Communication method and related device

Abstract

A communication method and a related device, for use in enabling the computing power of a communication node to be used for artificial intelligence (AI) data acquisition. In the method, after a first node receives first configuration information, the first node can perform AI data acquisition on the basis of the first configuration information to obtain first AI data; then, the first node can send the first AI data, and subsequently, a receiver of the first AI data can perform model processing on a first AI model on the basis of the first AI data. Therefore, when a communication node in a communication system serves as an AI participating node, the computing power of the communication node can serve as an AI data acquisition node to implement AI 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 8, 2023, with application number 202311691852.5 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 addition to processing communication signals in the communication network, communication nodes may also need to take into account artificial intelligence (AI) related processing.

[0006] To this end, how to achieve the integration of AI-related processing and communication networks is a technical problem that needs to be solved urgently. Summary of the Invention

[0007] The present application provides a communication method and related equipment for enabling a communication node to serve as an AI data collection node to realize AI data collection.

[0008] In a first aspect, the present application provides a communication method, which is performed by a first node. The first node may be a communication device (such as a network device or a terminal device), or the first node may be a component of the communication device (such as a processor, a chip, or a chip system), or the first node 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 node receives first configuration information, which is used to configure AI data collection; the first node sends first AI data, which is collected based on the first configuration information; wherein the first AI data is used for model processing of a first AI model.

[0009] Based on the above technical solution, after receiving the first configuration information, the first node can collect AI data based on the first configuration information to obtain first AI data. Thereafter, the first node can transmit the first AI data, and the recipient of the first AI data can subsequently perform model processing using the first AI model based on the first AI data. Thus, when a communication node in a communication system serves as an AI participating node, the communication node can function as an AI data collection node, thereby enabling AI data collection.

[0010] In addition, the first node acts as a communication node. After the first node sends the first AI data, the recipient of the first AI data can implement model processing of the AI ​​model based on the AI ​​data collected by the communication node.

[0011] It should be noted that the technical solution provided in this application can be applied to a communication system, which may include N distributed nodes and a control node, where N is a positive integer. Any of the N distributed nodes can serve as a first node, configured to execute the method of the first aspect and possible implementations thereof; the control node can be configured to execute the method of the second aspect and possible implementations thereof described below.

[0012] Optionally, the communication system may further include a central node, which may be used to execute the method described in the third aspect and its possible implementations below. Alternatively, the functions of the central node are executed by the control node, i.e., the control node is also used to execute the method involved in the central node.

[0013] Optionally, the communication system may further include a data receiving node, which may be used to perform the method described in the fourth aspect and its possible implementations below. Alternatively, the functions of the data receiving node are performed by the control node, i.e., the control node is also used to perform the method involved in the data receiving node.

[0014] 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.

[0015] In this application, terms such as data collection, data collection, data acquisition, and data capture can be used interchangeably.

[0016] It should be understood that the model processing involved in the embodiments of the present application includes at least one of model training, model inference, and model monitoring. Accordingly, the first AI data sent by the first node includes at least one of the AI ​​data used in the model training phase of the first AI model, the AI ​​data used in the model inference phase of the first AI model, and the AI ​​data used in the model monitoring phase of the first AI model.

[0017] In an implementation example, the AI ​​data used in the model training phase of the first AI model contained in the first AI data may include at least one of input data, feature data, and label data used for training the first AI model.

[0018] In another implementation example, the AI ​​data used in the model reasoning stage of the first AI model contained in the first AI data may include at least one of the input data, feature data, and reasoning result data used for the reasoning of the first AI model.

[0019] In another implementation example, the AI ​​data used in the model monitoring phase of the first AI model contained in the first AI data may include at least one of input data, feature data, label data, inference result data, AI model performance data, and communication performance data used for monitoring the first AI model.

[0020] 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 nodes (for example, the first node and other nodes mentioned later, including the second node, the control node, the central node, etc.). The AI ​​model involved in the embodiment of the present application (for example, the first AI model, the second AI model mentioned later, etc.) can be used to process the wireless communication signal (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 embodiment 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.

[0021] In a possible implementation of the first aspect, the first configuration information includes at least one of the following:

[0022] The identifier of the AI ​​model corresponding to the collected AI data;

[0023] The identifier of the AI ​​function corresponding to the collected AI data;

[0024] The identifier of the source node of the collected AI data;

[0025] Instruction information indicating model processing corresponding to collected AI data;

[0026] Instruction information indicating whether the configuration mode of AI data collection is centralized or decentralized;

[0027] Information indicating whether the AI ​​data collection method is centralized or decentralized;

[0028] Indicative information indicating AI data characteristics of collected AI data;

[0029] Instruction information for instructing AI data processing of collected AI data;

[0030] Indicative information indicating the AI ​​data collection period;

[0031] Instruction information indicating the transmission information of the collected AI data.

[0032] Optionally, the AI ​​data characteristics include one or more of the quantity, sample size, collection time, collection location, and distribution of the AI ​​data.

[0033] Optionally, AI data processing includes post-processing of output data of the AI ​​model and / or pre-processing of input data of the AI ​​model, such as one or more of dimensionality conversion and precision conversion.

[0034] Optionally, the AI ​​data collection cycle includes collecting AI data in a periodic manner, collecting AI data in a semi-static manner, or collecting AI data in a (dynamic) triggering manner, etc.

[0035] Optionally, the transmission information of the AI ​​data includes one or more of data structure, format, precision, dimension, and transmission resources.

[0036] It should be understood that when the first configuration information includes at least one of the above items, the first configuration information can be sent through one or more messages, that is, the first node can obtain the first configuration information through the receiving process of one or more messages.

[0037] Based on the above technical solution, the first configuration information used to configure AI data acquisition may include at least one of the above items to improve the flexibility of the solution implementation.

[0038] In a possible implementation manner of the first aspect, the first node is one of N distributed nodes, where N is an integer greater than or equal to 1.

[0039] Based on the above technical solution, the first node can be one of the N distributed nodes, that is, any node among the N distributed nodes can execute the method executed by the first node, so that the N distributed nodes can all serve as data collection nodes to realize AI data collection in distributed scenarios.

[0040] In a possible implementation of the first aspect, the first node receives the first configuration information, including: the first node receives the first configuration information from the control node, which is used to control data collection of the N distributed nodes; or, the first node receives the first configuration information from the control node through the central node; or, the first node receives the first configuration information from the second node, which is a node different from the first node among the N distributed nodes, and N is greater than 1.

[0041] Optionally, before the second node sends the first configuration information to the first node, the second node may receive one or more configuration information from the control node and send the first configuration information among the one or more configuration information to the first node. In other words, the first configuration information sent by the second node to the first node comes from the control node.

[0042] Optionally, the control node and the central node may be the same node, or the control node and the central node may be different logical nodes in a physical node, or the control node and the central node may be two independent and different nodes.

[0043] Based on the above technical solution, the first node can receive the first configuration information through the above-mentioned multiple methods. The first node is one of the N distributed nodes. In this way, the distributed node can receive the first configuration information in a variety of different scenarios and improve the flexibility of the solution implementation.

[0044] In a possible implementation of the first aspect, the first configuration information is configuration information corresponding to the first node of M configuration information, and the M configuration information is at least used to configure AI data collection of M distributed nodes among N distributed nodes, where M is less than or equal to N; the method also includes: the first node sends at least one configuration information of the M configuration information to at least one distributed node among the M distributed nodes.

[0045] Based on the above technical solution, the first node can receive M configuration information and determine the first configuration information from the M configuration information. The M configuration information is used to configure AI data collection for at least M distributed nodes among the N distributed nodes. Accordingly, the first node can send at least one configuration information from the M configuration information to other nodes among the M distributed nodes so that the other distributed nodes can obtain the corresponding configuration information and perform AI data collection.

