configured on demand. Optionally, the AI / ML functional entity type indication information is configured by default in the protocol or explicitly configured through an additional information field included in the second information.

[0096] In some embodiments, the second information includes n2 groups of sub-information, each group of sub-information in the n2 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n2 is a positive integer;

[0097] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations; or

[0098] When each group of sub-information includes at least two bits, each group of sub-information includes a fifth information field and a sixth information field, the fifth information field included in the i-th group of sub-information is used to indicate whether the functional unit within one or a group of first devices associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and the sixth information field included in the i-th group of sub-information is used to indicate a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional unit within one or a group of first devices associated with the i-th group of sub-information; or

[0099] When each group of sub-information includes at least one bit, each group of sub-information includes a seventh information field, and the seventh information field included in the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and indicates a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional units or the group of functional units within the first device associated with the i-th group of sub-information; or

[0100] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity configured on demand for processing AI / ML-related operations in one or a group of functional units within the first device associated with the i-th group of sub-information;

[0101] Wherein, i is a positive integer, and 1≤i≤n2.

[0102] In some embodiments, the fifth information field includes one bit, and / or the sixth information field includes at least one bit.

[0103] In some embodiments, each bit of the sixth information field is associated with one or a group of AI / ML functional entities, and the value of each bit contained in the sixth information field determines whether the associated one or a group of AI / ML functional entities support on-demand configuration; or, each bit state value of the sixth information field is associated with one or a group of AI / ML functional entities, and each bit state value of the sixth information field indicates that the corresponding associated one or a group of AI / ML functional entities support on-demand configuration.

[0104] In some embodiments, the type or group of AI / ML functional entities associated with each bit of the sixth information field is agreed upon by the protocol, or the type or group of AI / ML functional entities associated with each bit state value of the sixth information field is agreed upon by the protocol.

[0105] In some embodiments, each group of sub-information includes an eighth information field, and the eighth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0106] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n2 groups of sub-information are agreed upon by a protocol.

[0107] In some embodiments, the second information includes type information of at least one functional entity that processes AI / ML-related operations supported by the first device.

[0108] For example, the second information includes 3 bits, and the second information includes three groups of sub-information, each group of sub-information includes one bit, and each group of sub-information is associated with one or a group of functional units inside the first device, that is, each group of sub-information is used to indicate whether the functional units inside the first device or a group of functional units associated with the group of sub-information support the ability to configure the functional entity for processing AI / ML-related operations on demand. For example, the first group of sub-information of the second information (that is, the first bit of the second information) is associated with the PHY functional unit inside the first device, the second group of sub-information of the second information (that is, the second bit of the second information) is associated with the MAC and RLC functional units inside the first device, and the third group of sub-information of the second information (that is, the third bit of the second information) is associated with the RRC functional unit inside the first device.

[0109] For another specific example, the second information contains 9 bits, and the second information contains three groups of sub-information, each group of sub-information contains 3 bits and correspondingly contains the fifth information field and the sixth information field, the fifth information field contains one bit, the sixth information field contains 2 bits, and each bit of the sixth information field is associated with one or a group of types of AI / ML functional entities. For example, the value of the sixth information field is '01', indicating that the first bit of the sixth information field is associated with one or a group of types of AI / ML functional entities that do not support on-demand configuration, while the second bit of the sixth information field is associated with one or a group of types of AI / ML functional entities that support on-demand configuration.

[0110] For another specific example, the second information contains 9 bits, and the second information contains three groups of sub-information, each group of sub-information contains 3 bits and correspondingly contains the fifth information field and the sixth information field, the fifth information field contains one bit, the sixth information field contains 2 bits, the sixth information field has 4 values, each value is associated with one or a group of types of AI / ML functional entities, for example, the value of the sixth information field '01' indicates that the one or a group of types of AI / ML functional entities associated with the value '01' support on-demand configuration.

[0111] For another specific example, the second information includes 6 bits, and the second information includes three groups of sub-information, each group of sub-information includes two bits, each group of sub-information is associated with one or a group of functional units inside the first device, and each value of each group of sub-information corresponds to a type of AI / ML functional entity (here the two bits of each group of sub-information have four values ​​'00', '01', '10', '11', and some values ​​are allowed to have no specific meaning), wherein the value '00' indicates that the functional unit inside the one or a group of first devices associated with this group of sub-information does not support on-demand configuration of functional entities for processing AI / ML related operations, and the values ​​'01', '10', '11' indicate that the functional units inside the one or a group of first devices associated with this group of sub-information support on-demand configuration of functional entities for processing AI / ML related operations, and each value of '01', '10', '11' corresponds to one or a group of types of functional entities for processing AI / ML related operations.

[0112] In some embodiments, the second information is associated with configuration mode information, wherein the configuration mode information is used to indicate a method for triggering establishment of an on-demand configured AI / ML functional entity.

[0113] In some embodiments, the method of triggering the establishment of the on-demand configured AI / ML functional entity includes at least one of the following: the first device autonomously triggers the establishment of the on-demand configured AI / ML functional entity, the second device actively requests the first device to establish the on-demand configured AI / ML functional entity, and the first device initiates the establishment request and obtains confirmation from the second device before the first device establishes the on-demand configured AI / ML functional entity.

[0114] In some embodiments, the configuration mode information associated with the second information is specified by a protocol. For example, the protocol directly specifies that the configuration mode information associated with the second information means that the on-demand AI / ML functional entity is triggered by the second device, i.e., all on-demand AI / ML functional entities determined by the second information can only be established on the first device by triggering the second device.

[0115] In some embodiments, the second information includes a ninth information field, and the ninth information field is used to indicate configuration mode information associated with the second information.

[0116] In some embodiments, when the ninth information field is used to indicate the configuration mode information associated with the second information, the indication granularity of the ninth information field includes one of the following: the first device granularity, the sub-information group granularity contained in the second information, and the AI / ML functional entity type granularity configured on demand.

[0117] In some embodiments, when the indication granularity of the ninth information field is the granularity of the first device, the configuration modes corresponding to all on-demand AI / ML functional entities supported by the first device are the same. That is, the configuration modes corresponding to all on-demand AI / ML functional entities supported by the first device are the same, and the specific configuration mode is provided by the common configuration mode information.

[0118] In some embodiments, when the indication granularity of the ninth information field is the granularity of the sub-information group contained in the second information, the configuration modes corresponding to all on-demand configured AI / ML functional entities supported by each sub-information group contained in the second information are the same. That is, the configuration is performed according to the granularity of the sub-information group of the second information (each group of sub-information is associated with one or a group of functional units within the first device, so it can also be said to be configured according to the granularity of the functional units within the first device). In this manner, regardless of whether the type of on-demand configured AI / ML functional entity supported by a certain sub-information group of the second information is one or more, the configuration mode corresponding to all on-demand configured AI / ML functional entities supported by the group of sub-information is the same, and the specific configuration mode is given by the configuration mode information associated with the sub-information group.

[0119] In some embodiments, when the indication granularity of the ninth information field is the granularity of the on-demand configurable AI / ML functional entity type, each on-demand configurable AI / ML functional entity type supported by the second information is individually associated with a piece of configuration mode information indicating the configuration mode supported by that type of AI / ML functional entity. That is, each on-demand configurable AI / ML functional entity type supported by the second information is individually associated with a piece of configuration mode information indicating the configuration mode supported by that type of AI / ML functional entity.

[0120] In some embodiments, the types of functional entities for processing AI / ML related operations are divided based on the sources of the processable AI / ML related operations, or the types of functional entities for processing AI / ML related operations are divided based on the types of the processable AI / ML related operations.

[0121] In some embodiments, when the type of the functional entity for processing AI / ML-related operations is divided based on the source of the processable AI / ML-related operations, the type of the functional entity for processing AI / ML-related operations includes at least one of the following:

[0122] An AI / ML functional entity that processes AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity, an AI / ML functional entity that processes AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity, and an AI / ML functional entity that can process AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity and can also process AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity.

[0123] In some embodiments, when the types of functional entities for processing AI / ML-related operations are divided based on the types of AI / ML-related operations that can be processed, different types of AI / ML functional entities support different sets of AI / ML-related operation tasks.

[0124] In some embodiments, the AI / ML-related operations include at least one of the following operational tasks: data management tasks, storage management tasks, computing power management tasks, and model management tasks. Optionally, the data management tasks include, but are not limited to, at least one of the following: data collection, data storage, data modification, data update, data deletion, data replication, and data forwarding. Optionally, the storage management tasks include, but are not limited to, at least one of the following: remaining storage size indication, storage reservation, storage allocation, storage sharing, storage recycling, and storage formatting. Optionally, the computing power management tasks include, but are not limited to, at least one of the following: remaining computing power indication, computing power reservation, computing power allocation, computing power sharing, and computing power recycling. Optionally, the model management tasks include, but are not limited to, at least one of the following: model training, model verification, model testing, model deployment, model replication, model forwarding, model inference, model monitoring, model update, model activation, model deactivation, model deletion, and model switching.

[0125] In some embodiments, the M pieces of information include third information for indicating whether the first device supports data collection capabilities for AI / ML purposes.

[0126] In some embodiments, the third information includes a third bit; wherein the third bit is used to indicate whether the first device as a whole supports the data collection capability for AI / ML purposes. That is, the third information includes one bit to indicate whether the first device as a whole supports the data collection capability for AI / ML purposes, without distinguishing the type of collected data.

[0127] In some embodiments, the third information is associated with the type of data collected.

[0128] In some embodiments, the type of data collected in association with the third information is agreed upon by a protocol, or the type of data collected in association with the third information is indicated by an information field contained in the third information.

[0129] In some embodiments, the third information includes n3 groups of sub-information, each group of sub-information in the n3 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n3 is a positive integer;

[0130] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes; or

[0131] In the case where each group of sub-information includes at least two bits, each group of sub-information includes a tenth information field and an eleventh information field, the tenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or a group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and the eleventh information field included in the i-th group of sub-information is used to indicate a set of collected data types; or

[0132] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a twelfth information field, and the twelfth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and indicates a set of collected data types; or

[0133] In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a data type set collected by one or a group of functional units within the first device associated with the i-th group of sub-information;

[0134] Wherein, i is a positive integer, and 1≤i≤n3.

[0135] For example, the third information contains 6 bits, and the third information contains three groups of sub-information, each group of sub-information contains two bits, each group of sub-information is associated with one or a group of functional units inside the first device, and each value of each group of sub-information corresponds to a set of collected data types (here the two bits of each group of sub-information have four values ​​'00', '01', '10', '11', and some values ​​are allowed to not define specific meanings), wherein the value '00' indicates that the functional unit inside the one or a group of first devices associated with this group of sub-information does not support data collection capabilities for AI / ML purposes, and the values ​​'01', '10', '11' indicate that the functional unit inside the one or a group of first devices associated with this group of sub-information supports data collection capabilities for AI / ML purposes and each value of '01', '10', '11' corresponds to one or a group of types of collected data.

[0136] In some embodiments, the tenth information field includes one bit, and / or the eleventh information field includes at least one bit.

[0137] In some embodiments, each bit of the eleventh information field is associated with one or a group of types of collected data, and the value of each bit contained in the eleventh information field determines whether the associated one or a group of types of collected data support being collected by the first device; or, each bit state value of the eleventh information field is associated with one or a group of types of collected data, and each bit state value of the eleventh information field indicates that the corresponding associated one or a group of types of collected data support being collected by the first device.

[0138] In some embodiments, the type or group of collected data associated with each bit of the eleventh information field is agreed upon by the protocol, or the type or group of collected data associated with each bit state value of the eleventh information field is agreed upon by the protocol.

[0139] In some embodiments, each group of sub-information includes a thirteenth information field, and the thirteenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0140] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n3 groups of sub-information are agreed upon by a protocol.

[0141] In some embodiments, the third information includes type information of at least one type or a group of types of collected data supported by the first device. In some embodiments, the third information is associated with data collection trigger type indication information;

[0142] In which, the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by itself, or the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by the second device, or the bit state value of the data collection trigger type indication information is used to indicate that the first device supports both data collection triggered by itself and data collection triggered by the second device.

[0143] In some embodiments, the data collection trigger type indication information associated with the third information is agreed upon by a protocol, or the data collection trigger type indication information associated with the third information is indicated by an information field in the third information.

[0144] In some embodiments, the data type includes but is not limited to at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data assisted pre-processing data, AI / ML model output data assisted post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

[0145] In some embodiments, the M pieces of information include fourth information, where the fourth information is used to indicate whether the first device supports data reporting capabilities for AI / ML purposes.

[0146] In some embodiments, the fourth information includes a fourth bit; wherein the fourth bit is used to indicate whether the first device as a whole supports the data reporting capability for AI / ML purposes. That is, the fourth information includes one bit, which is used to indicate whether the first device as a whole supports the data reporting capability for AI / ML purposes, without distinguishing the type of reported data.

[0147] In some embodiments, the fourth information is associated with the reported data type.

[0148] In some embodiments, the type of data reported associated with the fourth information is agreed upon by a protocol, or the type of data reported associated with the fourth information is indicated by an information field included in the fourth information.

[0149] In some embodiments, the fourth information includes n4 groups of sub-information, each group of sub-information in the n4 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n4 is a positive integer;

[0150] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the data reporting capability for AI / ML purposes; or

[0151] When each group of sub-information includes at least two bits, each group of sub-information includes a fourteenth information field and a fifteenth information field, the fourteenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or a group of functional units within the first device associated with the i-th group of sub-information supports data reporting capabilities for AI / ML purposes, and the fifteenth information field included in the i-th group of sub-information is used to indicate a set of reported data types; or

[0152] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a sixteenth information field, and the sixteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports a data reporting capability for AI / ML purposes, and indicates a set of reported data types; or

[0153] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types reported by one or a group of functional units within the first device associated with the i-th group of sub-information;

[0154] Wherein, i is a positive integer, and 1≤i≤n4.

[0155] For example, the fourth information contains 6 bits, and the fourth information contains three groups of sub-information, each group of sub-information contains two bits, each group of sub-information is associated with one or a group of functional units inside the first device, and each value of each group of sub-information corresponds to a set of reported data types (here the two bits of each group of sub-information have four values ​​'00', '01', '10', and '11', and some values ​​are allowed to not define specific meanings), wherein the value '00' indicates that the functional unit inside the one or a group of first devices associated with this group of sub-information does not support data reporting capabilities for AI / ML purposes, and the values ​​'01', '10', and '11' indicate that the functional unit inside the one or a group of first devices associated with this group of sub-information supports data reporting capabilities for AI / ML purposes and each value of '01', '10', and '11' corresponds to one or a group of types of reported data.

[0156] In some embodiments, the fourteenth information field includes one bit, and / or the fifteenth information field includes at least one bit.

[0157] In some embodiments, each bit of the fifteenth information field is associated with one or a group of types of reported data, and the value of each bit contained in the fifteenth information field determines whether the associated one or a group of types of reported data support being reported by the first device; or, each bit state value of the fifteenth information field is associated with one or a group of types of reported data, and each bit state value of the fifteenth information field indicates that the corresponding associated one or a group of types of reported data support being reported by the first device.

[0158] In some embodiments, the type of reported data or a group of reported data associated with each bit of the fifteenth information field is agreed upon by the protocol, or the type of reported data or a group of reported data associated with each bit state value of the fifteenth information field is agreed upon by the protocol.

[0159] In some embodiments, each group of sub-information includes a seventeenth information field, and the seventeenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0160] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n4 groups of sub-information are agreed upon by a protocol.

[0161] In some embodiments, the fourth information includes type information of at least one type or a group of types of reported data supported by the first device.

[0162] In some embodiments, the fourth information is associated with data reporting trigger type indication information;

[0163] Among them, the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by itself, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by the second device, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device supports both data reporting triggered by itself and data reporting triggered by the second device.

[0164] In some embodiments, the data reporting trigger type indication information associated with the fourth information is agreed upon by a protocol, or the data reporting trigger type indication information associated with the fourth information is indicated by an information field in the fourth information.

[0165] In some embodiments, the data type includes but is not limited to at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data assisted pre-processing data, AI / ML model output data assisted post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

[0166] In some embodiments, the M pieces of information include fifth information for indicating whether the first device supports data measurement capabilities for AI / ML purposes.

[0167] In some embodiments, the fifth information includes a fifth bit; wherein the fifth bit is used to indicate whether the first device as a whole supports data measurement capabilities for AI / ML purposes.

[0168] In some embodiments, the fifth information is associated with the type of data measured.

[0169] In some embodiments, the data type of the measurement associated with the fifth information is agreed upon by a protocol, or the data type of the measurement associated with the fifth information is indicated by an information field included in the fifth information.

[0170] In some embodiments, the fifth information includes n5 groups of sub-information, each group of sub-information in the n5 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n5 is a positive integer;

[0171] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes; or

[0172] When each group of sub-information includes at least two bits, the each group of sub-information includes an eighteenth information field and a nineteenth information field, the eighteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and the nineteenth information field included in the i-th group of sub-information is used to indicate a set of measured data types; or

[0173] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a 20th information field, and the 20th information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and indicates a set of measured data types; or

[0174] In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a data type set measured by one or a group of functional units within the first device associated with the i-th group of sub-information;

[0175] Wherein, i is a positive integer, and 1≤i≤n5.

[0176] In some embodiments, the eighteenth information field includes one bit, and / or the nineteenth information field includes at least one bit.

[0177] In some embodiments, each bit of the nineteenth information field is associated with one or a group of types of measurement data, and the value of each bit contained in the nineteenth information field determines whether the associated one or a group of types of measurement data support being measured by the first device; or, each bit state value of the nineteenth information field is associated with one or a group of types of measurement data, and each bit state value of the nineteenth information field indicates that the corresponding associated one or a group of types of measurement data support being measured by the first device.

[0178] In some embodiments, the type of measurement data or a group of measurement data associated with each bit of the nineteenth information field is agreed upon by a protocol, or the type of measurement data or a group of measurement data associated with each bit state value of the nineteenth information field is agreed upon by a protocol.

[0179] In some embodiments, each group of sub-information includes a twenty-first information field, and the twenty-first information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0180] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n5 groups of sub-information are agreed upon by a protocol.

[0181] In some embodiments, the fifth information includes type information of at least one type or a group of types of measurement data supported by the first device.

[0182] In some embodiments, the fifth information is associated with data measurement trigger type indication information;

[0183] In which, the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by itself, or, the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by the second device, or, the bit state value of the data measurement trigger type indication information is used to indicate that the first device supports both data measurement triggered by itself and data measurement triggered by the second device.

[0184] In some embodiments, the data measurement trigger type indication information associated with the fifth information is agreed upon by a protocol, or the data measurement trigger type indication information associated with the fifth information is indicated by an information field in the fifth information.

[0185] In some embodiments, the data type includes but is not limited to at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data assisted pre-processing data, AI / ML model output data assisted post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

[0186] In some embodiments, the capabilities of the third information, the fourth information, and the fifth information may be further subdivided into at least one of data processing capability, data storage capability, and data calculation capability.

[0187] In some embodiments, the capabilities of the third information, fourth information, and fifth information are allowed to be merged into one capability for reporting, and the first device either supports all of them or does not support them at all. Optionally, the merged capabilities can also be associated with the data type information that can be processed. The merged capabilities can also be reported according to at least one of the following granularities: functional unit granularity, AI / ML model identification granularity, or processed data type granularity.

[0188] In some embodiments, the M pieces of information include sixth information, which is used to indicate whether the first device supports offline AI / ML model training capabilities.

[0189] In some embodiments, the sixth information is associated with offline AI / ML model training type indication information;

[0190] Among them, the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by itself, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by the second device, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device supports both offline AI / ML model training triggered by itself and offline AI / ML model training triggered by the second device.

[0191] In some embodiments, the offline AI / ML model training type indication information associated with the sixth information is agreed upon by the protocol, or the offline AI / ML model training type indication information associated with the sixth information is indicated by the information field in the sixth information.

[0192] In some embodiments, the M pieces of information include seventh information, which is used to indicate whether the first device supports online AI / ML model training capabilities.

[0193] In some embodiments, the seventh information is associated with online AI / ML model training type indication information;

[0194] Among them, the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by itself, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by the second device, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device supports both online AI / ML model training triggered by itself and online AI / ML model training triggered by the second device.

[0195] In some embodiments, the online AI / ML model training type indication information associated with the seventh information is agreed upon by the protocol, or the online AI / ML model training type indication information associated with the seventh information is indicated by the information field in the seventh information.

[0196] In some embodiments, the capabilities of the sixth information and the seventh information are allowed to be merged into one capability for reporting, and the first device either supports all or does not support all. Optionally, the merged capability can also be associated with training type indication information, and the merged capability can also be reported according to at least one of the following granularities: functional unit granularity, AI / ML model identification granularity, or associated training type granularity.

[0197] In some embodiments, the M pieces of information include eighth information, which is used to indicate an AI / ML model runtime or compilation format supported by the first device. Different first devices may support different runtime or compilation environments. Reporting this capability facilitates the second device transmitting an AI / ML model suitable for the first device's configuration environment to the first device.

[0198] In some embodiments, the M pieces of information include ninth information, which is used to indicate whether the first device supports AI / ML model reasoning capabilities.

[0199] In some embodiments, the ninth information is associated with AI / ML model reasoning type indication information;

[0200] In which, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports non-federated reasoning or centralized reasoning, or, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports federated reasoning or distributed reasoning, or, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device supports federated reasoning and non-federated reasoning.

[0201] In some embodiments, the AI / ML model reasoning type indication information associated with the ninth information is agreed upon by a protocol, or the AI / ML model reasoning type indication information associated with the ninth information is indicated by an information field in the ninth information.

[0202] In some embodiments, the M pieces of information include tenth information, which is used to indicate whether the first device supports AI / ML model switching capability.

[0203] In some embodiments, the tenth information is associated with AI / ML model switching type indication information;

[0204] In which, the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the first device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the second device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is allowed to be switched by the first device or the second device.

[0205] In some embodiments, the AI / ML model switching type indication information associated with the tenth information is agreed upon by a protocol, or the AI / ML model switching type indication information associated with the tenth information is indicated by an information field in the tenth information.

[0206] In some embodiments, the M pieces of information include eleventh information, which is used to indicate whether the first device supports AI / ML model activation or deactivation capabilities.

[0207] In some embodiments, the eleventh information is associated with AI / ML model activation / deactivation type indication information;

[0208] Among them, the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the first device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the second device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is allowed to be activated or deactivated by the first device or the second device.

[0209] In some embodiments, the AI / ML model activation / deactivation type indication information associated with the eleventh information is agreed upon by a protocol, or the AI / ML model activation / deactivation type indication information associated with the eleventh information is indicated by an information field in the eleventh information.

[0210] In some embodiments, the M pieces of information include twelfth information, which is used to indicate whether the first device supports AI / ML model performance monitoring capabilities.

