Communication method and related device

Abstract

A communication method and a related device, which are used to enable a communication apparatus in a communication system to serve as a data acquisition node, so as to achieve data acquisition. In the method, after a first communication apparatus has sent first information used to indicate state information of the first communication apparatus, the first communication apparatus can receive second information used to configure first data acquisition, and acquire data on the basis of the second information. The second information is determined on the basis of the state information of the first communication apparatus. In other words, the first communication apparatus can perform data acquisition on the basis of a configuration of a second communication apparatus. In this way, a communication apparatus in a communication system can serve as a data acquisition node, so as to realize data acquisition.

Description

A communication method and related equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on December 29, 2023, with application number 202311865121.8 and application name “A Communication Method and Related Equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communications, and in particular to a communication method and related equipment. Background Art

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

[0004] Currently, in wireless communication systems, communication nodes generally possess both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., processing both sending and receiving signals), enabling communication between the network device and other communication nodes.

[0005] However, in communication networks, communication nodes may have excess computing power beyond just supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue. Summary of the Invention

[0006] The present application provides a communication method and related equipment for enabling a communication device in a communication system to serve as a data collection node to achieve data collection.

[0007] In a first aspect, the present application provides a communication method, which is performed by a first communication device, which may be a communication device (such as a network device or a terminal device), or may be a component of a communication device (such as a processor, a chip, or a chip system, etc.), or may be a logic module or software that can implement all or part of the functions of the communication device. In this method, the first communication device sends first information, which is used to indicate status information of the first communication device; the first communication device receives second information, which is used to configure first data collection; wherein the second information is determined based on the status information of the first communication device; and the first communication device collects data based on the second information.

[0008] Based on the above technical solution, after a first communication device sends first information indicating its status, it can receive second information for configuring first data collection and collect data based on the second information. The second information is determined based on the status information of the first communication device. In other words, the first communication device can collect data based on the configuration of the second communication device. Thus, the communication devices in the communication system can serve as data collection nodes to implement data collection.

[0009] In addition, in the above technical solution, the second information used to configure the first data collection can be determined based on the status information of the first communication device. In this way, the configuration of the first data collection can be adapted to the status of the first communication device, thereby improving the success rate of data collection.

[0010] Optionally, the transmission of the second information for configuring the first data collection may be triggered by an artificial intelligence (AI) task functionality ID. For example, when the AI ​​task functionality ID is enabled, the second communication device may transmit the second information, causing the first communication device to perform data collection based on the second information.

[0011] In this application, terms such as data collection, data collection, data acquisition, and data capture can be used interchangeably.

[0012] In a possible implementation manner of the first aspect, the second information includes indication information indicating N collection conditions for the first data collection, where N is a positive integer; wherein the state indicated by the state information satisfies the N collection conditions.

[0013] Optionally, the indication information in the second information may indicate the N conditions in a variety of ways. For example, the indication information may include the N conditions themselves, or may include indexes / identifiers of the N conditions.

[0014] Based on the above technical solution, the second information used to configure the first data collection may include N collection conditions for the first data collection, and the state indicated by the state information satisfies the N collection conditions. In this way, the first communication device can perform data collection based on the collection conditions that satisfy the state of the first communication device.

[0015] In a possible implementation manner of the first aspect, the second information includes instruction information for instructing to perform the first data acquisition based on the N acquisition conditions.

[0016] Based on the above technical solution, through the above indication information, the first communication device can clearly define the N collection conditions for the first data collection of the first communication device, and subsequently the first communication device can perform data collection based on the N collection conditions.

[0017] In a possible implementation manner of the first aspect, the second information further includes indication information indicating M acquisition conditions, where the M acquisition conditions are conditions for second data acquisition, and M is a positive integer.

[0018] Optionally, the second information further includes indication information for indicating not to perform the first data acquisition based on the M acquisition conditions.

[0019] Optionally, the M collection conditions can be determined based on the status information of the first communication device sent by the first communication device, or based on the status information of other communication devices, or based on the status information of multiple communication devices (including the first communication device and other communication devices).

[0020] Optionally, the second data collection is different from the first data collection of the second information configuration. The second data collection can be used in other data collection processes of the first communication device, and can also be used in data collection processes of other communication devices, which is not limited here.

[0021] Based on the above technical solution, the second information received by the first communication device may also include conditions indicating other data collection. In this way, the second communication device can configure conditions for other data collection processes in the second information, and flexibly indicate whether to collect data based on the M collection conditions through additional indication information, thereby reducing implementation complexity.

[0022] In a possible implementation manner of the first aspect, the second information includes at least one of the following: an AI task identifier corresponding to the first data collection, an AI function identifier corresponding to the first data collection, and data set size information of the first data collection.

[0023] Based on the above technical solution, the second information used to configure the first data collection may include at least one of the above items to indicate the task and / or data set size corresponding to the data collection.

[0024] In a possible implementation manner of the first aspect, the status information includes at least one of the following: system status information, AI status information, data set status information, and scene status information.

[0025] Based on the above technical solution, the status information of the first communication device may include at least one of the above items, so that the second communication device can determine the above at least one status of the first communication device based on the first information, thereby improving the flexibility of the solution implementation.

[0026] Optionally, each of the above states satisfies one or more of the following:

[0027] The system state information includes at least one of a system parameter set and a signal processing configuration;

[0028] The AI ​​state information includes at least one of an AI model parameter and an AI computing resource;

[0029] The scene state information includes at least one of signal-to-noise ratio (SNR) state information, motion state information, reference signal configuration information, and antenna configuration information;

[0030] The data set status information includes at least one of data set collection time information, data set location information, data set collection stage information, indication information indicating whether the data set is existing data, and indication information indicating a reporting method of the data set.

[0031] In a possible implementation manner of the first aspect, the second information is determined based on the status information of the first communication device, including: the second information is determined based on the status information of the first communication device and the task requirements of the AI ​​task.

[0032] Based on the above technical solution, the basis for determining the second information may include not only the status information of the first communication device, but also the task requirements of the AI ​​task. In this way, the data collected by the first communication device based on the second information can meet the task requirements.

[0033] Optionally, the data obtained by the first communication device through data collection can be used for model processing of the AI ​​model (for example, at least one of model training, model reasoning, and model monitoring). In this way, the communication device in the communication system can serve as a collection node for AI-related data to realize the collection of AI-related data.

[0034] Optionally, wireless communication signals (such as the transmission and reception of configuration information of communication resources, the transmission and reception of reference signals, etc.) can be transmitted between different communication devices, and the AI ​​models involved in the embodiments of the present application (such as the first AI model, the second AI model, etc. mentioned later) can be used to process the wireless communication signals (including at least one of management, configuration, update, and optimization). For example, the AI ​​model may include an AI model for modulation and / or demodulation, an AI model for channel prediction, an AI model for beam management, an AI model for assisted positioning, an AI model for channel compression, an AI model for resource scheduling, an AI model for mobility management, an AI model for load balancing, an AI model for network energy saving, and one or more AI models for replacing one or more modules in a transmitter and / or receiver. Alternatively, the AI ​​model involved in the embodiments of the present application may also be an AI model for other AI tasks, such as an AI model for image recognition, an AI model for natural language processing, an AI model for computer vision, etc.

[0035] In this application, terms such as AI model, neural network model, AI neural network model, machine learning model, and AI processing model can be used interchangeably.

[0036] In a possible implementation of the first aspect, part or all of the data is used for processing a first AI model, and the first AI model is deployed on the first communication device.

[0037] Based on the above technical solution, of the data collected by the first communication device based on the second information, part or all of the data can be used to process the first AI model deployed on the first communication device. In this way, the first communication device can collect the first data configured based on the second information to achieve the collection of data used for model processing of the AI ​​model deployed on the first communication device.

[0038] In a possible implementation of the first aspect, part or all of the data is also used for processing a second AI model, and the second AI model is deployed on a second communication device; wherein the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; the method also includes: the first communication device sends part or all of the data.

[0039] Based on the above technical solution, part or all of the data collected by the first communication device based on the second information can also be used to process the second AI model deployed on the second communication device. In this way, the first communication device can collect the first data configured based on the second information to achieve the collection of data used for model processing of the AI ​​models deployed on both sides.

[0040] In a possible implementation of the first aspect, part or all of the data is used for processing a second AI model, and the second AI model is deployed on a second communication device; the method also includes: the first communication device sends part or all of the data.

[0041] Based on the above technical solution, of the data collected by the first communication device based on the second information, part or all of the data can be used to process the second AI model deployed on the second communication device. In this way, the first communication device can collect the first data configured based on the second information to achieve the collection of data used for model processing of the AI ​​model deployed on the second communication device.

[0042] The second aspect of the present application provides a communication method, which is performed by a second communication device, which can be a communication device (such as a network device or a terminal device), or the second communication device can be a component of the communication device (such as a processor, a chip or a chip system, etc.), or the second communication device can also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the second communication device receives first information, which is used to indicate status information of the first communication device; the second communication device sends second information, which is used to configure data collection; wherein the second information is determined based on the status information of the first communication device.

[0043] Based on the above technical solution, after a second communication device receives first information indicating the status of the first communication device, it can send second information for configuring first data collection, enabling the first communication device to collect data based on the second information after receiving the first information. The second information is determined based on the status information of the first communication device. In other words, the first communication device can collect data based on the configuration of the second communication device. Thus, the communication devices in the communication system can serve as data collection nodes to implement data collection.

[0044] In addition, in the above technical solution, the second information used to configure the first data collection can be determined based on the status information of the first communication device. In this way, the configuration of the first data collection can be adapted to the status of the first communication device, thereby improving the success rate of data collection.