[0046] Optionally, the first node sends at least one of the M configuration information to at least one of the M distributed nodes in order to enable the M distributed nodes to obtain their respective corresponding configuration information, so that the M distributed nodes can realize AI data collection based on their respective corresponding configuration information. Further optionally, during the sending process, the first node may send the configuration information corresponding to each distributed node to each of the M distributed nodes, or the first node may send the M configuration information to each of the M distributed nodes, or the first node may send the M configuration information to some of the M distributed nodes, and the some distributed nodes send the configuration information corresponding to the other distributed nodes to other distributed nodes, or other methods may be used to enable the M distributed nodes to obtain their respective corresponding configuration information, which is not limited here.

[0047] Optionally, among the N distributed nodes, the configuration information corresponding to different distributed nodes may be different. For this reason, the M configuration information can be used to configure the AI ​​data collection of M distributed nodes among the N distributed nodes.

[0048] Optionally, among N distributed nodes, the configuration information corresponding to different distributed nodes may be the same. To this end, M configuration information can be used to configure the AI ​​data collection of M distributed nodes among the N distributed nodes, and at least one configuration information among the M configuration information can also be used to configure the AI ​​data collection of at least one other distributed node among the N distributed nodes except the M distributed nodes.

[0049] In a possible implementation of the first aspect, the first node is one of N distributed nodes, where N is an integer greater than or equal to 1. The first node sending the first AI data includes: the first node sending the first AI data to a data receiving node, where the data receiving node is used for AI data collection; or the first node sending the first AI data to a central node.

[0050] Optionally, the control node and the data receiving node may be the same node, or the control node and the data receiving node may be different logical nodes in a physical node, or the control node and the data receiving node may be two independent and different nodes.

[0051] Based on the above technical solution, the first node can send the first AI data through the above-mentioned multiple methods. The first node is one of the N distributed nodes. In this way, the distributed nodes can send the collected AI data in a variety of different scenarios and improve the flexibility of the solution implementation.

[0052] In a possible implementation manner of the first aspect, the method further includes: the first node sending data of the first node to other nodes in the N distributed nodes, and the data of the first node is used for data collection of the other nodes.

[0053] Optionally, the data of the first node may include part or all of the first AI data.

[0054] Optionally, the data of the first node may include communication data of the first node, such as a reference signal, positioning data, etc.

[0055] Optionally, the data of the first node may include model data of the local AI model of the first node, such as at least one of the AI ​​data used in the model training phase of the local AI model, the AI ​​data used in the model inference phase of the local AI model, and the AI ​​data used in the model monitoring phase of the local AI model.

[0056] Based on the above technical solution, for N distributed nodes, some or all of the AI ​​data collected by one distributed node can be determined based on data sent by other distributed nodes. Accordingly, a first node can also send its data to other nodes in the N distributed nodes, so that the other nodes can collect data based on the first node's data.

[0057] In a possible implementation of the first aspect, the method further includes: the first node receiving data from other nodes in the N distributed nodes, and the data of the other nodes is used to determine part or all of the first AI data. Based on the above technical solution, for the N distributed nodes, part or all of the AI ​​data collected by one distributed node can be determined by data sent by other distributed nodes. Accordingly, the first node can also receive data from other nodes in the N distributed nodes (for example, second data from the second node), so that the first node can realize data collection based on the data of the other nodes.

[0058] Optionally, the data of the other node may include communication data of the other node, such as reference signals, positioning data, etc.

[0059] Optionally, the data of the other node may include model data of the local AI model of the other node, such as the AI ​​data used in the model training phase of the local AI model, the AI ​​data used in the model reasoning phase of the local AI model, and at least one of the AI ​​data used in the model monitoring phase of the local AI model. Exemplarily, taking the other node as the second node as an example, the local AI model of the second node can be recorded as a second AI model, and the model data of the second AI model can be recorded as second AI data. The implementation of the second AI data is similar to that of the first AI data, and the second AI data received by the first node may include at least one of the AI ​​data used in the model training phase of the second AI model, the AI ​​data used in the model reasoning phase of the second AI model, and the AI ​​data used in the model monitoring phase of the second AI model.

[0060] In one implementation example, the AI ​​data used in the model training phase of the second AI model included in the second AI data includes at least one of input data, feature data, and label data used for training the second AI model;

[0061] In another implementation example, the AI ​​data used in the model inference phase of the second AI model included in the second AI data includes at least one of input data, feature data, and inference result data used for inference of the second AI model;

[0062] In another implementation example, the AI ​​data used in the model monitoring phase of the second AI model contained in the second AI data includes at least one of input data, feature data, label data, inference result data, AI model performance data, and communication performance data used for monitoring the second AI model.

[0063] The second aspect of the present application provides a communication method, which is performed by a control node. The control node can be a communication device (such as a network device or a terminal device), or the control node can be a component in the communication device (such as a processor, a chip or a chip system, etc.), or the control node 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 control node determines first configuration information, which is used for AI data collection; the control node sends the first configuration information.

[0064] Based on the above technical solution, the first configuration information sent by the control node is used for AI data collection. That is, after receiving the first configuration information, the first node can perform AI data collection based on the first configuration information to obtain first AI data. Afterward, the first node can send the first AI data, and the recipient of the first AI data can subsequently perform model processing of the first AI model based on the first AI data. Thus, when the communication nodes in the communication system serve as AI participating nodes, the communication nodes can serve as AI data collection nodes to achieve AI data collection.

[0065] In a possible implementation of the second aspect, the first configuration information includes at least one of the following:

[0066] The identifier of the AI ​​model corresponding to the collected AI data;

[0067] The identifier of the AI ​​function corresponding to the collected AI data;

[0068] The identifier of the source node of the collected AI data;

[0069] Instruction information indicating model processing corresponding to collected AI data;

[0070] Instruction information indicating whether the configuration mode of AI data collection is centralized or decentralized;

[0071] Information indicating whether the AI ​​data collection method is centralized or decentralized;

[0072] Indicative information indicating AI data characteristics of collected AI data;

[0073] Instruction information for instructing AI data processing of collected AI data;

[0074] Indicative information indicating the AI ​​data collection period;

[0075] Instruction information indicating the transmission information of the collected AI data.

[0076] Based on the above technical solution, the first configuration information used to configure AI data acquisition may include at least one of the above items to improve the flexibility of the solution implementation.

[0077] Optionally, the AI ​​data characteristics include one or more of the quantity, sample size, collection time, collection location, and distribution of the AI ​​data.

[0078] Optionally, AI data processing includes post-processing of output data of the AI ​​model and / or pre-processing of input data of the AI ​​model, such as one or more of dimensionality conversion and precision conversion.

[0079] Optionally, the AI ​​data collection cycle includes collecting AI data in a periodic manner, collecting AI data in a semi-static manner, or collecting AI data in a (dynamic) triggering manner, etc.

[0080] Optionally, the transmission information of the AI ​​data includes one or more of data structure, format, precision, dimension, and transmission resources.

[0081] It should be understood that when the first configuration information includes at least one of the above items, the first configuration information can be sent through one or more messages, that is, the first node can obtain the first configuration information through the process of receiving one or more messages.

[0082] In a possible implementation of the second aspect, the control node is used to control data collection of N distributed nodes, where N is an integer greater than or equal to 1; and the first configuration information is used for AI data collection of a first AI node among the N distributed nodes.

[0083] Based on the above technical solution, the first node that performs data collection based on the first configuration information can be one of the N distributed nodes, that is, any node among the N distributed nodes can execute the method executed by the first node, so that the N distributed nodes can all serve as data collection nodes to realize AI data collection in distributed scenarios.

