Communication method and related device

Abstract

Provided in the present application are a communication method and a related device, which are used for determining and deploying an AI model in a communication network by means of interaction between different communication apparatuses, so that the computing power of the communication apparatuses can be applied to the processing of the AI model. In the method, after a first communication apparatus sends first information for determining a first AI model group, the first communication apparatus can receive second information, which comprises a model parameter of the first AI model group or a model parameter of a first AI model in the first AI model group, wherein the first AI model group comprises the first AI model deployed in the first communication apparatus and a second AI model deployed in a second communication apparatus.

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 November 24, 2023, with application number 202311600304.7 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 communication technology, and in particular to a communication method and related equipment. Background Art

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

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

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

[0006] Summary of the Invention

[0007] The present application provides a communication method and related equipment for determining and deploying artificial intelligence (AI) models in a communication network through interaction between different communication devices, so that the computing power of the communication devices can be applied to the processing of AI models.

[0008] The first aspect of the present application provides a communication method, which is performed by a first communication device, which may be a communication device (such as a terminal device), or the first communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the first communication device sends first information, and the first information is used to determine a first AI model group, and the first AI model group includes the first AI model and the second AI model; wherein the first AI model is deployed on the first communication device and the second AI model is deployed on the second communication device; the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; the first communication device receives second information from the second communication device, and the second information includes the model parameters of the first AI model group or the model parameters of the first AI model.

[0009] Based on the above technical solution, the second communication device, as the recipient of the first information, can determine the first AI model group based on the first information from the first communication device and deploy the first AI model to the first communication device via the second information. Thus, when the communication device in the communication system acts as an AI participating node, while enabling the computing power of the communication device to be applied to the processing of the AI ​​model, the first information from the first communication device can also serve as one of the bases for the second communication device to determine the AI ​​model, so that the AI ​​model determined by the second communication device can be adapted to the first communication device as much as possible, thereby improving the processing performance of the subsequent model processing based on the AI ​​model by the first communication device.

[0010] 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.

[0011] It should be understood that the first AI model group includes a first AI model and a second AI model. It can be understood that the function of the first AI model group is implemented at least through the model processing of the first AI model and the model processing of the second AI model. In other words, after the first communication device receives the second information, the first communication device can deploy the first AI model on the first communication device through the model parameters of the first AI model group or the model parameters of the first AI model contained in the second information, and perform model processing on the first AI model; accordingly, the second communication device can perform model processing on the second AI model deployed on the second communication device. Optionally, the model processing may include one or more of model update processing, model training processing, and model inference processing.

[0012] It can be understood that the second communication device can be implemented in many ways.

[0013] For example, the second communication device may be a terminal device, and accordingly, the first communication device and the second communication device may communicate on a sidelink (SL). In this case, the first AI model and the second AI model may be referred to as an end-to-end model, or an end-to-end collaborative model, etc.

[0014] For another example, the second communication device may be a network device (e.g., an access network device), and accordingly, the first communication device and the second communication device may communicate on uplink and downlink communication links. In this case, the first AI model and the second AI model may be referred to as an edge-end model, an edge-end collaborative model, an edge-end model, an edge-end collaborative model, etc.

[0015] Optionally, when the first AI model group is regarded as one AI model, the first AI model and the second AI model can be understood as two AI sub-models in the one AI model.

[0016] In this application, an AI model is deployed on a communication device (for example, a first AI model is deployed on a first communication device, a second AI model is deployed on a second communication device, etc.). It can be understood that after the communication device obtains the model parameters of the AI ​​model, it obtains / generates / constructs the AI ​​model based on the model parameters of the AI ​​model, and subsequently the communication device can perform model processing on the AI ​​model.

[0017] Optionally, the model parameters may include one or more of the model's hyperparameters, the model's dataset (including the model's input data and label data corresponding to the input data), and the model's structural parameters.

[0018] Optionally, an AI model group may include two or more AI models. For example, in addition to the first AI model and the second AI model, the first AI model group may also include other AI models. The other AI models can be deployed on other communication devices different from the first communication device and the second communication device. This is not limited here.

[0019] It should be understood that wireless communication signals (such as the transmission and reception of configuration information of communication resources, the transmission and reception of reference signals, etc.) can be transmitted between different communication devices (such as the first communication device and the second communication device).

[0020] Optionally, the AI ​​model involved in this application (such as the first AI model, the second AI model, the third to sixth AI models mentioned below, etc.) can be used to manage the wireless communication signal (including at least one of 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, or one or more AI models for replacing one or more modules in a transmitter and / or receiver. Alternatively, the AI ​​model involved in this 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] The second aspect of the present application provides a communication method, which is performed by a second communication device, which may be a communication device (such as a network device or a terminal device), or the second communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the second communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the second communication device receives first information; the second communication device determines a first AI model group based on the first information, and the first AI model group includes the first AI model and the second AI model; wherein the first AI model is deployed on the first communication device, and the second AI model is deployed on the second communication device, and the input of the first AI model includes the output of the second AI model, or the input of the second AI includes the output of the first AI model; the second communication device sends second information, and the second information includes the model parameters of the first AI model group or the model parameters of the first AI model.