[0211] In some embodiments, the twelfth information is associated with AI / ML model performance monitoring type indication information;

[0212] In which, the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the first device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the second device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is allowed to be executed by the first device or the second device.

[0213] In some embodiments, the AI / ML model performance monitoring type indication information associated with the twelfth information is agreed upon by the protocol, or the AI / ML model performance monitoring type indication information associated with the twelfth information is indicated by the information field in the twelfth information.

[0214] In some embodiments, the M pieces of information include thirteenth information, and the thirteenth information is used to indicate whether the first device supports AI / ML model transmission capability.

[0215] In some embodiments, the thirteenth information is associated with AI / ML model transmission type indication information;

[0216] Among them, the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model download, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model upload, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device supports AI / ML model download and AI / ML model upload.

[0217] In some embodiments, the AI / ML model transmission type indication information associated with the thirteenth information is agreed upon by a protocol, or the AI / ML model transmission type indication information associated with the thirteenth information is indicated by an information field in the thirteenth information.

[0218] In some embodiments, the AI / ML model transmission capability includes an AI / ML model data transmission format, where the AI / ML model data transmission format is used to indicate an AI / ML model transmission format supported by the first device. Of course, the AI / ML model transmission capability may also include other AI / ML model transmission information, which is not limited in the embodiments of the present application.

[0219] In some embodiments, the M pieces of information include fourteenth information, where the fourteenth information is used to indicate whether the first device supports AI / ML model update capability.

[0220] In some embodiments, the fourteenth information is associated with AI / ML model update type indication information;

[0221] In which, the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the first device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the second device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is allowed to be updated by the first device or the second device.

[0222] In some embodiments, the AI / ML model update type indication information associated with the fourteenth information is agreed upon by a protocol, or the AI / ML model update type indication information associated with the fourteenth information is indicated by an information field in the fourteenth information.

[0223] In some embodiments, the reporting granularity of some or all of the M information is at least one of the following: the granularity of the first device (regardless of the functional units within the first device), the granularity of the functional units contained within the first device, and the granularity of the AI / ML model identifier. Composite granularity reporting is permitted, for example, reporting at the granularity of the AI / ML model identifier and the granularity of the functional units contained within the first device.

[0224] In some embodiments, the first message further includes at least one AI / ML model identification information, and the AI / ML model identification information and the corresponding capabilities of the above-mentioned first to fourteenth information are in a one-to-one mapping relationship or a many-to-one mapping relationship.

[0225] One-to-one mapping relationship means: an association between an AI / ML model identifier and at least one capability corresponding to the first to fourteenth pieces of information;

[0226] Many-to-one mapping relationship means: multiple AI / ML model identifiers are associated with at least one capability corresponding to the first to fourteenth pieces of information.

[0227] In some embodiments, a portion of the capability information included in the first message is allowed to be reported at one granularity, and another portion of the capability information included in the first message is allowed to be reported at another granularity, and this application does not impose any restrictions.

[0228] In some embodiments, the M pieces of information are identification information of M capability sets;

[0229] The content of capability information included in different capability sets in the M capability sets is at least partially different, or the types of capability information included in different capability sets in the M capability sets are at least partially different;

[0230] Among them, the type of capability information included in the j-th capability set in the M capability sets is at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the capability of configuring the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capability for AI / ML purposes, whether the first device supports data reporting capability for AI / ML purposes, whether the first device supports data measurement capability for AI / ML purposes, whether the first device supports offline AI / ML model training capability, whether the first device supports online AI / ML model training capability, AI / ML model running or compilation format supported by the first device, whether the first device supports AI / ML model reasoning capability, whether the first device supports AI / ML model switching capability, whether the first device supports AI / ML model activation or deactivation capability, whether the first device supports AI / ML model performance monitoring capability, whether the first device supports AI / ML model transmission capability, and whether the first device supports AI / ML model update capability;

[0231] Wherein, j is a positive integer, and 1≤j≤M.

[0232] In some embodiments, the type of capability information contained in each of the M capability sets is agreed upon by a protocol, or the type of capability information contained in each of the M capability sets is indicated or configured by a network device.

[0233] That is, in this embodiment, capability reporting can be performed based on the capability template agreed upon in the protocol. The protocol directly stipulates one or more capability sets. The capability information type contained in each capability set is given by the protocol default method. Each capability set is associated with an identity (ID). The capability information content contained in different capability sets varies. The first device can implement capability information reporting by reporting the identity (ID) information associated with the supported capability sets to the second device.

[0234] In some embodiments, the AI / ML model transmission capability includes an AI / ML model data transmission format. Of course, the AI / ML model transmission capability may also include other AI / ML model transmission information, which is not limited in the embodiments of the present application.

[0235] In some embodiments, the first device receives a second message sent by the second device;

[0236] The second message includes second AI / ML-related capability information, the second AI / ML-related capability information is associated with the second device, and the second AI / ML-related capability information includes S pieces of information, where S is a positive integer;

[0237] Among them, the S information is used to indicate at least one of the following: whether the second device is deployed with a functional entity for processing AI / ML related operations, whether the second device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the second device supports data collection capabilities for AI / ML purposes, whether the second device supports data reporting capabilities for AI / ML purposes, whether the second device supports data measurement capabilities for AI / ML purposes, whether the second device supports offline AI / ML model training capabilities, whether the second device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the second device, whether the second device supports AI / ML model reasoning capabilities, whether the second device supports AI / ML model switching capabilities, whether the second device supports AI / ML model activation or deactivation capabilities, whether the second device supports AI / ML model performance monitoring capabilities, whether the second device supports AI / ML model transmission capabilities, and whether the second device supports AI / ML model update capabilities.

[0238] In some embodiments, the reporting granularity of some or all of the S information is at least one of the following: second device granularity, functional unit granularity within the second device, and AI / ML model identifier granularity. Composite granularity reporting is permitted, for example, reporting at both the AI / ML model identifier granularity and the functional unit granularity within the second device.

[0239] In some embodiments, the second message also includes at least one AI / ML model identification information, and the AI / ML model identification information and the corresponding capabilities of the fifteenth to twenty-eighth information described later are in a one-to-one mapping relationship or a many-to-one mapping relationship.

[0240] One-to-one mapping relationship means: an association between an AI / ML model identifier and at least one capability corresponding to the fifteenth to twenty-eighth pieces of information described below;

[0241] Many-to-one mapping relationship means: multiple AI / ML model identifiers are associated with at least one of the corresponding capabilities of the fifteenth to twenty-eighth pieces of information described below.

[0242] In some embodiments, a portion of the capability information included in the second message is allowed to be reported at one granularity, and another portion of the capability information included in the second message is allowed to be reported at another granularity, and this application does not impose any restrictions.

[0243] In some embodiments, the AI / ML model transmission capability includes an AI / ML model data transmission format. Of course, the AI / ML model transmission capability may also include other AI / ML model transmission information, which is not limited in the embodiments of the present application.

[0244] In some embodiments, the second message is one of the following: a NAS message, an AS message, an interface message, or an AL / ML dedicated message. Optionally, the AS message may be an RRC message, an L2 message, or an L1 message, and the interface message may be an NG message, an Xn message, an F1 message, or an E1 message.

[0245] In some embodiments, the second message is a response message to the first message or a message actively triggered by the second device.

[0246] In some embodiments, the S pieces of information are identification information of S capability sets;

[0247] The content of capability information included in different capability sets in the S capability sets is at least partially different, or the types of capability information included in different capability sets in the S capability sets are at least partially different;

[0248] Among them, the type of capability information included in the j-th capability set in the S capability sets is at least one of the following: whether the second device is deployed with a functional entity for processing AI / ML related operations, whether the second device supports the capability of configuring the functional entity for processing AI / ML related operations on demand, whether the second device supports data collection capability for AI / ML purposes, whether the second device supports data reporting capability for AI / ML purposes, whether the second device supports data measurement capability for AI / ML purposes, whether the second device supports offline AI / ML model training capability, whether the second device supports online AI / ML model training capability, AI / ML model running or compilation format supported by the second device, whether the second device supports AI / ML model reasoning capability, whether the second device supports AI / ML model switching capability, whether the second device supports AI / ML model activation or deactivation capability, whether the second device supports AI / ML model performance monitoring capability, whether the second device supports AI / ML model transmission capability, and whether the second device supports AI / ML model update capability;

[0249] Wherein, j is a positive integer, and 1≤j≤S.

[0250] In some embodiments, the type of capability information contained in each of the S capability sets is agreed upon by a protocol, or the type of capability information contained in each of the S capability sets is indicated or configured by a network device.

[0251] In some embodiments, the S pieces of information include fifteenth information, which is used to indicate whether the second device is deployed with a functional entity that processes AI / ML related operations.

[0252] In some embodiments, the fifteenth information includes a first bit; wherein the first bit is used to indicate whether the second device as a whole deploys a functional entity for processing AI / ML-related operations, and some or all functional units within the second device share the capability indicated by the first bit. That is, the AI / ML-related operations involved in each functional unit within the second device can be processed by the functional entity for processing AI / ML-related operations.

[0253] In some embodiments, the fifteenth information is associated with AI / ML functional entity type indication information, where the AI / ML functional entity type indication information is used to indicate the type of functional entity that processes AI / ML-related operations. Optionally, the AI / ML functional entity type indication information associated with the fifteenth information is configured by default in the protocol or explicitly configured via an additional information field included in the fifteenth information.

[0254] In some embodiments, the fifteenth information includes n1 groups of sub-information, each group of sub-information in the n1 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the second device, and n1 is a positive integer;

[0255] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit inside the second device or the group of functional units associated with the i-th group of sub-information deploys a functional entity for processing AI / ML-related operations; or

[0256] When each group of sub-information includes at least two bits, each group of sub-information includes a first information field and a second information field, the first information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in the functional unit within the one or group of second devices associated with the i-th group of sub-information, and the second information field included in the i-th group of sub-information is used to indicate the type of the functional entity for processing AI / ML-related operations deployed in the functional unit within the one or group of second devices associated with the i-th group of sub-information; or

[0257] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a third information field, and the third information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in one or a group of functional units within the second device associated with the i-th group of sub-information, and to indicate the type of the functional entity for processing AI / ML-related operations deployed in the one or a group of functional units within the second device associated with the i-th group of sub-information; or

[0258] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity deployed in one or a group of functional units within the second device associated with the i-th group of sub-information and for processing AI / ML-related operations;

[0259] Wherein, i is a positive integer, and 1≤i≤n1.

[0260] In some embodiments, the first information field includes one bit, and / or the second information field includes at least one bit.

[0261] In some embodiments, each group of sub-information includes a fourth information field, and the fourth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the second device associated with the i-th group of sub-information.

[0262] In some embodiments, the functional unit or units within the second device associated with each set of sub-information in the n1 sets of sub-information are agreed upon by a protocol.

[0263] In some embodiments, the fifteenth information includes type information of a functional entity that has been deployed by the second device to process AI / ML related operations.

[0264] In some embodiments, the types of functional entities for processing AI / ML related operations are divided based on the sources of the processable AI / ML related operations, or the types of functional entities for processing AI / ML related operations are divided based on the types of the processable AI / ML related operations.

[0265] In some embodiments, when the types of functional entities used to process AI / ML-related operations are divided based on the source of the processable AI / ML-related operations, the types of functional entities used to process AI / ML-related operations include at least one of the following:

[0266] An AI / ML functional entity that processes AI / ML related operations triggered by functional units within one or a group of second devices associated with the AI / ML functional entity, an AI / ML functional entity that processes AI / ML related operations triggered by functional units other than functional units within one or a group of second devices associated with the AI / ML functional entity, and an AI / ML functional entity that can process AI / ML related operations triggered by functional units within one or a group of second devices associated with the AI / ML functional entity and can also process AI / ML related operations triggered by functional units other than functional units within one or a group of second devices associated with the AI / ML functional entity.

[0267] In some embodiments, when the types of functional entities used to process AI / ML-related operations are divided based on the types of AI / ML-related operations that can be processed, different types of AI / ML functional entities support different sets of AI / ML-related operation tasks.

[0268] In some embodiments, the AI / ML-related operations include at least one of the following operational tasks: data management tasks, storage management tasks, computing power management tasks, and model management tasks. Optionally, the data management tasks include, but are not limited to, at least one of the following: data collection, data storage, data modification, data update, data deletion, data replication, and data forwarding. Optionally, the storage management tasks include, but are not limited to, at least one of the following: remaining storage size indication, storage reservation, storage allocation, storage sharing, storage recycling, and storage formatting. Optionally, the computing power management tasks include, but are not limited to, at least one of the following: remaining computing power indication, computing power reservation, computing power allocation, computing power sharing, and computing power recycling. Optionally, the model management tasks include, but are not limited to, at least one of the following: model training, model verification, model testing, model deployment, model replication, model forwarding, model inference, model monitoring, model update, model activation, model deactivation, model deletion, and model switching.

[0269] In some embodiments, the S pieces of information include sixteenth information, which is used to indicate whether the second device supports the ability to configure on-demand functional entities for processing AI / ML related operations.

[0270] In some embodiments, the sixteenth information includes a second bit; wherein the second bit is used to indicate whether the second device supports the capability of configuring a functional entity for processing AI / ML-related operations on demand, and some or all functional units within the second device share the capability indicated by the second bit. That is, the functional units within the second device share this capability (of course, the set of functional units sharing this capability may also be a subset of the set of all functional units supported within the second device), that is, the capability either indicates that all functional units within the second device support the capability of configuring a functional entity for processing AI / ML-related operations on demand, or indicates that none of the functional units within the second device supports the capability of configuring a functional entity for processing AI / ML-related operations on demand.

[0271] In some embodiments, the sixteenth information is associated with on-demand configurable AI / ML functional entity type indication information, wherein the on-demand configurable AI / ML functional entity type indication information is used to indicate the type of the on-demand configurable AI / ML functional entity. Optionally, the AI / ML functional entity type indication information is configured by protocol default or explicitly configured through an additional information field included in the sixteenth information.

[0272] In some embodiments, the sixteenth information includes n2 groups of sub-information, each group of sub-information in the n2 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the second device, and n2 is a positive integer;

[0273] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit within one or a group of second devices associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations; or

[0274] When each group of sub-information includes at least two bits, each group of sub-information includes a fifth information field and a sixth information field, the fifth information field included in the i-th group of sub-information is used to indicate whether the functional unit within the one or group of second devices associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and the sixth information field included in the i-th group of sub-information is used to indicate a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional unit within the one or group of second devices associated with the i-th group of sub-information; or

[0275] When each group of sub-information includes at least one bit, each group of sub-information includes a seventh information field, and the seventh information field included in the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the second device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and indicates the type set of functional entities configured on-demand for processing AI / ML-related operations in the functional units or the group of functional units within the second device associated with the i-th group of sub-information; or

[0276] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity configured on demand for processing AI / ML-related operations in one or a group of functional units within the second device associated with the i-th group of sub-information;

[0277] Wherein, i is a positive integer, and 1≤i≤n2.

[0278] In some embodiments, the fifth information field includes one bit, and / or the sixth information field includes at least one bit.

[0279] In some embodiments, each bit of the sixth information field is associated with one or a group of AI / ML functional entities, and the value of each bit contained in the sixth information field determines whether the associated one or a group of AI / ML functional entities support on-demand configuration; or, each bit state value of the sixth information field is associated with one or a group of AI / ML functional entities, and each bit state value of the sixth information field indicates that the corresponding associated one or a group of AI / ML functional entities support on-demand configuration.

[0280] In some embodiments, the type or group of AI / ML functional entities associated with each bit of the sixth information field is agreed upon by the protocol, or the type or group of AI / ML functional entities associated with each bit state value of the sixth information field is agreed upon by the protocol.

[0281] In some embodiments, each group of sub-information includes an eighth information field, and the eighth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the second device associated with the i-th group of sub-information.

[0282] In some embodiments, the functional unit or units within the second device associated with each group of sub-information in the n2 groups of sub-information are agreed upon by a protocol.

[0283] In some embodiments, the sixteenth information includes type information of at least one functional entity that processes AI / ML-related operations supported by the second device.

[0284] In some embodiments, the sixteenth information is associated with configuration mode information, wherein the configuration mode information is used to indicate a method for triggering establishment of an on-demand configured AI / ML functional entity.

[0285] In some embodiments, the method of triggering the establishment of the on-demand configured AI / ML functional entity includes at least one of the following: the second device autonomously triggers the establishment of the on-demand configured AI / ML functional entity, the second device actively requests the second device to establish the on-demand configured AI / ML functional entity, and the second device initiates the establishment request and obtains confirmation from the second device, and then the second device establishes the on-demand configured AI / ML functional entity.

[0286] In some embodiments, the configuration mode information associated with the sixteenth information is determined by a protocol. For example, the protocol directly determines that the configuration mode information associated with the sixteenth information means that the on-demand AI / ML functional entity is triggered to be established by the second device, that is, all on-demand AI / ML functional entities determined by the sixteenth information can only be established on the second device side by being triggered by the second device.

[0287] In some embodiments, the sixteenth information includes a ninth information field, and the ninth information field is used to indicate configuration mode information associated with the sixteenth information.

[0288] In some embodiments, when the ninth information field is used to indicate the configuration mode information associated with the sixteenth information, the indication granularity of the ninth information field includes one of the following: the second device granularity, the sub-information group granularity contained in the sixteenth information, and the AI / ML functional entity type granularity configured on demand.

[0289] In some embodiments, when the indication granularity of the ninth information field is the granularity of the second device, the configuration modes corresponding to all on-demand configurable AI / ML functional entities supported by the second device are the same. That is, the configuration modes corresponding to all on-demand configurable AI / ML functional entities supported by the second device are the same, and the specific configuration mode is provided by the common configuration mode information.

[0290] In some embodiments, when the indication granularity of the ninth information field is the granularity of the sub-information group contained in the sixteenth information, the configuration modes corresponding to all on-demand configured AI / ML functional entities supported by each sub-information group contained in the sixteenth information are the same. That is, the configuration is performed according to the granularity of the sub-information group of the sixteenth information (each group of sub-information is associated with one or a group of functional units inside the second device, so it can also be said to be configured according to the granularity of the functional units inside the second device). In this way, regardless of whether the type of on-demand configured AI / ML functional entity supported by a certain sub-information group of the sixteenth information is one or more, the configuration mode corresponding to all on-demand configured AI / ML functional entities supported by the group of sub-information is the same, and the specific configuration mode is given by the configuration mode information associated with the sub-information group.

[0291] In some embodiments, when the indication granularity of the ninth information field is the granularity of the on-demand AI / ML functional entity type, each on-demand AI / ML functional entity type supported by the sixteenth information is individually associated with a piece of configuration mode information for indicating the configuration mode supported by the AI / ML functional entity of that type. That is, each on-demand AI / ML functional entity type supported by the sixteenth information is individually associated with a piece of configuration mode information for indicating the configuration mode supported by the AI / ML functional entity of that type.

[0292] In some embodiments, the types of functional entities for processing AI / ML related operations are divided based on the sources of the processable AI / ML related operations, or the types of functional entities for processing AI / ML related operations are divided based on the types of the processable AI / ML related operations.

[0293] In some embodiments, when the type of the functional entity for processing AI / ML-related operations is divided based on the source of the processable AI / ML-related operations, the type of the functional entity for processing AI / ML-related operations includes at least one of the following:

[0294] An AI / ML functional entity that processes AI / ML related operations triggered by functional units within one or a group of second devices associated with the AI / ML functional entity, an AI / ML functional entity that processes AI / ML related operations triggered by functional units other than functional units within one or a group of second devices associated with the AI / ML functional entity, and an AI / ML functional entity that can process AI / ML related operations triggered by functional units within one or a group of second devices associated with the AI / ML functional entity and can also process AI / ML related operations triggered by functional units other than functional units within one or a group of second devices associated with the AI / ML functional entity.

[0295] In some embodiments, when the types of functional entities for processing AI / ML-related operations are divided based on the types of AI / ML-related operations that can be processed, different types of AI / ML functional entities support different sets of AI / ML-related operation tasks.

[0296] In some embodiments, the AI / ML-related operations include at least one of the following operational tasks: data management tasks, storage management tasks, computing power management tasks, and model management tasks. Optionally, the data management tasks include, but are not limited to, at least one of the following: data collection, data storage, data modification, data update, data deletion, data replication, and data forwarding. Optionally, the storage management tasks include, but are not limited to, at least one of the following: remaining storage size indication, storage reservation, storage allocation, storage sharing, storage recycling, and storage formatting. Optionally, the computing power management tasks include, but are not limited to, at least one of the following: remaining computing power indication, computing power reservation, computing power allocation, computing power sharing, and computing power recycling. Optionally, the model management tasks include, but are not limited to, at least one of the following: model training, model verification, model testing, model deployment, model replication, model forwarding, model inference, model monitoring, model update, model activation, model deactivation, model deletion, and model switching.

[0297] In some embodiments, the S pieces of information include seventeenth information, which is used to indicate whether the second device supports data collection capabilities for AI / ML purposes.

[0298] In some embodiments, the seventeenth information includes a third bit; wherein the third bit is used to indicate whether the second device as a whole supports the data collection capability for AI / ML purposes. That is, the seventeenth information includes one bit to indicate whether the second device as a whole supports the data collection capability for AI / ML purposes, without distinguishing the type of collected data.

[0299] In some embodiments, the seventeenth information is associated with the type of data collected.

[0300] In some embodiments, the type of data collected associated with the seventeenth information is agreed upon by a protocol, or the type of data collected associated with the seventeenth information is indicated by an information field included in the seventeenth information.

[0301] In some embodiments, the seventeenth information includes n3 groups of sub-information, each group of sub-information in the n3 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the second device, and n3 is a positive integer;

[0302] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit inside the second device or the group of functional units associated with the i-th group of sub-information supports the data collection capability for AI / ML purposes; or

[0303] In the case where each group of sub-information includes at least two bits, each group of sub-information includes a tenth information field and an eleventh information field, the tenth information field included in the i-th group of sub-information is used to indicate whether the functional unit inside the second device or a group of functional units associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and the eleventh information field included in the i-th group of sub-information is used to indicate a set of collected data types; or

[0304] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a twelfth information field, and the twelfth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the second device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and indicates a set of collected data types; or

[0305] In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a data type set collected by one or a group of functional units within the second device associated with the i-th group of sub-information;

[0306] Wherein, i is a positive integer, and 1≤i≤n3.

[0307] In some embodiments, the tenth information field includes one bit, and / or the eleventh information field includes at least one bit.

[0308] In some embodiments, each bit of the eleventh information field is associated with one or a group of types of collected data, and the value of each bit contained in the eleventh information field determines whether the associated one or a group of types of collected data support being collected by the second device; or, each bit state value of the eleventh information field is associated with one or a group of types of collected data, and each bit state value of the eleventh information field indicates that the corresponding associated one or a group of types of collected data support being collected by the second device.