[0084] In a possible implementation of the second aspect, the control node sends the first configuration information, including: the control node sends the first configuration information to the first node; or, the control node sends the first configuration information to the first node through the central node; or, the control node sends the first configuration information to the first node through the second node, the second node being a node different from the first node among the N distributed nodes, and N is greater than 1.

[0085] Based on the above technical solution, the control node can send the first configuration information to the first node through the above-mentioned multiple methods. The first node is one of the N distributed nodes. In this way, the distributed node can receive the first configuration information in a variety of different scenarios and improve the flexibility of the solution implementation.

[0086] In a possible implementation of the second aspect, the first configuration information is one of K configuration information sent, and the K configuration information are respectively used to configure AI data collection of N distributed nodes, where K is less than or equal to N.

[0087] Based on the above technical solution, the control node can send K configuration information, where the K configuration information is used to configure the AI ​​data collection in N distributed nodes. Accordingly, each distributed node can obtain the corresponding configuration information based on the K configuration information and perform AI data collection.

[0088] Optionally, among the N distributed nodes, the configuration information corresponding to different distributed nodes may be different. To this end, the values ​​of K and N can be equal, that is, the K configuration information can be used to configure AI data collection for different nodes among the N distributed nodes.

[0089] Optionally, among the N distributed nodes, the configuration information corresponding to different distributed nodes may be identical. To this end, K can be smaller than N, i.e., at least one of the K configuration information is used to configure AI data collection for at least two of the N distributed nodes. Accordingly, the configuration information for AI data collection of the at least two nodes is identical.

[0090] In a possible implementation manner of the second aspect, the method further includes: receiving, by the control node, first AI data, where the first AI data is collected based on the first configuration information.

[0091] Based on the above technical solution, after the control node sends the first configuration information, the control node may also receive first AI data collected based on the first configuration information. The first AI data may come from the first node, which acts as a communication node. After the first node sends the AI ​​data, the control node can implement model processing of the AI ​​model based on the AI ​​data collected by the communication node.

[0092] It should be understood that the model processing involved in the embodiments of the present application includes at least one of model training, model inference, and model monitoring. Accordingly, the first AI data sent by the first node includes at least one of the AI ​​data used in the model training phase of the first AI model, the AI ​​data used in the model inference phase of the first AI model, and the AI ​​data used in the model monitoring phase of the first AI model.

[0093] In an implementation example, the AI ​​data used in the model training phase of the first AI model contained in the first AI data may include at least one of input data, feature data, and label data used for training the first AI model.

[0094] In another implementation example, the AI ​​data used in the model reasoning stage of the first AI model contained in the first AI data may include at least one of the input data, feature data, and reasoning result data used for the reasoning of the first AI model.

[0095] In another implementation example, the AI ​​data used in the model monitoring phase of the first AI model contained in the first AI data may include at least one of input data, feature data, label data, inference result data, AI model performance data, and communication performance data used for monitoring the first AI model.

[0096] In a possible implementation of the second aspect, the control node receiving the first AI data includes: the control node receiving the first AI data from the first node; or the control node receiving the first AI data through a central node.

[0097] Based on the above technical solution, the control node can receive the first AI data through the above-mentioned multiple methods to improve the flexibility of the solution implementation.

[0098] A third aspect of the present application provides a communication method, which is performed by a central node. The central node can be a communication device (such as a network device or a terminal device), or the central node can be a component of the communication device (such as a processor, chip, or chip system), or the central node 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 central node receives first configuration information from a control node; the central node sends the first configuration information to the first node.

[0099] The steps performed by the central node may also refer to the description in the above-mentioned first aspect or second aspect and possible implementation thereof.

[0100] A fourth aspect of the present application provides a communication method, which is performed by a data receiving node. The data receiving node can be a communication device (such as a network device or a terminal device), or the data receiving node can be a component in the communication device (such as a processor, chip, or chip system, etc.), or the data receiving node 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 data receiving node receives first AI data from a first node.

[0101] The steps performed by the data receiving node may also refer to the description in the above-mentioned first aspect or second aspect and possible implementation methods thereof.

[0102] In a fifth aspect, the present application provides a communication device, which is a first node or a partial component of the first node (such as a processor, chip, chip system, logic module or software, etc.), and the device includes a transceiver unit and a processing unit; the transceiver unit is used to receive first configuration information, and the first configuration information is used to configure AI data collection; the processing unit sends first AI data, and the first AI data is collected based on the first configuration information; wherein, the first AI data is used for model processing of a first AI model.

[0103] In the fifth aspect of this 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.

[0104] In a sixth aspect, the present application provides a communication device, which is a control node or a partial component in a control node (such as a processor, chip, chip system, logic module or software, etc.). The device includes a transceiver unit and a processing unit. The processing unit is used to determine first configuration information, which is used for AI data collection; the transceiver unit is used to send the first configuration information.

[0105] In the sixth aspect of this 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.

[0106] In the seventh aspect of the present application, a communication device is provided, which is a central node or a partial component in the central node (such as a processor, chip, chip system, logic module or software, etc.), and the device includes a transceiver unit; the transceiver unit is used to receive first configuration information from the control node; the transceiver unit is also used to send the first configuration information to the first node.

[0107] In the seventh 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 third aspect and achieve corresponding technical effects. For details, please refer to the third aspect and will not be repeated here.

[0108] In an eighth aspect, the present application provides a communication device, which is a data receiving node or a partial component in a data receiving node (such as a processor, chip, chip system, logic module or software, etc.), and the device includes a transceiver unit, which is used to receive first AI data from a first node.

[0109] In the eighth 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 fourth aspect and achieve corresponding technical effects. For details, please refer to the fourth aspect and will not be repeated here.

[0110] In a ninth aspect, the present application provides a communication device, comprising at least one processor 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 fourth aspects.

[0111] In a tenth 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 fourth aspects.

[0112] In an eleventh aspect, the present application provides a communication system, which includes the above-mentioned first node and a control node.

[0113] Optionally, the communication system further includes other nodes among the N distributed nodes, such as a second node.

[0114] Optionally, the communication system further includes a data receiving node.

[0115] Optionally, the communication system further includes a central node.

[0116] A twelfth aspect of the present application 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 any possible implementation of any aspect of the first to fourth aspects above.

[0117] The thirteenth aspect of the present application provides a computer program product (or computer program). When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of any one of the first to fourth aspects above.

[0118] A fourteenth aspect of the present application provides a chip system, which includes 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 fourth aspects above.

[0119] 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.

[0120] Among them, the technical effects brought about by any design method in the fifth to fourteenth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to fourth aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] Figures 1a to 1c are schematic diagrams of a communication system provided by this application;

[0122] Figures 2a to 2h are schematic diagrams of the AI ​​processing process involved in this application;

[0123] FIG3 is an interactive schematic diagram of the communication method provided by this application;

[0124] Figures 4a to 4e, 5a to 5c, and 6 are schematic diagrams of the AI ​​processing process provided by this application;

[0125] 7 to 11 are schematic diagrams of the communication device provided in this application. DETAILED DESCRIPTION

[0126] First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0127] (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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] (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.

[0134] 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).

[0135] 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).

[0136] 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.

[0137] 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.

[0138] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

[0139] Table 1

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] (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.

[0145] Furthermore, these values ​​and parameters can be changed or updated.

[0146] (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.

[0147] (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.

[0148] 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.

[0149] 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.

[0150] (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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0161] 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).

[0162] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The following is an exemplary description of the implementation process of the neural network with reference to the accompanying drawings.

[0177] 1. Fully connected neural network, also known as multilayer perceptron (MLP).

[0178] 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.

[0179] Alternatively, considering neurons in two adjacent layers, the output h of the neurons in the next layer is the weighted sum of all neurons x in the previous layer connected to it and passes through the activation function, which can be expressed as:

[0180] h=f(wx+b).

