Communication method and related apparatus

Abstract

A communication method and a related apparatus. In the method, a first communication apparatus can process, on the basis of first data indicated by a second communication apparatus, one or more AI models of the first communication apparatus to obtain a processing result; and the first communication apparatus can then send second information obtained on the basis of the processing result, such that the second communication apparatus can determine some or all of the AI models among the one or more AI models on the basis of the second information (or determine an AI-enabled function supported by the first communication apparatus). In other words, after the second communication apparatus indicates the first data to the first communication apparatus, the first communication apparatus can indicate to the second communication apparatus an AI model or AI-enabled function corresponding to the first data. In this way, a communication apparatus in a communication system can participate in AI model processing, and provide an AI model processing capability corresponding to specified data or provide an AI-enabled function corresponding to the specified data.

Description

Communication method and related apparatus

[0001] This application claims priority from the Chinese patent application No. 202410529350.0, filed on April 28, 2024, and entitled "A communication method and related apparatus", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the field of communication, and in particular to a communication method and related apparatus. BACKGROUND

[0003] Wireless communication can be transmission communication between two or more communication nodes without propagation through conductors or cables, and the communication nodes generally include network devices and terminal devices.

[0004] At present, in a wireless communication system, a communication node generally has signal transceiving capability and computing capability. Taking a network device with computing capability as an example, the computing capability of the network device is mainly to provide computing power support for the signal transceiving capability (for example, to perform sending processing and receiving processing on signals) to realize communication between the network device and other communication nodes.

[0005] However, in a communication network, in addition to providing computing power support for the above communication tasks, the computing capability of the communication node can also have surplus computing capability. Therefore, how to utilize these computing capabilities is a technical problem to be solved. SUMMARY

[0006] The present application provides a communication method and related apparatus, which is used to enable a communication device in a communication system to participate in the processing of an artificial intelligence (AI) model and provide the processing capability of the AI model corresponding to specified data or provide an AI-enabled function corresponding to the specified data.

[0007] The first aspect of the present application provides a communication method, which is performed by a first communication device. The first communication device can be a communication apparatus (such as a terminal device or a network device), or the first communication device can be a part of the communication apparatus (such as a processor, a chip or a chip system, etc.), or the first communication device can also be a logic module or software that can realize all or part of the functions of the communication apparatus. In the method, the first communication device receives first information, which is used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of the first communication device; and the first communication device sends second information, which is determined based on the processing result; wherein the second information is used to determine part or all of the AI models in the one or more AI models, or the second information is used to determine AI-enabled functions supported by the first communication device.

[0008] Based on the above scheme, the first communication device can process one or more AI models of the first communication device based on the first data indicated by the second communication device to obtain a processing result. Thereafter, the first communication device can send second information obtained based on the processing result, so that the second communication device can determine part or all of the AI models in the one or more AI models (or determine AI-enabled functions supported by the first communication device) based on the second information. In other words, after the second communication device indicates the first data to the first communication device, the first communication device can indicate AI models or AI-enabled functions corresponding to the first data to the second communication device. In this way, the communication devices in the communication system can participate in the processing of the AI models and provide the processing capability of the AI models corresponding to the specified data or provide AI-enabled functions corresponding to the specified data.

[0009] In the present application, the AI model can be replaced by other terms, such as neural network, neural network model, AI neural network model, machine learning model, or AI processing model, etc.

[0010] In the present application, the AI-enabled function can be replaced by other terms, such as AI-enabled feature, AI capability, or AI function, etc.

[0011] In a possible implementation manner of the first aspect, the first data includes first training data, which is used for model training of the one or more AI models; wherein the processing result is obtained through model testing of a trained model after model training based on the first training data, by using preconfigured test data.

[0012] Based on the above scheme, the first data obtained by the first communication device through the first information can include first training data, so that the first communication device can indicate the AI model or AI enabled function corresponding to the model training process implemented by the first training data to the second communication device.

[0013] In a possible implementation manner of the first aspect, the first data includes first test data, the first test data being used for model testing of the one or more AI models; and the processing result is obtained after model training based on the preconfigured training data and model testing of the trained model by using the first test data.

[0014] Based on the above scheme, the first data obtained by the first communication device through the first information can include first test data, so that the first communication device can indicate the AI model or AI enabled function corresponding to the model testing process implemented by the first test data to the second communication device.

[0015] In a possible implementation manner of the first aspect, the first data includes second training data and second test data, the second training data being used for model training of the one or more AI models, and the second test data being used for model testing of the one or more AI models; and the processing result is obtained after model training based on the second training data and model testing of the trained model by using the second test data.

[0016] Based on the above scheme, the first data obtained by the first communication device through the first information can include second training data and second test data, so that the first communication device can indicate the AI model or AI enabled function corresponding to the model training process implemented by the second training data and the model testing process implemented by the second test data to the second communication device.

[0017] In a possible implementation manner of the first aspect, the first information includes configuration information used for collecting part or all of the first data, and / or the first information includes part or all of the first data.

[0018] Based on the above scheme, the first information received by the first communication device can contain one or more information contents described above, so that the first communication device can obtain the first data in multiple ways.

[0019] In a possible implementation manner of the first aspect, the first information further includes at least one of the following:

[0020] first indication information used for indicating a scenario corresponding to the first data;

[0021] The second indication information is used for indicating a preprocessing rule corresponding to the first data.

[0022] The third indication information is used for indicating area information applicable to the first data.

[0023] Based on the above scheme, the first information received by the first communication device can also include the at least one item, so that the first communication device obtains the first data based on the at least one item.

[0024] In a possible implementation manner of the first aspect, the second information includes any one of the following:

[0025] The fourth indication information is used for indicating the processing result.

[0026] The fifth indication information is used for indicating part or all of the AI models.

[0027] The sixth indication information is used for indicating an AI-enabled function supported by the first communication device.

[0028] Based on the above scheme, the second information sent by the first communication device can include any one of the above, so that the second communication device determines the AI model or the AI-enabled function corresponding to the first data in multiple ways.

[0029] In a possible implementation manner of the first aspect, the method further includes: the first communication device receives third information, the third information being used for indicating auxiliary information corresponding to the one or more AI models, the auxiliary information being used for indicating at least one of the following: model function, model structure parameter, data format of model input, data format of model output.

[0030] Optionally, the first information and the third information can be carried in the same message or in different messages, which is not limited here.

[0031] Based on the above scheme, the first communication device can also determine, through the received third information, auxiliary information corresponding to one or more AI models in the first communication device, so that the first communication device obtains an AI model or an AI-enabled function matched with the auxiliary information based on the auxiliary information.

[0032] The second aspect of the present application provides a communication method, which is performed by a second communication device. The second communication device can be a communication device (such as a terminal device or a network device), or the second communication device can be a part of the communication device (such as a processor, a chip, a chip system, or the like), or the second communication device can also be a logic module or software that can realize all or part of the functions of the communication device. In the method, the second communication device sends first information, which is used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of a first communication device; and the second communication device receives second information, which is determined based on the processing result; wherein the second information is used to determine part or all of the AI models in the one or more AI models, or the second information is used to determine AI-enabled functions supported by the first communication device.

[0033] Based on the above scheme, after the second communication device indicates the first data to the first communication device through the first information, the first communication device can process one or more AI models of the first communication device to obtain a processing result. Thereafter, the second communication device can receive second information based on the processing result, so that the second communication device can determine part or all of the AI models in the one or more AI models (or determine AI-enabled functions supported by the first communication device) based on the second information. In other words, after the second communication device indicates the first data to the first communication device, the first communication device can indicate AI models or AI-enabled functions corresponding to the first data to the second communication device. In this way, the communication devices in the communication system can participate in the processing of the AI models and provide the processing capability of the AI models corresponding to the specified data or provide the AI-enabled functions corresponding to the specified data.

[0034] In a possible implementation manner of the second aspect, the first data includes first training data, which is used for model training of the one or more AI models; wherein the processing result is obtained through model testing of a trained model on preconfigured test data after the trained model is obtained through model training based on the first training data.

[0035] Based on the above scheme, the first data indicated by the second communication device through the first information can include first training data, so that the first communication device can indicate AI models or AI-enabled functions corresponding to a model training process implemented by the first training data to the second communication device.

[0036] In a possible implementation manner of the second aspect, the first data includes first test data, the first test data being used for model testing of the one or more AI models; and the processing result is obtained by performing model testing on the trained model by using the first test data after model training based on the preconfigured training data.

[0037] Based on the above scheme, the first data indicated by the first information can include first test data, so that the first communication device can indicate to the second communication device an AI model or an AI-enabled function corresponding to a model testing process implemented by the first test data.

[0038] In a possible implementation manner of the second aspect, the first data includes second training data and second test data, the second training data being used for model training of the one or more AI models, and the second test data being used for model testing of the one or more AI models; and the processing result is obtained by performing model testing on the trained model by using the second test data after model training based on the second training data.