[0022] Based on the above technical solution, the second communication device, as the recipient of the first information, can determine the first AI model group based on the first information from the first communication device and deploy the first AI model to the first communication device via the second information. Thus, when the communication device in the communication system acts as an AI participating node, while enabling the computing power of the communication device to be applied to the processing of the AI ​​model, the first information from the first communication device can also serve as one of the bases for the second communication device to determine the AI ​​model, so that the AI ​​model determined by the second communication device can be adapted to the first communication device as much as possible, thereby improving the processing performance of the subsequent model processing based on the AI ​​model by the first communication device.

[0023] In a possible implementation of the first aspect or the second aspect, the second communication device is a functional entity that determines an AI model group list based on the first information, and the AI ​​model group list includes one or more AI model groups, and the one or more AI model groups include the first AI model group.

[0024] Based on the above technical solution, the second communication device can communicate with one or more first communication devices, and the second communication device can receive information (e.g., one or more first information) from the one or more first communication devices to generate / obtain / determine one or more AI model groups. In other words, the second communication device can perform information collection and perform model generation based on the collected information, and subsequently the second communication device can deploy the AI ​​model on one or more first communication devices.

[0025] Optionally, the AI ​​model group list may include one or more AI model groups, and each AI model group may include two or more AI models. As mentioned above, the relationship between an AI model group and an AI model can also be understood as the relationship between an AI model and an AI sub-model. To this end, the AI ​​model group list may also be replaced by an AI model list, that is, the AI ​​model list may include one or more AI models.

[0026] Optionally, list can be replaced with other terms such as set, dictionary, combination, space, etc.

[0027] In a possible implementation of the first aspect or the second aspect, the second communication device is a functional entity that determines an AI model group list based on the first information, and selects some or all of the AI ​​model groups used by the first communication device from the AI ​​model group list.

[0028] Based on the above technical solution, after the second communication device determines the AI ​​model group list based on the first information, the function implemented by the second communication device may also include selecting some or all AI model groups used by the first communication device from the AI ​​model group list. In other words, in addition to performing information collection and performing model generation based on the collected information, the second communication device may also perform model selection so that the second communication device can subsequently deploy AI models compatible with the one or more first communication devices on one or more first communication devices.

[0029] In a possible implementation of the first or second aspect, the first information includes first dimensional information or second dimensional information; when the input of the first AI model includes the output of the second AI model, the first dimensional information is used to determine the dimensional information of the input data of the first AI model or the dimensional information of the output data of the second AI model; when the input of the second AI model includes the output of the first AI model, the second dimensional information is used to determine the dimensional information of the output data of the first AI model or the dimensional information of the input data of the second AI model.

[0030] Based on the above technical solution, the second communication device can determine the dimension information of the data transmitted on the communication link through the first information. When the communication bandwidth between the first communication device and the second communication device is constant, since the dimension of the data transmitted on the communication link is related to the processing performance of the AI ​​model, this method can simplify the implementation process of the second communication device determining the first AI model group, reducing the complexity of the second communication device while also improving the processing performance of the AI ​​models included in the first AI model group.

[0031] In a possible implementation of the first aspect or the second aspect, the dimension information includes at least one of the following: an upper limit value of the dimension, a lower limit value of the dimension, the dimension expected by the first communication device (or the dimension not expected by the first communication device), and a value range of the dimension expected by the first communication device (or the value range of the dimension not expected by the first communication device).

[0032] Optionally, the dimensional information may include the value of at least one of the above items, or the quantized value of the value of at least one of the above items, or the index of the value of at least one of the above items, the index of the quantized value of the value of at least one of the above items, etc., or may be implemented in other ways, which are not limited here.

[0033] Based on the above technical solution, the dimensional information determined by the first dimensional information or the second dimensional information may include at least one of the above items to enhance the flexibility of the solution implementation.

[0034] For example, when the above-mentioned dimensional information includes the upper limit value and / or the lower limit value of the dimension, the second communication device can use the range indicated by the upper limit value and / or the lower limit value as one of the bases for determining the AI ​​model, which can improve the flexibility of the implementation of the solution.

[0035] For example, when the above-mentioned dimensional information includes the dimension expected by the first communication device and / or the value range of the dimension expected by the first communication device, the AI ​​model determined by the second communication device based on the dimensional information can meet the expectations of the first communication device.