[0045] In a possible implementation manner of the second aspect, the second information includes indication information indicating N collection conditions for the data collection, where N is a positive integer; wherein the state indicated by the state information satisfies the N collection conditions.

[0046] Optionally, the indication information in the second information may indicate the N conditions in a variety of ways. For example, the indication information may include the N conditions themselves, or may include indexes / identifiers of the N conditions.

[0047] Based on the above technical solution, the second information used to configure the first data collection may include N collection conditions for the first data collection, and the state indicated by the state information satisfies the N collection conditions. In this way, the first communication device can perform data collection based on the collection conditions that satisfy the state of the first communication device.

[0048] In a possible implementation manner of the second aspect, the second information includes instruction information for instructing to perform data collection based on the N collection conditions.

[0049] Based on the above technical solution, through the above indication information, the first communication device can clearly define the N collection conditions for the first data collection of the first communication device, and subsequently the first communication device can perform data collection based on the N collection conditions.

[0050] In a possible implementation manner of the second aspect, the second information further includes indication information indicating M acquisition conditions, where the M acquisition conditions are conditions for second data acquisition, and M is a positive integer.

[0051] Optionally, the second information further includes indication information for indicating not to perform data collection based on the M collection conditions.

[0052] Optionally, the M collection conditions can be determined based on the status information of the first communication device sent by the first communication device, or based on the status information of other communication devices, or based on the status information of multiple communication devices (including the first communication device and other communication devices).

[0053] Optionally, the second data collection is different from the first data collection of the second information configuration. The second data collection can be used in other data collection processes of the first communication device, and can also be used in data collection processes of other communication devices, which is not limited here.

[0054] Based on the above technical solution, the second information sent by the second communication device may also include conditions indicating other data collection. In this way, the second communication device can configure conditions for other data collection processes in the second information, and flexibly indicate whether to collect data based on the M collection conditions through additional indication information, thereby reducing implementation complexity.

[0055] In a possible implementation manner of the second aspect, the second information further includes at least one of the following: an AI task identifier corresponding to the data collection, and data set size information of the data collection.

[0056] Based on the above technical solution, the second information used to configure the first data collection may include at least one of the above items to indicate the task and / or data set size corresponding to the data collection.

[0057] In a possible implementation manner of the second aspect, the status information includes at least one of the following: system status information, AI status information, data set status information, and scene status information.

[0058] Based on the above technical solution, the status information of the first communication device may include at least one of the above items, so that the second communication device can determine the above at least one status of the first communication device based on the first information, thereby improving the flexibility of the solution implementation.

[0059] Optionally, each of the above states satisfies one or more of the following:

[0060] The system state information includes at least one of a system parameter set and a signal processing configuration;

[0061] The AI ​​state information includes at least one of an AI model parameter and an AI computing resource;

[0062] The scene state information includes at least one of SNR state information, motion state information, reference signal configuration information, and antenna configuration information;

[0063] The data set status information includes at least one of data set collection time information, data set location information, data set collection stage information, indication information indicating whether the data set is existing data, and indication information indicating a reporting method of the data set.

[0064] In a possible implementation of the second aspect, the second information is determined based on the status information of the first communication device, including: the second information is determined based on the status information of the first communication device and the task requirements of the AI ​​task, and the data meets the task requirements.

[0065] Based on the above technical solution, the basis for determining the second information may include not only the status information of the first communication device, but also the task requirements of the AI ​​task. In this way, the data collected by the first communication device based on the second information can meet the task requirements.

[0066] Optionally, the data obtained by the first communication device through data collection can be used for model processing of the AI ​​model (for example, at least one of model training, model reasoning, and model monitoring). In this way, the communication device in the communication system can serve as a collection node for AI-related data to realize the collection of AI-related data.

[0067] Optionally, wireless communication signals (such as the transmission and reception of configuration information of communication resources, the transmission and reception of reference signals, etc.) can be transmitted between different communication devices, and the AI ​​models involved in the embodiments of the present application (such as the first AI model, the second AI model, etc. mentioned later) can be used to process the wireless communication signals (including at least one of management, configuration, update, and optimization). For example, the AI ​​model may include an AI model for modulation and / or demodulation, an AI model for channel prediction, an AI model for beam management, an AI model for assisted positioning, an AI model for channel compression, an AI model for resource scheduling, an AI model for mobility management, an AI model for load balancing, an AI model for network energy saving, and one or more AI models for replacing one or more modules in a transmitter and / or receiver. Alternatively, the AI ​​model involved in the embodiments of the present application may also be an AI model for other AI tasks, such as an AI model for image recognition, an AI model for natural language processing, an AI model for computer vision, etc.

[0068] In this application, terms such as AI model, neural network model, AI neural network model, machine learning model, and AI processing model can be used interchangeably.

[0069] In a possible implementation of the second aspect, part or all of the data is used for processing a first AI model, and the first AI model is deployed on the first communication device.

[0070] Based on the above technical solution, of the data collected by the first communication device based on the second information, part or all of the data can be used to process the first AI model deployed on the first communication device. In this way, the first communication device can collect the first data configured based on the second information to achieve the collection of data used for model processing of the AI ​​model deployed on the first communication device.

[0071] In a possible implementation of the second aspect, part or all of the data is also used for processing a second AI model, and the second AI model is deployed on a second communication device; wherein the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; the method also includes: the second communication device receives part or all of the data.

[0072] Based on the above technical solution, part or all of the data collected by the first communication device based on the second information can also be used to process the second AI model deployed on the second communication device. In this way, the first communication device can collect the first data configured based on the second information to achieve the collection of data used for model processing of the AI ​​models deployed on both sides.

[0073] In a possible implementation of the second aspect, part or all of the data is used for processing a second AI model, and the second AI model is deployed on a second communication device; the method also includes: the second communication device receives part or all of the data.

[0074] Based on the above technical solution, of the data collected by the first communication device based on the second information, part or all of the data can be used to process the second AI model deployed on the second communication device. In this way, the first communication device can collect the first data configured based on the second information to achieve the collection of data used for model processing of the AI ​​model deployed on the second communication device.

[0075] The third aspect of the present application provides a communication device, which is a first communication device, and includes a transceiver unit and a processing unit; the transceiver unit is used to send first information, and the first information is used to indicate the status information of the first communication device; the transceiver unit is also used to receive second information, and the second information is used to configure data collection; wherein the second information is determined based on the status information of the first communication device; the processing unit collects data based on the second information.

[0076] In the third aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects. For details, please refer to the first aspect and will not be repeated here.

[0077] The fourth aspect of the present application provides a communication device, which is a second communication device, and includes a transceiver unit and a processing unit. The transceiver unit is used to receive first information, and the first information is used to indicate the status information of the first communication device; the processing unit is used to determine second information; the transceiver unit is also used to send second information, and the second information is used to configure data collection; wherein the second information is determined based on the status information of the first communication device.

[0078] In the fourth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the second aspect and achieve corresponding technical effects. For details, please refer to the second aspect and will not be repeated here.

[0079] In a fifth aspect, the present application provides a communication device, comprising at least one processor, wherein the at least one processor is coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the program or instructions so that the device implements the method described in any possible implementation method of any one of the first to second aspects.

[0080] In a sixth aspect, the present application provides a communication device comprising at least one logic circuit and an input / output interface; the logic circuit is used to execute the method described in any possible implementation of any one of the first to second aspects.

[0081] In a seventh aspect, the present application provides a communication system, which includes the above-mentioned first communication device and second communication device.

[0082] In an eighth aspect, the present application provides a computer-readable storage medium for storing one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in any possible implementation of any one of the first to second aspects above.

[0083] In a ninth aspect, 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 possible implementation of any one of the first to second aspects above.

[0084] In a tenth aspect, the present application provides a chip system comprising at least one processor for supporting a communication device to implement the method described in any possible implementation of any one of the first to second aspects.

[0085] In one possible design, the chip system may further include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of a chip or may include a chip and other discrete components. Optionally, the chip system may further include an interface circuit for providing program instructions and / or data to the at least one processor.

[0086] Among them, the technical effects brought about by any design method in the third to tenth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to second aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figures 1a to 1c are schematic diagrams of a communication system provided by this application;

[0088] Figures 2a to 2h are schematic diagrams of the AI ​​processing process involved in this application;

[0089] FIG3 is an interactive schematic diagram of the communication method provided by this application;

[0090] Figures 4 and 5 are schematic diagrams of the second information provided by this application;

[0091] FIG6 is another interactive schematic diagram of the communication method provided by this application;

[0092] 7 to 11 are schematic diagrams of the communication device provided in this application. DETAILED DESCRIPTION

[0093] First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0094] (1) Terminal device: It can be a wireless terminal device that can receive network device scheduling and instruction information. The wireless terminal device can be a device that provides voice and / or data connectivity to the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.

[0095] Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN). Terminal devices can be mobile terminal devices, such as mobile phones (also known as "cellular" phones, mobile phones), computers, and data cards. For example, they can be portable, pocket-sized, handheld, computer-built-in, or vehicle-mounted mobile devices that exchange voice and / or data with the radio access network. Examples include personal communication service (PCS) phones, cordless phones, Session Initiation Protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablet computers, and computers with wireless transceiver capabilities. Wireless terminal equipment can also be called system, subscriber unit, subscriber station, mobile station, mobile station (MS), remote station, access point (AP), remote terminal equipment (remote terminal), access terminal equipment (access terminal), user terminal equipment (user terminal), user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

[0096] As an example and not a limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices may also be referred to as wearable smart devices or smart wearable devices, etc., which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, etc., as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0097] The terminal may 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 remote medical, 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.