[0309] In some embodiments, the type or group of collected data associated with each bit of the eleventh information field is agreed upon by the protocol, or the type or group of collected data associated with each bit state value of the eleventh information field is agreed upon by the protocol.

[0310] In some embodiments, each group of sub-information includes a thirteenth information field, and the thirteenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the second device associated with the i-th group of sub-information.

[0311] In some embodiments, the functional unit or units within the second device associated with each group of sub-information in the n3 groups of sub-information are agreed upon by a protocol.

[0312] In some embodiments, the seventeenth information includes type information of at least one type or a group of types of collected data supported by the second device.

[0313] In some embodiments, the seventeenth information is associated with data collection trigger type indication information;

[0314] In which, the bit state value of the data collection trigger type indication information is used to indicate that the second device only supports data collection triggered by itself, or the bit state value of the data collection trigger type indication information is used to indicate that the second device only supports data collection triggered by the second device, or the bit state value of the data collection trigger type indication information is used to indicate that the second device supports both data collection triggered by itself and data collection triggered by the second device.

[0315] In some embodiments, the data collection trigger type indication information associated with the seventeenth information is agreed upon by a protocol, or the data collection trigger type indication information associated with the seventeenth information is indicated by an information field in the seventeenth information.

[0316] In some embodiments, the data type includes but is not limited to at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data assisted pre-processing data, AI / ML model output data assisted post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

[0317] In some embodiments, the S pieces of information include eighteenth information, where the eighteenth information is used to indicate whether the second device supports data reporting capabilities for AI / ML purposes.

[0318] In some embodiments, the eighteenth information includes a fourth bit; wherein the fourth bit is used to indicate whether the second device as a whole supports the data reporting capability for AI / ML purposes. That is, the eighteenth information includes one bit for indicating whether the second device as a whole supports the data reporting capability for AI / ML purposes, without distinguishing the type of reported data.

[0319] In some embodiments, the eighteenth information is associated with the reported data type.

[0320] In some embodiments, the type of data reported associated with the eighteenth information is agreed upon by a protocol, or the type of data reported associated with the eighteenth information is indicated by an information field included in the eighteenth information.

[0321] In some embodiments, the eighteenth information includes n4 groups of sub-information, each group of sub-information in the n4 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the second device, and n4 is a positive integer;

[0322] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit inside the second device or the group of functional units associated with the i-th group of sub-information supports the data reporting capability for AI / ML purposes; or

[0323] When each group of sub-information includes at least two bits, each group of sub-information includes a fourteenth information field and a fifteenth information field, the fourteenth information field included in the i-th group of sub-information is used to indicate whether the functional unit inside the second device or a group of functional units associated with the i-th group of sub-information supports data reporting capabilities for AI / ML purposes, and the fifteenth information field included in the i-th group of sub-information is used to indicate a set of reported data types; or

[0324] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a sixteenth information field, and the sixteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the second device associated with the i-th group of sub-information supports a data reporting capability for AI / ML purposes, and indicates a set of reported data types; or

[0325] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types reported by one or a group of functional units within the second device associated with the i-th group of sub-information;

[0326] Wherein, i is a positive integer, and 1≤i≤n4.

[0327] In some embodiments, the fourteenth information field includes one bit, and / or the fifteenth information field includes at least one bit.

[0328] In some embodiments, each bit of the fifteenth information field is associated with one or a group of types of reported data, and the value of each bit contained in the fifteenth information field determines whether the associated one or a group of types of reported data support being reported by the second device; or, each bit state value of the fifteenth information field is associated with one or a group of types of reported data, and each bit state value of the fifteenth information field indicates that the corresponding associated one or a group of types of reported data support being reported by the second device.

[0329] In some embodiments, the type of reported data or a group of reported data associated with each bit of the fifteenth information field is agreed upon by the protocol, or the type of reported data or a group of reported data associated with each bit state value of the fifteenth information field is agreed upon by the protocol.

[0330] In some embodiments, each group of sub-information includes a seventeenth information field, and the seventeenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the second device associated with the i-th group of sub-information.

[0331] In some embodiments, the functional unit or units within the second device associated with each group of sub-information in the n4 groups of sub-information are agreed upon by a protocol.

[0332] In some embodiments, the eighteenth information includes type information of at least one type or a group of types of reported data supported by the second device.

[0333] In some embodiments, the eighteenth information is associated with data reporting trigger type indication information;

[0334] Among them, the bit state value of the data reporting trigger type indication information is used to indicate that the second device only supports data reporting triggered by itself, or the bit state value of the data reporting trigger type indication information is used to indicate that the second device only supports data reporting triggered by the second device, or the bit state value of the data reporting trigger type indication information is used to indicate that the second device supports both data reporting triggered by itself and data reporting triggered by the second device.

[0335] In some embodiments, the data reporting trigger type indication information associated with the eighteenth information is agreed upon by a protocol, or the data reporting trigger type indication information associated with the eighteenth information is indicated by an information field in the eighteenth information.

[0336] In some embodiments, the data type includes but is not limited to at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data assisted pre-processing data, AI / ML model output data assisted post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

[0337] In some embodiments, the S pieces of information include nineteenth information, which is used to indicate whether the second device supports data measurement capabilities for AI / ML purposes.

[0338] In some embodiments, the nineteenth information includes a fifth bit; wherein the fifth bit is used to indicate whether the second device as a whole supports data measurement capabilities for AI / ML purposes.

[0339] In some embodiments, the nineteenth information is associated with a type of data being measured.

[0340] In some embodiments, the data type of the measurement associated with the nineteenth information is agreed upon by a protocol, or the data type of the measurement associated with the nineteenth information is indicated by an information field included in the nineteenth information.

[0341] In some embodiments, the nineteenth information includes n5 groups of sub-information, each group of sub-information in the n5 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the second device, and n5 is a positive integer;

[0342] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit inside the second device or the group of functional units associated with the i-th group of sub-information supports data measurement capability for AI / ML purposes; or

[0343] When each group of sub-information includes at least two bits, the each group of sub-information includes an eighteenth information field and a nineteenth information field, the eighteenth information field included in the i-th group of sub-information is used to indicate whether the functional unit inside the second device or a group of functional units associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and the nineteenth information field included in the i-th group of sub-information is used to indicate a set of measured data types; or

[0344] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a 20th information field, and the 20th information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the second device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and indicates a set of measured data types; or

[0345] In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a data type set measured by one or a group of functional units within the second device associated with the i-th group of sub-information;

[0346] Wherein, i is a positive integer, and 1≤i≤n5.

[0347] In some embodiments, the eighteenth information field includes one bit, and / or the nineteenth information field includes at least one bit.

[0348] In some embodiments, each bit of the nineteenth information field is associated with one or a group of types of measurement data, and the value of each bit contained in the nineteenth information field determines whether the associated one or a group of types of measurement data support being measured by the second device; or, each bit state value of the nineteenth information field is associated with one or a group of types of measurement data, and each bit state value of the nineteenth information field indicates that the corresponding associated one or a group of types of measurement data support being measured by the second device.

[0349] In some embodiments, the type of measurement data or a group of measurement data associated with each bit of the nineteenth information field is agreed upon by a protocol, or the type of measurement data or a group of measurement data associated with each bit state value of the nineteenth information field is agreed upon by a protocol.

[0350] In some embodiments, each group of sub-information includes a twenty-first information field, and the twenty-first information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the second device associated with the i-th group of sub-information.

[0351] In some embodiments, the functional unit or units within the second device associated with each group of sub-information in the n5 groups of sub-information are agreed upon by a protocol.

[0352] In some embodiments, the nineteenth information includes type information of at least one type or a group of types of measurement data supported by the second device. In some embodiments, the nineteenth information is associated with data measurement trigger type indication information;

[0353] In which, the bit state value of the data measurement trigger type indication information is used to indicate that the second device only supports data measurement triggered by itself, or, the bit state value of the data measurement trigger type indication information is used to indicate that the second device only supports data measurement triggered by the second device, or, the bit state value of the data measurement trigger type indication information is used to indicate that the second device supports both data measurement triggered by itself and data measurement triggered by the second device.

[0354] In some embodiments, the data measurement trigger type indication information associated with the nineteenth information is agreed upon by a protocol, or the data measurement trigger type indication information associated with the nineteenth information is indicated by an information field in the nineteenth information.

[0355] In some embodiments, the data type includes but is not limited to at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data assisted pre-processing data, AI / ML model output data assisted post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

[0356] In some embodiments, the capabilities of the seventeenth information, the eighteenth information, and the nineteenth information may be further subdivided into at least one of data processing capability, data storage capability, and data calculation capability.

[0357] In some embodiments, the capabilities of the seventeenth information, the eighteenth information, and the nineteenth information are allowed to be merged into one capability for reporting, and the second device either supports all of them or does not support them at all. Optionally, the merged capabilities can also be associated with the data type information that can be processed. The merged capabilities can also be reported according to at least one of the following granularities: functional unit granularity, AI / ML model identification granularity, or processed data type granularity.

[0358] In some embodiments, the S pieces of information include twentieth information, which is used to indicate whether the second device supports offline AI / ML model training capabilities.

[0359] In some embodiments, the twentieth information is associated with offline AI / ML model training type indication information;

[0360] Among them, the bit state value of the offline AI / ML model training type indication information is used to indicate that the second device only supports offline AI / ML model training triggered by itself, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the second device only supports offline AI / ML model training triggered by the second device, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the second device supports both offline AI / ML model training triggered by itself and offline AI / ML model training triggered by the second device.

[0361] In some embodiments, the offline AI / ML model training type indication information associated with the twentieth information is agreed upon by the protocol, or the offline AI / ML model training type indication information associated with the twentieth information is indicated by the information field in the twentieth information.

[0362] In some embodiments, the M pieces of information include twenty-first information, which is used to indicate whether the second device supports online AI / ML model training capabilities.

[0363] In some embodiments, the twenty-first information is associated with online AI / ML model training type indication information;

[0364] Among them, the bit state value of the online AI / ML model training type indication information is used to indicate that the second device only supports online AI / ML model training triggered by itself, or the bit state value of the online AI / ML model training type indication information is used to indicate that the second device only supports online AI / ML model training triggered by the second device, or the bit state value of the online AI / ML model training type indication information is used to indicate that the second device supports both online AI / ML model training triggered by itself and online AI / ML model training triggered by the second device.

[0365] In some embodiments, the online AI / ML model training type indication information associated with the twenty-first information is agreed upon by the protocol, or the online AI / ML model training type indication information associated with the twenty-first information is indicated by the information field in the twenty-first information.

[0366] In some embodiments, the capabilities of the above-mentioned twentieth information and twenty-first information are allowed to be merged into one capability for reporting, and the second device either supports all of them or does not support them at all. Optionally, the merged capabilities can also be associated with training type indication information, and the merged capabilities can also be reported according to at least one of the following granularities: functional unit granularity, AI / ML model identification granularity, or associated training type granularity.

[0367] In some embodiments, the M pieces of information include a 22nd piece of information, which is used to indicate an AI / ML model runtime or compilation format supported by the second device. Different second devices may support different runtime or compilation environments, and reporting this capability facilitates the first device transmitting an AI / ML model suitable for the second device's configuration environment to the second device.

[0368] In some embodiments, the M pieces of information include twenty-third information, and the twenty-third information is used to indicate whether the second device supports AI / ML model reasoning capabilities.

[0369] In some embodiments, the twenty-third information is associated with AI / ML model reasoning type indication information;

[0370] In which, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the second device only supports non-federated reasoning or centralized reasoning, or, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the second device only supports federated reasoning or distributed reasoning, or, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the second device supports federated reasoning and non-federated reasoning.

[0371] In some embodiments, the AI / ML model reasoning type indication information associated with the twenty-third information is agreed upon by a protocol, or the AI / ML model reasoning type indication information associated with the twenty-third information is indicated by an information field in the twenty-third information.

[0372] In some embodiments, the M pieces of information include twenty-fourth information, which is used to indicate whether the second device supports AI / ML model switching capability.

[0373] In some embodiments, the twenty-fourth information is associated with AI / ML model switching type indication information;

[0374] In which, the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the second device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the second device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is allowed to be switched by the second device or the second device.

[0375] In some embodiments, the AI / ML model switching type indication information associated with the twenty-fourth information is agreed upon by a protocol, or the AI / ML model switching type indication information associated with the twenty-fourth information is indicated by an information field in the twenty-fourth information.

[0376] In some embodiments, the M pieces of information include twenty-fifth information, which is used to indicate whether the second device supports AI / ML model activation or deactivation capabilities.

[0377] In some embodiments, the twenty-fifth information is associated with AI / ML model activation / deactivation type indication information;

[0378] In which, the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the second device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the second device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is allowed to be activated or deactivated by the second device or the second device.

[0379] In some embodiments, the AI / ML model activation / deactivation type indication information associated with the twenty-fifth information is agreed upon by the protocol, or the AI / ML model activation / deactivation type indication information associated with the twenty-fifth information is indicated by the information field in the twenty-fifth information.

[0380] In some embodiments, the M pieces of information include twenty-sixth information, and the twenty-sixth information is used to indicate whether the second device supports AI / ML model performance monitoring capabilities.

[0381] In some embodiments, the twenty-sixth information is associated with AI / ML model performance monitoring type indication information;

[0382] In which, the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the second device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the second device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is allowed to be executed by the second device or the second device.

[0383] In some embodiments, the AI / ML model performance monitoring type indication information associated with the twenty-sixth information is agreed upon by the protocol, or the AI / ML model performance monitoring type indication information associated with the twenty-sixth information is indicated by the information field in the twenty-sixth information.

[0384] In some embodiments, the S pieces of information include twenty-seventh information, and the twenty-seventh information is used to indicate whether the second device supports AI / ML model transmission capability.

[0385] In some embodiments, the twenty-seventh information is associated with AI / ML model transmission type indication information;

[0386] Among them, the bit state value of the AI / ML model transmission type indication information is used to indicate that the second device only supports AI / ML model download, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the second device only supports AI / ML model upload, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the second device supports AI / ML model download and AI / ML model upload.

[0387] In some embodiments, the AI / ML model transmission type indication information associated with the twenty-seventh information is agreed upon by a protocol, or the AI / ML model transmission type indication information associated with the twenty-seventh information is indicated by an information field in the twenty-seventh information.

[0388] In some embodiments, the AI / ML model transmission capability includes an AI / ML model data transmission format, where the AI / ML model data transmission format is used to indicate an AI / ML model transmission format supported by the first device. Of course, the AI / ML model transmission capability may also include other AI / ML model transmission information, which is not limited in the embodiments of the present application.

[0389] In some embodiments, the S pieces of information include twenty-eighth information, where the twenty-eighth information is used to indicate whether the second device supports AI / ML model update capability.

[0390] In some embodiments, the twenty-eighth information is associated with AI / ML model update type indication information;

[0391] In which, the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the second device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the second device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is allowed to be updated by the second device or the second device.

[0392] In some embodiments, the AI / ML model update type indication information associated with the twenty-eighth information is agreed upon by a protocol, or the AI / ML model update type indication information associated with the twenty-eighth information is indicated by an information field in the twenty-eighth information.

[0393] In some embodiments, the first device sends a third message to the second device;

[0394] The third message includes capability update indication information, where the capability update indication information is used to indicate that capability information related to the first AI / ML has been updated.

[0395] In some embodiments, the third message is one of the following: a NAS message, an AS message, an interface message, or an AL / ML dedicated message. Optionally, the AS message may be an RRC message, an L2 message, or an L1 message, and the interface message may be an NG message, an Xn message, an F1 message, or an E1 message.

[0396] In some embodiments, the capability update indication information includes a sixth bit, wherein the sixth bit is used to indicate that the capability information related to the first AI / ML has been updated; or

[0397] The capability update indication information includes multiple bits, wherein each of the multiple bits is associated with one or a group of AI / ML-related capability information, and each bit is used to indicate whether the corresponding one or a group of AI / ML-related capability information has been updated.

[0398] In some embodiments, the capability update indication information is triggered based on a preset event, where the preset event is given by a protocol agreement or configured by the second device.

[0399] In some embodiments, the first device receives a fourth message sent by the second device;

[0400] The fourth message is used to instruct the first device to report the updated capability information related to the first AI / ML, or the fourth message is used to instruct the first device to report the updated capability information in the capability information related to the first AI / ML.

[0401] In some embodiments, the fourth message is one of the following: a NAS message, an AS message, an interface message, or an AL / ML dedicated message. Optionally, the AS message may be an RRC message, an L2 message, or an L1 message, and the interface message may be an NG message, an Xn message, an F1 message, or an E1 message.

[0402] In some embodiments, the fourth message includes capability type indication information, where the capability type indication information is used to indicate the type of updated capability information reported by the first device.

[0403] In some embodiments, the fourth message is a response message to the third message or a message actively triggered by the second device.

[0404] In some embodiments, the first device sends a fifth message to the second device;

[0405] The fifth message includes the updated capability information related to the first AI / ML, or the fifth message includes the updated capability information specified to be reported by the second device.

[0406] In some embodiments, the fifth message is one of the following: a NAS message, an AS message, an interface message, or an AL / ML dedicated message. Optionally, the AS message may be an RRC message, an L2 message, or an L1 message, and the interface message may be an NG message, an Xn message, an F1 message, or an E1 message.

[0407] In some embodiments, when the first device is a terminal device, the functional units inside the first device include at least one of the following: non-access stratum (NAS) layer functional entity, service data adaptation protocol (SDAP) layer functional entity, radio resource control (RRC) layer functional entity, packet data convergence protocol (PDCP) layer functional entity, radio link control (RLC) layer functional entity, backhaul adaptation protocol (BAP) layer functional entity, media access control (MAC) layer functional entity, and physical layer (PHY) layer functional entity.

[0408] In some embodiments, when the first device is an access network device, the functional units inside the first device include at least one of the following: a centralized unit (CU), a distributed unit (DU), a centralized unit control plane (CU-CP), a centralized unit user plane (CU-UP), a NAS layer functional entity, a SDAP layer functional entity, a RRC layer functional entity, a PDCP layer functional entity, a RLC layer functional entity, a MAC layer functional entity, a PHY layer functional entity, and a BAP layer functional entity.

[0409] In some embodiments, when the first device is a core network device, the functional units inside the first device include at least one of the following: Access and Mobility Management Function (AMF) network element, Authentication Server Function (AUSF) network element, User Plane Function (UPF) network element, Session Management Function (SMF) network element, Location Management Function (LMF) network element, Policy Control Function (PCF) network element, and Unified Data Management (UDM) network element.

[0410] In some embodiments, the message (i.e., the first to fifth messages described above) is one of the following: an NAS message, an AS message, an interface message, or an AL / ML dedicated message. Optionally, the AS message may be an RRC message, an L2 message, or an L1 message, and the interface message may be an NG message, an Xn message, an F1 message, or an E1 message.

[0411] Therefore, in an embodiment of the present application, the first device may send first AI / ML-related capability information to the second device, where the M information included in the first AI / ML-related capability information is used to indicate at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML-related operations, whether the first device supports the capability of configuring the functional entity for processing AI / ML-related operations on demand, whether the first device supports data collection capability for AI / ML purposes, whether the first device supports data reporting capability for AI / ML purposes, whether the first device supports data measurement capability for AI / ML purposes, whether the first device supports offline AI / ML model training capability, whether the first device supports online AI / ML model training capability, AI / ML model running or compilation format supported by the first device, whether the first device supports AI / ML model reasoning capability, whether the first device supports AI / ML model switching capability, whether the first device supports AI / ML model activation or deactivation capability, whether the first device supports AI / ML model performance monitoring capability, whether the first device supports AI / ML model transmission capability, and whether the first device supports AI / ML model update capability. That is, through the above technical solution, AI / ML capability interactions of different types and granularities can be realized, and AI / ML-related capability information can be flexibly exchanged between the first device and the second device, facilitating the refined application of AI / ML functions in communication devices.

[0412] The above text, in conjunction with Figure 2, describes in detail the method embodiment of the present application. The following text, in conjunction with Figures 3 to 7, describes in detail the device embodiment of the present application. It should be understood that the device embodiment and the method embodiment correspond to each other, and similar descriptions can refer to the method embodiment.

[0413] FIG3 shows a schematic block diagram of a wireless communication device 300 according to an embodiment of the present application. The wireless communication device 300 is a first device. As shown in FIG3 , the wireless communication device 300 includes:

[0414] The first communication unit 310 is configured to send a first message to the second device;

[0415] The first message includes first artificial intelligence (AI) / machine learning (ML) related capability information, the first AI / ML related capability information is associated with the first device, and the first AI / ML related capability information includes M pieces of information, where M is a positive integer;

[0416] Among them, the M information is used to indicate at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capabilities for AI / ML purposes, whether the first device supports data reporting capabilities for AI / ML purposes, whether the first device supports data measurement capabilities for AI / ML purposes, whether the first device supports offline AI / ML model training capabilities, whether the first device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the first device, whether the first device supports AI / ML model reasoning capabilities, whether the first device supports AI / ML model switching capabilities, whether the first device supports AI / ML model activation or deactivation capabilities, whether the first device supports AI / ML model performance monitoring capabilities, whether the first device supports AI / ML model transmission capabilities, and whether the first device supports AI / ML model update capabilities.

[0417] In some embodiments, the M pieces of information include first information, which is used to indicate whether the first device is deployed with a functional entity that processes AI / ML related operations.

[0418] In some embodiments, the first information includes a first bit;

[0419] Among them, the first bit is used to indicate whether the first device as a whole is deployed with a functional entity for processing AI / ML related operations, and some or all functional units within the first device share the capability indicated by the first bit.

[0420] In some embodiments, the first information includes n1 groups of sub-information, each group of sub-information in the n1 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n1 is a positive integer;

[0421] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information deploys a functional entity for processing AI / ML-related operations; or

[0422] When each group of sub-information includes at least two bits, each group of sub-information includes a first information field and a second information field, the first information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information, and the second information field included in the i-th group of sub-information is used to indicate the type of the functional entity for processing AI / ML-related operations deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information; or

[0423] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a third information field, and the third information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in one or a group of functional units within the first device associated with the i-th group of sub-information, and to indicate the type of the functional entity for processing AI / ML-related operations deployed in the one or a group of functional units within the first device associated with the i-th group of sub-information; or

[0424] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity deployed in one or a group of functional units within the first device associated with the i-th group of sub-information and for processing AI / ML-related operations;

[0425] Wherein, i is a positive integer, and 1≤i≤n1.

[0426] In some embodiments, the first information field includes one bit, and / or the second information field includes at least one bit.

[0427] In some embodiments, each group of sub-information includes a fourth information field, and the fourth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0428] In some embodiments, the functional unit or units within the first device associated with each set of sub-information in the n1 sets of sub-information are agreed upon by a protocol.

[0429] In some embodiments, the M pieces of information include second information, where the second information is used to indicate whether the first device supports the ability to configure on-demand functional entities for processing AI / ML related operations.