[0181] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0182] Alternatively, the output of the neural network can be recursively expressed as:

[0183] y=f n (w n f n-1 (…)+b n ).

[0184] Where n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0185] 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.

[0186] Optionally, a specific training method is to use a loss function to evaluate the output results of the neural network.

[0187] 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.

[0188] Alternatively, the gradient descent process can be expressed as:

[0189] Among them, θ is the parameter to be optimized (including w and b), L is the loss function, and 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.

[0190] Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

[0191] 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:

[0192] Among them, w ij is the weight of node j connecting to node i, s i is the weighted sum of the inputs to node i.

[0193] 2. Federated Learning (FL)

[0194] 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.

[0195] 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:

[0196] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0197] (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.

[0198] (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.

[0199] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0200] 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.

[0201] 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.

[0202] 3. Decentralized learning: Different from federated learning, decentralized learning is another distributed learning architecture.

[0203] 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:

[0204] in, represents the parameters of the local model after the k+1th (k is a natural number) update in the i-th node, Represents 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.

[0205] 4. Segmented learning.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] However, in communication networks, communication nodes not only need to process communication signals within the communication network, but may also need to take into account AI-related processing. Therefore, how to achieve the integration of AI-related processing and communication networks is a technical problem that needs to be solved urgently.

[0212] To address the above-mentioned issues, the present application provides a communication method and related equipment for enabling communication nodes to collect artificial intelligence (AI) data, which will be described in detail below with reference to the accompanying drawings.

[0213] 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.

[0214] It should be noted that in Figure 3, the method is illustrated by taking the first node and the control node 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 Figure 3 and Figure 6 below, the execution subject of the method can be replaced by a chip, a chip system, a processor, a logic module or software in a communication device. Among them, the first node can be a terminal device and the control node can be a network device, or the first node and the control node are both terminal devices (for example, the method can be applied to the communication process of different terminal devices in a sidelink communication scenario).

[0215] S301. A control node sends first configuration information, and a first node receives the first configuration information in response, wherein the first configuration information is used to configure AI data collection.

[0216] S302: The first node collects data based on the first configuration information to obtain first AI data, wherein the first AI data is used for model processing of the first AI model.

[0217] In this application, terms such as data collection, data collection, data acquisition, and data capture can be used interchangeably.

[0218] 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.

[0219] 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 nodes (such as the first node, the control node, and other nodes mentioned later, including the second node, the central node, etc.). The AI ​​model involved in the embodiment of the present application (such as the first AI model, the second AI model mentioned later, etc.) can be used to process the wireless communication signal (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 embodiment 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.

[0220] In a possible implementation of the solution shown in FIG3 , the first node is one of N distributed nodes, where N is an integer greater than or equal to 1.

[0221] As an implementation example, as shown in FIG4a , where N is greater than 2, the N nodes may include node 1, node 2, ..., and node N in FIG4a , and the control node may communicate with the N distributed nodes. The first node may be any of the N nodes. In other words, any of the N distributed nodes may execute the method executed by the first node, enabling all N distributed nodes to serve as data collection nodes, thereby enabling AI data collection in distributed scenarios.

[0222] In a possible implementation, as shown in the example of FIG4 b , in addition to communicating with the control node, the N distributed nodes may also communicate with the data receiving node.

[0223] As shown in the example of Figure 4c, when the control node and the data receiving node are different nodes, compared with the implementation process shown in Figure 3, after step S302, the first node can also send first AI data to the data receiving node in step A, and the data receiving node can subsequently perform model processing on the first AI model based on the first AI data.

[0224] In one possible implementation, as shown in the example of Figure 4d, in addition to communicating with the control node, the N distributed nodes can also communicate with the data receiving node. Furthermore, if the control node and the data receiving node are the same node, the same node performs both the functions of the control node (e.g., determining and issuing configuration information) and the functions of the data receiving node (e.g., receiving AI data).

[0225] Similarly, as shown in the example of Figure 4e, compared with the implementation process shown in Figure 3, after step S302, the first node may also send first AI data to the control node (data receiving node) in step B, and the control node (data receiving node) may subsequently perform model processing on the first AI model based on the first AI data.

[0226] It should be noted that the data receiving node can implement the model processing process of the first AI model based on the first AI data in a variety of ways. For example, when the first AI model is deployed on the data receiving node, the data receiving node can perform model processing on the first AI data locally based on the first AI model. For another example, when the first AI model is deployed on other nodes (for example, one or more nodes among the N distributed nodes, or other nodes other than the N distributed nodes (such as central nodes) not shown in Figure 4b / Figure 4d), the data receiving node can send the first AI data to the other node, so that the other node can subsequently perform model processing on the first AI data based on the first AI model.

[0227] Optionally, the model processing includes at least one of model training, model inference, and model monitoring. Accordingly, the first AI data sent by the first node in step A or step B includes at least one of the AI ​​data used in the model training phase of the first AI model, the AI ​​data used in the model inference phase of the first AI model, and the AI ​​data used in the model monitoring phase of the first AI model.

[0228] In an implementation example, the AI ​​data used in the model training phase of the first AI model contained in the first AI data may include at least one of input data, feature data, and label data used for training the first AI model.

[0229] In another implementation example, the AI ​​data used in the model reasoning stage of the first AI model contained in the first AI data may include at least one of the input data, feature data, and reasoning result data used for the reasoning of the first AI model.

[0230] In another implementation example, the AI ​​data used in the model monitoring phase of the first AI model contained in the first AI data may include at least one of input data, feature data, label data, inference result data, AI model performance data, and communication performance data used for monitoring the first AI model.

[0231] Optionally, the feature data may indicate intermediate data, intermediate results, etc. of the reasoning of the AI ​​model.

[0232] Optionally, AI model performance data may refer to learning performance, such as accuracy, inference latency, inference complexity, etc.

[0233] Optionally, the communication performance data may refer to communication system performance, such as throughput, packet loss rate, latency, etc.

[0234] It should be noted that, for a distributed node, in the process of the distributed node collecting data based on configuration information to obtain AI data (for example, the process of the first node collecting data based on the first configuration information to obtain the first AI data in step S302), the distributed node can collect AI data based on the following multiple methods.

[0235] Method 1: Distributed nodes collect AI data based on the communication process.

[0236] For example, in approach 1, the distributed node acts as a communication node, and the communication process involved may include signal modulation and / or demodulation, reference signal measurement, and the transmission and reception of perception signals. Accordingly, the AI ​​data collected by the distributed node based on the communication process may include one or more of the following: signal modulation and / or demodulation results, reference signal measurement results, and perception results of the perception signal.

[0237] Method 2: Distributed nodes can collect AI data based on the data involved in the model processing of the locally deployed AI model.

[0238] Exemplarily, in method 2, the AI ​​data collected by the distributed nodes may include one or more of data for model training of the local AI model, data for model inference of the local AI model, and data for model monitoring of the local AI model.

[0239] It can be understood from the above description that, for the first node, the AI ​​model deployed locally on the first node may include the above-mentioned first AI model, and may also include other AI models different from the first AI model, which is not limited here.

[0240] It is understandable that the AI ​​data collected through the above-mentioned methods 1 and 2 may include the same parts, that is, the data obtained through method 1 may be the same as the data obtained through method 2. For example, when the AI ​​model deployed locally on a distributed node is an AI model for signal modulation and / or demodulation, the communication signals sent and received by the distributed node can be the data exchanged during the communication process in method 1, or the data involved in the model processing in method 2.