[0039] Based on the above scheme, the first data indicated by the first information can include second training data and second test data, so that the first communication device can indicate to the second communication device an AI model or an AI-enabled function corresponding to a model training process implemented by the second training data and a model testing process implemented by the second test data.

[0040] In a possible implementation manner of the second aspect, the first information includes configuration information used for collecting part or all of the first data, and / or the first information includes part or all of the first data.

[0041] Based on the above scheme, the first information sent by the second communication device to the first communication device can include one or more information contents described above, so that the first communication device can obtain the first data in multiple ways.

[0042] In a possible implementation manner of the second aspect, the first information further includes at least one of the following:

[0043] first indication information used for indicating a scenario corresponding to the first data;

[0044] second indication information used for indicating a preprocessing rule corresponding to the first data;

[0045] third indication information used for indicating region information to which the first data is applicable.

[0046] Based on the above scheme, the first information sent by the second communication device to the first communication device can further include the at least one, so that the first communication device obtains the first data based on the at least one.

[0047] In a possible implementation of the second aspect, the second information includes any of the following:

[0048] Fourth indication information, used to indicate the processing result;

[0049] Fifth indication information, used to indicate part or all of the one or more AI models;

[0050] Sixth indication information, used to indicate the AI-enabled function supported by the first communication device.

[0051] Based on the above scheme, the second information sent by the first communication device to the second communication device can include any of the above, so that the second communication device determines the AI model or the AI-enabled function corresponding to the first data in multiple ways.

[0052] In a possible implementation of the second aspect, the method further includes: the second communication device sends third information, the third information being used to indicate auxiliary information corresponding to the one or more AI models, the auxiliary information being used to indicate at least one of the following: model function, model structure parameter, data format of model input, data format of model output.

[0053] Based on the above scheme, the second communication device can further send third information to the first communication device, so that the first communication device determines auxiliary information corresponding to one or more AI models in the first communication device through the received third information, so that the first communication device obtains an AI model or an AI-enabled function matched with the auxiliary information based on the auxiliary information.

[0054] The third aspect of the present application provides a communication device, which is a first communication device, the communication device includes a transceiver unit and a processing unit; the transceiver unit is used to receive first information, the first information being used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of the first communication device; the processing unit determines second information based on the processing result, and the transceiver unit is further used to send the second information; wherein the second information is used to determine part or all of the one or more AI models, or the second information is used to determine an AI-enabled function supported by the first communication device.

[0055] In the third aspect of the present application, the component modules of the communication device can also be used to perform the steps performed in the various possible implementations of the first aspect and achieve the corresponding technical effects. For details, please refer to the first aspect, which will not be repeated here.

[0056] The fourth aspect of the present application provides a communication device, which is a second communication device, comprising a transceiver and a processing unit, the processing unit being configured to determine first information; the transceiver being configured to transmit the first information, the first information being used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of a first communication device; the transceiver is further configured to receive second information, the second information being determined based on the processing result; wherein the second information is used to determine part or all of the one or more AI models, or the second information is used to determine an AI-enabled function supported by the first communication device.

[0057] In the fourth aspect of the present application, the component modules of the communication device can also be used to perform the steps performed in the various possible implementation manners of the second aspect and achieve the corresponding technical effects. For details, please refer to the second aspect, which will not be described here again.

[0058] The fifth aspect of the present application provides a communication device, comprising at least one processor, the at least one processor being coupled with a memory; the memory being used to store programs or instructions; the at least one processor being used to execute the programs or instructions, so that the communication device implements the method described in any one of the possible implementation manners of the first aspect to the second aspect. Optionally, the communication device can comprise the memory.

[0059] The sixth aspect of the present application provides a communication device, comprising at least one logic circuit and an input-output interface; the logic circuit being used to execute the method described in any one of the possible implementation manners of the first aspect to the second aspect.

[0060] The seventh aspect of the present application provides a communication system, comprising the first communication device and the second communication device.

[0061] The eighth aspect of the present application provides a computer-readable storage medium, which is used to store one or more computer execution instructions, when the computer execution instructions are executed by a processor, the processor executes the method described in any one of the possible implementation manners of the first aspect to the second aspect.

[0062] The ninth aspect of the present application provides a computer program product (or computer program), when the computer program in the computer program product is executed by the processor, the processor executes the method described in any one of the possible implementation manners of the first aspect to the second aspect.

[0063] The tenth aspect of the present application provides a chip system, which comprises at least one processor configured to support a communication device to implement the method of any possible implementation of any one of the first aspect to the second aspect.

[0064] In a possible design, the chip system can further comprise a memory configured to store necessary program instructions and data of the communication device. The chip system can be composed of a chip, or can comprise the chip and other discrete devices. Optionally, the chip system further comprises an interface circuit configured to provide program instructions and / or data for the at least one processor.

[0065] The technical effects brought by any one of the third aspect to the tenth aspect can be referred to the technical effects brought by different design manners of the first aspect to the second aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0066] FIGS. 1a to 1c are schematic diagrams of a communication system provided by the present application;

[0067] FIGS. 2a to 2e are schematic diagrams of an AI processing process related to the present application;

[0068] FIG. 3 is an interaction diagram of a communication method provided by the present application;

[0069] FIGS. 4 to 8 are schematic diagrams of a communication device provided by the present application. DETAILED DESCRIPTION

[0070] First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0071] (1) Terminal device: can be a wireless terminal device capable of receiving network device scheduling and indication information, and the wireless terminal device can be a device providing voice and / or data connectivity to a user, or a handheld device with wireless connection function, or other processing devices connected to a wireless modem.

[0072] A terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN), and the terminal device can be a mobile terminal device, such as a mobile phone (or called "cellular" phone, mobile phone), a computer, and a data card, for example, which can be a portable, pocket, hand-held, computer- built-in, or vehicle-mounted mobile device that exchanges voice and / or data with a radio access network. For example, a personal communication service (PCS) phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a tablet, a computer with wireless transceiver function, and the like. The wireless terminal device can also be referred to as a system, a subscriber unit, a subscriber station, a mobile station or a mobile station (MS), a remote station, an access point (AP), a remote terminal, an access terminal, a user terminal, a user agent, a subscriber station (SS), customer premises equipment (CPE), a terminal, user equipment (UE), a mobile terminal (MT), and the like.

[0073] By way of example and not limitation, in embodiments of the present application, the terminal device can also be a wearable device. The wearable device can also be referred to as a smart wearable device or a smart wearable device, etc., which is a general term for devices that can be designed and developed by applying wearable technology to daily wear, such as glasses, gloves, watches, clothing, and shoes, etc. The wearable device is a portable device that can be directly worn on the body or integrated into the user's clothes or accessories. The wearable device is not only a hardware device, but also a powerful function realized through software support and data interaction, cloud interaction. The general wearable smart device includes a full function, large size, and can realize complete or partial functions without relying on a smart phone, such as smart watches or smart glasses, etc., and focuses on a certain application function, and needs to cooperate with other devices such as a smart phone, such as various smart wristbands, smart helmets, smart jewelry, etc.

[0074] The terminal can also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle to everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in telemedicine or telehealth services, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc.

[0075] In addition, the terminal device can also be a terminal device in a communication system evolved after the 5th generation (5G) communication system (such as 5G Advanced or 6th generation (6G) communication system, etc.) or a terminal device in a future evolved public land mobile network (PLMN), etc. For example, 5G Advanced or 6G network can further expand the form and function of 5G communication terminal, and 6G terminal includes but is not limited to vehicle, cellular network terminal (fusion satellite terminal function), drone, internet of things (IoT) device.

[0076] In the embodiments of the present application, the terminal device can also obtain an artificial intelligence (AI) service provided by the network device. Optionally, the terminal device can also have AI processing capability.

[0077] (2) Network device: can be a device in a wireless network, for example, the network device can be a RAN node (or device) for accessing the terminal device to the wireless network, which can also be referred to as a base station. At present, some examples of RAN devices are: base station, evolved NodeB (eNodeB), base station gNB (gNodeB) in 5G communication system, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (for example, home evolved Node B, or home Node B, HNB), baseband unit (BBU), or wireless fidelity (Wi-Fi) access point (AP) and the like. In addition, in one network structure, the network device can include a central unit (CU) node, or a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0078] Optionally, the RAN node can also be a macro base station, a micro base station or an indoor station, a relay node or a donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. The RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, etc. For example, the access network device in vehicle to everything (V2X) technology can be a road side unit (RSU).

[0079] In another possible scenario, multiple RAN nodes cooperate to assist a terminal to implement wireless access, and different RAN nodes respectively implement part of functions of a base station. For example, a RAN node can be a CU, a DU, a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. The CU and the DU can be separately configured, or can also be included in the same network element, for example, in a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, for example, included in a remote radio unit (RRU), an active antenna processing unit (AAU), a radio head (RH), or a remote radio head (RRH).