[0098] In addition, the terminal device may also be a terminal device in a communication system that has evolved after the fifth generation (5G) communication system (e.g., a sixth generation (6G) communication system) or a terminal device in a future public land mobile network (PLMN). For example, the 6G network can further expand the form and function of 5G communication terminals. 6G terminals include but are not limited to vehicles, cellular network terminals (with integrated satellite terminal functions), drones, and Internet of Things (IoT) devices.

[0099] In an embodiment of the present application, the terminal device may also obtain AI services provided by the network device. Optionally, the terminal device may also have AI processing capabilities.

[0100] (2) Network equipment: It can be a device in a wireless network. For example, the network equipment can be a RAN node (or device) that connects a terminal device to a wireless network, which can also be called a base station. Currently, some examples of RAN equipment include: base station, evolved NodeB (eNodeB), gNB (gNodeB) in a 5G communication system, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point AP, etc. In addition, in a network structure, the network equipment can include a centralized unit (CU) node, a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0101] Alternatively, a RAN node can be a macro base station, micro base station, indoor base station, relay node, donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. A RAN node can also be a server, wearable device, vehicle, or vehicle-mounted device. For example, the access network device in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

[0102] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, and different RAN nodes respectively implement part of the functions of the base station. For example, the RAN node can be a centralized unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). The CU and DU can be set separately, or they can be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0103] In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in an open access network (open RAN, O-RAN or ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. For the convenience of description, this application takes CU, CU-CP, CU-UP, DU and RU as examples for description. Any unit of CU (or CU-CP, CU-UP), DU and 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.

[0104] The communication between the access network device and the terminal device follows a certain protocol layer structure. The protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may 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. The user plane protocol layer may 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.

[0105] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

[0106] Table 1

[0107] The network device may be any other device that provides wireless communication functionality to the terminal device. The embodiments of this application do not limit the specific technology and device form used by the network device. For ease of description, the embodiments of this application do not limit this.

[0108] The network equipment may also include core network equipment, which may include, for example, a mobility management entity (MME), a home subscriber server (HSS), a serving gateway (S-GW), a policy and charging rules function (PCRF), and a public data network gateway (PDN gateway, P-GW) in a fourth generation (4G) network; and network elements such as an access and mobility management function (AMF), a user plane function (UPF), or a session management function (SMF) in a 5G network. In addition, the core network equipment may also include other core network equipment in a 5G network and a next generation network of a 5G network.

[0109] In an embodiment of the present application, the above-mentioned network device may also have a network node with AI capabilities, which can provide AI services for terminals or other network devices. For example, it can be an AI node on the network side (access network or core network), a computing power node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.

[0110] In the embodiments of the present application, the apparatus for implementing the function of the network device may be the network device, or may be a device capable of supporting the network device in implementing the function, such as a chip system, which may be installed in the network device. In the technical solutions provided in the embodiments of the present application, the technical solutions provided in the embodiments of the present application are described by taking the network device as an example.

[0111] (3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used simultaneously. Configuration refers to the network device / server sending some parameter configuration information or parameter values ​​to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission based on these values ​​or information. Pre-configuration is similar to configuration, and can be parameter information or parameter values ​​pre-negotiated between the network device / server and the terminal device, or parameter information or parameter values ​​used by the base station / network device or terminal device as specified in the standard protocol, or parameter information or parameter values ​​pre-stored in the base station / server or terminal device. This application does not limit this.

[0112] Furthermore, these values ​​and parameters can be changed or updated.

[0113] (4) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: 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 indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural 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 such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects.

[0114] (5) “Sending” and “receiving” in the embodiments of the present application indicate the direction of signal transmission. For example, “sending information to XX” can be understood as the destination of the information being XX, which can include direct sending through the air interface, as well as indirect sending through the air interface by other units or modules. “Receiving information from YY” can be understood as the source of the information being YY, which can include direct receiving from YY through the air interface, as well as indirect receiving from YY through the air interface from other units or modules. “Sending” can also be understood as the “output” of the chip interface, and “receiving” can also be understood as the “input” of the chip interface.

[0115] In other words, sending and receiving can be performed between devices, for example, between a network device and a terminal device, or can be performed within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, wiring or interface.

[0116] It is understandable that information may be processed between the source and destination of information transmission, such as coding, modulation, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated.

[0117] (6) In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated may also be indirectly indicated by indicating other information, wherein the other information is associated with the information to be indicated; or only a part of the information to be indicated may be indicated, while the other part of the information to be indicated is known or agreed in advance. For example, the indication of specific information may be achieved by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication. It is understandable that for the sender of the indication information, the indication information can be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information can be used to determine the information to be indicated.

[0118] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments of this application, and the various methods / designs / implementations in each embodiment, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments and the various methods / designs / implementations in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various methods / designs / implementations in each embodiment can be combined to form new embodiments, methods, or implementations according to their inherent logical relationships. The following description of the implementation methods of this application does not constitute a limitation on the scope of protection of this application.

[0119] 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.

[0120] Please refer to Figure 1a, which is a schematic diagram of a communication system in this application. Figure 1a exemplarily illustrates a network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. In the example shown in Figure 1a, terminal device 1 is a smart teacup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas pump, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

[0121] As shown in Figure 1a, the AI ​​configuration information sending entity can be a network device. The AI ​​configuration information receiving entity can be terminal devices 1-6. In this case, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 can send data to the network device, and the network device needs to receive data sent by terminal devices 1-6. At the same time, the network device can send configuration information to terminal devices 1-6.

[0122] For example, in Figure 1a, terminal devices 4 and 6 can also form a communication system. Terminal device 5 serves as a network device, i.e., the AI ​​configuration information sending entity; terminal devices 4 and 6 serve as terminal devices, i.e., the AI ​​configuration information receiving entities. For example, in a connected vehicle system, terminal device 5 sends AI configuration information to terminal devices 4 and 6, respectively, and receives data from them. Correspondingly, terminal devices 4 and 6 receive AI configuration information from terminal device 5 and send data to terminal device 5.

[0123] Taking the communication system shown in Figure 1a as an example, in addition to executing communication-related services, different devices (including between network devices, between network devices and terminal devices, and / or between terminal devices) may also execute AI-related services.

[0124] As shown in Figure 1b, taking the network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

[0125] As shown in Figure 1c, taking the terminal devices including a TV and a mobile phone as an example, communication-related services and AI-related services can also be performed between the TV and the mobile phone.

[0126] The technical solution provided in this application can be applied to a wireless communication system (e.g., the system shown in FIG. 1a , FIG. 1b , or FIG. 1c ). For example, an AI network element can be introduced into the communication system provided in this application to implement some or all AI-related operations. The AI ​​network element can also be referred to as an AI node, AI device, AI entity, AI module, AI model, or AI unit, etc. The AI ​​network element can be a network element built into the communication system. For example, the AI ​​network element can be an AI module built into: an access network device, a core network device, a cloud server, or a network management (OAM) to implement AI-related functions. The OAM can be a network management device for a core network device and / or a network management device for an access network device. Alternatively, the AI ​​network element can also be an independently set network element in the communication system. Optionally, the terminal or the chip built into the terminal can also include an AI entity to implement AI-related functions.

[0127] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0128] Artificial intelligence (AI) can imbue machines with human intelligence. For example, it can enable machines to simulate certain intelligent human behaviors using computer hardware and software. Machine learning methods can be used to achieve AI. In machine learning, a machine uses training data to learn (or train) a model. This model represents the mapping from input to output. The learned model can be used for inference (or prediction), meaning that the model can be used to predict the output corresponding to a given input. This output can also be called an inference result (or prediction result).

[0129] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0130] Supervised learning uses machine learning algorithms to learn the mapping relationship between sample values ​​and sample labels based on collected sample values ​​and sample labels, and then expresses this learned mapping relationship using an AI model. The process of training a machine learning model is the process of learning this mapping relationship. During training, sample values ​​are input into the model to obtain the model's predicted values. The model parameters are optimized by calculating the error between the model's predicted values ​​and the sample labels (ideal values). Once 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 mappings or nonlinear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0131] Unsupervised learning uses algorithms to discover inherent patterns in collected sample values. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping from one sample to another. This is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used in signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

[0132] 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 lack explicit label data for "correct" actions. Instead, the algorithm must interact with the environment to obtain reward signals from the environment, and then adjust its decision-making actions to maximize the reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmit power of each user based on the overall system throughput fed back by the wireless network, hoping to achieve higher system throughput. The goal of reinforcement learning is also to learn the mapping between environmental states and optimal (e.g., optimal) decision-making actions. However, because the labels for "correct actions" cannot be obtained in advance, network optimization cannot be achieved by calculating the error between actions and "correct actions." Reinforcement learning training is achieved through iterative interaction with the environment.

[0133] A neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, NNs can theoretically approximate any continuous function, enabling them to learn arbitrary mappings. Traditional communication systems require extensive expert knowledge to design communication modules. However, deep learning communication systems based on neural networks can automatically discover implicit patterns in massive data sets and establish mapping relationships between data, achieving performance superior to traditional modeling methods.

[0134] The idea of ​​a neural network is derived from the neuronal structure of the brain. For example, each neuron performs a weighted sum operation on its input values ​​and outputs the result through an activation function.

[0135] As shown in Figure 2a, it is a schematic diagram of the neuron structure. Assume that the input of the neuron is x = [x0, x1, ..., x n ], and the weights corresponding to each input are w=[w,w1,…,w n ], where n is a positive integer, w i and x i It can be a decimal, an integer (such as 0, a positive integer or a negative integer, etc.), or a complex number. i As x i The weight of x iWeighted. The bias of the weighted sum of the input values ​​according to the weight is, for example, b. The activation function can take many forms. Assuming that the activation function of a neuron is: y = f(z) = max(0,z), then the output of the neuron is: For another example, if the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: b can be a decimal, an integer (eg, 0, a positive integer, or a negative integer), or a complex number, etc. The activation functions of different neurons in a neural network can be the same or different.