[0241] In a possible implementation of the scheme shown in Figure 3, after step S302, the method further includes: the first node sends the data of the first node to other nodes in the N distributed nodes, and the data of the first node is used for data collection of the other nodes. Specifically, for N distributed nodes, part or all of the AI ​​data collected by one distributed node can be determined by the data sent by other distributed nodes. Accordingly, the first node can also send the first node data to other nodes in the N distributed nodes. For example, when the first node is node 1 in Figure 4a / Figure 4b / Figure 4d, the other node can be one or more nodes from node 2 to node N, so that the other nodes can realize data collection based on the data of the first node. Exemplarily, the other node can use the received data of the first node as data in method 1 and / or method 2 to determine the AI ​​data collected by the other node.

[0242] Optionally, the data of the first node may include part or all of the first AI data.

[0243] Optionally, the data of the first node may include communication data of the first node, such as a reference signal, positioning data, etc.

[0244] Optionally, the data of the first node may include model data of the local AI model of the first node, such as at least one of the AI ​​data used in the model training phase of the local AI model, the AI ​​data used in the model inference phase of the local AI model, and the AI ​​data used in the model monitoring phase of the local AI model.

[0245] In a possible implementation of the scheme shown in Figure 3, before step S302, the method further includes: the first node receives data from other nodes in the N distributed nodes, and the data of the other nodes is used to determine part or all of the first AI data. Similarly, for N distributed nodes, part or all of the AI ​​data collected by one distributed node can be determined by data sent by other distributed nodes. Accordingly, the first node can also receive data from other nodes in the N distributed nodes (for example, second data from the second node) so that the first node can realize data collection based on the data of the other nodes. Exemplarily, the first node can use the data received from other nodes as data in method 1 and / or method 2 to determine the first AI data collected by the first node.

[0246] Optionally, as described above, the first AI model may be deployed on a data receiving node or other node, and the second AI model may be deployed on another node among the N distributed nodes. The first AI model and the second AI model may be associated AI models.

[0247] For example, the two AI models may be an AI model deployed on a central node and an AI model deployed on a distributed node in the central learning scenario of FIG2f .

[0248] For another example, the two AI models may be AI models deployed on any two adjacent or non-adjacent distributed nodes in the central learning scenario of FIG2f.

[0249] For another example, the two AI models may be AI models deployed on any two adjacent or non-adjacent distributed nodes in the distributed learning of FIG. 2g .

[0250] For another example, the two AI models may be the AI ​​model deployed on the central node and the AI ​​model deployed on the distributed node in the segmentation learning of FIG2h.

[0251] For another example, the two AI models may be AI models deployed on any two adjacent or non-adjacent distributed nodes in the segmentation learning of FIG2h.

[0252] Optionally, the data of the other node may include communication data of the other node, such as reference signals, positioning data, etc.

[0253] Optionally, the data of the other node may include model data of the local AI model of the other node, such as the AI ​​data used in the model training phase of the local AI model, the AI ​​data used in the model reasoning phase of the local AI model, and at least one of the AI ​​data used in the model monitoring phase of the local AI model. Exemplarily, taking the other node as the second node as an example, the local AI model of the second node can be recorded as a second AI model, and the model data of the second AI model can be recorded as second AI data. The implementation of the second AI data is similar to that of the first AI data, and the second AI data received by the first node may include at least one of the AI ​​data used in the model training phase of the second AI model, the AI ​​data used in the model reasoning phase of the second AI model, and the AI ​​data used in the model monitoring phase of the second AI model.

[0254] In an implementation example, the AI ​​data used in the model training phase of the second AI model contained in the second AI data includes at least one of input data, feature data, and label data used for training the second AI model.

[0255] In another implementation example, the AI ​​data used in the model reasoning stage of the second AI model contained in the second AI data includes at least one of the input data, feature data, and reasoning result data used for the reasoning of the second AI model.

[0256] In another implementation example, the AI ​​data used in the model monitoring phase of the second AI model contained in the second AI data includes at least one of input data, feature data, label data, inference result data, AI model performance data, and communication performance data used for monitoring the second AI model.

[0257] As shown in the description of the AI ​​processing architecture shown in Figures 2f to 2h above, the interaction process between different distributed nodes may or may not involve intermediate nodes. This will be described below with more implementation examples.

[0258] In one possible implementation, in step S301, the first node may receive the first configuration information in a variety of ways. For example, the process of the first node receiving the first configuration information includes: the first node receiving the first configuration information from the control node, the control node being used to control the data collection of the N distributed nodes; or, the first node receiving the first configuration information from the control node through the central node; or, the first node receiving the first configuration information from the second node, the second node being a node different from the first node among the N distributed nodes, where N is greater than 1. Specifically, the first node may receive the first configuration information in the above-mentioned multiple ways, the first node being one of the N distributed nodes. In this way, the distributed node can receive the first configuration information in a variety of different scenarios and improve the flexibility of the solution implementation.

[0259] Optionally, the control node and the central node may be the same node, or the control node and the central node may be different logical nodes in a physical node, or the control node and the central node may be two independent and different nodes.

[0260] As an implementation example, the first configuration information received by the first node is the configuration information corresponding to the first node of M configuration information, and the M configuration information is at least used to configure the AI ​​data collection of M distributed nodes among N distributed nodes, where M is less than or equal to N; the method also includes: the first node sends at least one configuration information of the M configuration information to at least one distributed node among the M distributed nodes. Specifically, the first node can receive M configuration information and determine the first configuration information among the M configuration information. The M configuration information is at least used to configure the AI ​​data collection of M distributed nodes among N distributed nodes. Accordingly, the first node can send at least one configuration information of the M configuration information to other nodes among the M distributed nodes, so that other distributed nodes can obtain corresponding configuration information and perform AI data collection.

[0261] Optionally, the first node sends at least one of the M configuration information to at least one of the M distributed nodes in order to enable the M distributed nodes to obtain their respective corresponding configuration information, so that the M distributed nodes can realize AI data collection based on their respective corresponding configuration information. Further optionally, during the sending process, the first node may send the configuration information corresponding to each distributed node to each of the M distributed nodes, or the first node may send the M configuration information to each of the M distributed nodes, or the first node may send the M configuration information to some of the M distributed nodes, and the some distributed nodes send the configuration information corresponding to the other distributed nodes to other distributed nodes, or other methods may be used to enable the M distributed nodes to obtain their respective corresponding configuration information, which is not limited here.

[0262] Optionally, among the N distributed nodes, the configuration information corresponding to different distributed nodes may be different from each other. Therefore, the M configuration information is used to configure the AI ​​data collection of M distributed nodes among the N distributed nodes.

[0263] Optionally, among N distributed nodes, the configuration information corresponding to different distributed nodes may be the same. To this end, M configuration information can be used to configure the AI ​​data collection of M distributed nodes among the N distributed nodes, and at least one configuration information among the M configuration information can also be used to configure the AI ​​data collection of at least one other distributed node among the N distributed nodes except the M distributed nodes.

[0264] For example, taking the example where the configuration information corresponding to different distributed nodes may be different, that is, the M configuration information is used to configure AI data collection of the M distributed nodes respectively. In other words, the first node can send the other M-1 configuration information in the M configuration information except the first configuration information to the other M-1 distributed nodes in the M distributed nodes except the first node. The first node sends the other M-1 configuration information in the M configuration information except the first configuration information in multiple ways. For example, when the M-1 configuration information are used for AI data collection of M-1 different distributed nodes respectively, the first node can send the M-1 configuration information to the M-1 different distributed nodes respectively; or, the first node can send the M-1 configuration information to the next-hop node among the M-1 different distributed nodes, and the next-hop node will obtain local configuration information from the M-1 configuration information, and send the other M-2 configuration information in the M-1 configuration information except the local configuration information to the next-hop node of the next-hop node, and so on, until the M-1 different distributed nodes all obtain their respective configuration information.

[0265] It is understandable that, when the configuration information corresponding to different distributed nodes may be the same, the first node may send the M-1 configuration information with reference to the above-mentioned multiple methods.