[0036] In a possible implementation manner of the first aspect or the second aspect, the first dimension information or the second dimension information is determined based on channel state information (CSI).

[0037] Based on the above technical solution, the first dimension information or the second dimension information contained in the first information can be determined based on the channel state information, so that the first dimension information or the second dimension information can reflect the channel characteristics of the wireless channel between the first communication device and the second communication device to a certain extent, so that the AI ​​model that can be subsequently obtained based on the first information can be adapted to the channel characteristics of the wireless channel, in order to improve the transmission performance of the AI ​​data corresponding to the AI ​​model. Moreover, when the AI ​​model obtained based on the first information can be adapted to the channel characteristics of the wireless channel, the transmission data of the wireless link can also meet the channel bandwidth requirements as much as possible, thereby improving the model performance of the AI ​​model included in the first AI model group.

[0038] Optionally, the channel state information may include channel information between the first communication device and the second communication device, and / or channel information between the second communication device and the first communication device. Where the first communication device is a terminal device and the second communication device is a network device, the channel information between the first communication device and the second communication device may be understood as uplink channel information, and the channel information between the second communication device and the first communication device may be understood as downlink channel information.

[0039] Optionally, the channel state information may be obtained based on a reference signal.

[0040] For example, in the case where the first communication device and the second communication device communicate through a sidelink, the reference signal may include a sidelink synchronization signal / physical broadcast channel block (sidelink synchronization signal / physical broadcast channel block, sidelink SSB, SL-SSB, or S-SS / PSBCH block), a sidelink channel state information reference signal (sidelink channel state information reference signal, SL-CSI-RS), etc.

[0041] For example, when the first communication device and the second communication device communicate via uplink and downlink, the reference signal may include a channel state information reference signal (CSI-RS), a sounding reference signal (SRS), etc.

[0042] In a possible implementation of the first aspect or the second aspect, the first information includes at least one of the following: input data of the first AI model and label data of the input data of the first AI model, local computing power status information of the first communication device, and channel status information.

[0043] Based on the above technical solution, the first information may include at least one of the above information. In other words, the second communication device may determine the first AI model group based on the above at least one information to improve the flexibility of the solution implementation.

[0044] In an implementation example, when the first information includes input data of a first AI model and label data of the input data of the first AI model, since the input data can be used as input of the first AI model, the label data can be used as one of the bases for determining the model processing performance of the first AI model. Therefore, for the second communication device, the second communication device can obtain an AI model with better performance based on these two pieces of information.

[0045] In addition, for the second communication device, the second communication device can perform mathematical calculations based on mutual information based on these two pieces of information and the AI ​​data sent and received by the second communication device on the wireless link (such as the input data of the second AI model or the output data of the second AI model, etc.), and determine the first AI model group based on the result of the mathematical calculation, so as to improve the model performance of the AI ​​models included in the first AI model group under the premise that the wireless link data meets the bandwidth.

[0046] In another implementation example, when the first information includes local computing power status information of the first communication device, the complexity requirement of the AI ​​model's model processing may be related to the local computing power status of the first communication device. Therefore, for the second communication device, the first AI model group determined by the second communication device based on the local computing power status information can be adapted to the local computing power status of the first communication device to provide a first AI model that meets the local computing power status, thereby improving the success rate of the first communication device's model processing based on the first AI model.

[0047] In another implementation example, when the first information includes channel state information, since the channel state information can reflect the channel characteristics of the wireless channel between the first communication device and the second communication device, for the second communication device, the second communication device determines a first AI model group based on the channel state information to adapt to the channel characteristics, so that the transmission data of the wireless link meets the channel bandwidth requirements as much as possible, in order to improve the transmission performance of the AI ​​data corresponding to the AI ​​model.

[0048] It can be understood that when the first information includes two or more of the above-mentioned information, based on the technical gain brought by any one of the above-mentioned items, further superposition gain can be obtained through the two or more items of information.

[0049] In a possible implementation of the first aspect or the second aspect, the first information is used to determine a first AI model group, including: the first information is used to update the second AI model group to obtain the first AI model group; the second AI model group includes a third AI model and a fourth AI model, the third AI model is deployed on the first communication device, and the fourth AI model is deployed on the second communication device, the input of the third AI model includes the output of the fourth AI model, or the input of the fourth AI includes the output of the third AI model.

[0050] Optionally, update can be replaced by other terms such as modification, iteration, optimization, processing, etc.

[0051] Optionally, when the second AI model group is regarded as one AI model, the third AI model and the fourth AI model can be understood as two AI sub-models in the one AI model.

[0052] Based on the above technical solution, for the second communication device, after receiving the first information, the second communication device can update the second AI model group based on the first information to obtain the first AI model group. In other words, the first information sent by the first communication device can be used to update other AI models, making the solution applicable to AI model update scenarios.