[0430] In some embodiments, the second information includes a second bit;

[0431] The second bit is used to indicate whether the first device supports the capability of configuring functional entities for processing AI / ML related operations on demand, and some or all functional units within the first device share the capability indicated by the second bit.

[0432] In some embodiments, the second information includes n2 groups of sub-information, each group of sub-information in the n2 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n2 is a positive integer;

[0433] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations; or

[0434] When each group of sub-information includes at least two bits, each group of sub-information includes a fifth information field and a sixth information field, the fifth information field included in the i-th group of sub-information is used to indicate whether the functional unit within one or a group of first devices associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and the sixth information field included in the i-th group of sub-information is used to indicate a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional unit within one or a group of first devices associated with the i-th group of sub-information; or

[0435] When each group of sub-information includes at least one bit, each group of sub-information includes a seventh information field, and the seventh information field included in the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and indicates a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional units or the group of functional units within the first device associated with the i-th group of sub-information; or

[0436] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity configured on demand for processing AI / ML-related operations in one or a group of functional units within the first device associated with the i-th group of sub-information;

[0437] Wherein, i is a positive integer, and 1≤i≤n2.

[0438] In some embodiments, the fifth information field includes one bit, and / or the sixth information field includes at least one bit.

[0439] In some embodiments, each bit of the sixth information field is associated with one or a group of AI / ML functional entities, and the value of each bit contained in the sixth information field determines whether the associated one or a group of AI / ML functional entities supports on-demand configuration; or

[0440] Each bit state value of the sixth information field is associated with one or a group of AI / ML functional entities, and each bit state value of the sixth information field indicates that the corresponding associated one or a group of AI / ML functional entities supports on-demand configuration.

[0441] In some embodiments, the type or group of AI / ML functional entities associated with each bit of the sixth information field is agreed upon by the protocol, or the type or group of AI / ML functional entities associated with each bit state value of the sixth information field is agreed upon by the protocol.

[0442] In some embodiments, each group of sub-information includes an eighth information field, and the eighth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0443] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n2 groups of sub-information are agreed upon by a protocol.

[0444] In some embodiments, the second information is associated with configuration mode information, wherein the configuration mode information is used to indicate a method for triggering establishment of an on-demand configured AI / ML functional entity.

[0445] In some embodiments, the method of triggering the establishment of the on-demand configured AI / ML functional entity includes at least one of the following: the first device autonomously triggers the establishment of the on-demand configured AI / ML functional entity, the second device actively requests the first device to establish the on-demand configured AI / ML functional entity, and the first device initiates the establishment request and obtains confirmation from the second device before the first device establishes the on-demand configured AI / ML functional entity.

[0446] In some embodiments, the configuration mode information associated with the second information is agreed upon by a protocol; or,

[0447] The second information includes a ninth information field, and the ninth information field is used to indicate configuration mode information associated with the second information.

[0448] In some embodiments, when the ninth information field is used to indicate the configuration mode information associated with the second information, the indication granularity of the ninth information field includes one of the following: the first device granularity, the sub-information group granularity contained in the second information, and the AI / ML functional entity type granularity configured on demand.

[0449] In some embodiments, when the indication granularity of the ninth information field is the granularity of the first device, the configuration modes corresponding to all on-demand configured AI / ML functional entities supported by the first device are the same; or,

[0450] When the indication granularity of the ninth information field is the granularity of the sub-information group contained in the second information, the configuration modes corresponding to all on-demand AI / ML functional entities supported by each sub-information group contained in the second information are the same; or

[0451] When the indication granularity of the ninth information field is the on-demand configuration of the AI / ML functional entity type granularity, each on-demand configuration of the AI / ML functional entity type supported by the second information is separately associated with a configuration mode information for indicating the configuration mode supported by the AI / ML functional entity of this type.

[0452] In some embodiments, the types of functional entities for processing AI / ML related operations are divided based on the sources of the processable AI / ML related operations, or the types of functional entities for processing AI / ML related operations are divided based on the types of the processable AI / ML related operations.

[0453] In some embodiments, when the type of the functional entity for processing AI / ML-related operations is divided based on the source of the processable AI / ML-related operations, the type of the functional entity for processing AI / ML-related operations includes at least one of the following:

[0454] An AI / ML functional entity that processes AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity, an AI / ML functional entity that processes AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity, and an AI / ML functional entity that can process AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity and can also process AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity.

[0455] In some embodiments, when the types of functional entities for processing AI / ML-related operations are divided based on the types of AI / ML-related operations that can be processed, different types of AI / ML functional entities support different sets of AI / ML-related operation tasks.

[0456] In some embodiments, the AI / ML-related operation includes at least one of the following operation tasks: a data management task, a storage management task, a computing power management task, and a model management task;

[0457] The data management task includes at least one of the following: data collection, data storage, data modification, data update, data deletion, data replication, and data forwarding;

[0458] The storage management task includes at least one of the following: remaining storage size indication, storage reservation, storage allocation, storage sharing, storage recycling, and storage formatting;

[0459] The computing power management task includes at least one of the following: remaining computing power indication, computing power reservation, computing power allocation, computing power sharing, and computing power recovery;

[0460] Among them, the model management task includes at least one of the following: model training, model verification, model testing, model deployment, model replication, model forwarding, model reasoning, model monitoring, model update, model activation, model deactivation, model deletion, and model switching.

[0461] In some embodiments, the M pieces of information include third information for indicating whether the first device supports data collection capabilities for AI / ML purposes.

[0462] In some embodiments, the third information includes a third bit;

[0463] The third bit is used to indicate whether the first device as a whole supports data collection capabilities for AI / ML purposes.

[0464] In some embodiments, the third information is associated with the type of data collected.

[0465] In some embodiments, the type of data collected in association with the third information is agreed upon by a protocol, or the type of data collected in association with the third information is indicated by an information field contained in the third information.

[0466] In some embodiments, the third information includes n3 groups of sub-information, each group of sub-information in the n3 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n3 is a positive integer;

[0467] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes; or

[0468] In the case where each group of sub-information includes at least two bits, each group of sub-information includes a tenth information field and an eleventh information field, the tenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or a group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and the eleventh information field included in the i-th group of sub-information is used to indicate a set of collected data types; or

[0469] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a twelfth information field, and the twelfth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and indicates a set of collected data types; or

[0470] In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a data type set collected by one or a group of functional units within the first device associated with the i-th group of sub-information;

[0471] Wherein, i is a positive integer, and 1≤i≤n3.

[0472] In some embodiments, the tenth information field includes one bit, and / or the eleventh information field includes at least one bit.

[0473] In some embodiments, each bit of the eleventh information field is associated with one or a group of types of collected data, and the value of each bit contained in the eleventh information field determines whether the associated one or a group of types of collected data supports being collected by the first device; or,

[0474] Each bit state value of the eleventh information field is associated with one or a group of types of collected data, and each bit state value of the eleventh information field indicates that the corresponding associated one or a group of types of collected data supports being collected by the first device.

[0475] In some embodiments, the type or group of collected data associated with each bit of the eleventh information field is agreed upon by the protocol, or the type or group of collected data associated with each bit state value of the eleventh information field is agreed upon by the protocol.

[0476] In some embodiments, each group of sub-information includes a thirteenth information field, and the thirteenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0477] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n3 groups of sub-information are agreed upon by a protocol.

[0478] In some embodiments, the third information is associated with data collection trigger type indication information;

[0479] In which, the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by itself, or the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by the second device, or the bit state value of the data collection trigger type indication information is used to indicate that the first device supports both data collection triggered by itself and data collection triggered by the second device.

[0480] In some embodiments, the data collection trigger type indication information associated with the third information is agreed upon by a protocol, or the data collection trigger type indication information associated with the third information is indicated by an information field in the third information.

[0481] In some embodiments, the M pieces of information include fourth information, where the fourth information is used to indicate whether the first device supports data reporting capabilities for AI / ML purposes.

[0482] In some embodiments, the fourth information includes a fourth bit;

[0483] The fourth bit is used to indicate whether the first device as a whole supports data reporting capabilities for AI / ML purposes.

[0484] In some embodiments, the fourth information is associated with the reported data type.

[0485] In some embodiments, the type of data reported associated with the fourth information is agreed upon by a protocol, or the type of data reported associated with the fourth information is indicated by an information field included in the fourth information.

[0486] In some embodiments, the fourth information includes n4 groups of sub-information, each group of sub-information in the n4 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n4 is a positive integer;

[0487] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the data reporting capability for AI / ML purposes; or

[0488] When each group of sub-information includes at least two bits, each group of sub-information includes a fourteenth information field and a fifteenth information field, the fourteenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or a group of functional units within the first device associated with the i-th group of sub-information supports data reporting capabilities for AI / ML purposes, and the fifteenth information field included in the i-th group of sub-information is used to indicate a set of reported data types; or

[0489] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a sixteenth information field, and the sixteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports a data reporting capability for AI / ML purposes, and indicates a set of reported data types; or

[0490] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types reported by one or a group of functional units within the first device associated with the i-th group of sub-information;

[0491] Wherein, i is a positive integer, and 1≤i≤n4.

[0492] In some embodiments, the fourteenth information field includes one bit, and / or the fifteenth information field includes at least one bit.

[0493] In some embodiments, each bit of the fifteenth information field is associated with one or a group of types of reported data, and the value of each bit contained in the fifteenth information field determines whether the associated one or a group of types of reported data supports being reported by the first device; or,

[0494] Each bit state value of the fifteenth information field is associated with one or a group of types of reported data, and each bit state value of the fifteenth information field indicates that the corresponding associated one or a group of types of reported data supports being reported by the first device.

[0495] In some embodiments, the type of reported data or a group of reported data associated with each bit of the fifteenth information field is agreed upon by the protocol, or the type of reported data or a group of reported data associated with each bit state value of the fifteenth information field is agreed upon by the protocol.

[0496] In some embodiments, each group of sub-information includes a seventeenth information field, and the seventeenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0497] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n4 groups of sub-information are agreed upon by a protocol.

[0498] In some embodiments, the fourth information is associated with data reporting trigger type indication information;

[0499] Among them, the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by itself, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by the second device, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device supports both data reporting triggered by itself and data reporting triggered by the second device.

[0500] In some embodiments, the data reporting trigger type indication information associated with the fourth information is agreed upon by a protocol, or the data reporting trigger type indication information associated with the fourth information is indicated by an information field in the fourth information.

[0501] In some embodiments, the M pieces of information include fifth information for indicating whether the first device supports data measurement capabilities for AI / ML purposes.

[0502] In some embodiments, the fifth information includes a fifth bit;

[0503] The fifth bit is used to indicate whether the first device as a whole supports data measurement capabilities for AI / ML purposes.

[0504] In some embodiments, the fifth information is associated with the type of data measured.

[0505] In some embodiments, the data type of the measurement associated with the fifth information is agreed upon by a protocol, or the data type of the measurement associated with the fifth information is indicated by an information field included in the fifth information.

[0506] In some embodiments, the fifth information includes n5 groups of sub-information, each group of sub-information in the n5 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n5 is a positive integer;

[0507] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes; or

[0508] When each group of sub-information includes at least two bits, the each group of sub-information includes an eighteenth information field and a nineteenth information field, the eighteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and the nineteenth information field included in the i-th group of sub-information is used to indicate a set of measured data types; or

[0509] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a 20th information field, and the 20th information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and indicates a set of measured data types; or

[0510] In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a data type set measured by one or a group of functional units within the first device associated with the i-th group of sub-information;

[0511] Wherein, i is a positive integer, and 1≤i≤n5.

[0512] In some embodiments, the eighteenth information field includes one bit, and / or the nineteenth information field includes at least one bit.

[0513] In some embodiments, each bit of the nineteenth information field is associated with one or a group of types of measurement data, and the value of each bit contained in the nineteenth information field determines whether the associated one or a group of types of measurement data can be measured by the first device; or,

[0514] Each bit state value of the nineteenth information field is associated with one or a group of types of measurement data, and each bit state value of the nineteenth information field indicates that the corresponding associated one or a group of types of measurement data supports being measured by the first device.

[0515] In some embodiments, the type of measurement data or a group of measurement data associated with each bit of the nineteenth information field is agreed upon by a protocol, or the type of measurement data or a group of measurement data associated with each bit state value of the nineteenth information field is agreed upon by a protocol.

[0516] In some embodiments, each group of sub-information includes a twenty-first information field, and the twenty-first information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0517] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n5 groups of sub-information are agreed upon by a protocol.

[0518] In some embodiments, the fifth information is associated with data measurement trigger type indication information;

[0519] In which, the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by itself, or, the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by the second device, or, the bit state value of the data measurement trigger type indication information is used to indicate that the first device supports both data measurement triggered by itself and data measurement triggered by the second device.

[0520] In some embodiments, the data measurement trigger type indication information associated with the fifth information is agreed upon by a protocol, or the data measurement trigger type indication information associated with the fifth information is indicated by an information field in the fifth information.

[0521] In some embodiments, the data type includes at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data assisted pre-processing data, AI / ML model output data assisted post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

[0522] In some embodiments, the M pieces of information include sixth information, which is used to indicate whether the first device supports offline AI / ML model training capabilities.

[0523] In some embodiments, the sixth information is associated with offline AI / ML model training type indication information;

[0524] Among them, the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by itself, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by the second device, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device supports both offline AI / ML model training triggered by itself and offline AI / ML model training triggered by the second device.

[0525] In some embodiments, the offline AI / ML model training type indication information associated with the sixth information is agreed upon by the protocol, or the offline AI / ML model training type indication information associated with the sixth information is indicated by the information field in the sixth information.

[0526] In some embodiments, the M pieces of information include seventh information, which is used to indicate whether the first device supports online AI / ML model training capabilities.

[0527] In some embodiments, the seventh information is associated with online AI / ML model training type indication information;

[0528] Among them, the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by itself, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by the second device, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device supports both online AI / ML model training triggered by itself and online AI / ML model training triggered by the second device.

[0529] In some embodiments, the online AI / ML model training type indication information associated with the seventh information is agreed upon by the protocol, or the online AI / ML model training type indication information associated with the seventh information is indicated by the information field in the seventh information.

[0530] In some embodiments, the M pieces of information include eighth information, where the eighth information is used to indicate an AI / ML model running or compilation format supported by the first device.

[0531] In some embodiments, the M pieces of information include ninth information, which is used to indicate whether the first device supports AI / ML model reasoning capabilities.

[0532] In some embodiments, the ninth information is associated with AI / ML model reasoning type indication information;

[0533] In which, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports non-federated reasoning or centralized reasoning, or, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports federated reasoning or distributed reasoning, or, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device supports federated reasoning and non-federated reasoning.

[0534] In some embodiments, the AI / ML model reasoning type indication information associated with the ninth information is agreed upon by a protocol, or the AI / ML model reasoning type indication information associated with the ninth information is indicated by an information field in the ninth information.

[0535] In some embodiments, the M pieces of information include tenth information, which is used to indicate whether the first device supports AI / ML model switching capability.

[0536] In some embodiments, the tenth information is associated with AI / ML model switching type indication information;

[0537] In which, the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the first device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the second device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is allowed to be switched by the first device or the second device.

[0538] In some embodiments, the AI / ML model switching type indication information associated with the tenth information is agreed upon by a protocol, or the AI / ML model switching type indication information associated with the tenth information is indicated by an information field in the tenth information.

[0539] In some embodiments, the M pieces of information include eleventh information, which is used to indicate whether the first device supports AI / ML model activation or deactivation capabilities.

[0540] In some embodiments, the eleventh information is associated with AI / ML model activation / deactivation type indication information;

[0541] Among them, the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the first device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the second device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is allowed to be activated or deactivated by the first device or the second device.

[0542] In some embodiments, the AI / ML model activation / deactivation type indication information associated with the eleventh information is agreed upon by a protocol, or the AI / ML model activation / deactivation type indication information associated with the eleventh information is indicated by an information field in the eleventh information.

[0543] In some embodiments, the M pieces of information include twelfth information, which is used to indicate whether the first device supports AI / ML model performance monitoring capabilities.

[0544] In some embodiments, the twelfth information is associated with AI / ML model performance monitoring type indication information;

[0545] In which, the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the first device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the second device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is allowed to be executed by the first device or the second device.

[0546] In some embodiments, the AI / ML model performance monitoring type indication information associated with the twelfth information is agreed upon by the protocol, or the AI / ML model performance monitoring type indication information associated with the twelfth information is indicated by the information field in the twelfth information.

[0547] In some embodiments, the M pieces of information include thirteenth information, and the thirteenth information is used to indicate whether the first device supports AI / ML model transmission capability.

[0548] In some embodiments, the thirteenth information is associated with AI / ML model transmission type indication information;

[0549] Among them, the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model download, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model upload, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device supports AI / ML model download and AI / ML model upload.

[0550] In some embodiments, the AI / ML model transmission type indication information associated with the thirteenth information is agreed upon by a protocol, or the AI / ML model transmission type indication information associated with the thirteenth information is indicated by an information field in the thirteenth information.

[0551] In some embodiments, the M pieces of information include fourteenth information, where the fourteenth information is used to indicate whether the first device supports AI / ML model update capability.

[0552] In some embodiments, the fourteenth information is associated with AI / ML model update type indication information;

[0553] In which, the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the first device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the second device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is allowed to be updated by the first device or the second device.

[0554] In some embodiments, the AI / ML model update type indication information associated with the fourteenth information is agreed upon by a protocol, or the AI / ML model update type indication information associated with the fourteenth information is indicated by an information field in the fourteenth information.

[0555] In some embodiments, the reporting granularity of part or all of the M information is at least one of the following: the granularity of the first device, the granularity of the functional unit contained in the first device, and the granularity of the AI / ML model identification.

[0556] In some embodiments, the M pieces of information are identification information of M capability sets;

[0557] The content of capability information included in different capability sets in the M capability sets is at least partially different, or the types of capability information included in different capability sets in the M capability sets are at least partially different;

[0558] Among them, the type of capability information included in the j-th capability set in the M capability sets is at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the capability of configuring the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capability for AI / ML purposes, whether the first device supports data reporting capability for AI / ML purposes, whether the first device supports data measurement capability for AI / ML purposes, whether the first device supports offline AI / ML model training capability, whether the first device supports online AI / ML model training capability, AI / ML model running or compilation format supported by the first device, whether the first device supports AI / ML model reasoning capability, whether the first device supports AI / ML model switching capability, whether the first device supports AI / ML model activation or deactivation capability, whether the first device supports AI / ML model performance monitoring capability, whether the first device supports AI / ML model transmission capability, and whether the first device supports AI / ML model update capability;

[0559] Wherein, j is a positive integer, and 1≤j≤M.

[0560] In some embodiments, the type of capability information contained in each of the M capability sets is agreed upon by a protocol, or the type of capability information contained in each of the M capability sets is indicated or configured by a network device.

[0561] In some embodiments, the wireless communication device 300 further includes: a second communication unit 320;

[0562] The second communication unit 320 is configured to receive a second message sent by the second device;

[0563] The second message includes second AI / ML-related capability information, the second AI / ML-related capability information is associated with the second device, and the second AI / ML-related capability information includes S pieces of information, where S is a positive integer;

[0564] Among them, the S information is used to indicate at least one of the following: whether the second device is deployed with a functional entity for processing AI / ML related operations, whether the second device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the second device supports data collection capabilities for AI / ML purposes, whether the second device supports data reporting capabilities for AI / ML purposes, whether the second device supports data measurement capabilities for AI / ML purposes, whether the second device supports offline AI / ML model training capabilities, whether the second device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the second device, whether the second device supports AI / ML model reasoning capabilities, whether the second device supports AI / ML model switching capabilities, whether the second device supports AI / ML model activation or deactivation capabilities, whether the second device supports AI / ML model performance monitoring capabilities, whether the second device supports AI / ML model transmission capabilities, and whether the second device supports AI / ML model update capabilities.

[0565] In some embodiments, the reporting granularity of part or all of the S information is at least one of the following: the granularity of the second device, the granularity of the functional unit contained in the second device, and the granularity of the AI / ML model identification.

[0566] In some embodiments, the AI / ML model transmission capability includes an AI / ML model data transmission format.

[0567] In some embodiments, the first communication unit 310 is further configured to send a third message to the second device;

[0568] The third message includes capability update indication information, where the capability update indication information is used to indicate that capability information related to the first AI / ML has been updated.

[0569] In some embodiments, the capability update indication information includes a sixth bit, wherein the sixth bit is used to indicate that the capability information related to the first AI / ML has been updated; or

[0570] The capability update indication information includes multiple bits, wherein each of the multiple bits is associated with one or a group of AI / ML-related capability information, and each bit is used to indicate whether the corresponding one or a group of AI / ML-related capability information has been updated.

[0571] In some embodiments, the capability update indication information is triggered based on a preset event, where the preset event is given by a protocol agreement or configured by the second device.

[0572] In some embodiments, the second communication unit 320 is further configured to receive a fourth message sent by the second device;

[0573] The fourth message is used to instruct the first device to report the updated capability information related to the first AI / ML, or the fourth message is used to instruct the first device to report the updated capability information in the capability information related to the first AI / ML.

[0574] In some embodiments, the fourth message includes capability type indication information, where the capability type indication information is used to indicate the type of updated capability information reported by the first device.

[0575] In some embodiments, the first communication unit 310 is further configured to send a fifth message to the second device;

[0576] The fifth message includes the updated capability information related to the first AI / ML, or the fifth message includes the updated capability information specified to be reported by the second device.

[0577] In some embodiments, the first device is a terminal device, and the second device is a network device; or,

[0578] The first device is a network device, and the second device is a terminal device; or,

[0579] The first device is a terminal device, and the second device is another terminal device; or,

[0580] The first device is a network device, and the second device is another network device.

[0581] In some embodiments, when the first device is a terminal device, the functional units inside the first device include at least one of the following: a non-access stratum (NAS) layer functional entity, a service data adaptation protocol (SDAP) layer functional entity, a radio resource control (RRC) layer functional entity, a packet data convergence protocol (PDCP) layer functional entity, a radio link control (RLC) layer functional entity, a backhaul adaptation protocol (BAP) layer functional entity, a media access control (MAC) layer functional entity, and a physical layer (PHY) layer functional entity; or

[0582] When the first device is an access network device, the functional units inside the first device include at least one of the following: a centralized unit CU, a distributed unit DU, a centralized unit control plane CU-CP, a centralized unit user plane CU-UP, a NAS layer functional entity, a SDAP layer functional entity, an RRC layer functional entity, a PDCP layer functional entity, an RLC layer functional entity, a MAC layer functional entity, a PHY layer functional entity, and a BAP layer functional entity; or

[0583] When the first device is a core network device, the functional units inside the first device include at least one of the following: access and mobility management function AMF network element, authentication server function AUSF network element, user plane function UPF network element, session management function SMF network element, location management function LMF network element, policy control function PCF network element, and unified data management UDM network element.