[0080] In different systems, the CU (or CU-CP and CU-UP), the DU, or the RU can also have different names, but those skilled in the art can understand their meanings. For example, in an open RAN (O-RAN or ORAN) system, the CU can also be referred to as an O-CU (open CU), the DU can also be referred to as an O-DU, the CU-CP can also be referred to as an O-CU-CP, the CU-UP can also be referred to as an O-CU-UP, and the RU can also be referred to as an O-RU. For the convenience of description, the CU, the CU-CP, the CU-UP, the DU, and the RU are taken as examples for description in this application. Any one of the CU (or the CU-CP, the CU-UP), the DU, and the RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0081] The communication between the access network device and the terminal device follows a certain protocol layer structure. The protocol layer can include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer can include at least one of the following: a radio resource control (RRC) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, a media access control (MAC) layer, or a physical (PHY) layer, etc. The user plane protocol layer can include at least one of the following: a service data adaptation protocol (SDAP) layer, a PDCP layer, an RLC layer, a MAC layer, or a physical layer, etc.

[0082] For the correspondence between the network elements in the ORAN system and the protocol layer functions that can be implemented by the network elements, refer to Table 1 below.

[0083] Table 1

[0084] The network device can be another device that provides a wireless communication function for the terminal device. Embodiments of the present application do not limit the specific technology and specific device form adopted by the network device. For the convenience of description, embodiments of the present application do not limit.

[0085] The network device can also include a core network device, for example, a mobility management entity (MME) in a fourth generation (4G) network, a home subscriber server (HSS), a serving gateway (S-GW), a policy and charging rules function (PCRF), a public data network gateway (PDN gateway or P-GW), a network element such as an access and mobility management function (AMF) in a 5G network, a user plane function (UPF), or a session management function (SMF). In addition, the core network device can also include other core network devices in the 5G network and the next generation network of the 5G network.

[0086] In the embodiments of the present application, the network device mentioned above can also be an AI-capable network node, which can provide AI services for terminals or other network devices, for example, AI nodes, computing power nodes, AI-capable RAN nodes, AI-capable core network elements, etc. on the network side (access network or core network).

[0087] In the embodiments of the present application, the device for implementing the function of the network device can be a network device or a device capable of supporting the network device to implement the function, such as a chip system, which can be installed in the network device. In the technical solutions provided in the embodiments of the present application, the device for implementing the function of the network device is taken as an example to describe the technical solutions provided in the embodiments of the present application.

[0088] (3) Configuration and pre-configuration: in this application, configuration and pre-configuration will be used at the same time. Among them, configuration refers to that the network device / server sends some parameter configuration information or parameter values to the terminal through messages or signaling, so that the terminal determines the communication parameters or resource in transmission according to the values or information. Pre-configuration is similar to configuration, which can be parameter information or parameter values agreed by the network device / server and the terminal device in advance, or parameter information or parameter values adopted by the base station / network device or the terminal device according to the standard protocol, or parameter information or parameter values pre-stored in the base station / server or the terminal device. This application does not limit it.

[0089] Further, these values and parameters can be changed or updated.

[0090] (4) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" means two or more. "And / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which means that A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single item or multiple items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. In addition, unless otherwise specified, the ordinal numbers "first", "second", etc. mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, time sequence, priority or importance of multiple objects.

[0091] (5) In the embodiments of the present application, "sending" and "receiving" represent the direction of signal transmission. For example, "sending information to XX" can be understood as that the destination of the information is XX, which can include direct sending through the air interface, or indirect sending through the air interface by other units or modules. "Receiving information from YY" can be understood as that the source of the information is YY, which can include direct receiving from YY through the air interface, or indirect receiving from YY through the air interface by other units or modules. "Sending" can also be understood as "output" of chip interface, and "receiving" can also be understood as "input" of chip interface.

[0092] In other words, sending and receiving can be carried out between devices, such as between network devices and terminal devices, or within devices, such as between components, modules, chips, software modules or hardware modules within devices through buses, wires or interfaces.

[0093] It can be understood that the information can be processed, such as encoding and modulation, between the source end and the destination end of the information transmission, but the destination end can understand the effective information from the source end. Similar expressions in this application can be similarly understood, and will not be repeated here.

[0094] (6) In the embodiments of the present application, “indication” can include direct indication and indirect indication, and can also include explicit indication and implicit indication. The information indicated by certain information (indication information described below) is referred to as to-be-indicated information. In the implementation process, there are many ways to indicate the to-be-indicated information, for example, but not limited to, the to-be-indicated information can be directly indicated, such as the to-be-indicated information itself or the index of the to-be-indicated information. The to-be-indicated information can also be indirectly indicated by indicating other information, where the other information and the to-be-indicated information have an association relationship. The to-be-indicated information can also be indicated only by a part, and the other part of the to-be-indicated information is known or agreed in advance. For example, the indication of a specific information can be achieved by means of the arrangement order of each information agreed in advance (for example, protocol predefined), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific manner of indication. It can be understood that for the sender of the indication information, the indication information can be used to indicate the to-be-indicated information, and for the receiver of the indication information, the indication information can be used to determine the to-be-indicated information.

[0095] In the present application, the same or similar parts of each embodiment can be mutually referred to, unless otherwise specified. In the various embodiments of the present application, and the various methods / designs / implementation manners in each embodiment, the terms and / or descriptions of different embodiments, and the various methods / designs / implementation manners in each embodiment are consistent and can be mutually referred to, unless otherwise specified and logically conflicted. The technical features of different embodiments, and the various methods / designs / implementation manners in each embodiment can be combined to form new embodiments, methods, or implementation manners according to their inherent logical relationship. The implementation manners of the present application described below do not constitute a limitation on the protection scope of the present application.

[0096] The present application can be applied to a long term evolution (LTE) system, a new radio (NR) system, or a communication system evolved after 5G (such as 6G, etc.). The communication system includes at least one network device and / or at least one terminal device.

[0097] Please refer to FIG. 1a, which is a schematic diagram of a communication system in the present application. In FIG. 1a, a network device and six terminal devices are exemplarily shown, which are terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5 and terminal device 6. In the example shown in FIG. 1a, the terminal device 1 is exemplarily taken as a smart tea cup, the terminal device 2 is exemplarily taken as a smart air conditioner, the terminal device 3 is exemplarily taken as a smart fuel dispenser, the terminal device 4 is exemplarily taken as a vehicle, the terminal device 5 is exemplarily taken as a mobile phone, and the terminal device 6 is exemplarily taken as a printer.

[0098] As shown in FIG. 1a, the sending entity of the AI configuration information can be the network device. The receiving entity of the AI configuration information can be the terminal devices 1-6. In this case, the network device and the terminal devices 1-6 form a communication system, in which the terminal devices 1-6 can send data to the network device, and the network device needs to receive the data sent by the terminal devices 1-6. At the same time, the network device can send configuration information to the terminal devices 1-6.

[0099] Exemplarily, in FIG. 1a, the terminal devices 4-6 can also form a communication system. Among them, the terminal device 5 acts as a network device, i.e., the sending entity of the AI configuration information; the terminal devices 4 and 6 act as terminal devices, i.e., the receiving entity of the AI configuration information. For example, in a vehicle-to-everything system, the terminal device 5 sends AI configuration information to the terminal devices 4 and 6, and receives data sent by the terminal devices 4 and 6; correspondingly, the terminal devices 4 and 6 receive the AI configuration information sent by the terminal device 5, and send data to the terminal device 5.

[0100] Taking the communication system shown in FIG. 1a as an example, in addition to performing communication-related services, different devices (including network devices, network devices and terminal devices, and / or terminal devices and terminal devices) can also perform AI-related services.

[0101] As shown in FIG. 1b, taking a network device as a base station as an example, the base station and one or more terminal devices can perform communication-related services and AI-related services, and different terminal devices can also perform communication-related services and AI-related services.

[0102] As shown in FIG. 1c, taking a terminal device including a television and a mobile phone as an example, the television and the mobile phone can also perform communication-related services and AI-related services.

[0103] The technical solutions provided in the present application can be applied to a wireless communication system (for example, the system shown in FIG. 1a, FIG. 1b or FIG. 1c), and for example, an AI network element can be introduced in the communication system provided in the present application to implement part or all of AI-related operations. The AI network element can also be referred to as an AI node, an AI device, an AI entity, an AI module, an AI model, or an AI unit, etc. The AI network element can be built-in in a network element of the communication system. For example, the AI network element can be an AI module built-in in an access network device, a core network device, a cloud server, or an operation, administration and maintenance (OAM), to implement AI-related functions. The OAM can serve as a network management of the core network device and / or a network management of the access network device. Alternatively, the AI network element can also be a network element independently arranged in the communication system. Optionally, an AI entity can also be included in a terminal or a chip built-in in the terminal, to implement AI-related functions.

[0104] The AI possibly involved in the present application will be briefly introduced below.

[0105] AI can enable a machine to have human intelligence, for example, the machine can apply software and hardware of a computer to simulate certain intelligent behaviors of a human being. In order to realize artificial intelligence, a machine learning method can be adopted. In the machine learning method, the machine learns (or trains) a model by using training data. The model represents a mapping between input and output. The learned model can be used for inference (or prediction), that is, the model can be used to predict the output corresponding to a given input. The output can also be referred to as an inference result (or a prediction result).