[0136] Furthermore, neural networks generally include multiple layers, each of which may include one or more neurons. Increasing the depth and / or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it comprises, and the number of neurons in each layer can be referred to as the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, a hidden layer, and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to an intermediate hidden layer. The hidden layer performs calculations on the received processing results to obtain a calculation result, which is then passed to the output layer or the next adjacent hidden layer, which ultimately obtains the output of the neural network. A neural network can include one hidden layer or multiple hidden layers connected in sequence, without limitation.

[0137] A neural network is, for example, a deep neural network (DNN). Depending on how the network is constructed, a DNN can include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

[0138] Figure 2b is a schematic diagram of an FNN network. A characteristic of FNN networks is that neurons in adjacent layers are fully connected. This characteristic typically requires a large amount of storage space and results in high computational complexity.

[0139] CNN is a neural network specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling along the time axis) and image data (discrete sampling along two dimensions) can both be considered grid-like data. CNNs do not utilize all input information at once for computation. Instead, they use a fixed-size window to intercept a portion of the information for convolution operations, significantly reducing the computational complexity of model parameters. Furthermore, depending on the type of information intercepted by the window (e.g., people and objects in an image represent different types of information), each window can use a different convolution kernel, enabling CNNs to better extract features from the input data.

[0140] RNNs are a type of DNN that utilizes feedback time series information. Their input consists of a new input value at the current moment and their own output value at the previous moment. RNNs are suitable for capturing temporally correlated sequence features and are particularly well-suited for applications such as speech recognition and channel coding.

[0141] During the machine learning model training process, a loss function can be defined. This function describes the gap or discrepancy between the model's output and the ideal target value. Loss functions can be expressed in various forms, and there are no restrictions on their specific form. The model training process can be viewed as adjusting some or all of the model's parameters to keep the loss function below a threshold or meet the target.

[0142] A model may also be referred to as an AI model, rule, or other name. An AI model can be considered a specific method for implementing an AI function. An AI model represents a mapping relationship or function between the input and output of a model. AI functions may include one or more of the following: data collection, model training (or model learning), model information release, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model verification, or inference result release, etc. AI functions may also be referred to as AI (related) operations, or AI-related functions.

[0143] The following is an illustrative description of the implementation process of the neural network with reference to the accompanying drawings.

[0144] 1. Fully connected neural network, also known as multilayer perceptron (MLP).

[0145] As shown in Figure 2c, an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (center). Each layer of the MLP contains several nodes, called neurons. Neurons in adjacent layers are connected to each other.

[0146] Optionally, considering neurons in two adjacent layers, the output h of a neuron in the next layer is the weighted sum of all neurons x connected to it in the previous layer and passes through an activation function, which can be expressed as: h=f(wx+b).

[0147] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0148] Alternatively, the output of the neural network can be recursively expressed as: y = f n (w n f n-1 (…)+b n ).

[0149] Where n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0150] In other words, a neural network can be understood as a mapping from an input data set to an output data set. Neural networks are typically initialized randomly, and the process of obtaining this mapping from random w and b using existing data is called neural network training.

[0151] Optionally, a specific training method is to use a loss function to evaluate the output results of the neural network.

[0152] As shown in Figure 2d, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the loss function reaches a minimum, which is the "better point (e.g., optimal point)" in Figure 2d. It is understood that the neural network parameters corresponding to the "better point (e.g., optimal point)" in Figure 2d can be used as the neural network parameters in the trained AI model information.

[0153] Alternatively, the gradient descent process can be expressed as:

[0154] Among them, 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 derivative operation, represents the derivative of θ with respect to L.

[0155] Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

[0156] As shown in Figure 2e, the gradient of the previous layer parameters can be recursively calculated from the gradient of the next layer parameters, which can be expressed as:

[0157] Among them, w ij is the weight of node j connecting to node i, si is the weighted sum of the inputs to node i.

[0158] 2. Federated Learning (FL)

[0159] The concept of federated learning effectively solves the current difficulties faced by the development of artificial intelligence. On the premise of fully protecting user data privacy and security, it efficiently completes the model learning task by promoting the collaboration between various edge devices and central servers.

[0160] As shown in Figure 2f, the FL architecture is the most widely used training architecture in the current FL field. The FedAvg algorithm is the basic algorithm of FL. Its algorithm flow is roughly as follows:

[0161] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0162] (2) In the round t∈[1,T], client k∈[1,K] based on the local dataset For the received global model Perform E epochs of training to obtain local training results Report it to the central node.

[0163] (3) The central node aggregates and collects the local training results from all (or some) clients. Assume that the client set that uploads the local model in round t is The center will use the number of samples of the corresponding client as the weight to perform weighted averaging to obtain a new global model. The specific update rule is: The center then sends the latest version of the global model Broadcast to all client devices for a new round of training.

[0164] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0165] In addition to reporting local models You can also use the local gradient of training After reporting, the central node averages the local gradients and updates the global model according to the direction of the average gradient.

[0166] As you can see, in the FL framework, datasets exist on distributed nodes. Distributed nodes collect local datasets, perform local training, and report the local training results (models or gradients) to the central node. The central node itself does not have a dataset; it is only responsible for fusing the training results of distributed nodes to obtain a global model and send it to the distributed nodes.

[0167] 3. Decentralized learning: Different from federated learning, decentralized learning is another distributed learning architecture.

[0168] As shown in Figure 2g, consider a fully distributed system without a central node. The design goal f(x) of a decentralized learning system is generally the goal f of each node. i The mean of (x), that is Where n is the number of distributed nodes, x is the parameter to be optimized. In machine learning, x is the parameter of the machine learning (such as neural network) model. Each node uses local data and local target f i (x) Calculate local gradient Then it is sent to the neighboring nodes that can be communicated with. After any node receives the gradient information sent by its neighbor, it can update the parameter x of the local model according to the following formula:

[0169] in, represents the parameters of the local model after the k+1th (k is a natural number) update in the i-th node, Indicates the parameters of the local model after the kth update in the i-th node (if k is 0, it means is the parameter of the local model of the i-th node that does not participate in the update), α k Represents the tuning coefficient, N i is the set of neighbor nodes of node i, |N i | represents the number of elements in the neighbor node set of node i, that is, the number of neighbor nodes of node i. Through information interaction between nodes, the decentralized learning system will eventually learn a unified model.

[0170] 4. Segmented learning.

[0171] As shown in Figure 2h, in split learning, the complete neural network model is divided into two parts (i.e., two sub-networks). One part is deployed on distributed nodes (e.g., nodes 1, 2, and 3 in Figure 2h), and the other is deployed on a central node. The area where the complete neural network is split is called the "split layer." During forward inference, the distributed nodes input local data into their local sub-networks, inferring to the split layer. The split layer's result Fk (e.g., F1 / F2 / F3 in the figure) is then sent to the central node via a communication link. The central node then inputs the received Fk ​​into another sub-network deployed within itself and continues forward inference to obtain the final inference result. During the gradient backpropagation of model training, the gradient is propagated back through the central node's sub-network to the split layer, obtaining the backpropagation result Gk (e.g., G1 / G2 / G3 in the figure). The central node then sends Gk to the distributed nodes, and the gradient backpropagation continues on the distributed nodes' sub-networks.

[0172] Optionally, in segmentation learning, the models deployed by different distributed nodes may be the same or different, and may be determined based on the needs and capabilities of the different distributed nodes, which is not limited here.

[0173] Optionally, a distributed node can also send local model-related parameters to other distributed nodes. For example, in Figure 2h, node 1 can send local model-related parameters to nodes 2 and 3 respectively. In this example, node 1 first trains its local model (denoted as model 1). Optionally, node 1 can send the model-related parameters of model 1 to other nodes, allowing other nodes to continue training based on model 1 and obtain local models of other nodes more quickly.

[0174] As you can see, the forward inference and gradient backpropagation processes for segmentation learning may involve a distributed node and a central node. The trained subnetwork on a distributed node can be stored locally on the distributed node or on a dedicated model storage server. When a new distributed node joins the learning system, it can first download the trained subnetwork from the distributed node and then use local data for further training.

[0175] The technical solutions provided in this application can be applied to wireless communication systems (e.g., the systems shown in Figures 1a, 1b, or 1c). In wireless communication systems, communication nodes generally have both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., performing signal transmission and reception processing) to enable communication between the network device and other communication nodes.

[0176] However, in communication networks, communication nodes may have excess computing power beyond what is needed to support these communication tasks. Therefore, how to utilize this computing power, for example, to perform data collection, is a pressing technical issue.

[0177] In order to solve the above problems, the present application provides a communication method and related equipment, which are used to enable a communication device in a communication system to serve as a data collection node to achieve data collection.

[0178] Please refer to FIG3 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0179] It should be noted that, in the method shown in Figure 3 (or the method shown in Figure 6), the method is illustrated by taking the first communication device and the second communication device as the execution subjects of the interaction diagram as an example, but the present application does not limit the execution subjects of the interaction diagram. For example, in the method shown in Figure 3 (or the method shown in Figure 6), the execution subject of the method can be replaced by a chip, a chip system, a processor, a logic module or software in the communication device. The first communication device can be a terminal device and the second communication device can be a network device, or the first communication device and the second communication device are both terminal devices (for example, the method can be applied to the communication process of different terminal devices in a side link communication scenario).