[0266] In one possible implementation, after the first node obtains the first AI data in step S302, the first node may send the first AI data in various ways. For example, the process of the first node sending the first AI data includes: the first node sending the first AI data to a data receiving node, where the data receiving node is used for AI data collection; or the first node sending the first AI data to a central node. Specifically, the first node may send the first AI data in the various ways described above, where the first node is one of N distributed nodes. In this way, the distributed nodes can send collected AI data in a variety of different scenarios, thereby improving the flexibility of the solution implementation.

[0267] Optionally, the control node and the data receiving node may be the same node, or the control node and the data receiving node may be different logical nodes in a physical node, or the control node and the data receiving node may be two independent and different nodes.

[0268] As can be seen from the above implementation process, distributed nodes can receive configuration information and send AI data in a variety of ways. The following is an illustrative description of the scenarios shown in Figures 5a to 5c. It should be understood that the examples shown in Figures 5a to 5c below illustrate the example of the control node and data receiving node shown in Figure 4c being the same node. In actual applications, the control node and data receiving node can be different nodes.

[0269] In implementation method A, the control node (data receiving node) communicates with N distributed nodes respectively to send configuration information of the N distributed nodes and receive AI data of the N distributed nodes.

[0270] As an example implementation of Implementation A, as shown in FIG5a, when the control node (data receiving node) is reachable to communicate with N distributed nodes, the control node (data receiving node) can communicate with each distributed node. For example, a direct link exists between the control node (data receiving node) and each distributed node for communication, or the control node (data receiving node) and each distributed node can communicate through one or more relay nodes.

[0271] Accordingly, in Figure 5a, the N distributed nodes can each serve as the first node, i.e., the control node (data receiving node) can perform the process of sending the first configuration information N times in step S301, so that the N distributed nodes each receive their own configuration information. Furthermore, the N distributed nodes can collect AI data based on their respective configuration information in step S302. Furthermore, as shown in step B in Figure 4e, after step S302, the N distributed nodes can send their collected AI data to the control node (data receiving node).

[0272] In implementation B, the control node (data receiving node) communicates with one of the N distributed nodes to send configuration information of the N distributed nodes and receive AI data of the N distributed nodes.

[0273] As an example of implementation of Implementation B, as shown in FIG5b , when the control node (data receiving node) is in communication with one of the N distributed nodes (node ​​1 is used as an example in FIG5b ), and when communication between different distributed nodes is in communication, the control node (data receiving node) can communicate with node 1 to implement the transmission of configuration information of the N distributed nodes. For example, a direct link exists between the control node (data receiving node) and node 1 for communication, or the control node (data receiving node) and node 1 can communicate through one or more relay nodes.

[0274] Correspondingly, in FIG5 b , node 1 serves as the first node and may receive K pieces of configuration information including first configuration information in step S301 .

[0275] Optionally, among the N distributed nodes, the configuration information corresponding to different distributed nodes may be different. To this end, the values ​​of K and N can be equal, that is, the K configuration information can be used to configure AI data collection for different nodes among the N distributed nodes.

[0276] Optionally, among the N distributed nodes, the configuration information corresponding to different distributed nodes may be identical. To this end, K can be smaller than N, i.e., at least one of the K configuration information is used to configure AI data collection for at least two of the N distributed nodes. Accordingly, the configuration information for AI data collection of the at least two nodes is identical.

[0277] For example, taking the example of K configuration information that can be used to configure AI data collection for different nodes in N distributed nodes, that is, K is equal to N. For ease of understanding, the K configuration information will be referred to as N configuration information below. When N is greater than 1, if node 1 has a communication link with the other N-1 nodes (such as the dotted line in Figure 5b), the node 1 can send the other N-1 configuration information in the N configuration information except the first configuration information to the other N-1 nodes, so that the N distributed nodes can obtain their respective configuration information and perform data collection based on the configuration information. Alternatively, node 1 can send N-1 configuration information other than the first configuration information among the N configuration information to the neighboring node (for example, node 2). Similarly, after node 2 obtains its own corresponding configuration information (for example, the second configuration information) from the N-1 configuration information, node 2 can also send N-2 configuration information other than the first configuration information and the second configuration information among the N configuration information to the neighboring node, and so on, until the N distributed nodes can obtain their respective configuration information and perform data collection based on the configuration information.

[0278] Afterward, in Figure 5b, nodes 2 through N can follow the reverse transmission process described above and send their collected AI data to node 1. Node 1 can then send the AI ​​data collected by the N distributed nodes to the control node (data receiving node) through one or more transmissions. This allows the control node (data receiving node) to distribute configuration information to the N distributed nodes and receive AI data collected by the N distributed nodes.

[0279] It should be noted that the implementation processes of implementation method A and implementation method B can be combined with each other. For example, M (M is a positive integer) nodes among N distributed nodes communicate through implementation method B, and the other NM nodes communicate through implementation method A. In other words, for the M nodes, the sending of configuration information of the M distributed nodes and the reception of AI data of the M distributed nodes can be realized through the communication process between the control node (data receiving node) and one of the M nodes; for the NM nodes, the control node (data receiving node) communicates with the NM distributed nodes respectively to realize the sending of configuration information of the NM distributed nodes and the reception of AI data of the NM distributed nodes. The specific implementation process can refer to the implementation process of the above-mentioned implementation method A and implementation method B.

[0280] Implementation method C: The control node (data receiving node) communicates with the central node to send configuration information of N distributed nodes and receive AI data of N distributed nodes.

[0281] As an example of implementation method C, as shown in FIG5c, when the control node (data receiving node) and the central node are reachable, the control node (data receiving node) can communicate with the central node to implement the transmission of configuration information of N distributed nodes. For example, a direct link exists between the control node (data receiving node) and the central node for communication, or the control node (data receiving node) and the central node can communicate through one or more relay nodes.

[0282] Correspondingly, in Figure 5c, any one of the N distributed nodes can serve as the first node. In step S301, the first configuration information is received through the central node, so that the N distributed nodes can obtain their respective configuration information and perform data collection based on the configuration information in step S302.

[0283] Thereafter, in FIG5c , any of the N distributed nodes can serve as the first node and, after step S302 , send the AI ​​data collected by each node to the central node, so that the central node obtains N copies of AI data. Thereafter, the central node can process the N received AI data (e.g., data screening, data merging, data de-redundancy processing, etc.) and send them to the control node (data receiving node). Alternatively, the central node can transparently forward the N copies of AI data to the control node (data receiving node). Through this centralized implementation method, the control node (data receiving node) can send configuration information to the N distributed nodes through the central node, as well as receive AI data collected by the N distributed nodes.

[0284] Optionally, in Figure 5c, the control node may communicate with one or more of the N distributed nodes without going through the central node. To this end, the process of the distributed node receiving configuration information may be achieved by transmitting the configuration information through the central node, or may be achieved without going through the central node (see the implementation process shown in Figures 5a and 5b above), which is not limited here. Similarly, the process of the distributed node sending AI data may be achieved by transmitting the AI ​​data through the central node, or may be achieved without going through the central node (see the implementation process shown in Figures 5a and 5b above), which is not limited here.

[0285] It should be noted that there may be reachable communication links between the central node and the N distributed nodes, or there may be reachable communication links between the central node and only some of the distributed nodes. These two implementation processes can refer to the aforementioned implementation method A, implementation method B, and the implementation processes of M nodes and NM nodes in the previous text.

[0286] Alternatively, in FIG5c , the central node may be deployed on the same node as the control node and the data receiving node. In other words, the central node, the control node, and the data receiving node may be three different nodes, or any two or three of the three nodes may be the same node.

[0287] In a possible implementation manner, the first configuration information received by the first node in step S301 includes at least one item of the following information A to information J.