[0106] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. The unsupervised learning can also be referred to as non-supervised learning.

[0107] Supervised learning learns a mapping relationship from sample values to sample labels by using a machine learning algorithm according to the collected sample values and sample labels, and uses an AI model to express the learned mapping relationship. The process of training the machine learning model is the process of learning the mapping relationship. In the training process, the sample values are input into the model to obtain the predicted values of the model, and the model parameters are optimized by calculating the error between the predicted values of the model and the sample labels (ideal values). After the mapping relationship is learned, the learned mapping can be used to predict new sample labels. The mapping relationship learned by supervised learning can include linear mapping or nonlinear mapping. According to the type of label, the learned task can be divided into classification task and regression task.

[0108] Unsupervised learning relies on collected sample values ​​to discover inherent patterns within the samples themselves. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationship from sample to sample; this is called self-supervised learning. During training, model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used for signal compression and decompression recovery applications; common algorithms include autoencoders and generative adversarial networks.

[0109] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels. The algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and a better (e.g., optimal) decision action. However, because the label of the "correct action" cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action." Reinforcement learning training is achieved through iterative interaction with the environment.

[0110] Neural networks (NNs) are a specific model in machine learning techniques. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings. Traditional communication systems rely on extensive expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.

[0111] The idea behind neural networks comes from the neuronal structure of the brain. For example, each neuron performs a weighted summation of its input values ​​and outputs the result through an activation function.

[0112] Figure 2a shows a schematic diagram of a neuron structure. Assume the input to the neuron is x = [x0, x1, ..., x...]. n The weights corresponding to each input are w = [w0, w1, ..., w] n ], where n is a positive integer, w i and x i It can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. i As x i The weights are used to assign weights to x. iThe inputs are weighted. The bias for the weighted sum of the inputs according to the weights is, for example, b. The form of the activation function can be various. Assuming that the activation function of a neuron is y = f(z) = max(0, z), the output of the neuron is: For example, the activation function of a neuron is y = f(z) = z, the output of the neuron is: where b can be various possible types such as a decimal number, an integer (for example, 0, a positive integer, or a negative integer), or a complex number. The activation functions of different neurons in a neural network can be the same or different.

[0113] In addition, a neural network generally includes multiple layers, and each layer can include one or more neurons. By increasing the depth and / or width of the neural network, the expressive ability of the neural network can be improved, and the neural network can provide stronger information extraction and abstract modeling capabilities for complex systems. The depth of the neural network can refer to the number of layers included in the neural network, and the number of neurons included in each layer can be referred to as the width of the layer. In an implementation manner, the neural network includes an input layer and an output layer. The input layer of the neural network processes the received input information through neurons, and transmits the processing result to the output layer, and the output layer obtains the output result of the neural network. In another implementation manner, the neural network includes an input layer, a hidden layer, and an output layer. The input layer of the neural network processes the received input information through neurons, and transmits the processing result to the intermediate hidden layer. The hidden layer calculates the received processing result to obtain a calculation result, and transmits the calculation result to the output layer or the next adjacent hidden layer, and finally the output layer obtains the output result of the neural network. The neural network can include one hidden layer, or include multiple sequentially connected hidden layers, which is not limited.

[0114] The neural network is, for example, a deep neural network (DNN). According to the construction manner of the network, the DNN can include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

[0115] FIG. 2b is a schematic diagram of a FNN network. The characteristic of the FNN network is that the neurons of adjacent layers are completely connected two by two. This characteristic makes the FNN usually need a large amount of storage space, and leads to a high calculation complexity.

[0116] CNN is a kind of neural network specially designed to deal with data with similar grid structure. For example, time series data (e.g. time axis discrete sampling) and image data (e.g. two-dimensional discrete sampling) can be considered as similar grid structure data. CNN does not use all input information at once for operation, but uses a fixed size window to extract part of the information for convolution operation, which greatly reduces the calculation of model parameters. In addition, according to the different types of window extraction information (such as people and objects in the same picture are different types of information), each window can use different convolution kernel operation, which makes CNN better extract the features of input data.

[0117] RNN is a kind of DNN network that uses feedback time series information. The input of RNN includes the new input value at the current time and the output value of itself at the previous time. RNN is suitable for obtaining sequence characteristics with temporal correlation, and is particularly suitable for speech recognition, channel coding and decoding and other applications.

[0118] In the above model training process of machine learning, a loss function can be defined. The loss function describes the gap or difference between the output value of the model and the ideal target value. The loss function can be embodied in various forms, and the specific form of the loss function is not limited. The model training process can be regarded as the following process: by adjusting part or all of the parameters of the model, the value of the loss function is less than the threshold value or meets the target demand.

[0119] The model can also be called an AI model, a rule or other names. The AI model can be considered as a specific method to realize the AI function. The AI model represents the mapping relationship or function between the input and output of the model. The AI function can include one or more of the following: data collection, model training (or model learning), model information publishing, model inference (or model reasoning, reasoning, or prediction, etc.), model monitoring or model verification, or inference result publishing, etc. The AI function can also be called AI (related) operation, or AI related function.

[0120] The implementation process of the fully connected neural network will be described below with reference to the accompanying drawings. The fully connected neural network is also called multilayer perceptron (MLP).

[0121] As shown in FIG. 2c, an MLP includes an input layer (left side), an output layer (right side), and multiple hidden layers (middle). Each layer of the MLP includes a plurality of nodes, which are called neurons. The neurons of adjacent two layers are connected to each other.

[0122] Optionally, considering the neurons of two adjacent layers, the output h of the neuron of the next layer is the weighted sum of all the neurons x of the previous layer connected thereto and is subjected to an activation function, and can be expressed as: h = f(wx + b).

[0123] wherein w is a weight matrix, b is a bias vector, and f is an activation function.

[0124] Further optionally, the output of the neural network can be recursively expressed as: y = f n (w n f n-1 (…)+b n ).

[0125] wherein n is the index of the layer of the neural network, n is greater than or equal to 1, and n is less than or equal to N, wherein N is the total number of layers of the neural network.

[0126] In other words, the neural network can be understood as a mapping relationship from a set of input data to a set of output data. Usually, the neural network is randomly initialized, and the process of obtaining this mapping relationship from the random w and b using the existing data is called training of the neural network.

[0127] Optionally, the specific way of training is to evaluate the output result of the neural network by using a loss function.

[0128] As shown in FIG. 2d, the error can be back-propagated, and the neural network parameters (including w and b) can be iteratively optimized by the gradient descent method until the loss function reaches the minimum value, i.e., the “better point (e.g., optimal point)” in FIG. 2d. It can be understood that the neural network parameters corresponding to the “better point (e.g., optimal point)” in FIG. 2d can be used as the neural network parameters in the trained AI model information.

[0129] Further optionally, the process of gradient descent can be expressed as:

[0130] wherein θ is the parameter to be optimized (including w and b), L is the loss function, η is the learning rate, which controls the step size of gradient descent, represents the derivation operation, represents the derivative of L with respect to θ.

[0131] Further optionally, the process of back-propagation utilizes the chain rule of partial derivative.

[0132] As shown in FIG. 2e, the gradient of the parameters of the previous layer can be recursively calculated from the gradient of the parameters of the next layer, and can be expressed as:

[0133] wherein w ij is the weight of node j connected to node i, and si is the input weighted sum for node i.

[0134] The technical solutions provided in the present application can be applied to a wireless communication system (for example, the system shown in FIG. 1a or FIG. 1b or FIG. 1c), in which a communication node generally has signal transceiving capability and computing capability. Taking a network device with computing capability as an example, the computing capability of the network device is mainly to provide computing power support for the signal transceiving capability (for example, to perform sending processing and receiving processing on signals), so as to realize the communication task of the network device and other communication nodes.

[0135] The technical solutions provided in the present application can be applied to a wireless communication system (for example, the system shown in FIG. 1a or FIG. 1b), in which a communication node generally has signal transceiving capability and computing capability. Taking a network device with computing capability as an example, the computing capability of the network device is mainly to provide computing power support for the signal transceiving capability (for example, to perform sending processing and receiving processing on signals), so as to realize the communication task of the network device and other communication nodes. In addition, in addition to processing communication signals in the communication network, the communication device can also process other communication tasks (for example, channel prediction, beam management, resource scheduling, etc.).

[0136] However, in the communication network, in addition to providing computing power support for the above communication tasks, the computing capability of the communication node can also have surplus computing capability. Therefore, how to utilize these computing capabilities is a technical problem to be solved.

[0137] In a possible implementation manner, the communication device can serve as a participating node of an AI learning system, and the computing power of the communication device is applied to a certain link of the AI learning system. Generally, the AI function introduced in the wireless network needs to rely on an AI model to be implemented. Taking a communication device including a terminal device and a network device as an example, it is necessary to reach a consensus between the terminal device and the network device that the model or model pair needs to be called when implementing a certain AI-enabled function. In this case, both parties need to identify the existing model in advance, so as to facilitate subsequent calling and maintenance.