[0584] In some embodiments, the message is one of the following: a NAS message, an AS message, an interface message, an AL / ML dedicated message.

[0585] In some embodiments, the communication unit may be a communication interface or a transceiver, or an input / output interface of a communication chip or a system on chip.

[0586] It should be understood that the device 300 for wireless communication according to an embodiment of the present application may correspond to the first device in the method embodiment of the present application, and the above-mentioned and other operations and / or functions of each unit in the device 300 for wireless communication are respectively for implementing the corresponding processes of the first device in the method 200 shown in Figure 2. For the sake of brevity, they will not be repeated here.

[0587] FIG4 shows a schematic block diagram of a wireless communication device 400 according to an embodiment of the present application. The wireless communication device 400 is a second device. As shown in FIG4 , the wireless communication device 400 includes:

[0588] The first communication unit 410 is configured to receive a first message sent by a first device;

[0589] The first message includes first artificial intelligence (AI) / machine learning (ML) related capability information, the first AI / ML related capability information is associated with the first device, and the first AI / ML related capability information includes M pieces of information, where M is a positive integer;

[0590] Among them, the M information is used to indicate at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capabilities for AI / ML purposes, whether the first device supports data reporting capabilities for AI / ML purposes, whether the first device supports data measurement capabilities for AI / ML purposes, whether the first device supports offline AI / ML model training capabilities, whether the first device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the first device, whether the first device supports AI / ML model reasoning capabilities, whether the first device supports AI / ML model switching capabilities, whether the first device supports AI / ML model activation or deactivation capabilities, whether the first device supports AI / ML model performance monitoring capabilities, whether the first device supports AI / ML model transmission capabilities, and whether the first device supports AI / ML model update capabilities.

[0591] In some embodiments, the M pieces of information include first information, which is used to indicate whether the first device is deployed with a functional entity that processes AI / ML related operations.

[0592] In some embodiments, the first information includes a first bit;

[0593] Among them, the first bit is used to indicate whether the first device as a whole is deployed with a functional entity for processing AI / ML related operations, and some or all functional units within the first device share the capability indicated by the first bit.

[0594] In some embodiments, the first information includes n1 groups of sub-information, each group of sub-information in the n1 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n1 is a positive integer;

[0595] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information deploys a functional entity for processing AI / ML-related operations; or

[0596] When each group of sub-information includes at least two bits, each group of sub-information includes a first information field and a second information field, the first information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information, and the second information field included in the i-th group of sub-information is used to indicate the type of the functional entity for processing AI / ML-related operations deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information; or

[0597] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a third information field, and the third information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in one or a group of functional units within the first device associated with the i-th group of sub-information, and to indicate the type of the functional entity for processing AI / ML-related operations deployed in the one or a group of functional units within the first device associated with the i-th group of sub-information; or

[0598] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity deployed in one or a group of functional units within the first device associated with the i-th group of sub-information and for processing AI / ML-related operations;

[0599] Wherein, i is a positive integer, and 1≤i≤n1.

[0600] In some embodiments, the first information field includes one bit, and / or the second information field includes at least one bit.

[0601] In some embodiments, each group of sub-information includes a fourth information field, and the fourth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0602] In some embodiments, the functional unit or units within the first device associated with each set of sub-information in the n1 sets of sub-information are agreed upon by a protocol.

[0603] In some embodiments, the M pieces of information include second information, where the second information is used to indicate whether the first device supports the ability to configure on-demand functional entities for processing AI / ML related operations.

[0604] In some embodiments, the second information includes a second bit;

[0605] The second bit is used to indicate whether the first device supports the capability of configuring functional entities for processing AI / ML related operations on demand, and some or all functional units within the first device share the capability indicated by the second bit.

[0606] In some embodiments, the second information includes n2 groups of sub-information, each group of sub-information in the n2 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n2 is a positive integer;

[0607] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations; or

[0608] When each group of sub-information includes at least two bits, each group of sub-information includes a fifth information field and a sixth information field, the fifth information field included in the i-th group of sub-information is used to indicate whether the functional unit within one or a group of first devices associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and the sixth information field included in the i-th group of sub-information is used to indicate a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional unit within one or a group of first devices associated with the i-th group of sub-information; or

[0609] When each group of sub-information includes at least one bit, each group of sub-information includes a seventh information field, and the seventh information field included in the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and indicates a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional units or the group of functional units within the first device associated with the i-th group of sub-information; or

[0610] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity configured on demand for processing AI / ML-related operations in one or a group of functional units within the first device associated with the i-th group of sub-information;

[0611] Wherein, i is a positive integer, and 1≤i≤n2.

[0612] In some embodiments, the fifth information field includes one bit, and / or the sixth information field includes at least one bit.

[0613] In some embodiments, each bit of the sixth information field is associated with one or a group of AI / ML functional entities, and the value of each bit contained in the sixth information field determines whether the associated one or a group of AI / ML functional entities supports on-demand configuration; or

[0614] Each bit state value of the sixth information field is associated with one or a group of AI / ML functional entities, and each bit state value of the sixth information field indicates that the corresponding associated one or a group of AI / ML functional entities supports on-demand configuration.

[0615] In some embodiments, the type or group of AI / ML functional entities associated with each bit of the sixth information field is agreed upon by the protocol, or the type or group of AI / ML functional entities associated with each bit state value of the sixth information field is agreed upon by the protocol.

[0616] In some embodiments, each group of sub-information includes an eighth information field, and the eighth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0617] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n2 groups of sub-information are agreed upon by a protocol.

[0618] In some embodiments, the second information is associated with configuration mode information, wherein the configuration mode information is used to indicate a method for triggering establishment of an on-demand configured AI / ML functional entity.

[0619] In some embodiments, the method of triggering the establishment of the on-demand configured AI / ML functional entity includes at least one of the following: the first device autonomously triggers the establishment of the on-demand configured AI / ML functional entity, the second device actively requests the first device to establish the on-demand configured AI / ML functional entity, and the first device initiates the establishment request and obtains confirmation from the second device before the first device establishes the on-demand configured AI / ML functional entity.

[0620] In some embodiments, the configuration mode information associated with the second information is agreed upon by a protocol; or,

[0621] The second information includes a ninth information field, and the ninth information field is used to indicate configuration mode information associated with the second information.

[0622] In some embodiments, when the ninth information field is used to indicate the configuration mode information associated with the second information, the indication granularity of the ninth information field includes one of the following: the first device granularity, the sub-information group granularity contained in the second information, and the AI / ML functional entity type granularity configured on demand.

[0623] In some embodiments, when the indication granularity of the ninth information field is the granularity of the first device, the configuration modes corresponding to all on-demand configured AI / ML functional entities supported by the first device are the same; or,

[0624] When the indication granularity of the ninth information field is the granularity of the sub-information group contained in the second information, the configuration modes corresponding to all on-demand AI / ML functional entities supported by each sub-information group contained in the second information are the same; or

[0625] When the indication granularity of the ninth information field is the on-demand configuration of the AI / ML functional entity type granularity, each on-demand configuration of the AI / ML functional entity type supported by the second information is separately associated with a configuration mode information for indicating the configuration mode supported by the AI / ML functional entity of this type.

[0626] In some embodiments, the types of functional entities for processing AI / ML related operations are divided based on the sources of the processable AI / ML related operations, or the types of functional entities for processing AI / ML related operations are divided based on the types of the processable AI / ML related operations.

[0627] In some embodiments, when the type of the functional entity for processing AI / ML-related operations is divided based on the source of the processable AI / ML-related operations, the type of the functional entity for processing AI / ML-related operations includes at least one of the following:

[0628] An AI / ML functional entity that processes AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity, an AI / ML functional entity that processes AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity, and an AI / ML functional entity that can process AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity and can also process AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity.

[0629] In some embodiments, when the types of functional entities for processing AI / ML-related operations are divided based on the types of AI / ML-related operations that can be processed, different types of AI / ML functional entities support different sets of AI / ML-related operation tasks.

[0630] In some embodiments, the AI / ML-related operation includes at least one of the following operation tasks: a data management task, a storage management task, a computing power management task, and a model management task;

[0631] The data management task includes at least one of the following: data collection, data storage, data modification, data update, data deletion, data replication, and data forwarding;

[0632] The storage management task includes at least one of the following: remaining storage size indication, storage reservation, storage allocation, storage sharing, storage recycling, and storage formatting;

[0633] The computing power management task includes at least one of the following: remaining computing power indication, computing power reservation, computing power allocation, computing power sharing, and computing power recovery;

[0634] Among them, the model management task includes at least one of the following: model training, model verification, model testing, model deployment, model replication, model forwarding, model reasoning, model monitoring, model update, model activation, model deactivation, model deletion, and model switching.

[0635] In some embodiments, the M pieces of information include third information for indicating whether the first device supports data collection capabilities for AI / ML purposes.

[0636] In some embodiments, the third information includes a third bit;

[0637] The third bit is used to indicate whether the first device as a whole supports data collection capabilities for AI / ML purposes.

[0638] In some embodiments, the third information is associated with the type of data collected.

[0639] In some embodiments, the type of data collected in association with the third information is agreed upon by a protocol, or the type of data collected in association with the third information is indicated by an information field contained in the third information.

[0640] In some embodiments, the third information includes n3 groups of sub-information, each group of sub-information in the n3 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n3 is a positive integer;

[0641] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes; or

[0642] In the case where each group of sub-information includes at least two bits, each group of sub-information includes a tenth information field and an eleventh information field, the tenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or a group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and the eleventh information field included in the i-th group of sub-information is used to indicate a set of collected data types; or

[0643] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a twelfth information field, and the twelfth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and indicates a set of collected data types; or

[0644] In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a data type set collected by one or a group of functional units within the first device associated with the i-th group of sub-information;

[0645] Wherein, i is a positive integer, and 1≤i≤n3.

[0646] In some embodiments, the tenth information field includes one bit, and / or the eleventh information field includes at least one bit.

[0647] In some embodiments, each bit of the eleventh information field is associated with one or a group of types of collected data, and the value of each bit contained in the eleventh information field determines whether the associated one or a group of types of collected data supports being collected by the first device; or,

[0648] Each bit state value of the eleventh information field is associated with one or a group of types of collected data, and each bit state value of the eleventh information field indicates that the corresponding associated one or a group of types of collected data supports being collected by the first device.

[0649] In some embodiments, the type or group of collected data associated with each bit of the eleventh information field is agreed upon by the protocol, or the type or group of collected data associated with each bit state value of the eleventh information field is agreed upon by the protocol.

[0650] In some embodiments, each group of sub-information includes a thirteenth information field, and the thirteenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0651] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n3 groups of sub-information are agreed upon by a protocol.

[0652] In some embodiments, the third information is associated with data collection trigger type indication information;

[0653] In which, the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by itself, or the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by the second device, or the bit state value of the data collection trigger type indication information is used to indicate that the first device supports both data collection triggered by itself and data collection triggered by the second device.

[0654] In some embodiments, the data collection trigger type indication information associated with the third information is agreed upon by a protocol, or the data collection trigger type indication information associated with the third information is indicated by an information field in the third information.

[0655] In some embodiments, the M pieces of information include fourth information, where the fourth information is used to indicate whether the first device supports data reporting capabilities for AI / ML purposes.

[0656] In some embodiments, the fourth information includes a fourth bit;

[0657] The fourth bit is used to indicate whether the first device as a whole supports data reporting capabilities for AI / ML purposes.

[0658] In some embodiments, the fourth information is associated with the reported data type.

[0659] In some embodiments, the type of data reported associated with the fourth information is agreed upon by a protocol, or the type of data reported associated with the fourth information is indicated by an information field included in the fourth information.

[0660] In some embodiments, the fourth information includes n4 groups of sub-information, each group of sub-information in the n4 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n4 is a positive integer;

[0661] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the data reporting capability for AI / ML purposes; or

[0662] When each group of sub-information includes at least two bits, each group of sub-information includes a fourteenth information field and a fifteenth information field, the fourteenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or a group of functional units within the first device associated with the i-th group of sub-information supports data reporting capabilities for AI / ML purposes, and the fifteenth information field included in the i-th group of sub-information is used to indicate a set of reported data types; or

[0663] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a sixteenth information field, and the sixteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports a data reporting capability for AI / ML purposes, and indicates a set of reported data types; or

[0664] When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types reported by one or a group of functional units within the first device associated with the i-th group of sub-information;

[0665] Wherein, i is a positive integer, and 1≤i≤n4.

[0666] In some embodiments, the fourteenth information field includes one bit, and / or the fifteenth information field includes at least one bit.

[0667] In some embodiments, each bit of the fifteenth information field is associated with one or a group of types of reported data, and the value of each bit contained in the fifteenth information field determines whether the associated one or a group of types of reported data supports being reported by the first device; or,

[0668] Each bit state value of the fifteenth information field is associated with one or a group of types of reported data, and each bit state value of the fifteenth information field indicates that the corresponding associated one or a group of types of reported data supports being reported by the first device.

[0669] In some embodiments, the type of reported data or a group of reported data associated with each bit of the fifteenth information field is agreed upon by the protocol, or the type of reported data or a group of reported data associated with each bit state value of the fifteenth information field is agreed upon by the protocol.

[0670] In some embodiments, each group of sub-information includes a seventeenth information field, and the seventeenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0671] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n4 groups of sub-information are agreed upon by a protocol.

[0672] In some embodiments, the fourth information is associated with data reporting trigger type indication information;

[0673] Among them, the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by itself, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by the second device, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device supports both data reporting triggered by itself and data reporting triggered by the second device.

[0674] In some embodiments, the data reporting trigger type indication information associated with the fourth information is agreed upon by a protocol, or the data reporting trigger type indication information associated with the fourth information is indicated by an information field in the fourth information.

[0675] In some embodiments, the M pieces of information include fifth information for indicating whether the first device supports data measurement capabilities for AI / ML purposes.

[0676] In some embodiments, the fifth information includes a fifth bit;

[0677] The fifth bit is used to indicate whether the first device as a whole supports data measurement capabilities for AI / ML purposes.

[0678] In some embodiments, the fifth information is associated with the type of data measured.

[0679] In some embodiments, the data type of the measurement associated with the fifth information is agreed upon by a protocol, or the data type of the measurement associated with the fifth information is indicated by an information field included in the fifth information.

[0680] In some embodiments, the fifth information includes n5 groups of sub-information, each group of sub-information in the n5 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n5 is a positive integer;

[0681] In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes; or

[0682] When each group of sub-information includes at least two bits, the each group of sub-information includes an eighteenth information field and a nineteenth information field, the eighteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and the nineteenth information field included in the i-th group of sub-information is used to indicate a set of measured data types; or

[0683] In the case where each group of sub-information includes at least one bit, each group of sub-information includes a 20th information field, and the 20th information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and indicates a set of measured data types; or

[0684] In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a data type set measured by one or a group of functional units within the first device associated with the i-th group of sub-information;

[0685] Wherein, i is a positive integer, and 1≤i≤n5.

[0686] In some embodiments, the eighteenth information field includes one bit, and / or the nineteenth information field includes at least one bit.

[0687] In some embodiments, each bit of the nineteenth information field is associated with one or a group of types of measurement data, and the value of each bit contained in the nineteenth information field determines whether the associated one or a group of types of measurement data support being measured by the first device; or, each bit state value of the nineteenth information field is associated with one or a group of types of measurement data, and each bit state value of the nineteenth information field indicates that the corresponding associated one or a group of types of measurement data support being measured by the first device.

[0688] In some embodiments, the type of measurement data or a group of measurement data associated with each bit of the nineteenth information field is agreed upon by a protocol, or the type of measurement data or a group of measurement data associated with each bit state value of the nineteenth information field is agreed upon by a protocol.

[0689] In some embodiments, each group of sub-information includes a twenty-first information field, and the twenty-first information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

[0690] In some embodiments, the functional unit or units within the first device associated with each group of sub-information in the n5 groups of sub-information are agreed upon by a protocol.

[0691] In some embodiments, the fifth information is associated with data measurement trigger type indication information;

[0692] In which, the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by itself, or, the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by the second device, or, the bit state value of the data measurement trigger type indication information is used to indicate that the first device supports both data measurement triggered by itself and data measurement triggered by the second device.

[0693] In some embodiments, the data measurement trigger type indication information associated with the fifth information is agreed upon by a protocol, or the data measurement trigger type indication information associated with the fifth information is indicated by an information field in the fifth information.

[0694] In some embodiments, the data type includes at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data assisted pre-processing data, AI / ML model output data assisted post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

[0695] In some embodiments, the M pieces of information include sixth information, which is used to indicate whether the first device supports offline AI / ML model training capabilities.

[0696] In some embodiments, the sixth information is associated with offline AI / ML model training type indication information;

[0697] Among them, the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by itself, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by the second device, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device supports both offline AI / ML model training triggered by itself and offline AI / ML model training triggered by the second device.

[0698] In some embodiments, the offline AI / ML model training type indication information associated with the sixth information is agreed upon by the protocol, or the offline AI / ML model training type indication information associated with the sixth information is indicated by the information field in the sixth information.

[0699] In some embodiments, the M pieces of information include seventh information, which is used to indicate whether the first device supports online AI / ML model training capabilities.

[0700] In some embodiments, the seventh information is associated with online AI / ML model training type indication information;

[0701] Among them, the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by itself, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by the second device, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device supports both online AI / ML model training triggered by itself and online AI / ML model training triggered by the second device.

[0702] In some embodiments, the online AI / ML model training type indication information associated with the seventh information is agreed upon by the protocol, or the online AI / ML model training type indication information associated with the seventh information is indicated by the information field in the seventh information.

[0703] In some embodiments, the M pieces of information include eighth information, where the eighth information is used to indicate an AI / ML model running or compilation format supported by the first device.

[0704] In some embodiments, the M pieces of information include ninth information, which is used to indicate whether the first device supports AI / ML model reasoning capabilities.

[0705] In some embodiments, the ninth information is associated with AI / ML model reasoning type indication information;

[0706] In which, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports non-federated reasoning or centralized reasoning, or, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports federated reasoning or distributed reasoning, or, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device supports federated reasoning and non-federated reasoning.

[0707] In some embodiments, the AI / ML model reasoning type indication information associated with the ninth information is agreed upon by a protocol, or the AI / ML model reasoning type indication information associated with the ninth information is indicated by an information field in the ninth information.

[0708] In some embodiments, the M pieces of information include tenth information, which is used to indicate whether the first device supports AI / ML model switching capability.

[0709] In some embodiments, the tenth information is associated with AI / ML model switching type indication information;

[0710] In which, the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the first device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the second device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is allowed to be switched by the first device or the second device.

[0711] In some embodiments, the AI / ML model switching type indication information associated with the tenth information is agreed upon by a protocol, or the AI / ML model switching type indication information associated with the tenth information is indicated by an information field in the tenth information.

[0712] In some embodiments, the M pieces of information include eleventh information, which is used to indicate whether the first device supports AI / ML model activation or deactivation capabilities.

[0713] In some embodiments, the eleventh information is associated with AI / ML model activation / deactivation type indication information;

[0714] Among them, the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the first device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the second device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is allowed to be activated or deactivated by the first device or the second device.

[0715] In some embodiments, the AI / ML model activation / deactivation type indication information associated with the eleventh information is agreed upon by a protocol, or the AI / ML model activation / deactivation type indication information associated with the eleventh information is indicated by an information field in the eleventh information.

[0716] In some embodiments, the M pieces of information include twelfth information, which is used to indicate whether the first device supports AI / ML model performance monitoring capabilities.

[0717] In some embodiments, the twelfth information is associated with AI / ML model performance monitoring type indication information;

[0718] In which, the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the first device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the second device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is allowed to be executed by the first device or the second device.

[0719] In some embodiments, the AI / ML model performance monitoring type indication information associated with the twelfth information is agreed upon by the protocol, or the AI / ML model performance monitoring type indication information associated with the twelfth information is indicated by the information field in the twelfth information.

[0720] In some embodiments, the M pieces of information include thirteenth information, and the thirteenth information is used to indicate whether the first device supports AI / ML model transmission capability.

[0721] In some embodiments, the thirteenth information is associated with AI / ML model transmission type indication information;

[0722] Among them, the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model download, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model upload, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device supports AI / ML model download and AI / ML model upload.

[0723] In some embodiments, the AI / ML model transmission type indication information associated with the thirteenth information is agreed upon by a protocol, or the AI / ML model transmission type indication information associated with the thirteenth information is indicated by an information field in the thirteenth information.

[0724] In some embodiments, the M pieces of information include fourteenth information, where the fourteenth information is used to indicate whether the first device supports AI / ML model update capability.

[0725] In some embodiments, the fourteenth information is associated with AI / ML model update type indication information;

[0726] In which, the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the first device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the second device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is allowed to be updated by the first device or the second device.

[0727] In some embodiments, the AI / ML model update type indication information associated with the fourteenth information is agreed upon by a protocol, or the AI / ML model update type indication information associated with the fourteenth information is indicated by an information field in the fourteenth information.

[0728] In some embodiments, the reporting granularity of part or all of the M information is at least one of the following: the granularity of the first device, the granularity of the functional unit contained in the first device, and the granularity of the AI / ML model identification.

[0729] In some embodiments, the M pieces of information are identification information of M capability sets;

[0730] The content of capability information included in different capability sets in the M capability sets is at least partially different, or the types of capability information included in different capability sets in the M capability sets are at least partially different;

[0731] Among them, the type of capability information included in the j-th capability set in the M capability sets is at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the capability of configuring the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capability for AI / ML purposes, whether the first device supports data reporting capability for AI / ML purposes, whether the first device supports data measurement capability for AI / ML purposes, whether the first device supports offline AI / ML model training capability, whether the first device supports online AI / ML model training capability, AI / ML model running or compilation format supported by the first device, whether the first device supports AI / ML model reasoning capability, whether the first device supports AI / ML model switching capability, whether the first device supports AI / ML model activation or deactivation capability, whether the first device supports AI / ML model performance monitoring capability, whether the first device supports AI / ML model transmission capability, and whether the first device supports AI / ML model update capability;

[0732] Wherein, j is a positive integer, and 1≤j≤M.

[0733] In some embodiments, the type of capability information contained in each of the M capability sets is agreed upon by a protocol, or the type of capability information contained in each of the M capability sets is indicated or configured by a network device.