[0288] Information A. Identification (or index) of the AI ​​model corresponding to the collected AI data.

[0289] Information B. Identification (or index) of the AI ​​function corresponding to the collected AI data.

[0290] Information C: indicates information indicating model processing corresponding to the collected AI data.

[0291] Information D. Configuration information indicating whether the AI ​​data collection is configured in a centralized or decentralized manner.

[0292] Information E: indicates whether the AI ​​data collection method is centralized or decentralized.

[0293] Information F: Indicative information indicating AI data characteristics of the collected AI data.

[0294] Information G: Information indicating AI data processing of collected AI data.

[0295] Information H: Information indicating the AI ​​data collection period.

[0296] Information I. Indicative information indicating the transmission information of the collected AI data.

[0297] Information J. Identification of the source node of the collected AI data.

[0298] For information A. As can be seen from the definition of the AI ​​model above, the identifier of the AI ​​model in information A can be used to identify (or indicate) the AI ​​model. In other words, when the first configuration information includes information A, the first AI data obtained by the first node through data collection based on the first configuration information includes the data of the AI ​​model corresponding to the identifier of the AI ​​model indicated by the information A. For example, the identifier of the AI ​​model in information A can specifically be the identifier of an AI model for modulation and / or demodulation, the identifier of an AI model for channel prediction, the identifier of an AI model for beam management, the identifier of an AI model for assisted positioning, the identifier of an AI model for channel compression, the identifier of an AI model for resource scheduling, the AI ​​model for mobility management, the AI ​​model for load balancing, the AI ​​model for network energy saving, the identifier of an AI model for replacing one or more modules in a transmitter and / or receiver, the identifier of an AI model for image recognition, the identifier of an AI model for natural language processing, the identifier of an AI model for computer vision, etc.

[0299] For information B, as can be seen from the definition of the AI ​​model above, the identifier of the AI ​​function in information B can be used to identify (or indicate) the function of the AI ​​model. In other words, when the first configuration information includes information B, the first AI data obtained by the first node through data collection based on the first configuration information includes the AI ​​data corresponding to the identifier of the AI ​​function indicated by the information B. For example, the identifier of the AI ​​function in information B can specifically be a function identifier for modulation and / or demodulation, a function identifier for channel prediction, a function identifier for beam management, a function identifier for assisted positioning, a function identifier for channel compression, a function identifier for resource scheduling, an AI model for mobility management, an AI model for load balancing, an AI model for network energy saving, a function identifier for replacing one or more modules in a transmitter and / or receiver, a function identifier for image recognition, a function identifier for natural language processing, a function identifier for computer vision, etc.

[0300] With respect to information C, since model processing may include at least one of model training, model reasoning, and model monitoring, information C may include at least one of: indication information indicating that the collected AI data is used for (or corresponds to) model training, indication information indicating that the collected AI data is used for (or corresponds to) model reasoning, and indication information indicating that the collected AI data is used for (or corresponds to) model monitoring. In other words, when the first configuration information includes information C, the first AI data obtained by the first node through data collection based on the first configuration information includes at least one model-processed AI data indicated by information C.

[0301] For information D and information E, as shown in the examples of Figures 5a to 5c above, the data exchanged between the distributed node and the control node (data receiving node) can be transmitted through the central node or not. To this end, through the indication of information D, it can be determined whether the configuration process of the configuration information (such as the first configuration information) participates in the transmission through the central node. If so, the configuration method of the configuration information of the information D indicating AI data collection is centralized; if not, the configuration method of the configuration information of the information D indicating AI data collection is decentralized. In other words, when the first configuration information includes information D, if information D indicates centralized, then the first configuration information received by the first node is configured through the central node; if information D indicates decentralized, then the first configuration information received by the first node is not configured through the central node, but is configured through the control node.

[0302] Similarly, information E indicates whether the collection process of AI data (e.g., first AI data) involves transmission through a central node. If so, information E indicates that the AI ​​data collection method is centralized; if not, information E indicates that the AI ​​data collection method is decentralized. In other words, if the first configuration information includes information E, and if information E indicates centralized, then after the first node collects the first AI data based on the first configuration information, the first node sends the first AI data to the central node. If information E indicates decentralized, then after the first node collects the first AI data based on the first configuration information, the first node sends the first AI data to the data receiving node, rather than to the central node.

[0303] Information F indicates that the AI ​​data characteristics include one or more of the following: quantity, sample size, collection time, collection location, and distribution of the AI ​​data. In other words, when the first configuration information includes information F, the first AI data obtained by the first node through data collection based on the first configuration information satisfies the AI ​​data characteristics indicated by information F.

[0304] For information G, AI data processing includes post-processing of the AI ​​model's output data and / or pre-processing of the AI ​​model's input data, such as one or more of dimensionality conversion and precision conversion. In other words, when the first configuration information includes information G, the first AI data obtained by the first node through data collection based on the first configuration information satisfies the post-processing and / or pre-processing indicated by information G.

[0305] For information H, the AI ​​data collection period includes AI data collection in a periodic manner, AI data collection in a semi-static manner, or AI data collection in a (dynamic) triggered manner. In other words, when the first configuration information includes information H, the first node collects data based on the collection period indicated by the information H, thereby obtaining the first AI data.

[0306] For information I, the transmission information of the AI ​​data includes one or more of the following: data structure, format, precision, dimension, and transmission resources. In other words, when the first configuration information includes information I, the first AI data obtained by the first node through data collection based on the first configuration information satisfies the transmission information indicated by information I.

[0307] For information J, the first node can request / obtain / collect data from the source node of the collected AI data based on the identifier of the source node indicated by the information J, and determine part or all of the first AI data based on the data from the source node.

[0308] It should be understood that when the first configuration information includes at least one item of the above-mentioned information A to information J, in step S301, the first configuration information can be sent through one or more messages, that is, the first node can obtain the first configuration information through the receiving process of one or more messages.

[0309] Based on the technical solution shown in Figure 3, after the first node receives the first configuration information in step S301, it can perform AI data collection based on the first configuration information in step S302 to obtain first AI data. Thereafter, the first node can transmit the first AI data, and the recipient of the first AI data can subsequently perform model processing using the first AI model based on the first AI data. Thus, when a communication node in a communication system serves as an AI participating node, the communication node can function as an AI data collection node, enabling AI data collection.

[0310] In addition, the first node acts as a communication node. After the first node sends the first AI data, the recipient of the first AI data can implement model processing of the AI ​​model based on the AI ​​data collected by the communication node.

[0311] Please refer to FIG6 . This application also provides a communication architecture that can be used for data collection.

[0312] In Figure 6, the communication architecture includes at least a data storage module and a data transmission module. The data transmission module can be used to transmit data between nodes (including between distributed nodes and between distributed nodes and central nodes). The data storage module can be used to store data received from other nodes.

[0313] Exemplarily, the data storage module may include the data receiving node in any of the above embodiments, or the data receiving node in any of the above embodiments may be used to execute the process executed by the data storage model.

[0314] Exemplarily, the data transmission module may include the distributed nodes (e.g., N distributed nodes) and / or the central node in any of the above embodiments, or the distributed nodes (e.g., N distributed nodes) and / or the central node in any of the above embodiments may be used to execute the process performed by the data transmission model.

[0315] Optionally, the communication architecture may further include a data measurement module. The data measurement module may be configured to obtain measurement data through measurement (including reference signal-based measurement and measurement performed through a sensing function). Accordingly, the data storage module may also be configured to store data obtained by the node through measurement.

[0316] Exemplarily, the data measurement module may include the nodes in any of the above embodiments (e.g., N distributed nodes, a control node, a central node, a data receiving node), or the nodes in any of the above embodiments (e.g., N distributed nodes, a control node, a central node, a data receiving node) may be used to execute the process performed by the data measurement module.