[0138] In addition, in the case that the communication device can serve as a participating node of an AI learning system, one communication device can store or deploy one or more AI models, and the AI models stored (or deployed) by different communication devices can be different (or, one communication device can provide one or more AI-enabled functions, and the AI-enabled functions provided by different communication devices can be different). Therefore, how to indicate the AI model (or the AI-enabled function) in different communication devices has not been solved by the current related solutions.

[0139] To solve the above problems, the present application provides a communication method and related device, which will be described in detail below in conjunction with the accompanying drawings.

[0140] Please refer to FIG. 3, which is an implementation schematic diagram of the communication method provided by the present application. The method includes the following steps.

[0141] It should be noted that in the following, the first communication device and other communication devices (for example, the second communication device) in FIG. 3 are taken as an example to illustrate the execution subject of the interaction, but the present application does not limit the execution subject of the interaction. For example, the communication device can be a communication equipment (for example, a terminal equipment or a network equipment), or a chip, a baseband chip, a modem chip, a system on chip (SoC) chip containing a modem core, a system in package (SIP) chip, a communication module, a chip system, a processor, a logic module or software in the communication equipment, etc.

[0142] S301. The second communication device sends first information, and correspondingly, the first communication device receives the first information. The first information is used to indicate first data. The first data is used to obtain a processing result through processing of one or more AI models of the first communication device.

[0143] S302. The first communication device sends second information, and correspondingly, the second communication device receives the second information. The second information is determined based on the processing result. The second information is used to determine part or all of the one or more AI models, or the second information is used to determine an AI-enabled function supported by the first communication device.

[0144] In the present application, the AI model can be replaced by other terms, such as neural network, neural network model, AI neural network model, machine learning model, or AI processing model, etc.

[0145] In the present application, the AI-enabled function can be replaced by other terms, such as AI-enabled feature, AI capability, or AI function, etc.

[0146] It should be noted that in step S301, the first data indicated by the first information can be implemented in various ways, which will be described in conjunction with some implementation examples.

[0147] Implementation example one, the first data indicated by the first information includes first training data.

[0148] In an implementation example one, the first training data is used for model training of one or more AI models of the first communication device, i.e., the second communication device indicates, through the first information, specific training data (i.e., the first training data) to the first communication device, so as to facilitate the first communication device to perform a model training process based on the specific training data. Correspondingly, the processing result obtained by the first communication device based on the first training data can be obtained after a pre-configured test data is used to test a trained model obtained based on the first training data. Thus, the first communication device can indicate, in step S302, an AI model or an AI-enabled function corresponding to the model training process implemented by the first training data to the second communication device.

[0149] In an implementation example two, the first data indicated by the first information includes first test data.

[0150] In the implementation example two, the first test data is used for model testing of one or more AI models of the first communication device, i.e., the second communication device indicates, through the first information, specific test data (i.e., the first test data) to the first communication device, so as to facilitate the first communication device to perform a model testing process based on the specific test data. Correspondingly, the processing result obtained by the first communication device based on the first training data is obtained after a pre-configured training data is used to train a model, and then the trained model is tested by using the first test data. Thus, the first communication device can indicate, in step S302, an AI model or an AI-enabled function corresponding to the model testing process implemented by the first test data to the second communication device.

[0151] In an implementation example three, the first data indicated by the first information includes second training data and second test data.

[0152] In the implementation example three, the second training data is used for model training of one or more AI models of the first communication device, and the second test data is used for model testing of the one or more AI models; wherein the processing result is obtained after a pre-configured training data is used to train a model, and then the trained model is tested by using the second test data. That is, the second communication device indicates, through the first information, specific training data (i.e., the second training data) and specific test data (i.e., the second test data) to the first communication device, so as to facilitate the first communication device to perform a model training process based on the specific training data, and to perform a model testing process based on the specific test data. Thus, the first communication device can indicate, in step S302, an AI model or an AI-enabled function corresponding to the model training process implemented by the second training data and the model testing process implemented by the second test data to the second communication device.

[0153] For example, the training data and / or the test data can be data corresponding to the AI model.

[0154] For example, in a case where the certain AI model of the first communication device is an AI model for modulation and / or demodulation (which can be referred to as a smart modulation model, a smart demodulation model, or a smart modulation and demodulation model, etc.), the training data and / or the test data for the AI model can include one or more of a modulation and coding scheme (MCS), a frequency domain resource indication, a transmit power control command (TPC command), and a transmitted precoding matrix indicator (TPMI).

[0155] For example, in a case where the certain AI model of the first communication device is an AI model for channel prediction (which can be referred to as a channel estimation model, a channel prediction model, a channel analog recovery model, a channel reconstruction model, a channel acquisition model, or a channel inference model, etc.), the training data and / or the test data for the AI model can include one or more of time domain configuration information, frequency domain configuration information, spatial domain configuration information, port information, periodicity information, and codebook configuration information.

[0156] For example, in a case where the certain AI model of the first communication device is an AI model for beam management (which can be referred to as a beam management model or a beam optimization model, etc.), the training data and / or the test data for the AI model can include channel characteristic information of a cell and a number of beams associated with the AI model for beam management.

[0157] It should be understood that after the first communication device determines the first data based on the first information in step S301, the first communication device can obtain a processing result based on processing of one or more AI models of the first communication device; thereafter, the first communication device can obtain second information based on the processing result, the second information being used to determine part or all of the one or more AI models, or the second information being used to determine an AI-enabled function supported by the first communication device. Based on the above implementation examples one to three, it can be known that the first data and the AI model or the AI-enabled function can have an associated relationship.

[0158] As an example, in the implementation of example one, it can be understood that the second communication device indicates a specific training data to the first communication device, so that the first communication device trains based on the specific training data, and an AI model or AI enabled function corresponding to the first training data is obtained through the training process. Wherein, the AI model or AI enabled function corresponding to the first training data can be that the AI model or AI enabled function has the ability to process the first training data (or the same or similar data as the first training data).

[0159] As another example, in the implementation of example two, it can be understood that the second communication device indicates a specific test data to the first communication device, so that the first communication device tests based on the specific test data, and determines (or selects, filters, etc.) an AI model or AI enabled function corresponding to the first test data through the test process. Wherein, the AI model or AI enabled function corresponding to the first test data can be that the AI model or AI enabled function has better (or higher, or higher than a threshold, etc.) processing performance for processing the first test data (or the same or similar data as the first training data).

[0160] As another example, in the implementation of example three, it can be understood that the second communication device indicates a specific training data and a specific test data to the first communication device, so that the first communication device trains based on the specific training data and tests based on the specific test data, and an AI model or AI enabled function corresponding to the second training data and the second test data is obtained and determined (or selected, filtered, etc.) through the training process and the test process. Wherein, the AI model or AI enabled function corresponding to the second training data and the second test data can be that the AI model or AI enabled function has the ability to process the second training data (or the same or similar data as the second training data), and the AI model or AI enabled function has better (or higher, or higher than a threshold, etc.) processing performance for processing the first test data (or the same or similar data as the first training data).

[0161] Based on the scheme shown in FIG. 3, the first communication device can process one or more AI models of the first communication device based on the first data indicated by the second communication device to obtain a processing result in step S302. Thereafter, the first communication device can send second information based on the processing result, so that the second communication device can determine part or all of the AI models (or determine the AI-enabled functions supported by the first communication device) based on the second information. In other words, after the second communication device indicates the first data to the first communication device, the first communication device can indicate the AI model or the AI-enabled function corresponding to the first data to the second communication device. In this way, the communication devices in the communication system can participate in the processing of the AI model and provide the processing capability of the AI model corresponding to the specified data or provide the AI-enabled function corresponding to the specified data.

[0162] In a possible implementation of the method shown in FIG. 3, the first information received by the first communication device in step S301 includes configuration information for collecting part or all of the data in the first data, and / or the first information includes part or all of the data in the first data. Wherein, the first information received by the first communication device can contain one or more information contents described above, so that the first communication device can obtain the first data in various ways.

[0163] As an example, in the case where the first information includes configuration information for collecting part or all of the data in the first data, the configuration information can include configuration information of resources (such as time domain resources, frequency domain resources, etc.) for collecting the part or all of the data. Correspondingly, the first communication device can collect based on the configuration information to obtain the part or all of the data.

[0164] As another example, in the case where the first information includes part or all of the data in the first data, the first communication device can obtain the part or all of the data based on the first information.

[0165] Optionally, in the case where the first information includes part or all of the data in the first data, the first information can further include data composition information (such as one or more of data size, data format, data type) of the part or all of the data, in this way, the first communication device can obtain the part or all of the data from the first information based on the data composition information.

[0166] Optionally, the first information can further include at least one of the following:

[0167] First indication information for indicating a scenario corresponding to the first data;

[0168] Second indication information for indicating a preprocessing rule corresponding to the first data;

[0169] a third indication information, used to indicate area information to which the first data is applicable.