[0053] Optionally, the third AI model and the second AI model included in the second AI model group may be general models or dedicated models to enable updates of different types of models.

[0054] It should be understood that the general model can be called the basic model, large model or L0 model, and the specialized model can be called the small model, L1 model, L2 model, etc.

[0055] Taking large models as an example, large models can refer to machine learning models with a large number of parameters and complex structures, which can process massive data and complete various complex tasks, such as natural language processing, computer vision, speech recognition, etc.

[0056] Alternatively, large models are typically built from deep neural networks, with billions or even hundreds of billions of parameters.

[0057] Optionally, the purpose of designing a large model can be to improve the model's expressiveness and predictive performance, and to be able to handle more complex tasks and data.

[0058] Alternatively, large models can learn complex patterns and features by training on massive amounts of data, have stronger generalization capabilities, and can make accurate predictions on unprocessed data.

[0059] In contrast, a small model can refer to a model with fewer parameters and fewer layers. Generally speaking, compared to small models, large models usually have more parameters and deeper layers, and have stronger expressive power and higher accuracy, but also require more computing resources and time for training and inference. They are suitable for scenarios with large data volumes and abundant computing resources, such as cloud computing, high-performance computing, and artificial intelligence.

[0060] Alternatively, small models have the advantages of being lightweight, efficient, and easy to deploy, and are suitable for scenarios with small data volumes and limited computing resources, such as mobile applications, embedded devices, and the Internet of Things.

[0061] In a possible implementation manner of the first aspect or the second aspect, the first information is information sent periodically, and / or the second information is information sent periodically.

[0062] Based on the above technical solution, the first information can be one of the bases for determining the AI ​​model, and the second information can deploy the AI ​​model. The first communication device and the second communication device can periodically send the first information and / or the second information to achieve periodic determination and / or periodic deployment of the AI ​​model, so as to achieve multiple iterative updates of the AI ​​model through a periodic process.

[0063] In a possible implementation of the first aspect or the second aspect, the AI ​​model of the first AI model group is a dedicated model.

[0064] Based on the above technical solution, the first communication device can be a terminal device. For this purpose, the AI ​​model deployed on the terminal device can be a dedicated model, and the first information sent by the terminal device can be used to determine the dedicated model. Since different terminal devices may have different end-side characteristics (such as different local data, different local computing power, different channel characteristics, etc.), by deploying a dedicated model in the terminal device, the AI ​​model deployed on the terminal device can be adapted to the end-side characteristics of the terminal device, in order to improve the model processing performance of the AI ​​model.

[0065] The third aspect of the present application provides a communication method, which is performed by a second communication device, which may be a communication device (such as a cloud server or core network device), or the second communication device may be a partial component in the communication device (such as a processor, chip or chip system, etc.), or the second communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the second communication device sends third information, and the third information is used to determine a third AI model group, and the third AI model group includes the fifth AI model and the sixth AI model; wherein the fifth AI model is deployed on the first communication device, and the sixth AI model is deployed on the second communication device, and the input of the fifth AI model includes the output of the sixth AI model, or the input of the sixth AI includes the output of the fifth AI model; the second communication device receives fourth information from the third communication device, and the fourth information includes the model parameters of the third AI model group.

[0066] Based on the above technical solution, the third communication device, as the recipient of the third information, can determine the third AI model group based on the third information from the second communication device, and through the fourth information, enable the second communication device to subsequently deploy the fifth AI model in the first communication device and the sixth AI model in the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, while enabling the computing power of the communication device to be applied to the processing of the AI ​​model, the third information from the second communication device can also serve as one of the bases for the third communication device to determine the AI ​​model, so that the AI ​​model determined by the third communication device can be adapted to the second communication device as much as possible, thereby improving the success rate of the subsequent AI model processing by the second communication device.

[0067] It should be understood that the third AI model group includes the fifth AI model and the sixth AI model. It can be understood that the functions of the third AI model group are implemented at least through the model processing of the fifth AI model and the model processing of the sixth AI model. In other words, after the second communication device receives the fourth information, the second communication device can determine the model parameters of the fifth AI model and the model parameters of the sixth AI model, and the second communication device can deploy the fifth AI model in the first communication device and deploy the sixth AI model in the second communication device to implement model processing of the fifth AI model and the sixth AI model. Optionally, the model processing can include one or more of model update processing, model training processing, and model inference processing.

[0068] Optionally, when the third AI model group is regarded as one AI model, the fifth AI model and the sixth AI model can be understood as two AI sub-models in the one AI model.