[0180] For example, taking the second communication device as a network device, the network device can be implemented by the ORAN network element shown in Table 1 above. For example, the receiving and sending processes of the second communication device in steps S301 and S302 below can be implemented by one or more of the O-CU-CP, O-CU-UP, O-DU, and O-RU in the ORAN network element.

[0181] S301. A first communication device sends first information, and correspondingly, a second communication device receives the first information, wherein the first information is used to indicate status information of the first communication device.

[0182] S302: The second communication device sends second information, and the first communication device receives the second information accordingly, wherein the second information is used to configure the first data collection, and the second information is determined based on the state information of the first communication device.

[0183] S303. The first communication device collects data based on the second information.

[0184] In this application, terms such as data collection, data collection, data acquisition, and data capture can be used interchangeably.

[0185] In a possible implementation, in step S301, the state information indicated by the first information sent by the first communication device includes at least one of the following information 1 to information 4.

[0186] Information 1. System status information.

[0187] Exemplarily, the system status information in information 1 may include at least one of a system parameter set and a signal processing configuration. For example, the system parameter set (numerology) may include subcarrier spacing, frame structure, system bandwidth, etc. For another example, the signal processing configuration may include modulation order, coding rate, waveform, precoding, resource mapping method, multi-antenna configuration, etc.

[0188] Information 2. AI status information.

[0189] Exemplarily, the AI ​​state information in information 2 includes at least one of AI model parameters and AI computing resources. For example, AI model parameters may include model hyperparameters, model structural parameters, etc. In another example, AI computing resources may include graphics processing unit (GPU) floating point numbers, central processing unit (CPU) frequency, etc.

[0190] Information 3. Dataset status information.

[0191] Exemplarily, the dataset status information in information 4 includes at least one of the collection time information of the dataset, the geographic location information of the dataset, the collection stage information corresponding to the dataset, indication information indicating whether the dataset is existing data, and indication information indicating the reporting method of the dataset.

[0192] Information 4. Scene status information.

[0193] Exemplarily, the scene state information in information 4 includes at least one of signal-to-noise ratio (SNR) state information, motion state information, reference signal configuration information, and antenna configuration information.

[0194] Therefore, the status information of the first communication device may include at least one of the above items, so that the second communication device can determine the at least one status of the first communication device based on the first information, thereby improving the flexibility of the solution implementation.

[0195] Optionally, one or more contents included in the first information may be transmitted through one message or multiple messages, which is not limited here.

[0196] Optionally, the sending of the first information may be triggered based on signaling from the second communication device, such as scheduling signaling, request signaling, indication signaling, etc. In this way, the first communication device can clearly identify the status information that needs to be reported in the first information based on the signaling, which helps the second communication device obtain the status information desired by the second communication device.

[0197] In one possible implementation, in step S302, the second information sent by the second communication device includes indication information indicating N collection conditions for the first data collection, where N is a positive integer; wherein the state indicated by the state information satisfies the N collection conditions. Specifically, the second information for configuring the first data collection may include the N collection conditions for the first data collection, and the state indicated by the state information satisfies the N collection conditions. In this manner, the first communication device can perform data collection based on the collection conditions that satisfy the state of the first communication device.

[0198] Optionally, the indication information in the second information may indicate the N conditions in a variety of ways. For example, the indication information may include the N conditions themselves, or may include indexes / identifiers of the N conditions.

[0199] As an example, taking the first information including information 1 as an example, for example, the information 1 may include the system bandwidth of the first communication device. Accordingly, one or more of the N collection conditions configured by the second information can configure the first communication device to collect communication information under the system bandwidth (such as signal reception quality information, signal demodulation performance information, etc.).

[0200] As another example, taking the first information including information 2 as an example, for example, the information 2 may include the AI ​​model parameters of the first communication device. Accordingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to collect model processing information of the AI ​​model corresponding to the AI ​​model parameters (for example, the data dimension of the model input data, the data dimension of the model output data, etc.).

[0201] As another example, taking the first information including information 3 as an example, for example, information 3 may include acquisition phase information corresponding to a data set of the first communication device. Accordingly, one or more of the N acquisition conditions configured in the second information may configure the first communication device to acquire model processing information of the AI ​​model corresponding to the acquisition phase information corresponding to the data set. The acquisition phase information may indicate that the acquisition phase is at least one of a model training phase, a model inference phase, and a model monitoring phase.

[0202] As another example, taking the first information including information 4 as an example, for example, the information 4 may include the SNR status information of the first communication device. Accordingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to collect communication information under the SNR state (for example, high SNR state, low SNR state) indicated by the SNR status information.

[0203] As another example, taking the first information including information 4 as an example, for example, the information 4 may include the motion state information of the first communication device. Accordingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to collect communication information under the motion state indicated by the motion state information (for example, high-speed motion state, low-speed motion state, etc.).

[0204] As another example, taking the first information including information 4 as an example, for example, the information 4 may include the reference signal configuration information of the first communication device. Accordingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to collect communication information under the configuration indicated by the reference signal configuration information (for example, a reference signal configuration with a larger number of resources, a reference signal configuration with a smaller number of resources).

[0205] As another example, taking the first information including information 4 as an example, for example, the information 4 may include the antenna configuration information of the first communication device. Accordingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to collect communication information under the configuration indicated by the antenna configuration information (for example, the number of ports in the horizontal direction, the number of ports in the vertical direction).

[0206] Optionally, the second information further includes instruction information for instructing to perform the first data collection based on the N collection conditions. Specifically, through the above instruction information, the first communication device can clearly specify that the N collection conditions are used for the first data collection of the first communication device, and the first communication device can subsequently perform data collection based on the N collection conditions.

[0207] Optionally, the indication information indicating that the first data acquisition is performed based on the N acquisition conditions may include the specific acquisition parameter ranges of the N acquisition conditions (for example, SNR interval, motion speed interval, etc.), and may also include the index of the specific acquisition parameter ranges of the N acquisition conditions (the specific acquisition parameter range can be configured through other signaling, such as RRC message).

[0208] In one possible implementation, the second information further includes indication information indicating M collection conditions, where the M collection conditions are conditions for the second data collection, and M is a positive integer. Optionally, the second information further includes indication information for indicating that the first data collection is not to be performed based on the M collection conditions. Specifically, the second information received by the first communication device may also include conditions indicating other data collection conditions. In this manner, the second communication device can configure conditions for other data collection processes in the second information and flexibly indicate whether to perform data collection based on the M collection conditions through additional indication information, thereby reducing implementation complexity.

[0209] Optionally, the M collection conditions can be determined based on the status information of the first communication device sent by the first communication device, or based on the status information of other communication devices, or based on the status information of multiple communication devices (including the first communication device and other communication devices).

[0210] In one possible implementation, the second information includes at least one of the following: an AI task identifier corresponding to the first data collection, an AI function identifier corresponding to the first data collection, and data set size information for the first data collection. Specifically, the second information used to configure the first data collection may include at least one of the above items to indicate the task and / or data set size corresponding to the data collection.

[0211] Optionally, the second data collection is different from the first data collection of the second information configuration. The second data collection can be used in other data collection processes of the first communication device, and can also be used in data collection processes of other communication devices, which is not limited here.

[0212] It should be noted that, as described above, the second information may include multiple pieces of information (e.g., indication information indicating N acquisition conditions for the first data acquisition, indication information indicating that the first data acquisition is performed based on the N acquisition conditions, indication information indicating M acquisition conditions, an artificial intelligence (AI) task identifier corresponding to the first data acquisition, an AI function identifier corresponding to the first data acquisition, and data set size information for the first data acquisition), wherein the multiple pieces of information may be carried by one message / signaling or by multiple messages / signaling. In other words, the second information may be carried by one message / signaling or by multiple messages / signaling. For example, the second information may be carried by one signaling, which may be a MAC CE (MAC control element) in the example of FIG. 4 below. For another example, the second information may be carried by a first message (e.g., an RRC message) and a second message (e.g., a MAC CE / DCI, etc.), the first message including N acquisition conditions and M acquisition conditions, and the second message including indication information indicating that the first data acquisition is performed based on the N acquisition conditions and indication information indicating that the first data acquisition is not performed based on the M acquisition conditions.

[0213] Optionally, the second information can be carried in RRC, MAC CE, DCI (downlink control information), sidelink control information (SCI) or other signaling / messages.

[0214] As an implementation example, as shown in FIG4 , taking the second information carried by a MAC CE as an example, the second information may include the following fields:

[0215] "R" field: reserved field.

[0216] "Task ID" field: indicates the task ID, i.e., the "AI task ID corresponding to the first data collection" mentioned above.

[0217] "Dataset Size" field: indicates the size of the data subset required for data collection, that is, the "dataset size information of the first data collection" mentioned above.

[0218] "Data Collection Condition i" field: Indicates the i-th condition for data collection, or the index / identifier of the i-th condition. For example, some or all of the fields in "Data Collection Condition 0-n" shown in Figure 4 may indicate information indicating N collection conditions for a first data collection. Alternatively, at least one field in "Data Collection Condition 0-n" shown in Figure 4 may indicate information indicating M collection conditions for a second data collection.

[0219] "Ci" field: indicates whether the i-th condition needs to be met. For example, 0 indicates that the condition does not need to be met, and 1 indicates collecting data that meets the requirements of Data Collection Condition i. For example, when the collection condition indicated by the "Ci" field shown in Figure 4 is any one of the N collection conditions, the value of the "Ci" field can be 1. For another example, when the collection condition indicated by the "Ci" field shown in Figure 4 is any one of the M collection conditions, the value of the "Ci" field can be 0.