[0170] Therefore, the first information received by the first communication device can also include the at least one described above, so that the first communication device obtains the first data based on the at least one described above. In the following, the indication information included in the first information will be exemplarily described through some examples.

[0171] In example A, in the case where the first information includes the first indication information, the first communication device can obtain, as the first data, data corresponding to a scene indicated by the first indication information based on the first indication information.

[0172] For example, in example A, the scene indicated by the first indication information can include one or more of an indoor scene, an outdoor scene, a line of sight (LOS) scene, a non line of sight (NLOS), a terrestrial network (TN) scene, and a non-terrestrial network (NTN) scene.

[0173] Optionally, in the case where the first data includes training data (such as the first training data, the second training data, etc. described above), example A described above can be understood as that the second communication device can indicate, through the first information, training data of a specific scene to the first communication device, so that the first communication device obtains an AI model (or an AI enabled function) suitable for the specific scene based on the training data of the specific scene.

[0174] Optionally, in the case where the first data includes test data (such as the first test data, the second test data, etc. described above), example A described above can be understood as that the second communication device can indicate, through the first information, test data of a specific scene to the first communication device, so that the first communication device determines (or selects, filters, etc.) an AI model (or an AI enabled function) suitable for the specific scene based on the test data of the specific scene.

[0175] In example B, in the case where the first information includes the second indication information, the first communication device can perform data processing based on a pre-processing rule indicated by the second indication information based on the second indication information, to obtain the first data.

[0176] For example, in example B, the pre-processing rule indicated by the second indication information can include one or more of data enhancement, data filtering, data cleaning, data noise reduction, and data expansion based on sample data.

[0177] In an example, the pre-processing rule includes data augmentation based on sample data, and the sample data can be channel data of one or more time units. The first communication device can perform data augmentation on the sample data based on the pre-processing rule, and the obtained channel data of other time units can be part or all of the first data.

[0178] In another example, the pre-processing rule includes data denoising, and the first communication device can perform noise addition on the collected (or configured) channel data based on the pre-processing rule, and the obtained noise-added channel data can be part or all of the first data.

[0179] In example C, when the first information includes third indication information, the first communication device can obtain the data corresponding to the region information indicated by the third indication information as the first data based on the third indication information.

[0180] For example, in example C, the applicable region information indicated by the third indication information can include one or more of the following: location coordinates of a geographic region, latitude and longitude information, and altitude information.

[0181] Similarly, when the first data includes training data (e.g., the first training data, the second training data, etc. described above), example C can be understood as the second communication device indicating the training data of the region information to the first communication device through the first information, so that the first communication device obtains the AI model (or AI-enabled function) suitable for the specific region based on the training data of the specific region.

[0182] Similarly, when the first data includes test data (e.g., the first test data, the second test data, etc. described above), example C can be understood as the second communication device indicating the test data of the specific region to the first communication device through the first information, so that the first communication device determines (or selects, filters, etc.) the AI model (or AI-enabled function) suitable for the specific region based on the test data of the specific region.

[0183] In a possible implementation of the method shown in FIG. 3, the second information sent by the first communication device in step S302 includes any of the following:

[0184] Fourth indication information for indicating the processing result;

[0185] Fifth indication information for indicating part or all of the one or more AI models;

[0186] Sixth indication information for indicating the AI-enabled function supported by the first communication device.

[0187] Thus, the second information sent by the first communication device can include any of the above, so that the second communication device determines the AI model or AI-enabled function corresponding to the first data in multiple ways. The above-mentioned indication information included in the second information will be described exemplarily through some examples.

[0188] In the first way, when the second information includes the fourth indication information, the second communication device can determine part or all of the one or more AI models in the first communication device based on the processing result indicated by the fourth indication information, or the second communication device can determine the AI-enabled function supported by the first communication device based on the processing result indicated by the fourth indication information. Since the first communication device can directly indicate the processing result through the second information after obtaining the processing result through the processing of the one or more AI models in the first communication device, the processing complexity of the first communication device can be reduced.

[0189] In the second way, when the second information includes the fifth indication information, the second communication device can determine part or all of the one or more AI models deployed (or stored, existing) by the first communication device that match the first data based on the fifth indication information. Subsequently, when the second communication device performs an AI task associated with the first data, the second communication device can schedule the part or all of the AI models based on the fifth indication information to improve the performance of the AI task.

[0190] In the third way, when the second information includes the sixth indication information, the second communication device can determine the AI-enabled function that matches the first data from one or more processing capabilities provided by the first communication device based on the sixth indication information. Subsequently, when the second communication device performs an AI task associated with the first data, the second communication device can schedule the AI-enabled function based on the sixth indication information to improve the performance of the AI task.

[0191] In one possible implementation of the method shown in FIG. 3, the method further includes that the first communication device receives third information, the third information being used to indicate auxiliary information corresponding to the one or more AI models, the auxiliary information being used to indicate at least one of the following: model function, model structure parameter, data format of model input, data format of model output. In other words, the first communication device can also determine the auxiliary information corresponding to the one or more AI models in the first communication device through the received third information, so that the first communication device obtains the AI model or AI-enabled function that matches the auxiliary information based on the auxiliary information.

[0192] It should be understood that, in the case that the assistance information indicates the model function, it can be understood that the assistance information is used to indicate the target model, i.e., the first communication apparatus can obtain the target model of a specific model function based on the assistance information (or the first communication apparatus can obtain the AI-enabled function same as or similar to the specific model function based on the assistance information).

[0193] It should be understood that, in the case that the assistance information indicates at least one of the model structure parameter, the data format of the model input (denoted as format 1), and the data format of the model output (denoted as format 2), it can be understood that the assistance information is used to indicate the reference model, i.e., the first communication apparatus can determine at least one of the specific model structure of the reference model, the model input of the reference model conforms to the format 1, and the model output of the reference model conforms to the format 2 based on the assistance information. Correspondingly, the first communication apparatus can obtain the AI model same as or similar to the reference model based on the assistance information (or the first communication apparatus can obtain the AI-enabled function provided by the AI model same as or similar to the reference model based on the assistance information).

[0194] Optionally, the first information and the third information can be carried in the same message or in different messages, which is not limited here. For example, in the case that the first communication apparatus is a terminal device and the second communication apparatus is a network device, the message can include an RRC message, a downlink control information (DCI), or a media access control control element (MAC CE), or other messages.

[0195] Referring to FIG. 4, an embodiment of the present application provides a communication apparatus 400, which can implement the functions of the first communication apparatus (or the second communication apparatus) in the above-mentioned method embodiments, and thus can also achieve the beneficial effects possessed by the above-mentioned method embodiments. In the embodiment of the present application, the communication apparatus 400 can be the first communication apparatus (or the second communication apparatus), or an integrated circuit or element etc. inside the first communication apparatus (or the second communication apparatus), such as a chip, a baseband chip, a modem chip, a SoC chip containing a modem core, a system in package (SIP) chip, a communication module, a chip system, a processor, etc.

[0196] It should be noted that the transceiver unit 402 can include a sending unit and a receiving unit, which are respectively used for performing sending and receiving.

[0197] In a possible implementation, when the apparatus 400 is configured to perform the method performed by the first communication apparatus in FIG. 3 and related embodiments, the apparatus 400 includes a processing unit 401 and a transceiver unit 402; the transceiver unit 402 is configured to receive first information, the first information being used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of the first communication apparatus; the processing unit 401 is configured to determine second information based on the processing result; and the transceiver unit 402 is further configured to send the second information; wherein the second information is used to determine part or all of the one or more AI models, or the second information is used to determine an AI-enabled function supported by the first communication apparatus.

[0198] In a possible implementation, when the apparatus 400 is configured to perform the method performed by the second communication apparatus in FIG. 3 and related embodiments, the apparatus 400 includes a processing unit 401 and a transceiver unit 402; the processing unit 401 is configured to determine first information; and the transceiver unit 402 is configured to send the first information, the first information being used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of the first communication apparatus; and the transceiver unit 402 is further configured to receive second information, the second information being determined based on the processing result; wherein the second information is used to determine part or all of the one or more AI models, or the second information is used to determine an AI-enabled function supported by the first communication apparatus.

[0199] In a possible design, when the communication apparatus 400 is a communication module in a terminal device or a terminal, the function of the processing unit 401 can be implemented by one or more processors. Specifically, the processor can include a modem chip, or a SoC chip or a SIP chip including a modem core. The function of the transceiver unit 402 can be implemented by a transceiver circuit.

[0200] In a possible design, when the communication apparatus 400 is a circuit or a chip responsible for communication functions in a terminal, such as a modem chip or a SoC chip or a SIP chip including a modem core, the function of the processing unit 401 can be implemented by a circuit system including one or more processors or processor cores in the chip. The function of the transceiver unit 402 can be implemented by an interface circuit or a data transceiver circuit on the chip.

[0201] It should be noted that the information execution process and the like of the units of the communication apparatus 400 described above can be specifically refer to the descriptions in the method embodiments described above, and will not be described here again.