[0734] In some embodiments, the wireless communication device 400 further includes: a second communication unit 420;

[0735] The second communication unit 420 is configured to send a second message to the first device;

[0736] The second message includes second AI / ML-related capability information, the second AI / ML-related capability information is associated with the second device, and the second AI / ML-related capability information includes S pieces of information, where S is a positive integer;

[0737] Among them, the S information is used to indicate at least one of the following: whether the second device is deployed with a functional entity for processing AI / ML related operations, whether the second device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the second device supports data collection capabilities for AI / ML purposes, whether the second device supports data reporting capabilities for AI / ML purposes, whether the second device supports data measurement capabilities for AI / ML purposes, whether the second device supports offline AI / ML model training capabilities, whether the second device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the second device, whether the second device supports AI / ML model reasoning capabilities, whether the second device supports AI / ML model switching capabilities, whether the second device supports AI / ML model activation or deactivation capabilities, whether the second device supports AI / ML model performance monitoring capabilities, whether the second device supports AI / ML model transmission capabilities, and whether the second device supports AI / ML model update capabilities.

[0738] In some embodiments, the reporting granularity of part or all of the S information is at least one of the following: the granularity of the second device, the granularity of the functional unit contained in the second device, and the granularity of the AI / ML model identification.

[0739] In some embodiments, the AI / ML model transmission capability includes an AI / ML model data transmission format.

[0740] In some embodiments, the first communication unit 410 is further configured to receive a third message sent by the first device;

[0741] The third message includes capability update indication information, where the capability update indication information is used to indicate that capability information related to the first AI / ML has been updated.

[0742] In some embodiments, the capability update indication information includes a sixth bit, wherein the sixth bit is used to indicate that the capability information related to the first AI / ML has been updated; or

[0743] The capability update indication information includes multiple bits, wherein each of the multiple bits is associated with one or a group of AI / ML-related capability information, and each bit is used to indicate whether the corresponding one or a group of AI / ML-related capability information has been updated.

[0744] In some embodiments, the second communication unit 420 is further configured to send a fourth message to the first device;

[0745] The fourth message is used to instruct the first device to report the updated capability information related to the first AI / ML, or the fourth message is used to instruct the first device to report the updated capability information in the capability information related to the first AI / ML.

[0746] In some embodiments, the fourth message includes capability type indication information, where the capability type indication information is used to indicate the type of updated capability information reported by the first device.

[0747] In some embodiments, the first communication unit 410 is further configured to receive a fifth message sent by the first device;

[0748] The fifth message includes the updated capability information related to the first AI / ML, or the fifth message includes the updated capability information specified to be reported by the second device.

[0749] In some embodiments, the first device is a terminal device, and the second device is a network device; or,

[0750] The first device is a network device, and the second device is a terminal device; or,

[0751] The first device is a terminal device, and the second device is another terminal device; or,

[0752] The first device is a network device, and the second device is another network device.

[0753] In some embodiments, when the first device is a terminal device, the functional units inside the first device include at least one of the following: a non-access stratum (NAS) layer functional entity, a service data adaptation protocol (SDAP) layer functional entity, a radio resource control (RRC) layer functional entity, a packet data convergence protocol (PDCP) layer functional entity, a radio link control (RLC) layer functional entity, a backhaul adaptation protocol (BAP) layer functional entity, a media access control (MAC) layer functional entity, and a physical layer (PHY) layer functional entity; or

[0754] When the first device is an access network device, the functional units inside the first device include at least one of the following: a centralized unit CU, a distributed unit DU, a centralized unit control plane CU-CP, a centralized unit user plane CU-UP, a NAS layer functional entity, a SDAP layer functional entity, an RRC layer functional entity, a PDCP layer functional entity, an RLC layer functional entity, a MAC layer functional entity, a PHY layer functional entity, and a BAP layer functional entity; or

[0755] When the first device is a core network device, the functional units inside the first device include at least one of the following: access and mobility management function AMF network element, authentication server function AUSF network element, user plane function UPF network element, session management function SMF network element, location management function LMF network element, policy control function PCF network element, and unified data management UDM network element.

[0756] In some embodiments, the message is one of the following: a NAS message, an AS message, an interface message, an AL / ML dedicated message.

[0757] In some embodiments, the communication unit may be a communication interface or a transceiver, or an input / output interface of a communication chip or a system on chip.

[0758] It should be understood that the device 400 for wireless communication according to an embodiment of the present application may correspond to the second device in the method embodiment of the present application, and the above-mentioned and other operations and / or functions of each unit in the device 400 for wireless communication are respectively for implementing the corresponding processes of the second device in the method 200 shown in Figure 2. For the sake of brevity, they will not be repeated here.

[0759] Figure 5 is a schematic structural diagram of a communication device 500 provided in an embodiment of the present application. The communication device 500 shown in Figure 5 includes a processor 510, which can call and run a computer program from a memory to implement the method in the embodiment of the present application.

[0760] In some embodiments, as shown in FIG5 , the communication device 500 may further include a memory 520. The processor 510 may call and execute a computer program from the memory 520 to implement the method in the embodiment of the present application.

[0761] The memory 520 may be a separate device independent of the processor 510 , or may be integrated into the processor 510 .

[0762] In some embodiments, as shown in FIG5 , the communication device 500 may further include a transceiver 530 , and the processor 510 may control the transceiver 530 to communicate with other devices, specifically, to send information or data to other devices, or to receive information or data sent by other devices.

[0763] The transceiver 530 may include a transmitter and a receiver. The transceiver 530 may further include an antenna, and the number of antennas may be one or more.

[0764] In some embodiments, the processor 510 may implement the functionality of a processing unit in the first device, or the processor 510 may implement the functionality of a processing unit in the second device, which will not be described in detail here for the sake of brevity.

[0765] In some embodiments, the transceiver 530 may implement the functionality of the communication unit in the first device, which will not be described in detail here for the sake of brevity.

[0766] In some embodiments, the transceiver 530 may implement the functionality of a communication unit in the second device, which will not be described in detail here for the sake of brevity.

[0767] In some embodiments, the communication device 500 may specifically be the first device of the embodiment of the present application, and the communication device 500 may implement the corresponding processes implemented by the first device in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0768] In some embodiments, the communication device 500 may specifically be the second device of the embodiment of the present application, and the communication device 500 may implement the corresponding processes implemented by the second device in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0769] Figure 6 is a schematic structural diagram of an apparatus according to an embodiment of the present application. The apparatus 600 shown in Figure 6 includes a processor 610, which can call and execute a computer program from a memory to implement the method according to the embodiment of the present application.

[0770] In some embodiments, as shown in FIG6 , the apparatus 600 may further include a memory 620 , wherein the processor 610 may call and execute a computer program from the memory 620 to implement the method in the embodiment of the present application.

[0771] The memory 620 may be a separate device independent of the processor 610 , or may be integrated into the processor 610 .

[0772] In some embodiments, the apparatus 600 may further include an input interface 630. The processor 610 may control the input interface 630 to communicate with other devices or chips, and specifically, may obtain information or data sent by other devices or chips. Optionally, the processor 610 may be located inside or outside the chip.

[0773] In some embodiments, the processor 610 may implement the functionality of a processing unit in the first device, or the processor 610 may implement the functionality of a processing unit in the second device, which will not be described in detail here for the sake of brevity.

[0774] In some embodiments, the input interface 630 may implement the functionality of a communication unit in the first device, or the input interface 630 may implement the functionality of a communication unit in the second device.

[0775] In some embodiments, the apparatus 600 may further include an output interface 640. The processor 610 may control the output interface 640 to communicate with other devices or chips, and specifically, may output information or data to other devices or chips. Optionally, the processor 610 may be located inside or outside the chip.

[0776] In some embodiments, the output interface 640 may implement the functionality of a communication unit in the first device, or the output interface 640 may implement the functionality of a communication unit in the second device.

[0777] In some embodiments, the apparatus can be applied to the first device in the embodiments of the present application, and the apparatus can implement the corresponding processes implemented by the first device in each method of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0778] In some embodiments, the apparatus can be applied to the second device in the embodiments of the present application, and the apparatus can implement the corresponding processes implemented by the second device in each method of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0779] In some embodiments, the device mentioned in the embodiments of the present application may also be a chip, such as a system-on-chip, a system-on-chip, a chip system, or a system-on-chip chip.

[0780] FIG7 is a schematic block diagram of a communication system 700 provided in an embodiment of the present application. As shown in FIG7 , the communication system 700 includes a first device 710 and a second device 720 .

[0781] Among them, the first device 710 can be used to implement the corresponding functions implemented by the first device in the above method, and the second device 720 can be used to implement the corresponding functions implemented by the second device in the above method. For the sake of brevity, they will not be repeated here.

[0782] It should be understood that the processor of the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by hardware integrated logic circuits in the processor or software instructions. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0783] It is understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0784] It should be understood that the above-mentioned memories are exemplary but not restrictive. For example, the memories in the embodiments of the present application may also be static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM RAM (DR RAM), etc. In other words, the memories in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0785] An embodiment of the present application also provides a computer-readable storage medium for storing a computer program.

[0786] In some embodiments, the computer-readable storage medium can be applied to the first device in the embodiments of the present application, and the computer program enables the computer to execute the corresponding processes implemented by the first device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0787] In some embodiments, the computer-readable storage medium can be applied to the second device in the embodiments of the present application, and the computer program enables the computer to execute the corresponding processes implemented by the second device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0788] An embodiment of the present application also provides a computer program product, including computer program instructions.

[0789] In some embodiments, the computer program product can be applied to the first device in the embodiments of the present application, and the computer program instructions enable the computer to execute the corresponding processes implemented by the first device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0790] In some embodiments, the computer program product can be applied to the second device in the embodiments of the present application, and the computer program instructions enable the computer to execute the corresponding processes implemented by the second device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0791] The embodiment of the present application also provides a computer program.

[0792] In some embodiments, the computer program can be applied to the first device in the embodiments of the present application. When the computer program runs on a computer, the computer executes the corresponding processes implemented by the first device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0793] In some embodiments, the computer program can be applied to the second device in the embodiments of the present application. When the computer program runs on a computer, the computer executes the corresponding processes implemented by the second device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0794] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0795] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0796] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0797] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0798] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0799] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. In view of this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0800] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A wireless communication method, characterized in that: include: The first device sends a first message to the second device; The first message includes first artificial intelligence (AI) / machine learning (ML) related capability information, the first AI / ML related capability information is associated with the first device, and the first AI / ML related capability information includes M pieces of information, where M is a positive integer; Among them, the M information is used to indicate at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capabilities for AI / ML purposes, whether the first device supports data reporting capabilities for AI / ML purposes, whether the first device supports data measurement capabilities for AI / ML purposes, whether the first device supports offline AI / ML model training capabilities, whether the first device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the first device, whether the first device supports AI / ML model reasoning capabilities, whether the first device supports AI / ML model switching capabilities, whether the first device supports AI / ML model activation or deactivation capabilities, whether the first device supports AI / ML model performance monitoring capabilities, whether the first device supports AI / ML model transmission capabilities, and whether the first device supports AI / ML model update capabilities.

2. The method according to claim 1, wherein The M pieces of information include first information, where the first information is used to indicate whether the first device is deployed with a functional entity that processes AI / ML related operations.

3. The method according to claim 2, wherein The first information includes a first bit; Among them, the first bit is used to indicate whether the first device as a whole is deployed with a functional entity for processing AI / ML related operations, and some or all functional units within the first device share the capabilities indicated by the first bit.

4. The method according to claim 2 or 3, wherein: The first information includes n1 groups of sub-information, each group of sub-information in the n1 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units inside the first device, and n1 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in one or a group of functional units within the first device associated with the i-th group of sub-information; or In the case where each group of sub-information includes at least two bits, each group of sub-information includes a first information field and a second information field, the first information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information, and the second information field included in the i-th group of sub-information is used to indicate the type of the functional entity for processing AI / ML-related operations deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information; or In the case where each group of sub-information includes at least one bit, each group of sub-information includes a third information field, and the third information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in one or a group of functional units within the first device associated with the i-th group of sub-information, and to indicate the type of the functional entity for processing AI / ML-related operations deployed in the one or a group of functional units within the first device associated with the i-th group of sub-information; or When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity deployed in one or a group of functional units within the first device associated with the i-th group of sub-information for processing AI / ML-related operations; Wherein, i is a positive integer, and 1≤i≤n1.

5. The method according to claim 4, wherein The first information field includes one bit, and / or the second information field includes at least one bit.

6. The method according to claim 4 or 5, characterized in that Each group of sub-information includes a fourth information field, and the fourth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

7. The method according to claim 4 or 5, characterized in that The one or a group of functional units inside the first device associated with each group of sub-information in the n1 groups of sub-information is agreed upon by the protocol.

8. The method according to claim 1, wherein The M pieces of information include second information, where the second information is used to indicate whether the first device supports the ability to configure on-demand functional entities for processing AI / ML related operations.

9. The method according to claim 8, wherein The second information includes a second bit; The second bit is used to indicate whether the first device supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and some or all functional units within the first device share the capability indicated by the second bit.

10. The method according to claim 8, wherein The second information includes n2 groups of sub-information, each group of sub-information in the n2 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n2 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations; or When each group of sub-information includes at least two bits, each group of sub-information includes a fifth information field and a sixth information field, the fifth information field included in the i-th group of sub-information is used to indicate whether the functional unit within the first device or the group of functional units associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and the sixth information field included in the i-th group of sub-information is used to indicate a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional units within the first device or the group of functional units associated with the i-th group of sub-information; or In the case where each group of sub-information includes at least one bit, each group of sub-information includes a seventh information field, and the seventh information field included in the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and indicates a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional units or the group of functional units within the first device associated with the i-th group of sub-information; or When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity configured on demand for processing AI / ML-related operations in one or a group of functional units within the first device associated with the i-th group of sub-information; Wherein, i is a positive integer, and 1≤i≤n2.

11. The method according to claim 10, wherein The fifth information field includes one bit, and / or the sixth information field includes at least one bit.

12. The method according to claim 10 or 11, wherein: Each bit of the sixth information field is associated with one or a group of AI / ML functional entities, and the value of each bit contained in the sixth information field determines whether the associated one or a group of AI / ML functional entities supports on-demand configuration; or Each bit state value of the sixth information field is associated with one or a group of AI / ML functional entities, and each bit state value of the sixth information field indicates that the corresponding associated one or a group of AI / ML functional entities supports on-demand configuration.

13. The method according to claim 12, wherein: The type or group of AI / ML functional entities associated with each bit of the sixth information field is agreed upon by the protocol, or the type or group of AI / ML functional entities associated with each bit state value of the sixth information field is agreed upon by the protocol.

14. The method according to any one of claims 10 to 13, characterized in that Each group of sub-information includes an eighth information field, and the eighth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

15. The method according to any one of claims 10 to 13, characterized in that The one or a group of functional units inside the first device associated with each group of sub-information in the n2 groups of sub-information is agreed upon by the protocol.

16. The method according to any one of claims 8 to 15, characterized in that The second information is associated with configuration mode information, wherein the configuration mode information is used to indicate a method for triggering establishment of an on-demand configured AI / ML functional entity.

17. The method according to claim 16, wherein The method of triggering the establishment of the on-demand configured AI / ML functional entity includes at least one of the following: the first device autonomously triggers the establishment of the on-demand configured AI / ML functional entity, the second device actively requests the first device to establish the on-demand configured AI / ML functional entity, and the first device initiates the establishment request and obtains confirmation from the second device, and then the first device establishes the on-demand configured AI / ML functional entity.

18. The method according to claim 16 or 17, wherein: The configuration mode information associated with the second information is agreed upon by the protocol; or, The second information includes a ninth information field, and the ninth information field is used to indicate configuration mode information associated with the second information.

19. The method according to claim 18, wherein In the case where the ninth information field is used to indicate the configuration mode information associated with the second information, the indication granularity of the ninth information field includes one of the following: the first device granularity, the sub-information group granularity contained in the second information, and the AI / ML functional entity type granularity configured on demand.

20. The method according to claim 19, wherein When the indication granularity of the ninth information field is the granularity of the first device, the configuration modes corresponding to all on-demand configured AI / ML functional entities supported by the first device are the same; or When the indication granularity of the ninth information field is the granularity of the sub-information group contained in the second information, the configuration modes corresponding to all on-demand AI / ML functional entities supported by each sub-information group contained in the second information are the same; or When the indication granularity of the ninth information field is the on-demand configuration of the AI / ML functional entity type granularity, each on-demand configuration of the AI / ML functional entity type supported by the second information is separately associated with a configuration mode information for indicating the configuration mode supported by the AI / ML functional entity of this type.

21. The method according to any one of claims 4 to 7 and 10 to 15, characterized in that The types of functional entities for processing AI / ML related operations are divided based on the sources of the processable AI / ML related operations, or the types of functional entities for processing AI / ML related operations are divided based on the types of the processable AI / ML related operations.

22. The method according to claim 21, wherein In the case where the type of the functional entity for processing the AI / ML-related operation is divided based on the source of the processable AI / ML-related operation, the type of the functional entity for processing the AI / ML-related operation includes at least one of the following: An AI / ML functional entity that processes AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity, an AI / ML functional entity that processes AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity, and an AI / ML functional entity that can process AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity and can also process AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity.

23. The method according to claim 21, wherein When the types of functional entities for processing AI / ML related operations are divided based on the types of AI / ML related operations that can be processed, different types of AI / ML functional entities support different sets of AI / ML related operation tasks.

24. The method according to any one of claims 1 to 23, characterized in that The AI / ML related operations include at least one of the following operation tasks: data management tasks, storage management tasks, computing power management tasks, and model management tasks; The data management tasks include at least one of the following: data collection, data storage, data modification, data update, data deletion, data replication, and data forwarding; The storage management task includes at least one of the following: remaining storage size indication, storage reservation, storage allocation, storage sharing, storage recycling, and storage formatting; The computing power management tasks include at least one of the following: remaining computing power indication, computing power reservation, computing power allocation, computing power sharing, and computing power recovery; Among them, the model management tasks include at least one of the following: model training, model verification, model testing, model deployment, model replication, model forwarding, model reasoning, model monitoring, model update, model activation, model deactivation, model deletion, and model switching.

25. The method of claim 1, wherein The M pieces of information include third information, where the third information is used to indicate whether the first device supports data collection capabilities for AI / ML purposes.

26. The method of claim 25, wherein: The third information includes a third bit; The third bit is used to indicate whether the first device as a whole supports data collection capabilities for AI / ML purposes.

27. The method according to claim 25 or 26, wherein The third information is associated with the type of data collected.

28. The method of claim 27, wherein: The type of data collected in association with the third information is agreed upon by a protocol, or the type of data collected in association with the third information is indicated by an information field included in the third information.

29. The method of claim 25, wherein: The third information includes n3 groups of sub-information, each group of sub-information in the n3 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units inside the first device, and n3 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes; or When each group of sub-information includes at least two bits, each group of sub-information includes a tenth information field and an eleventh information field, the tenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and the eleventh information field included in the i-th group of sub-information is used to indicate a set of collected data types; or, When each group of sub-information includes at least one bit, each group of sub-information includes a twelfth information field, and the twelfth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and indicates a set of collected data types; or, In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types collected by one or a group of functional units within the first device associated with the i-th group of sub-information; Wherein, i is a positive integer, and 1≤i≤n3.

30. The method of claim 29, wherein: The tenth information field includes one bit, and / or the eleventh information field includes at least one bit.

31. The method according to claim 29 or 30, wherein Each bit of the eleventh information field is associated with one or a group of types of collected data, and the value of each bit contained in the eleventh information field determines whether the associated one or a group of types of collected data supports being collected by the first device; or, Each bit state value of the eleventh information field is associated with one or a group of types of collected data, and each bit state value of the eleventh information field indicates that the corresponding associated one or a group of types of collected data supports being collected by the first device.

32. The method of claim 31, wherein The type or group of collected data associated with each bit of the eleventh information field is agreed upon by the protocol, or the type or group of collected data associated with each bit state value of the eleventh information field is agreed upon by the protocol.

33. The method according to any one of claims 29 to 32, wherein Each group of sub-information includes a thirteenth information field, and the thirteenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

34. The method according to any one of claims 29 to 32, wherein The one or a group of functional units inside the first device associated with each group of sub-information in the n3 groups of sub-information is agreed upon by the protocol.

35. The method according to any one of claims 25 to 34, wherein The third information is associated with the data collection trigger type indication information; Among them, the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by itself, or the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by the second device, or the bit state value of the data collection trigger type indication information is used to indicate that the first device supports both data collection triggered by itself and data collection triggered by the second device.

36. The method of claim 35, wherein: The data collection trigger type indication information associated with the third information is agreed upon by a protocol, or the data collection trigger type indication information associated with the third information is indicated by an information field in the third information.

37. The method of claim 1, wherein The M pieces of information include fourth information, where the fourth information is used to indicate whether the first device supports data reporting capabilities for AI / ML purposes.

38. The method of claim 37, wherein The fourth information includes a fourth bit; The fourth bit is used to indicate whether the first device as a whole supports data reporting capabilities for AI / ML purposes.

39. The method according to claim 37 or 38, wherein The fourth information is associated with the reported data type.

40. The method of claim 39, wherein The type of data reported in association with the fourth information is agreed upon by a protocol, or the type of data reported in association with the fourth information is indicated by an information field included in the fourth information.

41. The method of claim 37, wherein: The fourth information includes n4 groups of sub-information, each group of sub-information in the n4 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units inside the first device, and n4 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit inside the first device or the group of functional units associated with the i-th group of sub-information supports data reporting capability for AI / ML purposes; or When each group of sub-information includes at least two bits, each group of sub-information includes a fourteenth information field and a fifteenth information field, the fourteenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or a group of functional units within the first device associated with the i-th group of sub-information supports data reporting capabilities for AI / ML purposes, and the fifteenth information field included in the i-th group of sub-information is used to indicate a set of reported data types; or When each group of sub-information includes at least one bit, each group of sub-information includes a sixteenth information field, and the sixteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports a data reporting capability for AI / ML purposes, and to indicate a set of reported data types; or, In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types reported by one or a group of functional units within the first device associated with the i-th group of sub-information; Wherein, i is a positive integer, and 1≤i≤n4.

42. The method of claim 41, wherein The fourteenth information field includes one bit, and / or the fifteenth information field includes at least one bit.

43. The method according to claim 41 or 42, wherein Each bit of the fifteenth information field is associated with one or a group of types of reported data, and the value of each bit contained in the fifteenth information field determines whether the associated one or a group of types of reported data can be reported by the first device; or, Each bit state value of the fifteenth information field is associated with one or a group of types of reported data, and each bit state value of the fifteenth information field indicates that the corresponding associated one or a group of types of reported data supports being reported by the first device.

44. The method of claim 43, wherein: The type of reported data or a group of reported data associated with each bit of the fifteenth information field is agreed upon by the protocol, or the type of reported data or a group of reported data associated with each bit state value of the fifteenth information field is agreed upon by the protocol.

45. The method according to any one of claims 41 to 44, characterized in that Each group of sub-information includes a seventeenth information field, and the seventeenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

46. ​​The method according to any one of claims 41 to 44, wherein The one or a group of functional units inside the first device associated with each group of sub-information in the n4 groups of sub-information is agreed upon by the protocol.