[0069] It should be noted that the second communication device and the third communication device can be implemented in multiple ways, wherein the second communication device can be a terminal device or an access network device, and the third communication device can be a cloud server or a core network device. For example, when the third communication device is a cloud server, the second communication device can communicate with the cloud server through the core network device. For another example, when the third communication device is a core network device, the second communication device can be a terminal device, and the terminal device can communicate with the core network device through the access network device. For another example, when the third communication device is a core network device, the second communication device can be an access network device, and the access network device can communicate through the communication interface between the access network device and the core network device.

[0070] The fourth aspect of the present application provides a communication method, which is performed by a third communication device, which may be a network device (such as an access network device, a core network device, a cloud server, etc.), or the third communication device may be a partial component in the network device (such as a processor, a chip or a chip system, etc.), or the third communication device may also be a logic module or software that can implement all or part of the network device functions. In this method, the third communication device receives third information; the third communication device determines a third AI model group based on the third information, and the third AI model group includes the fifth AI model and the sixth AI model; wherein the fifth AI model is deployed on the first communication device and the sixth AI model is deployed on the second communication device; the input of the fifth AI model includes the output of the sixth AI model, or the input of the sixth AI model includes the output of the fifth AI model; the third communication device sends fourth information, and the fourth information includes the model parameters of the third AI model group.

[0071] Based on the above technical solution, after the third communication device receives the third information for determining the third AI model group, the third communication device can send fourth information, which includes the model parameters of the third AI model group. In other words, as the recipient of the third information, the third communication device can determine the third AI model group based on the third information from the second communication device, and through the fourth information, enable the subsequent second communication device to deploy the fifth AI model in the first communication device and the sixth AI model in the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, while enabling the computing power of the communication device to be applied to the processing of the AI ​​model, the third information from the second communication device can also be used as one of the determination bases for the third communication device to determine the AI ​​model, so that the AI ​​model determined by the third communication device can be adapted to the second communication device as much as possible, thereby improving the success rate of the subsequent model processing of the AI ​​model by the second communication device.

[0072] In a possible implementation of the third aspect or the fourth aspect, the third communication device is a functional entity that determines the third AI model group based on the third information.

[0073] Based on the above technical solution, the third communication device can communicate with one or more second communication devices, and the third communication device can receive information (e.g., one or more third information) from the one or more second communication devices to generate / obtain / determine the third AI model group. In other words, the second communication device can perform information collection and perform model generation based on the collected information, and subsequently the second communication device can deploy the AI ​​model on one or more second communication devices (and the corresponding first communication device).

[0074] Optionally, the AI ​​model of the third AI model group is a universal model. In this way, the third communication device can determine the universal model through information from the one or more second communication devices (e.g., one or more third information), and subsequently multiple second communication devices and the first communication devices connected to each second communication device can deploy a universal model with high generalization and good universality.

[0075] In a possible implementation of the third or fourth aspect, the third information includes third dimensional information or fourth dimensional information; when the input of the fifth AI model includes the output of the sixth AI model, the third dimensional information is used to determine the dimensional information of the input data of the fifth AI model or the dimensional information of the output data of the sixth AI model; when the input of the sixth AI model includes the output of the fifth AI model, the fourth dimensional information is used to determine the dimensional information of the output data of the fifth AI model or the dimensional information of the input data of the sixth AI model.

[0076] Based on the above technical solution, the third communication device can determine the dimension information of the data transmitted on the communication link between the first communication device and the second communication device through the third information. When the communication bandwidth between the first communication device and the second communication device is constant, since the dimension of the data transmitted on the communication link is related to the processing performance of the AI ​​model, this method can simplify the implementation process of the third communication device determining the first AI model group, reducing the complexity of the third communication device while also improving the processing performance of the AI ​​models included in the first AI model group.

[0077] In a possible implementation of the third aspect or the fourth aspect, the dimension information includes at least one of the following: an upper limit value of the dimension, a lower limit value of the dimension, the dimension expected by the second communication device (or the dimension not expected by the first communication device), and a value range of the dimension expected by the second communication device (or the value range of the dimension not expected by the first communication device).

[0078] Optionally, the dimensional information may include the value of at least one of the above items, or the quantized value of the value of at least one of the above items, or the index of the value of at least one of the above items, the index of the quantized value of the value of at least one of the above items, etc., or may be implemented in other ways, which are not limited here.

[0079] Based on the above technical solution, the dimensional information determined by the third dimensional information or the fourth dimensional information may include at least one of the above items to enhance the flexibility of the solution implementation.

[0080] For example, when the above-mentioned dimensional information includes the upper limit value and / or the lower limit value of the dimension, the third communication device can use the range indicated by the upper limit value and / or the lower limit value as one of the bases for determining the AI ​​model, which can improve the flexibility of the implementation of the solution.