[0220] As an implementation example, as shown in FIG5 , the "Data Collection Condition i" field shown in FIG4 can be used to indicate the i-th condition for data collection. Accordingly, the mapping relationship between the "Data Collection Condition i" field and the specific collection parameter range of the collection condition can refer to the method shown in FIG5 . The mapping relationship can be carried in the second information, or can be preconfigured or predefined in the first communication device and / or the second communication device, or can be configured by the second communication device before step S302 (e.g., via an RRC message), which is not limited here.

[0221] In one possible implementation, the second information is determined based on the status information of the first communication device, including: the second information is determined based on the status information of the first communication device and the task requirements of the AI ​​task. Specifically, the second information may be determined based on the task requirements of the AI ​​task in addition to the status information of the first communication device. In this manner, the data collected by the first communication device based on the second information can meet the task requirements.

[0222] Based on the technical solution in Figure 3, after a first communication device sends first information indicating its status in step S301, it can receive second information for configuring first data collection in step S302 and collect data based on the second information in step S303. The second information is determined based on the status information of the first communication device. In other words, the first communication device can collect data based on the configuration of the second communication device. Thus, the communication devices in the communication system can serve as data collection nodes to implement data collection.

[0223] In addition, in the above technical solution, the second information used to configure the first data collection can be determined based on the status information of the first communication device. In this way, the configuration of the first data collection can be adapted to the status of the first communication device, thereby improving the success rate of data collection.

[0224] In one possible implementation of the method shown in Figure 3, the data obtained by the first communication device through data collection in step S303 can be used for model processing of the AI ​​model (for example, at least one of model training, model reasoning, and model monitoring). In this way, the communication device in the communication system can serve as a collection node for AI-related data to realize the collection of AI-related data.

[0225] Optionally, wireless communication signals (such as the transmission and reception of configuration information of communication resources, the transmission and reception of reference signals, etc.) can be transmitted between different communication devices, and the AI ​​models involved in the embodiments of the present application (such as the first AI model, the second AI model, etc. mentioned later) can be used to process the wireless communication signals (including at least one of management, configuration, update, and optimization). For example, the AI ​​model may include an AI model for modulation and / or demodulation, an AI model for channel prediction, an AI model for beam management, an AI model for assisted positioning, an AI model for channel compression, an AI model for resource scheduling, an AI model for mobility management, an AI model for load balancing, an AI model for network energy saving, and one or more AI models for replacing one or more modules in a transmitter and / or receiver. Alternatively, the AI ​​model involved in the embodiments of the present application may also be an AI model for other AI tasks, such as an AI model for image recognition, an AI model for natural language processing, an AI model for computer vision, etc.

[0226] Optionally, the AI ​​model can be a model that is strongly related to the acquisition conditions described above (for example, N acquisition conditions), or it can be a model corresponding to the AI ​​task identifier or AI function identifier described above, or it can be a model whose input data size meets the "data set size" described above.

[0227] For ease of understanding, the following will provide an exemplary description of various implementations of the AI ​​model applied to the data obtained by the first communication device in performing data collection in step S303.

[0228] Implementation method 1: After the first communication device performs data collection in step S303, part or all of the data is used for processing the first AI model, and the first AI model is deployed on the first communication device.

[0229] Through implementation method one, the first communication device can collect the first data based on the second information configuration to collect data used for model processing of the AI ​​model deployed on the first communication device.

[0230] In one possible implementation of the first implementation, part or all of the data is also used for processing a second AI model, which is deployed on a second communication device; wherein the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model. Accordingly, the method shown in Figure 3 also includes: after step S303, the first communication device sends part or all of the data to the second communication device. In this way, the first communication device can collect the first data based on the second information configuration to realize the collection of data used for model processing of the AI ​​models deployed on both sides.

[0231] Implementation Method 2: After the first communication device collects data in step S303, part or all of the data is used to process the second AI model, which is deployed on the second communication device. Accordingly, the method shown in Figure 3 also includes: after step S303, the first communication device sends part or all of the data to the second communication device.

[0232] Through the second implementation method, the first communication device can collect the first data based on the second information configuration to realize the collection of data used for model processing of the AI ​​model deployed on the second communication device.

[0233] Optionally, in addition to the above-mentioned implementation method 1 and implementation method 2, the AI ​​model can also be implemented in other ways.

[0234] For example, the first communication device and the second communication device can deploy the same AI model. Accordingly, the first communication device can send part or all of the collected data to the second communication device with reference to the above implementation method, so that both the first communication device and the second communication device can process the AI ​​model based on the data.

[0235] For example, the AI ​​model can be deployed on other devices different from the first communication device and the second communication device. Accordingly, the first communication device can send part or all of the collected data to the other device, so that the other device can process the AI ​​model based on the data.

[0236] In addition, when the AI ​​model is deployed on the first communication device or the second communication device, the AI ​​model can be called a single-end deployed model; and when the AI ​​model is deployed on the first communication device and the second communication device, the AI ​​model can be called a dual-end deployed model.

[0237] In a possible implementation of the method shown in Figure 3, in step S301, the second communication device may receive first information from one or more first communication devices to obtain status information of the one or more first communication devices. Thereafter, the second communication device may classify data based on the status information of the one or more first communication devices to determine one or more second information of the one or more first communication devices, and send the one or more second information to the one or more first communication devices respectively in step S302. Thereafter, in step S303, the one or more first communication devices may perform data collection based on the one or more second information. To facilitate understanding of the scheme, the following will take the example of the one or more first communication devices including UE 1 and UE 2, and the second communication device being a network device as an example, and describe it in conjunction with the example shown in Figure 6 below.

[0238] Please refer to FIG6 , which is a schematic diagram of the communication method provided in this application. The method includes the following steps.

[0239] Exemplarily, in the method shown in FIG6 , the network device may be implemented by the ORAN network element shown in Table 1 above. For example, the network device receiving and sending processes in steps A / B / D / E below may be implemented by one or more of the O-CU-CP, O-CU-UP, O-DU, and O-RU in the ORAN network element, and the network device performing data classification in steps C / H / I may be implemented by one or more of the O-CU-CP, O-CU-UP, O-DU, and O-RU in the ORAN network element.

[0240] A. UE 1 sends first information_1, and correspondingly, the network device receives the first information_1. The first information_1 is used to indicate the status information of UE 1.

[0241] B. UE 2 sends first information_2, and correspondingly, the network device receives the first information_2. The first information_2 is used to indicate the status information of UE 2.

[0242] It should be noted that both step A and step B are implementation examples of the aforementioned step S301. Accordingly, the implementation process of the first information_1 and the first information_2 can refer to the implementation process of the first information in the aforementioned step S301.

[0243] C. The network device divides the data into categories based on the status information of each UE.

[0244] D. The network device sends second information_1 to UE 1, and UE 1 receives the second information_1. The second information_1 is used to configure data collection of UE 1. Furthermore, the second information_1 is determined based on the first information_1.

[0245] E. The network device sends second information_2 to UE 2, and UE 2 receives the second information_2. The second information_2 is used to configure data collection of UE 2. Furthermore, the second information_2 is determined based on the first information_2.

[0246] It should be noted that step D and step E are both implementation examples of the aforementioned step S302. Accordingly, the implementation process of the second information_1 and the second information_2 can refer to the implementation process of the second information in the aforementioned step S302.

[0247] F. UE 1 collects data_1 based on the second information_1.

[0248] G. UE 2 collects data_2 based on the second information_2.

[0249] It should be noted that step F and step G are both implementation examples of the aforementioned step S303. Accordingly, the implementation process of collecting data_1 based on the second information_1 and collecting data_2 based on the second information_2 can refer to the implementation process of collecting data in the aforementioned step S303.

[0250] H.UE_1 and / or the network device performs model processing of the AI ​​model based on data_1.

[0251] I.UE_2 and / or network devices perform model processing of the AI ​​model based on data_2.

[0252] It should be understood that step H and step I can refer to the implementation of the above-mentioned single-end deployment or dual-end deployment, and step H and step I are optional steps.

[0253] As an implementation example, in the method shown in Figure 6, the first information_1 in step A and the first information_2 in step B indicate different SNR status information as an example. Accordingly, the network device can determine that the data collected by UE 1 and UE 2 can be used as data for an AI model that is strongly correlated with SNR. Among them, one of the N collection conditions corresponding to the "Data Collection Condition i" shown in Figure 4 above can indicate SNR status information or an index of SNR status information. Accordingly, the "Data Collection Condition i" field shown in Figure 5 above can be represented as the implementation process of Table 2 below.

[0254] Table 2

[0255] In other words, the SNR status information indicated by UE 1 through first information_1 can be one row of information in Table 2. Similarly, the SNR status information indicated by UE 2 through first information_2 can be one row of information in Table 2.

[0256] Exemplarily, an AI model that is strongly related to SNR may include an intelligent demodulation model, an intelligent modulation model, etc. deployed on a network device. Taking the intelligent demodulation model as an example, the input of the intelligent demodulation model is a bit and the output is a symbol. In addition, the symbol is sent to the UE through the data channel after adding noise, and the UE needs to collect the received symbols under different SNR intervals and send them to the network device. That is, the network device can divide the data categories in step C based on the SNR status information of each UE (for example, according to the reported SNR, divide the SNR intervals shown in Table 2), determine and send the second information _1 for configuring the data collection of UE 1, and the second information _2 for configuring the data collection of UE 2. Thereafter, after performing data collection in steps F and G, UE 1 and UE 2 can send relevant information of the received symbols under different SNR intervals, so that the network device can train the models under different SNR intervals based on the relevant information of the received symbols under different SNR intervals in steps H and I.