[0202] Please refer to FIG. 5, which is another schematic structural diagram of a communication apparatus 500 provided in the present application, the communication apparatus 500 comprises a logic circuit 501 and an input / output interface 502. Wherein, the communication apparatus 500 can be a chip or an integrated circuit.

[0203] Wherein, the transceiver unit 402 shown in FIG. 4 can be a communication interface, which can be the input / output interface 502 in FIG. 5, the input / output interface 502 can comprise an input interface and an output interface. Alternatively, the communication interface can also be a transceiver circuit, which can comprise an input interface circuit and an output interface circuit.

[0204] In a possible implementation, when the apparatus 500 is used to perform the method performed by the first communication apparatus in FIG. 3 and related embodiments, the input / output interface 502 is configured to receive first information, the first information being used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of the first communication apparatus; the logic circuit 501 is configured to determine second information based on the processing result; and the input / output interface 502 is further configured to send the second information; wherein the second information is used to determine part or all of the one or more AI models, or the second information is used to determine an AI-enabled function supported by the first communication apparatus.

[0205] In a possible implementation, when the apparatus 500 is used to perform the method performed by the second communication apparatus in FIG. 3 and related embodiments, the logic circuit 501 is configured to determine first information; the input / output interface 502 is configured to send the first information, the first information being used to indicate first data; wherein the first data is used to obtain a processing result through processing of one or more AI models of the first communication apparatus; the input / output interface 502 is further configured to receive second information, the second information being determined based on the processing result; wherein the second information is used to determine part or all of the one or more AI models, or the second information is used to determine an AI-enabled function supported by the first communication apparatus.

[0206] Wherein, the logic circuit 501 and the input / output interface 502 can also perform other steps performed by the first communication apparatus or the second communication apparatus in any of the embodiments and achieve the corresponding beneficial effects, which will not be described here.

[0207] In a possible implementation, the processing unit 401 shown in FIG. 4 can be the logic circuit 501 in FIG. 5.

[0208] Optionally, the logic circuit 501 can be a processing apparatus, and the functions of the processing apparatus can be partially or entirely implemented through software. Wherein, the functions of the processing apparatus can be partially or entirely implemented through software.

[0209] Optionally, the processing apparatus can include a memory and a processor, wherein the memory is configured to store a computer program, and the processor is configured to read and execute the computer program stored in the memory to perform the corresponding processing and / or steps in any one of the method embodiments.

[0210] Optionally, the processing apparatus can only include the processor. The memory for storing the computer program is located outside the processing apparatus, and the processor is connected with the memory through the circuit / wire to read and execute the computer program stored in the memory. The memory and the processor can be integrated together, or can also be physically independent of each other.

[0211] Optionally, the processing apparatus can be one or more chips, or one or more integrated circuits. For example, the processing apparatus can be one or more field-programmable gate arrays (FPGA), application specific integrated circuits (ASIC), system on chips (SoC), central processing units (CPU), network processors (NP), digital signal processors (DSP), micro controller units (MCU), programmable logic devices (PLD) or other integrated chips, or any combination of the above chips or processors, etc.

[0212] Please refer to FIG. 6, the communication apparatus 600 involved in the above embodiments provided by the embodiments of the present application, and the communication apparatus 600 can be specifically the communication apparatus as the terminal device in the above embodiments.

[0213] Optionally, the communication apparatus 600 can include but is not limited to at least one processor 601 and a communication port 602.

[0214] Optionally, the transceiver unit 402 shown in FIG. 4 can be a communication interface, which can be the communication port 602 in FIG. 6. The communication port 602 can include an input interface and an output interface. Alternatively, the communication port 602 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0215] Further, the apparatus can further include at least one of a memory 603, a bus 604, and in embodiments of the present application, the at least one processor 601 is configured to control actions of the communication apparatus 600.

[0216] Further, the processor 601 can be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array, or other programmable logic device, transistor logic, hardware component, or any combination thereof. It can implement or execute various example logical blocks, modules, and circuits described in connection with the disclosure. The processor can also be a combination of computing functionality, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, or the like. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, apparatus, and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0217] It should be noted that the communication apparatus 600 shown in FIG. 6 can be specifically used to implement the steps implemented by the terminal device in the foregoing method embodiments, and achieve the corresponding technical effects of the terminal device. The specific implementation of the communication apparatus shown in FIG. 6 can refer to the description in the foregoing method embodiments, which will not be described here.

[0218] Please refer to FIG. 7, which is a structural schematic diagram of a communication apparatus 700 involved in the foregoing embodiments provided by embodiments of the present application. The communication apparatus 700 can be specifically the communication apparatus as the network device in the foregoing embodiments.

[0219] The communication apparatus 700 includes at least one processor 711 and at least one network interface 714. Further, the communication apparatus can further include at least one memory 712, at least one transceiver 713, and one or more antennas 714. The processor 711, the memory 712, the transceiver 713, and the network interface 714 are connected, for example, through a bus, and in embodiments of the present application, the connection can include various interfaces, transmission lines, or buses, etc., which are not limited in the embodiments. The antenna 715 is connected to the transceiver 713. The network interface 714 is configured to enable the communication apparatus to communicate with other communication devices through a communication link. For example, the network interface 714 can include a network interface between the communication apparatus and a core network device, such as an S1 interface. The network interface can include a network interface between the communication apparatus and other communication apparatuses (such as other network devices or core network devices), such as an X2 or Xn interface.

[0220] The transceiving unit 402 shown in FIG. 4 can be a communication interface, which can be the network interface 714 in FIG. 7, and can include an input interface and an output interface. Alternatively, the network interface 714 can also be a transceiving circuit, which can include an input interface circuit and an output interface circuit.

[0221] The processor 711 is mainly used for processing communication protocols and communication data, and controlling the entire communication device, executing software programs, and processing data of the software programs, for example, for supporting the communication device to perform the actions described in the embodiments. The communication device can include a baseband processor and a central processor, the baseband processor is mainly used for processing communication protocols and communication data, and the central processor is mainly used for controlling the entire terminal device, executing software programs, and processing data of the software programs. The processor 711 in FIG. 7 can integrate the functions of the baseband processor and the central processor, and those skilled in the art can understand that the baseband processor and the central processor can also be independent processors interconnected by a bus or the like. Those skilled in the art can understand that the terminal device can include multiple baseband processors to adapt to different network modes, and the terminal device can include multiple central processors to enhance its processing capability, and various components of the terminal device can be connected by various buses. The baseband processor can also be referred to as a baseband processing circuit or a baseband processing chip. The central processor can also be referred to as a central processing circuit or a central processing chip. The function of processing communication protocols and communication data can be built into the processor, or stored in the memory in the form of a software program, and the processor executes the software program to realize the baseband processing function.

[0222] The memory is mainly used for storing software programs and data. The memory 712 can exist independently and be connected to the processor 711. Alternatively, the memory 712 can be integrated with the processor 711, for example, integrated in a chip. The memory 712 can store program codes for executing the technical solutions of the embodiments of the present application, and the processor 711 controls the execution. Various computer programs executed can also be regarded as a driver of the processor 711.

[0223] FIG. 7 only shows one memory and one processor. In actual terminal devices, there can be multiple processors and multiple memories. The memory can also be referred to as a storage medium or a storage device, etc. The memory can be a storage element on the same chip as the processor, that is, an on-chip storage element, or an independent storage element, and the embodiments of the present application do not limit this.

[0224] The transceiver 713 can be configured to support the receiving or transmitting of radio frequency signals between the communication device and a terminal. The transceiver 713 can be connected to the antenna 715. The transceiver 713 includes a transmitter Tx and a receiver Rx. Specifically, the one or more antennas 715 can receive radio frequency signals, the receiver Rx of the transceiver 713 is configured to receive the radio frequency signals from the antenna and convert the radio frequency signals into digital baseband signals or digital intermediate frequency signals, and provide the digital baseband signals or digital intermediate frequency signals to the processor 711 for further processing, such as demodulation processing and decoding processing, by the processor 711. In addition, the transmitter Tx in the transceiver 713 is also configured to receive modulated digital baseband signals or digital intermediate frequency signals from the processor 711, and convert the modulated digital baseband signals or digital intermediate frequency signals into radio frequency signals, and transmit the radio frequency signals through the one or more antennas 715. Specifically, the receiver Rx can selectively perform one or more levels of down-mixing processing and analog-to-digital conversion processing on the radio frequency signals to obtain the digital baseband signals or digital intermediate frequency signals, and the order of the down-mixing processing and the analog-to-digital conversion processing can be adjustable. The transmitter Tx can selectively perform one or more levels of up-mixing processing and digital-to-analog conversion processing on the modulated digital baseband signals or digital intermediate frequency signals to obtain the radio frequency signals, and the order of the up-mixing processing and the digital-to-analog conversion processing can be adjustable. The digital baseband signals and the digital intermediate frequency signals can be collectively referred to as digital signals.