[0317] Optionally, the communication architecture may further include a data usage module. The data usage module may retrieve data used for model training, model reasoning, and model monitoring from the data storage, and restore the data that may be generated during model training, model reasoning, and model monitoring. Accordingly, the data storage module may also be used to store data generated during the data usage process.

[0318] Exemplarily, the data usage module may include a node in any of the above embodiments on which an AI model is deployed (e.g., N distributed nodes, a control node, a central node, a data receiving node), or the node in any of the above embodiments (e.g., N distributed nodes, a control node, a central node, a data receiving node) may be used to execute the process performed by the data usage module.

[0319] Referring to Figure 7, an embodiment of the present application provides a communication device 700. The communication device 700 can implement the functions of the first node (or control node) in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment. In the embodiment of the present application, the communication device 700 can be the first node (or control node), or it can be an integrated circuit or component within the first node (or control node), such as a chip.

[0320] 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.

[0321] In one possible implementation, when the device 700 is used to execute the method executed by the first node 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 configuration information, and the first configuration information is used to configure AI data collection; the processing unit 701 sends first AI data, and the first AI data is collected based on the first configuration information; wherein the first AI data is used for model processing of the first AI model.

[0322] In one possible implementation, when the device 700 is used to execute the method executed by the control node in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the processing unit 701 is used to determine first configuration information, and the first configuration information is used for AI data collection; the transceiver unit 702 is used to send the first configuration information.

[0323] 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.

[0324] 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.

[0325] 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.

[0326] Optionally, the input-output interface 802 is used to receive first configuration information, which is used to configure AI data acquisition; the logic circuit 801 sends first AI data, which is collected based on the first configuration information; wherein, the first AI data is used for model processing of the first AI model.

[0327] Optionally, the logic circuit 801 is used to determine first configuration information, which is used for AI data collection; the input and output interface 802 is used to send the first configuration information.

[0328] The logic circuit 801 and the input / output interface 802 may also execute other steps executed by the first node or the control node in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

[0329] In a possible implementation, the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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).

[0335] Here, 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 .

[0336] 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.

[0337] 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.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] 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.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] 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.

[0348] 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.

[0349] 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.

[0350] 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.

[0351] 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 ).

[0352] 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.

[0353] 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.

[0354] 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.

[0355] 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.

[0356] 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.

[0357] An embodiment of the present application also 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 method of the first node or control node in the above embodiment.

[0358] 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 of the possible implementation of the above-mentioned first node or control node.

[0359] 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 for storing program instructions and data necessary for the communication device. The chip system may be composed of chips, or may include chips and other discrete devices, wherein the communication device may specifically be the first node or control node in the aforementioned method embodiment.

[0360] An embodiment of the present application also provides a communication system, wherein the network system architecture includes the first node and the control node in any of the above embodiments.

[0361] Optionally, the communication system further includes other nodes among the N distributed nodes, such as a second node.

[0362] Optionally, the communication system further includes a data receiving node.

[0363] Optionally, the communication system further includes a central node.

[0364] 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.

[0365] 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.

[0366] 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: include: Receive first configuration information, where the first configuration information is used to configure AI data collection; Send first AI data, where the first AI data is collected based on the first configuration information; wherein the first AI data is used for model processing of a first AI model.

2. The method according to claim 1, characterized in that The method is applied to a first node, where the first node is one of N distributed nodes, where N is an integer greater than 1; The receiving first configuration information comprises: receiving the first configuration information from a control node, where the control node is used to control data collection of the N distributed nodes; or, Receiving the first configuration information from the control node through the central node; or, The first configuration information is received from a second node, where the second node is a node different from the first node among the N distributed nodes.

3. The method according to claim 1 or 2, characterized in that: The first configuration information is configuration information of M configuration information corresponding to the first node, and the M configuration information is used to configure AI data collection of M distributed nodes among N distributed nodes at least, where M is less than or equal to N; The method further comprises: One or more configuration information among the M configuration information are sent to at least one node among the M distributed nodes.

4. The method according to any one of claims 1 to 3, characterized in that: The method is applied to a first node, where the first node is one of N distributed nodes, where N is an integer greater than or equal to 1; The sending of the first AI data includes: Sending the first AI data to a data receiving node, where the data receiving node is used for AI data collection; or, Send the first AI data to the central node.

5. The method according to claim 4, characterized in that The method further comprises: The data of the first node is sent to other nodes in the N distributed nodes, where the first AI data is used for data collection of the other nodes.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Receive data from other nodes among the N distributed nodes, where the data from other nodes is used to determine part or all of the first AI data.

7. A communication method, characterized in that: include: Determine first configuration information, where the first configuration information is used for AI data collection; Send the first configuration information.

8. The method according to claim 7, characterized in that The method is applied to a control node, the control node is used to control data collection of N distributed nodes, N is an integer greater than or equal to 1; the first configuration information is used for AI data collection of a first AI node among the N distributed nodes; The sending the first configuration information includes: sending the first configuration information to the first node; or, Sending the first configuration information to the first node through a central node; or, The first configuration information is sent to the first node through a second node, where the second node is a node different from the first node among the N distributed nodes.

9. The method according to claim 8, characterized in that The first configuration information is one of the K configuration information sent, and the K configuration information are respectively used to configure AI data collection of N distributed nodes, where K is less than or equal to N.

10. The method according to any one of claims 7 to 9, characterized in that: The method further comprises: First AI data is received, where the first AI data is collected based on the first configuration information.

11. The method according to claim 10, characterized in that The method is applied to a control node, the control node is used to control data collection of N distributed nodes, N is an integer greater than or equal to 1; the first configuration information is used for AI data collection of a first AI node among the N distributed nodes; The receiving first AI data includes: receiving the first AI data from the first node; The first AI data is received through a central node.

12. The method according to any one of claims 1 to 11, characterized in that: The first configuration information includes at least one of the following: The identifier of the AI ​​model corresponding to the collected AI data; The identifier of the AI ​​function corresponding to the collected AI data; The identification of the source node of the collected AI data; Instruction information indicating model processing corresponding to collected AI data; Indication information indicating whether the configuration mode of the configuration information of AI data collection is centralized or decentralized; Indicative information indicating whether the AI ​​data collection method is centralized or decentralized; Indicative information indicating AI data characteristics of collected AI data; Instruction information indicating AI data processing of collected AI data; Indicative information indicating the AI ​​data collection period; Indicative information indicating transmission information of collected AI data.

13. The method according to any one of claims 1 to 12, characterized in that: The model processing includes at least one of model training, model reasoning, and model monitoring.

14. The method according to any one of claims 1 to 13, characterized in that: The first AI data includes at least one of AI data used in the model training phase of the first AI model, AI data used in the model reasoning phase of the first AI model, and AI data used in the model monitoring phase of the first AI model.

15. The method according to claim 14, characterized in that The AI ​​data used in the model training phase of the first AI model includes at least one of input data, feature data, and label data used for training the first AI model; The AI ​​data used in the model reasoning phase of the first AI model includes at least one of input data, feature data, and reasoning result data used for reasoning of the first AI model; The AI ​​data used in the model monitoring stage of the first AI model includes at least one of input data, feature data, label data, inference result data, AI model performance data, and communication performance data used for monitoring the first AI model.

16. A communication device, characterized in that: Comprising means for performing the method as claimed in any one of claims 1 to 15.

17. A communication device, characterized in that: The method comprises at least one processor coupled to a memory; the at least one processor is configured to execute the method according to any one of claims 1 to 15.

18. The communication device according to claim 17, characterized in that: The communication device is a chip or a chip system.

19. A readable storage medium, characterized in that: The storage medium stores a computer program or an instruction. When the computer program or the instruction is executed by the communication device, the method according to any one of claims 1 to 15 is implemented.

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