[0081] For example, when the above-mentioned dimensional information includes the dimension expected by the second communication device and / or the value range of the dimension expected by the second communication device, the AI ​​model determined by the third communication device based on the dimensional information can meet the expectations of the second communication device.

[0082] In a possible implementation manner of the third aspect or the fourth aspect, the third dimension information or the fourth dimension information is determined based on channel state information.

[0083] Based on the above technical solution, the third dimension information or fourth dimension information included in the third information can be determined based on the channel state information, so that the third dimension information or fourth dimension information can reflect the channel characteristics of the wireless channel between the first communication device and the second communication device to a certain extent, so that the AI ​​model that can be subsequently obtained based on the third information can be adapted to the channel characteristics of the wireless channel, in order to improve the transmission performance of the AI ​​data corresponding to the AI ​​model. Moreover, when the AI ​​model obtained based on the third information is able to adapt to the channel characteristics of the wireless channel, the transmission data of the wireless link can also meet the channel bandwidth requirements as much as possible, thereby improving the model performance of the AI ​​models included in the third AI model group.

[0084] In a possible implementation of the third aspect or the fourth aspect, the third information includes at least one of the following: model parameters of the AI ​​model in one or more AI model groups; wherein each AI model group in the one or more AI model groups includes a dedicated model deployed on the first communication device and a dedicated model deployed on the second communication device; data from one or more first communication devices connected to the second communication device; input data of the AI ​​model deployed on the second communication device and label data of the input data of the AI ​​model deployed on the second communication device.

[0085] Based on the above technical solution, the third information can be implemented through at least one of the above items to improve the flexibility of the solution implementation.

[0086] In an implementation example, when the third information includes model parameters of AI models in one or more AI model groups, the third communication device can obtain one or more dedicated models deployed in the first communication device and the second communication device based on the third information. In this way, the third communication device can obtain the model characteristics of the one or more dedicated models and embody the obtained characteristics in the third AI model group to improve the generalization (or universality) of the general model contained in the third AI model group.

[0087] In another implementation example, when the third information includes data from one or more first communication devices (such as terminal devices) connected to the second communication device, since different terminal devices may have different data characteristics (for example, different data may be collected at different geographical locations, different data may be collected at different times, different data may correspond to different wireless channels where users are located, etc.), in this way, the third communication device obtains a third AI model group based on these data characteristics to improve the generalization (or universality) of the general model contained in the third AI model group.

[0088] In another implementation example, when the third information includes the input data of the AI ​​model deployed on the second communication device and the label data of the input data of the AI ​​model deployed on the second communication device, since the input data can be used as the input of the sixth AI model, the label data can be used as one of the bases for determining the model processing performance of the sixth AI model. Therefore, for the third communication device, the third communication device can obtain an AI model with better performance based on these two pieces of information.

[0089] In addition, for the third communication device, the third communication device can perform mathematical calculations based on mutual information based on these two information and the AI ​​data sent and received by the second communication device on the wireless link (for example, the input data of the sixth AI model or the output data of the sixth AI model, etc.), and determine the third AI model group based on the result of the mathematical calculation, so as to improve the model performance of the AI ​​models included in the third AI model group under the premise that the wireless link data meets the bandwidth.

[0090] It can be understood that when the third information includes two or more of the above-mentioned information, based on the technical gain brought by any one of the above-mentioned items, further superposition gain can be obtained through the two or more items of information.

[0091] Optionally, each item of information included in the third information may be partial information obtained by the second communication device through screening from multiple copies.

[0092] In a possible implementation of the third aspect or the fourth aspect, the third information is used to determine a third AI model group, including: the third information is used to update the fourth AI model group to obtain the third AI model group; the fourth AI model group includes a seventh AI model and an eighth AI model, the seventh AI model is deployed on the first communication device, and the eighth AI model is deployed on the second communication device, the input of the seventh AI model includes the output of the eighth AI model, or the input of the eighth AI includes the output of the seventh AI model.

[0093] Optionally, when the fourth AI model group is regarded as one AI model, the seventh AI model and the eighth AI model can be understood as two AI sub-models in the one AI model.

[0094] Based on the above technical solution, after receiving the third information, the third communication device can update the fourth AI model group based on the third information to obtain the third AI model group. In other words, the third information sent by the second communication device can be used to update other AI models, making the solution applicable to AI model update scenarios.

[0095] In a possible implementation manner of the third aspect or the fourth aspect, the third information is information sent periodically, and / or the fourth information is information sent periodically.

[0096] Based on the above technical solution, the third information can be one of the bases for determining the AI ​​model, and the fourth information can deploy the AI ​​model. The second communication device and the third communication device can periodically send the third information and / or fourth information to achieve periodic determination and / or periodic deployment of the AI ​​model, so as to achieve multiple iterative updates of the AI ​​model through a periodic process.