47. The method according to any one of claims 37 to 46, wherein The fourth information is associated with the data reporting trigger type indication information; Among them, the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by itself, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by the second device, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device supports both data reporting triggered by itself and data reporting triggered by the second device.

48. The method of claim 47, wherein The data reporting trigger type indication information associated with the fourth information is agreed upon by a protocol, or the data reporting trigger type indication information associated with the fourth information is indicated by an information field in the fourth information.

49. The method of claim 1, wherein The M pieces of information include fifth information, where the fifth information is used to indicate whether the first device supports data measurement capabilities for AI / ML purposes.

50. The method of claim 49, wherein The fifth information includes a fifth bit; The fifth bit is used to indicate whether the first device as a whole supports data measurement capabilities for AI / ML purposes.

51. The method according to claim 49 or 50, wherein The fifth information is associated with the measured data type.

52. The method of claim 51, wherein The type of measurement data associated with the fifth information is agreed upon by a protocol, or the type of measurement data associated with the fifth information is indicated by an information field included in the fifth information.

53. The method of claim 49, wherein: The fifth information includes n5 groups of sub-information, each group of sub-information in the n5 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units inside the first device, and n5 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes; or When each group of sub-information includes at least two bits, each group of sub-information includes an eighteenth information field and a nineteenth information field, the eighteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and the nineteenth information field included in the i-th group of sub-information is used to indicate a set of measured data types; or When each group of sub-information includes at least one bit, each group of sub-information includes a 20th information field, and the 20th information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and to indicate a set of measured data types; or, In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types measured by one or a group of functional units within the first device associated with the i-th group of sub-information; Wherein, i is a positive integer, and 1≤i≤n5.

54. The method of claim 53, wherein: The eighteenth information field includes one bit, and / or the nineteenth information field includes at least one bit.

55. The method according to claim 53 or 54, wherein Each bit of the nineteenth information field is associated with one or a group of measurement data, and a value of each bit contained in the nineteenth information field determines whether the associated one or a group of measurement data supports measurement by the first device; or, Each bit state value of the nineteenth information field is associated with one or a group of types of measurement data, and each bit state value of the nineteenth information field indicates that the corresponding associated one or a group of types of measurement data supports being measured by the first device.

56. The method of claim 55, wherein: The type of measurement data or a group of measurement data associated with each bit of the nineteenth information field is agreed upon by the protocol, or the type of measurement data or a group of measurement data associated with each bit state value of the nineteenth information field is agreed upon by the protocol.

57. The method according to any one of claims 53 to 56, wherein Each group of sub-information includes a twenty-first information field, and the twenty-first information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

58. The method according to any one of claims 53 to 56, wherein The one or a group of functional units inside the first device associated with each group of sub-information in the n5 groups of sub-information is agreed upon by the protocol.

59. The method according to any one of claims 49 to 58, wherein The fifth information is associated with the data measurement trigger type indication information; Among them, the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by itself, or the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by the second device, or the bit state value of the data measurement trigger type indication information is used to indicate that the first device supports both data measurement triggered by itself and data measurement triggered by the second device.

60. The method of claim 59, wherein The data measurement trigger type indication information associated with the fifth information is agreed upon by a protocol, or the data measurement trigger type indication information associated with the fifth information is indicated by an information field in the fifth information.

61. The method of any one of claims 27 to 34, 39 to 46, 51 to 58, wherein: The data type includes at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data auxiliary pre-processing data, AI / ML model output data auxiliary post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

62. The method of claim 1, wherein The M pieces of information include sixth information, where the sixth information is used to indicate whether the first device supports offline AI / ML model training capabilities.

63. The method of claim 62, wherein: The sixth information is associated with offline AI / ML model training type indication information; Among them, the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by itself, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by the second device, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device supports both offline AI / ML model training triggered by itself and offline AI / ML model training triggered by the second device.

64. The method of claim 63, wherein: The offline AI / ML model training type indication information associated with the sixth information is agreed upon by the protocol, or the offline AI / ML model training type indication information associated with the sixth information is indicated by the information field in the sixth information.

65. The method of claim 1, wherein The M pieces of information include seventh information, and the seventh information is used to indicate whether the first device supports the capability of online AI / ML model training.

66. The method of claim 65, wherein The seventh information is associated with the online AI / ML model training type indication information; Among them, the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by itself, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by the second device, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device supports both online AI / ML model training triggered by itself and online AI / ML model training triggered by the second device.

67. The method of claim 66, wherein: The online AI / ML model training type indication information associated with the seventh information is agreed upon by the protocol, or the online AI / ML model training type indication information associated with the seventh information is indicated by the information field in the seventh information.

68. The method of claim 1, wherein The M pieces of information include eighth information, where the eighth information is used to indicate an AI / ML model running or compilation format supported by the first device.

69. The method of claim 1, wherein The M pieces of information include ninth information, and the ninth information is used to indicate whether the first device supports AI / ML model reasoning capabilities.

70. The method of claim 69, wherein The ninth information is associated with the AI / ML model reasoning type indication information; Among them, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports non-federated reasoning or centralized reasoning, or the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports federated reasoning or distributed reasoning, or the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device supports federated reasoning and non-federated reasoning.

71. The method of claim 70, wherein The AI / ML model reasoning type indication information associated with the ninth information is agreed upon by the protocol, or the AI / ML model reasoning type indication information associated with the ninth information is indicated by the information field in the ninth information.

72. The method of claim 1, wherein The M pieces of information include tenth information, and the tenth information is used to indicate whether the first device supports AI / ML model switching capability.

73. The method of claim 72, wherein: The tenth information is associated with the AI / ML model switching type indication information; Among them, the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the first device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the second device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is allowed to be switched by the first device or the second device.

74. The method of claim 73, wherein The AI / ML model switching type indication information associated with the tenth information is agreed upon by the protocol, or the AI / ML model switching type indication information associated with the tenth information is indicated by the information field in the tenth information.

75. The method of claim 1, wherein The M pieces of information include eleventh information, and the eleventh information is used to indicate whether the first device supports AI / ML model activation or deactivation capabilities.

76. The method of claim 75, wherein The eleventh information is associated with the AI / ML model activation / deactivation type indication information; Among them, the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the first device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the second device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is allowed to be activated or deactivated by the first device or the second device.

77. The method of claim 76, wherein The AI / ML model activation / deactivation type indication information associated with the eleventh information is agreed upon by the protocol, or the AI / ML model activation / deactivation type indication information associated with the eleventh information is indicated by the information field in the eleventh information.

78. The method of claim 1, wherein The M pieces of information include twelfth information, and the twelfth information is used to indicate whether the first device supports AI / ML model performance monitoring capabilities.

79. The method of claim 78, wherein The twelfth information is associated with the AI / ML model performance monitoring type indication information; Among them, the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the first device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the second device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is allowed to be executed by the first device or the second device.

80. The method of claim 79, wherein The AI / ML model performance monitoring type indication information associated with the twelfth information is agreed upon by the protocol, or the AI / ML model performance monitoring type indication information associated with the twelfth information is indicated by the information field in the twelfth information.

81. The method of claim 1, wherein The M pieces of information include thirteenth information, and the thirteenth information is used to indicate whether the first device supports AI / ML model transmission capability.

82. The method of claim 81, wherein The thirteenth information is associated with the AI / ML model transmission type indication information; Among them, the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model download, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model upload, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device supports AI / ML model download and AI / ML model upload.

83. The method of claim 82, wherein The AI / ML model transmission type indication information associated with the thirteenth information is agreed upon by the protocol, or the AI / ML model transmission type indication information associated with the thirteenth information is indicated by the information field in the thirteenth information.

84. The method of claim 1, wherein The M pieces of information include fourteenth information, and the fourteenth information is used to indicate whether the first device supports AI / ML model update capability.

85. The method of claim 84, wherein The fourteenth information is associated with the AI / ML model update type indication information; Among them, the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the first device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the second device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is allowed to be updated by the first device or the second device.

86. The method of claim 85, wherein The AI / ML model update type indication information associated with the fourteenth information is agreed upon by the protocol, or the AI / ML model update type indication information associated with the fourteenth information is indicated by the information field in the fourteenth information.

87. The method according to any one of claims 1 to 86, wherein The reporting granularity of part or all of the M information is at least one of the following: the granularity of the first device, the granularity of the functional unit contained in the first device, and the granularity of the AI / ML model identification.

88. The method of claim 1, wherein The M pieces of information are identification information of M capability sets; The content of capability information included in different capability sets in the M capability sets is at least partially different, or the types of capability information included in different capability sets in the M capability sets are at least partially different; Among them, the type of capability information included in the j-th capability set of the M capability sets is at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the capability of configuring the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capability for AI / ML purposes, whether the first device supports data reporting capability for AI / ML purposes, whether the first device supports data measurement capability for AI / ML purposes, whether the first device supports offline AI / ML model training capability, whether the first device supports online AI / ML model training capability, AI / ML model running or compilation format supported by the first device, whether the first device supports AI / ML model reasoning capability, whether the first device supports AI / ML model switching capability, whether the first device supports AI / ML model activation or deactivation capability, whether the first device supports AI / ML model performance monitoring capability, whether the first device supports AI / ML model transmission capability, and whether the first device supports AI / ML model update capability; Wherein, j is a positive integer, and 1≤j≤M.

89. The method of claim 88, wherein The type of capability information included in each of the M capability sets is agreed upon by a protocol, or the type of capability information included in each of the M capability sets is indicated or configured by a network device.

90. The method according to any one of claims 1 to 89, wherein The method further comprises: The first device receives a second message sent by the second device; The second message includes second AI / ML-related capability information, the second AI / ML-related capability information is associated with the second device, and the second AI / ML-related capability information includes S pieces of information, where S is a positive integer; Among them, the S information is used to indicate at least one of the following: whether the second device is deployed with a functional entity for processing AI / ML related operations, whether the second device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the second device supports data collection capabilities for AI / ML purposes, whether the second device supports data reporting capabilities for AI / ML purposes, whether the second device supports data measurement capabilities for AI / ML purposes, whether the second device supports offline AI / ML model training capabilities, whether the second device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the second device, whether the second device supports AI / ML model reasoning capabilities, whether the second device supports AI / ML model switching capabilities, whether the second device supports AI / ML model activation or deactivation capabilities, whether the second device supports AI / ML model performance monitoring capabilities, whether the second device supports AI / ML model transmission capabilities, and whether the second device supports AI / ML model update capabilities.

91. The method of claim 90, wherein The reporting granularity of part or all of the S information is at least one of the following: the granularity of the second device, the granularity of the functional unit contained in the second device, and the granularity of the AI / ML model identification.

92. The method of claim 81, 82, 83, 88, 89, 90 or 91, wherein The AI / ML model transmission capability includes an AI / ML model data transmission format.

93. The method according to any one of claims 1 to 92, wherein The method further comprises: The first device sends a third message to the second device; The third message includes capability update indication information, where the capability update indication information is used to indicate that the capability information related to the first AI / ML has been updated.

94. The method of claim 93, wherein The capability update indication information includes a sixth bit, wherein the sixth bit is used to indicate that the first AI / ML-related capability information has been updated; or The capability update indication information includes multiple bits, wherein each of the multiple bits is associated with one or a group of AI / ML-related capability information, and each bit is used to indicate whether the corresponding one or a group of AI / ML-related capability information has been updated.

95. The method of claim 93 or 94, wherein: The first device receives a fourth message sent by the second device; The fourth message is used to instruct the first device to report the updated first AI / ML-related capability information, or the fourth message is used to instruct the first device to report the updated capability information in the first AI / ML-related capability information.

96. The method of claim 95, wherein The fourth message includes capability type indication information, where the capability type indication information is used to indicate a type of updated capability information reported by the first device.

97. The method of claim 95 or 96, wherein: The first device sends a fifth message to the second device; The fifth message includes the updated capability information related to the first AI / ML, or the fifth message includes the updated capability information specified to be reported by the second device.

98. The method according to any one of claims 1 to 97, wherein The first device is a terminal device, and the second device is a network device; or, The first device is a network device, and the second device is a terminal device; or, The first device is a terminal device, and the second device is another terminal device; or, The first device is a network device, and the second device is another network device.

99. The method according to any one of claims 1 to 98, wherein In the case where the first device is a terminal device, the functional units inside the first device include at least one of the following: a non-access stratum (NAS) layer functional entity, a service data adaptation protocol (SDAP) layer functional entity, a radio resource control (RRC) layer functional entity, a packet data convergence protocol (PDCP) layer functional entity, a radio link control (RLC) layer functional entity, a backhaul adaptation protocol (BAP) layer functional entity, a media access control (MAC) layer functional entity, and a physical layer (PHY) layer functional entity; or In the case where the first device is an access network device, the functional units inside the first device include at least one of the following: a centralized unit CU, a distributed unit DU, a centralized unit control plane CU-CP, a centralized unit user plane CU-UP, a NAS layer functional entity, a SDAP layer functional entity, an RRC layer functional entity, a PDCP layer functional entity, an RLC layer functional entity, a MAC layer functional entity, a PHY layer functional entity, and a BAP layer functional entity; or When the first device is a core network device, the functional units inside the first device include at least one of the following: access and mobility management function AMF network element, authentication server function AUSF network element, user plane function UPF network element, session management function SMF network element, location management function LMF network element, policy control function PCF network element, and unified data management UDM network element.

100. The method according to any one of claims 1 to 99, wherein The message is one of the following: NAS message, AS message, interface message, AL / ML dedicated message.

101. A wireless communication method, characterized in that: include: The second device receives the first message sent by the first device; The first message includes first artificial intelligence (AI) / machine learning (ML) related capability information, the first AI / ML related capability information is associated with the first device, and the first AI / ML related capability information includes M pieces of information, where M is a positive integer; Among them, the M information is used to indicate at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capabilities for AI / ML purposes, whether the first device supports data reporting capabilities for AI / ML purposes, whether the first device supports data measurement capabilities for AI / ML purposes, whether the first device supports offline AI / ML model training capabilities, whether the first device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the first device, whether the first device supports AI / ML model reasoning capabilities, whether the first device supports AI / ML model switching capabilities, whether the first device supports AI / ML model activation or deactivation capabilities, whether the first device supports AI / ML model performance monitoring capabilities, whether the first device supports AI / ML model transmission capabilities, and whether the first device supports AI / ML model update capabilities.

102. The method of claim 101, wherein: The M pieces of information include first information, where the first information is used to indicate whether the first device is deployed with a functional entity that processes AI / ML related operations.

103. The method of claim 102, wherein: The first information includes a first bit; Among them, the first bit is used to indicate whether the first device as a whole is deployed with a functional entity for processing AI / ML related operations, and some or all functional units within the first device share the capabilities indicated by the first bit.

104. The method according to claim 102 or 103, wherein: The first information includes n1 groups of sub-information, each group of sub-information in the n1 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units inside the first device, and n1 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in one or a group of functional units within the first device associated with the i-th group of sub-information; or In the case where each group of sub-information includes at least two bits, each group of sub-information includes a first information field and a second information field, the first information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information, and the second information field included in the i-th group of sub-information is used to indicate the type of the functional entity for processing AI / ML-related operations deployed in the functional unit or group of functional units within the first device associated with the i-th group of sub-information; or In the case where each group of sub-information includes at least one bit, each group of sub-information includes a third information field, and the third information field included in the i-th group of sub-information is used to indicate whether a functional entity for processing AI / ML-related operations is deployed in one or a group of functional units within the first device associated with the i-th group of sub-information, and to indicate the type of the functional entity for processing AI / ML-related operations deployed in the one or a group of functional units within the first device associated with the i-th group of sub-information; or When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity deployed in one or a group of functional units within the first device associated with the i-th group of sub-information for processing AI / ML-related operations; Wherein, i is a positive integer, and 1≤i≤n1.

105. The method of claim 104, wherein: The first information field includes one bit, and / or the second information field includes at least one bit.

106. The method according to claim 104 or 105, wherein: Each group of sub-information includes a fourth information field, and the fourth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

107. The method according to claim 104 or 105, wherein: The one or a group of functional units inside the first device associated with each group of sub-information in the n1 groups of sub-information is agreed upon by the protocol.

108. The method of claim 101, wherein: The M pieces of information include second information, where the second information is used to indicate whether the first device supports the ability to configure on-demand functional entities for processing AI / ML related operations.

109. The method of claim 108, wherein The second information includes a second bit; The second bit is used to indicate whether the first device supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and some or all functional units within the first device share the capability indicated by the second bit.

110. The method of claim 108, wherein: The second information includes n2 groups of sub-information, each group of sub-information in the n2 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units within the first device, and n2 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations; or When each group of sub-information includes at least two bits, each group of sub-information includes a fifth information field and a sixth information field, the fifth information field included in the i-th group of sub-information is used to indicate whether the functional unit within the first device or the group of functional units associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and the sixth information field included in the i-th group of sub-information is used to indicate a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional units within the first device or the group of functional units associated with the i-th group of sub-information; or In the case where each group of sub-information includes at least one bit, each group of sub-information includes a seventh information field, and the seventh information field included in the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports the capability of on-demand configuration of functional entities for processing AI / ML-related operations, and indicates a type set of functional entities configured on-demand for processing AI / ML-related operations in the functional units or the group of functional units within the first device associated with the i-th group of sub-information; or When each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a type of a functional entity configured on demand for processing AI / ML-related operations in one or a group of functional units within the first device associated with the i-th group of sub-information; Wherein, i is a positive integer, and 1≤i≤n2.

111. The method of claim 110, wherein: The fifth information field includes one bit, and / or the sixth information field includes at least one bit.

112. The method according to claim 110 or 111, wherein: Each bit of the sixth information field is associated with one or a group of AI / ML functional entities, and the value of each bit contained in the sixth information field determines whether the associated one or a group of AI / ML functional entities supports on-demand configuration; or Each bit state value of the sixth information field is associated with one or a group of AI / ML functional entities, and each bit state value of the sixth information field indicates that the corresponding associated one or a group of AI / ML functional entities supports on-demand configuration.

113. The method of claim 112, wherein: The type or group of AI / ML functional entities associated with each bit of the sixth information field is agreed upon by the protocol, or the type or group of AI / ML functional entities associated with each bit state value of the sixth information field is agreed upon by the protocol.

114. The method according to any one of claims 110 to 113, wherein Each group of sub-information includes an eighth information field, and the eighth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

115. The method according to any one of claims 110 to 113, wherein The one or a group of functional units inside the first device associated with each group of sub-information in the n2 groups of sub-information is agreed upon by the protocol.

116. The method according to any one of claims 108 to 115, wherein The second information is associated with configuration mode information, wherein the configuration mode information is used to indicate a method for triggering establishment of an on-demand configured AI / ML functional entity.

117. The method of claim 116, wherein: The method of triggering the establishment of the on-demand configured AI / ML functional entity includes at least one of the following: the first device autonomously triggers the establishment of the on-demand configured AI / ML functional entity, the second device actively requests the first device to establish the on-demand configured AI / ML functional entity, and the first device initiates the establishment request and obtains confirmation from the second device, and then the first device establishes the on-demand configured AI / ML functional entity.

118. The method according to claim 116 or 117, wherein The configuration mode information associated with the second information is agreed upon by the protocol; or, The second information includes a ninth information field, and the ninth information field is used to indicate configuration mode information associated with the second information.

119. The method of claim 118, wherein In the case where the ninth information field is used to indicate the configuration mode information associated with the second information, the indication granularity of the ninth information field includes one of the following: the first device granularity, the sub-information group granularity contained in the second information, and the AI / ML functional entity type granularity configured on demand.

120. The method of claim 119, wherein: When the indication granularity of the ninth information field is the granularity of the first device, the configuration modes corresponding to all on-demand configured AI / ML functional entities supported by the first device are the same; or When the indication granularity of the ninth information field is the granularity of the sub-information group contained in the second information, the configuration modes corresponding to all on-demand AI / ML functional entities supported by each sub-information group contained in the second information are the same; or When the indication granularity of the ninth information field is the on-demand configuration of the AI / ML functional entity type granularity, each on-demand configuration of the AI / ML functional entity type supported by the second information is separately associated with a configuration mode information for indicating the configuration mode supported by the AI / ML functional entity of this type.

121. The method according to any one of claims 104 to 107 and 110 to 115, wherein The types of functional entities for processing AI / ML related operations are divided based on the sources of the processable AI / ML related operations, or the types of functional entities for processing AI / ML related operations are divided based on the types of the processable AI / ML related operations.

122. The method of claim 121, wherein: In the case where the type of the functional entity for processing the AI / ML-related operation is divided based on the source of the processable AI / ML-related operation, the type of the functional entity for processing the AI / ML-related operation includes at least one of the following: An AI / ML functional entity that processes AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity, an AI / ML functional entity that processes AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity, and an AI / ML functional entity that can process AI / ML related operations triggered by one or a group of functional units within the first device associated with the AI / ML functional entity and can also process AI / ML related operations triggered by functional units other than one or a group of functional units within the first device associated with the AI / ML functional entity.

123. The method of claim 121, wherein: When the types of functional entities for processing AI / ML related operations are divided based on the types of AI / ML related operations that can be processed, different types of AI / ML functional entities support different sets of AI / ML related operation tasks.

124. The method according to any one of claims 101 to 123, wherein The AI / ML related operations include at least one of the following operation tasks: data management tasks, storage management tasks, computing power management tasks, and model management tasks; The data management tasks include at least one of the following: data collection, data storage, data modification, data update, data deletion, data replication, and data forwarding; The storage management task includes at least one of the following: remaining storage size indication, storage reservation, storage allocation, storage sharing, storage recycling, and storage formatting; The computing power management tasks include at least one of the following: remaining computing power indication, computing power reservation, computing power allocation, computing power sharing, and computing power recovery; Among them, the model management tasks include at least one of the following: model training, model verification, model testing, model deployment, model replication, model forwarding, model reasoning, model monitoring, model update, model activation, model deactivation, model deletion, and model switching.

125. The method of claim 101, wherein: The M pieces of information include third information, where the third information is used to indicate whether the first device supports data collection capabilities for AI / ML purposes.

126. The method of claim 125, wherein: The third information includes a third bit; The third bit is used to indicate whether the first device as a whole supports data collection capabilities for AI / ML purposes.

127. The method of claim 125 or 126, wherein: The third information is associated with the type of data collected.

128. The method of claim 127, wherein: The type of data collected in association with the third information is agreed upon by a protocol, or the type of data collected in association with the third information is indicated by an information field included in the third information.

129. The method of claim 125, wherein: The third information includes n3 groups of sub-information, each group of sub-information in the n3 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units inside the first device, and n3 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes; or When each group of sub-information includes at least two bits, each group of sub-information includes a tenth information field and an eleventh information field, the tenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and the eleventh information field included in the i-th group of sub-information is used to indicate a set of collected data types; or, When each group of sub-information includes at least one bit, each group of sub-information includes a twelfth information field, and the twelfth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data collection capabilities for AI / ML purposes, and indicates a set of collected data types; or, In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types collected by one or a group of functional units within the first device associated with the i-th group of sub-information; Wherein, i is a positive integer, and 1≤i≤n3.