[0225] The transceiver 713 can also be referred to as a transceiving unit, a transceiver, a transceiving device, etc. Optionally, the devices in the transceiving unit for implementing the receiving function can be regarded as a receiving unit, and the devices in the transceiving unit for implementing the transmitting function can be regarded as a transmitting unit, i.e., the transceiving unit includes the receiving unit and the transmitting unit, the receiving unit can also be referred to as a receiver, an input port, a receiving circuit, etc., and the transmitting unit can be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0226] It should be noted that the communication device 700 shown in FIG. 7 can be specifically configured to implement the steps implemented by the network device in the foregoing method embodiments, and achieve the corresponding technical effects of the network device. The specific implementation mode of the communication device 700 shown in FIG. 7 can be referred to the description in the foregoing method embodiments, which will not be described here one by one.

[0227] Please refer to FIG. 8, which is a structural schematic diagram of a communication device involved in the above embodiments provided by the embodiments of the present application.

[0228] It can be understood that the communication apparatus 800 includes, for example, modules, units, elements, circuits, or interfaces, and the like, which are properly configured together to perform the technical solutions provided in the present application. The communication apparatus 800 can be a terminal device or a network device described above, or can be a component (for example, a chip) of the devices, to implement the methods described in the following method embodiments. The communication apparatus 800 includes one or more processors 801. The processor 801 can be a general purpose processor or a special purpose processor, etc. For example, it can be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication apparatus (such as a RAN node, a terminal, or a chip, etc.), execute software programs, and process data of the software programs.

[0229] Optionally, in one design, the processor 801 can include a program 803 (which can also be referred to as code or instructions at times) that can be run on the processor 801, so that the communication apparatus 800 performs the methods described in the following embodiments. In yet another possible design, the communication apparatus 800 includes a circuit (not shown in FIG. 8).

[0230] Optionally, the communication apparatus 800 can include one or more memories 802 having a program 804 (which can also be referred to as code or instructions at times) stored thereon, which can be run on the processor 801, so that the communication apparatus 800 performs the methods described in the above method embodiments.

[0231] Optionally, the processor 801 and / or the memory 802 can include an AI module 807, 808, which is used to implement AI-related functions. The AI module can be implemented in software, hardware, or a combination of software and hardware. For example, the AI module can include a radio intelligence control (RIC) module. For example, the AI module can be a near-real-time RIC or a non-real-time RIC.

[0232] Optionally, the processor 801 and / or the memory 802 can also store data. The processor and the memory can be separately arranged or integrated together.

[0233] Optionally, the communication apparatus 800 can also include a transceiver 805 and / or an antenna 806. The processor 801 can also be referred to as a processing unit, which controls the communication apparatus (such as a RAN node or a terminal). The transceiver 805 can also be referred to as a transceiving unit, a transceiver, a transceiving circuit, or a transceiver, etc., which is used to realize the transceiving function of the communication apparatus through the antenna 806.

[0234] The processing unit 401 shown in FIG. 4 can be the processor 801. The transceiving unit 402 shown in FIG. 4 can be a communication interface, which can be the transceiver 805 in FIG. 8, which can include an input interface and an output interface. Alternatively, the transceiver 805 can also be a transceiving circuit, which can include an input interface circuit and an output interface circuit.

[0235] The embodiments of the present application further provide a computer readable storage medium for storing one or more computer-executable instructions, which, when executed by a processor, cause the processor to perform the method described in the possible implementation manners of the first communication device or the second communication device.

[0236] The embodiments of the present application further provide a computer program product (or computer program), which, when executed by a processor, causes the processor to perform the method described in the possible implementation manners of the first communication device or the second communication device.

[0237] The embodiments of the present application further provide a chip system, which includes at least one processor for supporting the communication device to implement the functions involved in the possible implementation manners of the communication device. Optionally, the chip system further includes an interface circuit for providing program instructions and / or data for the at least one processor. In a possible design, the chip system can further include a memory for storing necessary program instructions and data of the communication device. The chip system can be composed of a chip, or can include a chip and other discrete devices. The communication device can be the first communication device or the second communication device in the method embodiments.

[0238] The embodiments of the present application further provide a communication system, which includes the first communication device and / or the second communication device in any of the above embodiments.

[0239] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0240] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0241] In addition, each functional unit in the embodiments of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially or substantially, or all or part of the technical solutions, can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing 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 methods described in the embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and various other media that can store program codes.

Claims

1. A communication method characterized by comprising: The method comprises: The first communication device receives first information, wherein the first information is used to indicate first data; wherein the first data is used to obtain a processing result by processing of one or more artificial intelligence (AI) models of the first communication device; The first communication device sends second information, wherein the second information is determined based on the processing result; wherein the second information is used to determine part or all of the one or more AI models, or the second information is used to determine an AI-enabled function supported by the first communication device.

2. The method of claim 1, wherein, The first data comprises first training data, wherein the first training data is used for model training of the one or more AI models. The processing result is obtained by model testing of a pre-configured test data on a trained model after model training based on the first training data.

3. The method of claim 1, wherein, The first data comprises first test data, wherein the first test data is used for model testing of the one or more AI models. The processing result is obtained by model testing of the first test data on the trained model after model training based on pre-configured training data.

4. The method of claim 1, wherein, The first data comprises second training data and second test data, wherein the second training data is used for model training of the one or more AI models, and the second test data is used for model testing of the one or more AI models. The processing result is obtained by model testing of the second test data on the trained model after model training based on the second training data.

5. The method according to any one of claims 1 to 4, characterized in that, The first information comprises configuration information for collecting part or all of the first data, and / or the first information comprises part or all of the first data.

6. The method of claim 5, wherein, The first information further comprises at least one of the following: First indication information, used to indicate a scenario corresponding to the first data; Second indication information, used to indicate a preprocessing rule corresponding to the first data; Third indication information, used to indicate area information applicable to the first data.

7. The method according to any one of claims 1 to 6, characterized in that, The second information comprises any one of the following: Fourth indication information, used to indicate the processing result; Fifth indication information, used to indicate part or all of the one or more AI models; Sixth indication information, used to indicate an AI-enabled function supported by the first communication device.

8. The method according to any one of claims 1 to 7, characterized in that, The method further comprises: The first communication device receives third information, wherein the third information is used to indicate auxiliary information corresponding to the one or more AI models, and the auxiliary information is used to indicate at least one of the following: Model function, model structure parameter, data format of model input, and data format of model output.

9. A communication method characterized by comprising: The method comprises: The second communication device sends first information, wherein the first information is used to indicate first data; wherein the first data is used to obtain a processing result by processing of one or more artificial intelligence (AI) models of the first communication device; The second communication device receives second information, the second information being determined based on the processing result; wherein the second information is used to determine part or all of the one or more AI models, or the second information is used to determine AI-enabled functions supported by the first communication device.

10. The method of claim 9, wherein, The first data includes first training data, the first training data being used for model training of the one or more AI models. The processing result is obtained after model training based on the first training data, and model testing of the trained model based on preconfigured test data.

11. The method of claim 9, wherein, The first data includes first test data, the first test data being used for model testing of the one or more AI models. The processing result is obtained after model training based on preconfigured training data, and model testing of the trained model based on the first test data.

12. The method of claim 9, wherein, The first data includes second training data and second test data, the second training data being used for model training of the one or more AI models, and the second test data being used for model testing of the one or more AI models. The processing result is obtained after model training based on the second training data, and model testing of the trained model based on the second test data.

13. The method according to any one of claims 9 to 12, characterized in that, The first information includes configuration information for collecting part or all of the first data, and / or the first information includes part or all of the first data.

14. The method of claim 13, wherein, The first information further includes at least one of the following: First indication information, used to indicate a scenario corresponding to the first data; Second indication information, used to indicate a preprocessing rule corresponding to the first data; Third indication information, used to indicate area information to which the first data is applicable.

15. The method according to any one of claims 9 to 14, characterized in that, The second information includes any one of the following: Fourth indication information, used to indicate the processing result; Fifth indication information, used to indicate part or all of the one or more AI models; Sixth indication information, used to indicate AI-enabled functions supported by the first communication device.

16. The method according to any one of claims 9 to 15, characterized in that, The method further includes: The second communication device sends third information, the third information being used to indicate auxiliary information corresponding to the one or more AI models, the auxiliary information being used to indicate at least one of the following: Model function, model structure parameter, data format of model input, and data format of model output.

17. A communications device, characterized by A module for performing the method of any one of claims 1 to 16.

18. A communications device, characterized by At least one processor coupled with a memory, the at least one processor being configured to perform the method of any one of claims 1 to 16.

19. The communication apparatus according to claim 18, wherein The communication device is a chip or a chip system.

20. A readable storage medium, characterized by, The storage medium stores a computer program or instructions, which, when executed by the communication device, implement the method of any one of claims 1 to 16. The storage medium stores a computer program or instructions, which, when executed by the communication device, implement the method of any one of claims 1 to 16.

21. A computer program product, characterised in that, When the computer program product is run on a computer, it causes the computer to perform a method as claimed in any one of claims 1 to 16.

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