[0097] In a fifth aspect, the present application provides a communication device, which is a first communication device and includes a transceiver unit and a processing unit; the processing unit is used to determine first information; the transceiver unit is used to send first information, and the first information is used to determine a first artificial intelligence (AI) model group, and the first AI model group includes the first AI model and the second AI model; wherein the first AI model is deployed on the first communication device and the second AI model is deployed on the second communication device; the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; the transceiver unit is also used to receive second information from the second communication device, and the second information includes model parameters of the first AI model group or model parameters of the first AI model.

[0098] 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.

[0099] In a sixth aspect, the present application provides a communication device, which is a second communication device, and includes a transceiver unit and a processing unit. The transceiver unit receives first information; the processing unit is used to determine a first AI model group based on the first information, and the first AI model group includes the first AI model and the second AI model; wherein the first AI model is deployed in the first communication device, and the second AI model is deployed in the second communication device, and the input of the first AI model includes the output of the second AI model, or the input of the second AI includes the output of the first AI model; the transceiver unit is also used to send second information, and the second information includes the model parameters of the first AI model group or the model parameters of the first AI model.

[0100] 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.

[0101] In a seventh aspect, the present application provides a communication device, which is a second communication device and includes a transceiver unit and a processing unit; the processing unit is used to determine third information; the transceiver unit is used to send third information, and the third information is used to determine a third AI model group, and the third AI model group includes the fifth AI model and the sixth AI model; wherein the fifth AI model is deployed on the first communication device, and the sixth AI model is deployed on the second communication device, and the input of the fifth AI model includes the output of the sixth AI model, or the input of the sixth AI includes the output of the fifth AI model; the transceiver unit is also used to receive fourth information from the third communication device, and the fourth information includes the model parameters of the third AI model group.

[0102] 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.

[0103] In an eighth aspect, the present application provides a communication device, which is a third communication device, and includes a transceiver unit and a processing unit. The transceiver unit is used to receive third information; the processing unit is used to determine a third AI model group based on the third information, and the third AI model group includes the fifth AI model and the sixth AI model; wherein the fifth AI model is deployed on the first communication device and the sixth AI model is deployed on the second communication device; the input of the fifth AI model includes the output of the sixth AI model, or the input of the sixth AI model includes the output of the fifth AI model; the transceiver unit is also used to send fourth information, and the fourth information includes model parameters of the third AI model group.

[0104] 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.

[0105] 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.

[0106] In a possible implementation, the communication device further includes a memory. Optionally, the processor and the memory are integrated together.

[0107] 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.

[0108] In an eleventh aspect of the present application, a communication system is provided, comprising the first communication device and the second communication device. Alternatively, the communication system comprises the second communication device and the third communication device. Alternatively, the communication system comprises the first communication device, the second communication device, and the third communication device.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Among them, the technical effects brought about by any design method in the third to tenth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to fourth aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figures 1a and 1b are schematic diagrams of a communication system provided by this application;

[0115] Figures 2a to 2g are schematic diagrams of the AI ​​processing process involved in this application;

[0116] FIG3 is an interactive schematic diagram of the communication method provided by this application;

[0117] Figures 4 to 6 are interactive schematic diagrams of the communication method provided by this application;

[0118] 7 to 11 are schematic diagrams of the communication device provided in this application. DETAILED DESCRIPTION

[0119] First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0120] (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.

[0121] 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.

[0122] 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.

[0123] The terminal can also be a drone, a robot, a terminal in device-to-device communication (D2D), 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 a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc.

[0124] 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.

[0125] 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.

[0126] (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.

[0127] Optionally, the 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 V2X technology can be a road side unit (RSU).

[0128] 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).

[0129] 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.

[0130] 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.

[0131] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

[0132] Table 1

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] (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.

[0138] Furthermore, these values ​​and parameters can be changed or updated.

[0139] (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.

[0140] (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.

[0141] 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.

[0142] 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.

[0143] (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.

[0144] 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.

[0145] 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.

[0146] Please refer to Figure 1a, which is a schematic diagram of the architecture of a communication system 1000 used in an embodiment of the present application. As shown in Figure 1a, the communication system includes a RAN 100 and a core network 200. Optionally, the communication system 1000 may also include the Internet 300. The RAN 100 includes at least one RAN node (such as 110a and 110b in Figure 1a, collectively referred to as 110) and may also include at least one terminal (such as 120a-120j in Figure 1a, collectively referred to as 120). The RAN 100 may also include other RAN nodes, such as wireless relay devices and / or wireless backhaul devices (not shown in Figure 1a). The terminal 120 is wirelessly connected to the RAN node 110, and the RAN node 110 is wirelessly or wiredly connected to the core network 200. The core network devices in the core network 200 and the RAN node 110 in the RAN 100 may be independent and different physical devices, or they may be the same physical device that integrates the logical functions of the core network devices and the logical functions of the RAN nodes. Terminals and RAN nodes may be connected to each other via wired or wireless means.