130. The method of claim 129, wherein: The tenth information field includes one bit, and / or the eleventh information field includes at least one bit.

131. The method of claim 129 or 130, wherein: Each bit of the eleventh information field is associated with one or a group of types of collected data, and the value of each bit contained in the eleventh information field determines whether the associated one or a group of types of collected data supports being collected by the first device; or, Each bit state value of the eleventh information field is associated with one or a group of types of collected data, and each bit state value of the eleventh information field indicates that the corresponding associated one or a group of types of collected data supports being collected by the first device.

132. The method of claim 131, wherein The type or group of collected data associated with each bit of the eleventh information field is agreed upon by the protocol, or the type or group of collected data associated with each bit state value of the eleventh information field is agreed upon by the protocol.

133. The method according to any one of claims 129 to 132, wherein Each group of sub-information includes a thirteenth information field, and the thirteenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

134. The method according to any one of claims 129 to 132, wherein The one or a group of functional units inside the first device associated with each group of sub-information in the n3 groups of sub-information is agreed upon by the protocol.

135. The method of any one of claims 125 to 134, wherein The third information is associated with the data collection trigger type indication information; Among them, the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by itself, or the bit state value of the data collection trigger type indication information is used to indicate that the first device only supports data collection triggered by the second device, or the bit state value of the data collection trigger type indication information is used to indicate that the first device supports both data collection triggered by itself and data collection triggered by the second device.

136. The method of claim 135, wherein: The data collection trigger type indication information associated with the third information is agreed upon by a protocol, or the data collection trigger type indication information associated with the third information is indicated by an information field in the third information.

137. The method of claim 101, wherein: The M pieces of information include fourth information, where the fourth information is used to indicate whether the first device supports data reporting capabilities for AI / ML purposes.

138. The method of claim 137, wherein The fourth information includes a fourth bit; The fourth bit is used to indicate whether the first device as a whole supports data reporting capabilities for AI / ML purposes.

139. The method of claim 137 or 138, wherein: The fourth information is associated with the reported data type.

140. The method of claim 139, wherein The type of data reported in association with the fourth information is agreed upon by a protocol, or the type of data reported in association with the fourth information is indicated by an information field included in the fourth information.

141. The method of claim 137, wherein The fourth information includes n4 groups of sub-information, each group of sub-information in the n4 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units inside the first device, and n4 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit inside the first device or the group of functional units associated with the i-th group of sub-information supports data reporting capability for AI / ML purposes; or When each group of sub-information includes at least two bits, each group of sub-information includes a fourteenth information field and a fifteenth information field, the fourteenth information field included in the i-th group of sub-information is used to indicate whether the functional unit or a group of functional units within the first device associated with the i-th group of sub-information supports data reporting capabilities for AI / ML purposes, and the fifteenth information field included in the i-th group of sub-information is used to indicate a set of reported data types; or When each group of sub-information includes at least one bit, each group of sub-information includes a sixteenth information field, and the sixteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports a data reporting capability for AI / ML purposes, and to indicate a set of reported data types; or, In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types reported by one or a group of functional units within the first device associated with the i-th group of sub-information; Wherein, i is a positive integer, and 1≤i≤n4.

142. The method of claim 141, wherein The fourteenth information field includes one bit, and / or the fifteenth information field includes at least one bit.

143. The method of claim 141 or 142, wherein: Each bit of the fifteenth information field is associated with one or a group of types of reported data, and the value of each bit contained in the fifteenth information field determines whether the associated one or a group of types of reported data can be reported by the first device; or, Each bit state value of the fifteenth information field is associated with one or a group of types of reported data, and each bit state value of the fifteenth information field indicates that the corresponding associated one or a group of types of reported data supports being reported by the first device.

144. The method of claim 143, wherein The type of reported data or a group of reported data associated with each bit of the fifteenth information field is agreed upon by the protocol, or the type of reported data or a group of reported data associated with each bit state value of the fifteenth information field is agreed upon by the protocol.

145. The method according to any one of claims 141 to 144, wherein Each group of sub-information includes a seventeenth information field, and the seventeenth information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

146. The method of any one of claims 141 to 144, wherein The one or a group of functional units inside the first device associated with each group of sub-information in the n4 groups of sub-information is agreed upon by the protocol.

147. The method of any one of claims 137 to 146, wherein The fourth information is associated with the data reporting trigger type indication information; Among them, the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by itself, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device only supports data reporting triggered by the second device, or the bit state value of the data reporting trigger type indication information is used to indicate that the first device supports both data reporting triggered by itself and data reporting triggered by the second device.

148. The method of claim 147, wherein The data reporting trigger type indication information associated with the fourth information is agreed upon by a protocol, or the data reporting trigger type indication information associated with the fourth information is indicated by an information field in the fourth information.

149. The method of claim 101, wherein The M pieces of information include fifth information, where the fifth information is used to indicate whether the first device supports data measurement capabilities for AI / ML purposes.

150. The method of claim 149, wherein: The fifth information includes a fifth bit; The fifth bit is used to indicate whether the first device as a whole supports data measurement capabilities for AI / ML purposes.

151. The method of claim 149 or 150, wherein: The fifth information is associated with the measured data type.

152. The method of claim 151, wherein The type of measurement data associated with the fifth information is agreed upon by a protocol, or the type of measurement data associated with the fifth information is indicated by an information field included in the fifth information.

153. The method of claim 149, wherein: The fifth information includes n5 groups of sub-information, each group of sub-information in the n5 groups of sub-information includes one or more bits, each group of sub-information is associated with one or a group of functional units inside the first device, and n5 is a positive integer; In the case where each group of sub-information includes one bit, the i-th group of sub-information is used to indicate whether the functional unit or the group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes; or When each group of sub-information includes at least two bits, each group of sub-information includes an eighteenth information field and a nineteenth information field, the eighteenth information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and the nineteenth information field included in the i-th group of sub-information is used to indicate a set of measured data types; or When each group of sub-information includes at least one bit, each group of sub-information includes a 20th information field, and the 20th information field included in the i-th group of sub-information is used to indicate whether one or a group of functional units within the first device associated with the i-th group of sub-information supports data measurement capabilities for AI / ML purposes, and to indicate a set of measured data types; or, In a case where each group of sub-information includes at least one bit, the i-th group of sub-information is used to indicate a set of data types measured by one or a group of functional units within the first device associated with the i-th group of sub-information; Wherein, i is a positive integer, and 1≤i≤n5.

154. The method of claim 153, wherein: The eighteenth information field includes one bit, and / or the nineteenth information field includes at least one bit.

155. The method of claim 153 or 154, wherein: Each bit of the nineteenth information field is associated with one or a group of measurement data, and a value of each bit contained in the nineteenth information field determines whether the associated one or a group of measurement data supports measurement by the first device; or, Each bit state value of the nineteenth information field is associated with one or a group of types of measurement data, and each bit state value of the nineteenth information field indicates that the corresponding associated one or a group of types of measurement data supports being measured by the first device.

156. The method of claim 155, wherein: The type of measurement data or a group of measurement data associated with each bit of the nineteenth information field is agreed upon by the protocol, or the type of measurement data or a group of measurement data associated with each bit state value of the nineteenth information field is agreed upon by the protocol.

157. The method according to any one of claims 153 to 156, wherein Each group of sub-information includes a twenty-first information field, and the twenty-first information field included in the i-th group of sub-information is used to indicate one or a group of functional units inside the first device associated with the i-th group of sub-information.

158. The method of any one of claims 153 to 156, wherein The one or a group of functional units inside the first device associated with each group of sub-information in the n5 groups of sub-information is agreed upon by the protocol.

159. The method of any one of claims 149 to 158, wherein The fifth information is associated with the data measurement trigger type indication information; Among them, the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by itself, or the bit state value of the data measurement trigger type indication information is used to indicate that the first device only supports data measurement triggered by the second device, or the bit state value of the data measurement trigger type indication information is used to indicate that the first device supports both data measurement triggered by itself and data measurement triggered by the second device.

160. The method of claim 159, wherein: The data measurement trigger type indication information associated with the fifth information is agreed upon by a protocol, or the data measurement trigger type indication information associated with the fifth information is indicated by an information field in the fifth information.

161. The method of any one of claims 127 to 134, 139 to 146, 151 to 158, wherein The data type includes at least one of the following: AI / ML model input data, AI / ML model output data, AI / ML model input data auxiliary pre-processing data, AI / ML model output data auxiliary post-processing data, AI / ML model training data, AI / ML model inference data, and AI / ML model performance monitoring data.

162. The method of claim 101, wherein: The M pieces of information include sixth information, where the sixth information is used to indicate whether the first device supports offline AI / ML model training capabilities.

163. The method of claim 162, wherein: The sixth information is associated with offline AI / ML model training type indication information; Among them, the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by itself, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device only supports offline AI / ML model training triggered by the second device, or the bit state value of the offline AI / ML model training type indication information is used to indicate that the first device supports both offline AI / ML model training triggered by itself and offline AI / ML model training triggered by the second device.

164. The method of claim 163, wherein: The offline AI / ML model training type indication information associated with the sixth information is agreed upon by the protocol, or the offline AI / ML model training type indication information associated with the sixth information is indicated by the information field in the sixth information.

165. The method of claim 101, wherein: The M pieces of information include seventh information, and the seventh information is used to indicate whether the first device supports the capability of online AI / ML model training.

166. The method of claim 165, wherein: The seventh information is associated with the online AI / ML model training type indication information; Among them, the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by itself, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device only supports online AI / ML model training triggered by the second device, or the bit state value of the online AI / ML model training type indication information is used to indicate that the first device supports both online AI / ML model training triggered by itself and online AI / ML model training triggered by the second device.

167. The method of claim 166, wherein: The online AI / ML model training type indication information associated with the seventh information is agreed upon by the protocol, or the online AI / ML model training type indication information associated with the seventh information is indicated by the information field in the seventh information.

168. The method of claim 101, wherein: The M pieces of information include eighth information, where the eighth information is used to indicate an AI / ML model running or compilation format supported by the first device.

169. The method of claim 101, wherein: The M pieces of information include ninth information, and the ninth information is used to indicate whether the first device supports AI / ML model reasoning capabilities.

170. The method of claim 169, wherein: The ninth information is associated with the AI / ML model reasoning type indication information; Among them, the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports non-federated reasoning or centralized reasoning, or the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device only supports federated reasoning or distributed reasoning, or the bit state value of the AI / ML model reasoning type indication information is used to indicate that the first device supports federated reasoning and non-federated reasoning.

171. The method of claim 170, wherein: The AI / ML model reasoning type indication information associated with the ninth information is agreed upon by the protocol, or the AI / ML model reasoning type indication information associated with the ninth information is indicated by the information field in the ninth information.

172. The method of claim 101, wherein: The M pieces of information include tenth information, and the tenth information is used to indicate whether the first device supports AI / ML model switching capability.

173. The method of claim 172, wherein: The tenth information is associated with the AI / ML model switching type indication information; Among them, the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the first device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is only allowed to be switched by the second device, or the bit state value of the AI / ML model switching type indication information is used to indicate that the AI / ML model is allowed to be switched by the first device or the second device.

174. The method of claim 173, wherein The AI / ML model switching type indication information associated with the tenth information is agreed upon by the protocol, or the AI / ML model switching type indication information associated with the tenth information is indicated by the information field in the tenth information.

175. The method of claim 101, wherein: The M pieces of information include eleventh information, and the eleventh information is used to indicate whether the first device supports AI / ML model activation or deactivation capabilities.

176. The method of claim 175, wherein: The eleventh information is associated with the AI / ML model activation / deactivation type indication information; Among them, the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the first device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is only allowed to be activated or deactivated by the second device, or the bit state value of the AI / ML model activation / deactivation type indication information is used to indicate that the AI / ML model is allowed to be activated or deactivated by the first device or the second device.

177. The method of claim 176, wherein: The AI / ML model activation / deactivation type indication information associated with the eleventh information is agreed upon by the protocol, or the AI / ML model activation / deactivation type indication information associated with the eleventh information is indicated by the information field in the eleventh information.

178. The method of claim 101, wherein: The M pieces of information include twelfth information, and the twelfth information is used to indicate whether the first device supports AI / ML model performance monitoring capabilities.

179. The method of claim 178, wherein The twelfth information is associated with the AI / ML model performance monitoring type indication information; Among them, the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the first device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is only allowed to be executed by the second device, or the bit state value of the AI / ML model performance monitoring type indication information is used to indicate that the AI / ML model performance monitoring is allowed to be executed by the first device or the second device.

180. The method of claim 179, wherein: The AI / ML model performance monitoring type indication information associated with the twelfth information is agreed upon by the protocol, or the AI / ML model performance monitoring type indication information associated with the twelfth information is indicated by the information field in the twelfth information.

181. The method of claim 101, wherein: The M pieces of information include thirteenth information, and the thirteenth information is used to indicate whether the first device supports AI / ML model transmission capability.

182. The method of claim 181, wherein: The thirteenth information is associated with the AI / ML model transmission type indication information; Among them, the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model download, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device only supports AI / ML model upload, or the bit state value of the AI / ML model transmission type indication information is used to indicate that the first device supports AI / ML model download and AI / ML model upload.

183. The method of claim 182, wherein: The AI / ML model transmission type indication information associated with the thirteenth information is agreed upon by the protocol, or the AI / ML model transmission type indication information associated with the thirteenth information is indicated by the information field in the thirteenth information.

184. The method of claim 101, wherein: The M pieces of information include fourteenth information, and the fourteenth information is used to indicate whether the first device supports AI / ML model update capability.

185. The method of claim 184, wherein: The fourteenth information is associated with the AI / ML model update type indication information; Among them, the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the first device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is only allowed to be updated by the second device, or the bit state value of the AI / ML model update type indication information is used to indicate that the AI / ML model is allowed to be updated by the first device or the second device.

186. The method of claim 185, wherein: The AI / ML model update type indication information associated with the fourteenth information is agreed upon by the protocol, or the AI / ML model update type indication information associated with the fourteenth information is indicated by the information field in the fourteenth information.

187. The method of any one of claims 101 to 186, wherein The reporting granularity of part or all of the M information is at least one of the following: the granularity of the first device, the granularity of the functional unit contained in the first device, and the granularity of the AI / ML model identification.

188. The method of claim 101, wherein: The M pieces of information are identification information of M capability sets; The content of capability information included in different capability sets in the M capability sets is at least partially different, or the types of capability information included in different capability sets in the M capability sets are at least partially different; Among them, the type of capability information included in the j-th capability set of the M capability sets is at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the capability of configuring the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capability for AI / ML purposes, whether the first device supports data reporting capability for AI / ML purposes, whether the first device supports data measurement capability for AI / ML purposes, whether the first device supports offline AI / ML model training capability, whether the first device supports online AI / ML model training capability, AI / ML model running or compilation format supported by the first device, whether the first device supports AI / ML model reasoning capability, whether the first device supports AI / ML model switching capability, whether the first device supports AI / ML model activation or deactivation capability, whether the first device supports AI / ML model performance monitoring capability, whether the first device supports AI / ML model transmission capability, and whether the first device supports AI / ML model update capability; Wherein, j is a positive integer, and 1≤j≤M.

189. The method of claim 188, wherein The type of capability information included in each of the M capability sets is agreed upon by a protocol, or the type of capability information included in each of the M capability sets is indicated or configured by a network device.

190. The method of any one of claims 101 to 189, wherein The method further comprises: The second device sends a second message to the first device; The second message includes second AI / ML-related capability information, the second AI / ML-related capability information is associated with the second device, and the second AI / ML-related capability information includes S pieces of information, where S is a positive integer; Among them, the S information is used to indicate at least one of the following: whether the second device is deployed with a functional entity for processing AI / ML related operations, whether the second device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the second device supports data collection capabilities for AI / ML purposes, whether the second device supports data reporting capabilities for AI / ML purposes, whether the second device supports data measurement capabilities for AI / ML purposes, whether the second device supports offline AI / ML model training capabilities, whether the second device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the second device, whether the second device supports AI / ML model reasoning capabilities, whether the second device supports AI / ML model switching capabilities, whether the second device supports AI / ML model activation or deactivation capabilities, whether the second device supports AI / ML model performance monitoring capabilities, whether the second device supports AI / ML model transmission capabilities, and whether the second device supports AI / ML model update capabilities.

191. The method of claim 190, wherein: The reporting granularity of part or all of the S information is at least one of the following: the granularity of the second device, the granularity of the functional unit contained in the second device, and the granularity of the AI / ML model identification.

192. The method of claim 181, 182, 183, 188, 189, 190 or 191, wherein The AI / ML model transmission capability includes an AI / ML model data transmission format.

193. The method according to any one of claims 101 to 192, wherein The method further comprises: The second device receives a third message sent by the first device; The third message includes capability update indication information, where the capability update indication information is used to indicate that the capability information related to the first AI / ML has been updated.

194. The method of claim 193, wherein: The capability update indication information includes a sixth bit, wherein the sixth bit is used to indicate that the first AI / ML-related capability information has been updated; or The capability update indication information includes multiple bits, wherein each of the multiple bits is associated with one or a group of AI / ML-related capability information, and each bit is used to indicate whether the corresponding one or a group of AI / ML-related capability information has been updated.

195. The method of claim 193 or 194, wherein: The second device sends a fourth message to the first device; The fourth message is used to instruct the first device to report the updated first AI / ML-related capability information, or the fourth message is used to instruct the first device to report the updated capability information in the first AI / ML-related capability information.

196. The method of claim 195, wherein: The fourth message includes capability type indication information, where the capability type indication information is used to indicate a type of updated capability information reported by the first device.

197. The method of claim 195 or 196, wherein: The second device receives a fifth message sent by the first device; The fifth message includes the updated capability information related to the first AI / ML, or the fifth message includes the updated capability information specified to be reported by the second device.

198. The method of any one of claims 101 to 197, wherein The first device is a terminal device, and the second device is a network device; or, The first device is a network device, and the second device is a terminal device; or, The first device is a terminal device, and the second device is another terminal device; or, The first device is a network device, and the second device is another network device.

199. The method of any one of claims 101 to 198, wherein In the case where the first device is a terminal device, the functional units inside the first device include at least one of the following: a non-access stratum (NAS) layer functional entity, a service data adaptation protocol (SDAP) layer functional entity, a radio resource control (RRC) layer functional entity, a packet data convergence protocol (PDCP) layer functional entity, a radio link control (RLC) layer functional entity, a backhaul adaptation protocol (BAP) layer functional entity, a media access control (MAC) layer functional entity, and a physical layer (PHY) layer functional entity; or In the case where the first device is an access network device, the functional units inside the first device include at least one of the following: a centralized unit CU, a distributed unit DU, a centralized unit control plane CU-CP, a centralized unit user plane CU-UP, a NAS layer functional entity, a SDAP layer functional entity, an RRC layer functional entity, a PDCP layer functional entity, an RLC layer functional entity, a MAC layer functional entity, a PHY layer functional entity, and a BAP layer functional entity; or When the first device is a core network device, the functional units inside the first device include at least one of the following: access and mobility management function AMF network element, authentication server function AUSF network element, user plane function UPF network element, session management function SMF network element, location management function LMF network element, policy control function PCF network element, and unified data management UDM network element.

200. The method according to any one of claims 101 to 199, wherein The message is one of the following: NAS message, AS message, interface message, AL / ML dedicated message.

201. A wireless communication device, characterized in that include: A first communication unit, configured to send a first message to a second device; The first message includes first artificial intelligence (AI) / machine learning (ML) related capability information, the first AI / ML related capability information is associated with the first device, and the first AI / ML related capability information includes M pieces of information, where M is a positive integer; Among them, the M information is used to indicate at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capabilities for AI / ML purposes, whether the first device supports data reporting capabilities for AI / ML purposes, whether the first device supports data measurement capabilities for AI / ML purposes, whether the first device supports offline AI / ML model training capabilities, whether the first device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the first device, whether the first device supports AI / ML model reasoning capabilities, whether the first device supports AI / ML model switching capabilities, whether the first device supports AI / ML model activation or deactivation capabilities, whether the first device supports AI / ML model performance monitoring capabilities, whether the first device supports AI / ML model transmission capabilities, and whether the first device supports AI / ML model update capabilities.

202. A wireless communication device, characterized in that include: A first communication unit, configured to receive a first message sent by a first device; The first message includes first artificial intelligence (AI) / machine learning (ML) related capability information, the first AI / ML related capability information is associated with the first device, and the first AI / ML related capability information includes M pieces of information, where M is a positive integer; Among them, the M information is used to indicate at least one of the following: whether the first device is deployed with a functional entity for processing AI / ML related operations, whether the first device supports the ability to configure the functional entity for processing AI / ML related operations on demand, whether the first device supports data collection capabilities for AI / ML purposes, whether the first device supports data reporting capabilities for AI / ML purposes, whether the first device supports data measurement capabilities for AI / ML purposes, whether the first device supports offline AI / ML model training capabilities, whether the first device supports online AI / ML model training capabilities, AI / ML model running or compilation formats supported by the first device, whether the first device supports AI / ML model reasoning capabilities, whether the first device supports AI / ML model switching capabilities, whether the first device supports AI / ML model activation or deactivation capabilities, whether the first device supports AI / ML model performance monitoring capabilities, whether the first device supports AI / ML model transmission capabilities, and whether the first device supports AI / ML model update capabilities.

203. A communication device, characterized in that include: A processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory, so that the communication device executes the method according to any one of claims 1 to 100.

204. A communication device, characterized in that include: A processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory, so that the communication device executes the method according to any one of claims 101 to 200.

205. A chip, characterized in that include: A processor, configured to call and run a computer program from a memory, so that a device equipped with the chip executes the method according to any one of claims 1 to 100.

206. A chip, characterized in that include: A processor, configured to call and run a computer program from a memory, so that a device equipped with the chip executes a method as claimed in any one of claims 101 to 200.

207. A computer-readable storage medium, characterized in that For storing a computer program, when the computer program is executed, the method according to any one of claims 1 to 100 is implemented.

208. A computer-readable storage medium, characterized in that Used for storing a computer program, when the computer program is executed, the method according to any one of claims 101 to 200 is implemented.

209. A computer program product, characterized in that The method comprises computer program instructions, and when the computer program instructions are executed, the method according to any one of claims 1 to 100 is implemented.

210. A computer program product, characterized in that The method comprises computer program instructions, and when the computer program instructions are executed, the method according to any one of claims 101 to 200 is implemented.

211. A computer program, characterized in that When the computer program is executed, the method according to any one of claims 1 to 100 is implemented.

212. A computer program, characterized in that When the computer program is executed, the method according to any one of claims 101 to 200 is implemented.

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