[0257] As another implementation example, in the method shown in Figure 6, take the example of the first information_1 in step A and the first information_2 in step B indicating different motion state information. Accordingly, the network device can determine that the data collected by UE 1 and UE 2 can be used as data of an AI model that is strongly related to the motion state. Among them, one of the conditions corresponding to the N collection conditions in the "Data Collection Condition i" shown in the aforementioned Figure 4 can indicate motion state information or an index of motion state information, and accordingly, the "Data Collection Condition i" field shown in the aforementioned Figure 5 can be represented as the implementation process of Table 3 below.

[0258] Table 3

[0259] In other words, the motion state information indicated by UE 1 through first information_1 may be one row of information in Table 3. Similarly, the motion state information indicated by UE 2 through first information_2 may be one row of information in Table 3.

[0260] Exemplarily, AI models that are strongly related to motion states may include channel prediction models, positioning prediction models, channel state information (CSI) feedback models, etc. deployed on network devices. Taking the channel prediction model as an example, the input data of the channel prediction model may include historical channel data (including channel data in a low-speed motion state, channel data in a high-speed motion state, etc.), and the output data may be predicted channel information. Accordingly, the UE may collect corresponding channel data based on the configured motion state information and send it to the network device. That is, the network device may classify the data in step C based on the motion state information of each UE, determine and send the second information _1 for configuring data collection of UE 1, and the second information _2 for configuring data collection of UE 2. Thereafter, after performing data collection in steps F and G, UE 1 and UE 2 may send channel data in different motion states, so that the network device can train the channel prediction model based on the channel data in different motion states in steps H and I.

[0261] As another implementation example, in the method shown in Figure 6, take the first information_1 in step A and the first information_2 in step B as an example indicating different reference signal configuration information. Accordingly, the network device can determine that the data collected by UE 1 and UE 2 can be used as data of the AI ​​model related to the reference signal configuration information. Among them, one of the conditions corresponding to the N acquisition conditions in the "Data Collection Condition i" shown in the aforementioned Figure 4 can indicate that the reference signal configuration information is or the index of the reference signal configuration information. Accordingly, taking the reference signal configuration information including the number of reference signals carried by a resource block (RB) as an example, the "Data Collection Condition i" field shown in the aforementioned Figure 5 can be expressed as the implementation process of Table 4 below.

[0262] Table 4

[0263] In other words, the reference signal configuration information indicated by UE 1 through first information_1 may be one row of information in Table 4. Similarly, the reference signal configuration information indicated by UE 2 through first information_2 may be one row of information in Table 4.

[0264] Exemplarily, an AI model that is strongly related to the reference signal configuration may include an intelligent receiver model, an intelligent transceiver model, etc. deployed on a network device. Taking the intelligent receiver model as an example, the input data of the intelligent receiver model may include a signal received over the air interface, and the output data is bits. Accordingly, the UE may collect corresponding data based on the configured reference signal configuration information and send it to the network device. That is, the network device may classify the data in step C based on the reference signal configuration information of each UE, determine and send the second information _1 for configuring the data collection of UE 1, and the second information _2 for configuring the data collection of UE 2. Thereafter, after performing data collection in steps F and G, UE 1 and UE 2 may send signals with different reference signal configuration information, so that the network device can train the intelligent receiver model based on the signals with different reference signal configuration information in steps H and I.

[0265] As another implementation example, in the method shown in Figure 6, take the example of the first information_1 in step A and the first information_2 in step B indicating different antenna configuration information. Accordingly, the network device can determine that the data collected by UE 1 and UE 2 can be used as data of the AI ​​model related to the antenna configuration information. Among them, one of the conditions corresponding to the N collection conditions in the "Data Collection Condition i" shown in the aforementioned Figure 4 can indicate that the antenna configuration information is or the index of the antenna configuration information. Accordingly, taking the example of the antenna configuration information including the number of transceiver antennas in the horizontal direction or the vertical direction, the "Data Collection Condition i" field shown in the aforementioned Figure 5 can be represented as the implementation process of the following Table 5.

[0266] Table 5

[0267] In other words, the antenna configuration information indicated by UE 1 through first information_1 may be one row of information in Table 5. Similarly, the antenna configuration information indicated by UE 2 through first information_2 may be one row of information in Table 5.

[0268] Exemplarily, AI models that are strongly related to antenna configuration may include beam prediction models, precoding information determination models, etc. deployed on network devices. Taking the beam prediction model as an example, the input data of the beam prediction model may include a small number of scanned fixed beams, and the output is the optimal beam direction (including horizontal and / or vertical directions). Accordingly, the UE can collect corresponding data based on the configured antenna configuration information and send it to the network device. That is, the network device can divide the data categories in step C based on the antenna configuration information of each UE, determine and send the second information _1 for configuring data collection of UE 1, and the second information _2 for configuring data collection of UE 2. Thereafter, after performing data collection in steps F and G, UE 1 and UE 2 can send signals with different antenna configuration information, so that the network device can train the beam prediction model based on the signals of different antenna configuration information in steps H and I.

[0269] Referring to Figure 7, an embodiment of the present application provides a communication device 700. This communication device 700 can implement the functions of the first communication device (or second communication device) in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment. In this embodiment of the present application, the communication device 700 can be the first communication device (or second communication device), or it can be an integrated circuit or component, such as a chip, within the first communication device (or second communication device).

[0270] It should be noted that the transceiver unit 702 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.

[0271] In one possible implementation, when the device 700 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to send first information, and the first information is used to indicate the status information of the first communication device; the transceiver unit 702 is also used to receive second information, and the second information is used to configure data collection; wherein the second information is determined based on the status information of the first communication device; and the processing unit 701 collects data based on the second information.

[0272] In one possible implementation, when the device 700 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive first information, and the first information is used to indicate the status information of the first communication device; the processing unit 701 is used to determine second information; the transceiver unit 702 is also used to send second information, and the second information is used to configure data collection; wherein the second information is determined based on the status information of the first communication device.

[0273] It should be noted that, for details on the information execution process of the units of the above-mentioned communication device 700, please refer to the description in the method embodiment shown above in this application, and no further details will be given here.

[0274] Please refer to Fig. 8, which is another schematic structural diagram of a communication device 800 provided in this application. The communication device 800 includes a logic circuit 801 and an input / output interface 802. The communication device 800 may be a chip or an integrated circuit.

[0275] The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the input / output interface 802 in FIG8 , which may include an input interface and an output interface. Alternatively, the communication interface may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0276] Optionally, the input-output interface 802 is used to send first information, which is used to indicate status information of the first communication device; the input-output interface 802 is also used to receive second information, which is used to configure data collection; wherein the second information is determined based on the status information of the first communication device; and the logic circuit 801 collects data based on the second information.

[0277] Optionally, the input-output interface 802 is used to receive first information, which is used to indicate status information of the first communication device; the logic circuit 801 is used to determine second information; the input-output interface 802 is also used to send second information, which is used to configure data acquisition; wherein the second information is determined based on the status information of the first communication device.

[0278] The logic circuit 801 and the input / output interface 802 may also execute other steps executed by the first communication device or the second communication device in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

[0279] In a possible implementation, the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .

[0280] Optionally, the logic circuit 801 may be a processing device, and the functions of the processing device may be partially or entirely implemented by software. The functions of the processing device may be partially or entirely implemented by software.

[0281] Optionally, the processing device may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform corresponding processing and / or steps in any one of the method embodiments.

[0282] Alternatively, the processing device may include only a processor. A memory for storing the computer program is located outside the processing device, and the processor is connected to the memory via circuits / wires to read and execute the computer program stored in the memory. The memory and processor may be integrated or physically separate.

[0283] Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processor units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic devices (PLDs), or other integrated chips, or any combination of the above chips or processors.

[0284] Please refer to Figure 9, which shows a communication device 900 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 900 can specifically be a communication device serving as a terminal device in the above-mentioned embodiments. The example shown in Figure 9 is that the terminal device is implemented through the terminal device (or a component in the terminal device).

[0285] Herein, a possible logical structure diagram of the communication device 900 is shown. The communication device 900 may include but is not limited to at least one processor 901 and a communication port 902 .

[0286] The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the communication port 902 in FIG9 , which may include an input interface and an output interface. Alternatively, the communication port 902 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0287] Further optionally, the device may also include at least one of a memory 903 and a bus 904. In an embodiment of the present application, the at least one processor 901 is used to control and process the actions of the communication device 900.

[0288] In addition, the processor 901 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, a transistor logic device, a hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0289] It should be noted that the communication device 900 shown in Figure 9 can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment and achieve the corresponding technical effects of the terminal device. The specific implementation methods of the communication device shown in Figure 9 can refer to the description in the aforementioned method embodiment and will not be repeated here.

[0290] Please refer to Figure 10, which is a structural diagram of the communication device 1000 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 1000 can specifically be a communication device as a network device in the above-mentioned embodiments. The example shown in Figure 10 is that the network device is implemented through the network device (or a component in the network device), wherein the structure of the communication device can refer to the structure shown in Figure 10.

[0291] The communication device 1000 includes at least one processor 1011 and at least one network interface 1014. Further optionally, the communication device also includes at least one memory 1012, at least one transceiver 1013 and one or more antennas 1015. The processor 1011, the memory 1012, the transceiver 1013 and the network interface 1014 are connected, for example, via a bus. In an embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. The antenna 1015 is connected to the transceiver 1013. The network interface 1014 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1014 may include a network interface between the communication device and the core network device, such as an S1 interface, and the network interface may include a network interface between the communication device and other communication devices (such as other network devices or core network devices), such as an X2 or Xn interface.

[0292] The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the network interface 1014 in FIG10 , which may include an input interface and an output interface. Alternatively, the network interface 1014 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0293] Processor 1011 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process software program data, for example, to support the communication device in performing the actions described in the embodiments. The communication device may include a baseband processor and a central processing unit. The baseband processor is primarily used to process communication protocols and communication data, while the central processing unit is primarily used to control the entire terminal device, execute software programs, and process software program data. Processor 1011 in Figure 10 may integrate the functions of both a baseband processor and a central processing unit. Those skilled in the art will appreciate that the baseband processor and the central processing unit may also be independent processors interconnected via a bus or other technology. Those skilled in the art will appreciate that a terminal device may include multiple baseband processors to accommodate different network standards, multiple central processing units to enhance its processing capabilities, and various components of the terminal device may be connected via various buses. The baseband processor may also be referred to as a baseband processing circuit or a baseband processing chip. The central processing unit may also be referred to as a central processing circuit or a central processing chip. The functionality for processing communication protocols and communication data may be built into the processor or stored in memory as a software program, which is executed by the processor to implement the baseband processing functionality.

[0294] The memory is primarily used to store software programs and data. Memory 1012 can exist independently and be connected to processor 1011. Alternatively, memory 1012 and processor 1011 can be integrated together, for example, within a single chip. Memory 1012 can store program code for executing the technical solutions of the embodiments of the present application, and execution is controlled by processor 1011. The various computer program codes executed can also be considered drivers for processor 1011.

[0295] Figure 10 shows only one memory and one processor. In an actual terminal device, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device. The memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the present embodiment.

[0296] The transceiver 1013 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal. The transceiver 1013 can be connected to the antenna 1015. The transceiver 1013 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1015 can receive radio frequency signals. The receiver Rx of the transceiver 1013 is used to receive the radio frequency signal from the antenna, convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or digital intermediate frequency signal to the processor 1011 so that the processor 1011 can further process the digital baseband signal or digital intermediate frequency signal, such as demodulation and decoding. In addition, the transmitter Tx in the transceiver 1013 is also used to receive a modulated digital baseband signal or digital intermediate frequency signal from the processor 1011, convert the modulated digital baseband signal or digital intermediate frequency signal into a radio frequency signal, and transmit the radio frequency signal through one or more antennas 1015. Specifically, the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal. The order of the down-mixing and analog-to-digital conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal. The order of the up-mixing and digital-to-analog conversion processes is adjustable. The digital baseband signal and the digital intermediate frequency signal may be collectively referred to as digital signals.

[0297] The transceiver 1013 may also be referred to as a transceiver unit, a transceiver, a transceiver device, etc. Optionally, a device in the transceiver unit that implements a receiving function may be referred to as a receiving unit, and a device in the transceiver unit that implements a transmitting function may be referred to as a transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the transmitting unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0298] It should be noted that the communication device 1000 shown in Figure 10 can be specifically used to implement the steps implemented by the network device in the aforementioned method embodiment, and to achieve the corresponding technical effects of the network device. The specific implementation methods of the communication device 1000 shown in Figure 10 can refer to the description in the aforementioned method embodiment, and will not be repeated here one by one.

[0299] Please refer to FIG11 , which is a schematic structural diagram of the communication device involved in the above-mentioned embodiment provided in an embodiment of the present application.

[0300] It can be understood that the communication device 110 includes, for example, modules, units, elements, circuits, or interfaces, which are appropriately configured together to implement the technical solutions provided in this application. The communication device 110 can be the terminal device or network device described above, or a component (such as a chip) in these devices, used to implement the method described in the following method embodiment. The communication device 110 includes one or more processors 111. The processor 111 can be a general-purpose processor or a dedicated processor. 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 device (such as a RAN node, terminal, or chip, etc.), execute software programs, and process data of software programs.

[0301] Optionally, in one design, the processor 111 may include a program 113 (sometimes also referred to as code or instructions), which may be executed on the processor 111 to cause the communication device 110 to perform the methods described in the following embodiments. In yet another possible design, the communication device 110 includes circuitry (not shown in FIG11 ).

[0302] Optionally, the communication device 110 may include one or more memories 112 on which a program 114 (sometimes also referred to as code or instructions) is stored. The program 114 can be run on the processor 111, so that the communication device 110 executes the method described in the above method embodiment.

[0303] Optionally, the processor 111 and / or the memory 112 may include AI modules 117 and 118, which are used to implement AI-related functions. The AI ​​module can be implemented through software, hardware, or a combination of software and hardware. For example, the AI ​​module may include a wireless intelligent control (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0304] Optionally, data may be stored in the processor 111 and / or the memory 112. The processor and the memory may be provided separately or integrated together.

[0305] Optionally, the communication device 110 may further include a transceiver 115 and / or an antenna 116. The processor 111 may also be referred to as a processing unit, and controls the communication device (e.g., a RAN node or terminal). The transceiver 115 may also be referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, and is configured to implement the transceiver functions of the communication device through the antenna 116.

[0306] The processing unit 701 shown in FIG7 may be the processor 111. The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the transceiver 115 shown in FIG11 . The transceiver 115 may include an input interface and an output interface. Alternatively, the transceiver 115 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0307] An embodiment of the present application further provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in the possible implementation methods of the first communication device or the second communication device in the aforementioned embodiment.

[0308] An embodiment of the present application also provides a computer program product (or computer program). When the computer program product is executed by the processor, the processor executes the method that may be implemented by the above-mentioned first communication device or second communication device.

[0309] An embodiment of the present application also provides a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation methods of the above-mentioned communication device. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data to the at least one processor. In one possible design, the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device. The chip system can be composed of chips, or it can include chips and other discrete devices, wherein the communication device can specifically be the first communication device or the second communication device in the aforementioned method embodiment.

[0310] An embodiment of the present application further provides a communication system, wherein the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

[0311] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0312] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0313] In addition, the functional units in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned 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 this understanding, the technical solution of the present application is essentially or the contributing part or all or part of the technical solution can be embodied in the form of a software product, which 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 method described in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Claims

1. A communication method, characterized in that, Including: Sending a first message, where the first message is used to indicate the status information of a first communication device; Receiving a second message, where the second message is used to configure first data collection; wherein, the second message is determined based on the status information of the first communication device; Collecting data based on the second message.

2. The method according to claim 1, characterized in that, Part or all of the collected data is used for the processing of a first AI model, and the first AI model is deployed on the first communication device.

3. The method according to claim 2, wherein Part or all of the data is also used for the processing of a second AI model, and the second AI model is deployed on a second communication device; wherein, the input of the first AI model includes the output of the second AI model, or, the input of the second AI model includes the output of the first AI model; The method further includes: Sending part or all of the data to the second communication device.

4. The method according to claim 1, characterized in that Part or all of the data is used for the processing of a second AI model, and the second AI model is deployed on a second communication device; The method further includes: Sending part or all of the data to the second communication device.

5. A communication method, characterized in that, Including: Receiving a first message, where the first message is used to indicate the status information of a first communication device; Sending a second message, where the second message is used to configure first data collection; wherein, the second message is determined based on the status information of the first communication device.

6. The method according to claim 5, wherein Part or all of the collected data is used for the processing of a first AI model, and the first AI model is deployed on the first communication device.

7. The method according to claim 6, wherein Part or all of the collected data is also used for the processing of a second AI model, and the second AI model is deployed on a second communication device; wherein, the input of the first AI model includes the output of the second AI model, or, the input of the second AI model includes the output of the first AI model; The method further includes: Receiving part or all of the data.

8. The method according to claim 5, characterized in that, Part or all of the collected data is used for the processing of a second AI model, and the second AI model is deployed on a second communication device; The method further includes: Receiving part or all of the data.

9. The method according to any one of claims 1 to 8, characterized in that, The second message includes indication information indicating N collection conditions for the first data collection, where N is a positive integer; wherein, the status indicated by the status information of the first communication device satisfies the N collection conditions.

10. The method according to claim 9, wherein The second message further includes indication information for indicating the first data collection based on the N collection conditions.

11. The method according to claim 9 or 10, characterized in that, The second message further includes indication information indicating M collection conditions, where the M collection conditions are conditions for second data collection, and M is a positive integer.

12. The method according to any one of claims 1 to 11, characterized in that, The second message includes at least one of the following: The artificial intelligence (AI) task identifier corresponding to the first data collection, the AI function identifier corresponding to the first data collection, the dataset size information of the first data collection.

13. The method according to any one of claims 1 to 12, characterized in that, The status information includes at least one of the following: System status information, AI status information, dataset status information, scenario status information.

14. The method according to claim 13, characterized in that, Satisfying one or more of the following: The system status information includes at least one of a system parameter set and a signal processing configuration; The AI status information includes at least one of AI model parameters and AI computing power resources; The scene status information includes at least one of signal-to-noise ratio (SNR) status information, motion status information, reference signal configuration information, and antenna configuration information; The dataset status information includes at least one of the acquisition time information of the dataset, the geographical location information of the dataset, the acquisition stage information corresponding to the dataset, the indication information indicating whether the dataset is existing data, and the indication information indicating the reporting method of the dataset.

15. The method according to any one of claims 1 to 14, characterized in that, The second information is determined based on the status information of the first communication device and includes: The second information is determined based on the status information of the first communication device and the task requirements of the AI task, and the collected data meets the task requirements.

16. A communication device, characterized in that, It includes a module for executing the method according to any one of claims 1 to 15.

17. A communication device, characterized in that, It includes at least one processor, and the at least one processor is coupled to a memory; the at least one processor is used to execute the method according to any one of claims 1 to 15.

18. The communication device according to claim 17, wherein The communication device is a chip or a chip system.

19. A readable storage medium, characterized in that, A computer program or instruction is stored in the storage medium, and when the computer program or instruction is executed by the communication device, the method according to any one of claims 1 to 15 is implemented.

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