[0147] The RAN 100 may be an evolved universal terrestrial radio access (E-UTRA) system, a NR system, or a future radio access system defined in the 3rd Generation Partnership Project (3GPP). The RAN 100 may also include two or more of the aforementioned different radio access systems. The RAN 100 may also be an open RAN (O-RAN).

[0148] For ease of description, a base station is taken as an example of a RAN node for description below.

[0149] Base stations and terminals can be fixed or mobile. They can be deployed on land, indoors or outdoors, handheld or vehicle-mounted; on water; or on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of base stations and terminals.

[0150] The roles of base stations and terminals can be relative. For example, the helicopter or drone 120i in Figure 1a can be configured as a mobile base station. To terminals 120j accessing the wireless access network 100 via 120i, terminal 120i is a base station. However, to base station 110a, 120i is a terminal, meaning that communication between 110a and 120i occurs via a wireless air interface protocol. Of course, communication between 110a and 120i can also occur via a base station-to-base station interface protocol. In this case, 120i is also a base station relative to 110a. Therefore, base stations and terminals can be collectively referred to as communication devices. 110a and 110b in Figure 1a can be referred to as communication devices with base station functionality, while 120a-120j in Figure 1a can be referred to as communication devices with terminal functionality.

[0151] Communication between base stations and terminals, between base stations, and between terminals can be carried out through authorized spectrum, unauthorized spectrum, or both; communication can be carried out through spectrum below 6 gigahertz (GHz), spectrum above 6 GHz, or spectrum below 6 GHz and spectrum above 6 GHz. The embodiments of the present application do not limit the spectrum resources used for wireless communication.

[0152] In the embodiments of the present application, the functions of the base station may also be performed by a module (such as a chip) in the base station, or by a control subsystem that includes the base station functions. The control subsystem that includes the base station functions here may be a control center in the above-mentioned application scenarios such as smart grid, industrial control, smart transportation, and smart city. The functions of the terminal may also be performed by a module (such as a chip or modem) in the terminal, or by a device that includes the terminal functions.

[0153] Figure 1b is another schematic diagram of a communication system provided by an embodiment of the present application. In Figure 1b, the network device is a base station as an example for illustration, and device 1 and device 2 are both terminal devices. As shown in Figure 1b, the communication link between device 1 and device 2 can be called a sidelink (SL), and the communication link between device 1 (or device 2) and the base station can be called an uplink and a downlink, including an uplink and a downlink. It can be seen that a sidelink is a communication mechanism that allows different terminal devices to communicate directly without going through a network device.

[0154] Optionally, in the sidelink (SL), generally speaking, the transmitting device and the receiving device can be a terminal device or network device of the same type, or a road side unit (RSU) and a terminal device, wherein the RSU is a road side station or road side unit from a physical entity point of view, and from a functional point of view, the RSU can be a terminal device or a network device, and this application does not impose any restrictions on this. That is, the transmitting device is a terminal device and the receiving device is also a terminal device; or, the transmitting device is a road side station and the receiving device is also a terminal device; or, the transmitting device is a terminal device and the receiving device is also a road side station. In addition, the sidelink can also be a base station device of the same type or different types. At this time, the function of the sidelink is similar to that of the relay link, but the air interface technology used can be the same or different.

[0155] Exemplarily, the sidelink supports broadcast, unicast, and multicast.

[0156] When a terminal device (eg, device 1 ) communicates directly with another terminal device (eg, device 2 ) without going through a network device, the two terminal devices may communicate based on a proximity-based services communication 5 (PC5) port.

[0157] A typical application of sidelink is V2X communication, which utilizes and enhances current cellular network functions and elements to achieve low-latency and high-reliability communication between various nodes in the vehicle network, including vehicle-to-vehicle (V2V) communication, vehicle-to-pedestrian (V2P) communication, vehicle-to-infrastructure (V2I) communication, and vehicle-to-network (V2N) communication.

[0158] The technical solution provided in this application can be applied to a wireless communication system (e.g., the system shown in FIG. 1a or FIG. 1b ). 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: a terminal device, 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.

[0159] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0160] 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).

[0161] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 i Weighted. 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The following is an exemplary description of the implementation process of the neural network with reference to the accompanying drawings.

[0176] 1. Fully connected neural network, also known as multilayer perceptron (MLP).

[0177] 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.

[0178] 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:

[0179] h=f(wx+b).

[0180] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0181] Alternatively, the output of the neural network can be recursively expressed as:

[0182] y=f n (w n f n-1 (…)+b n ).

[0183] Where n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0184] 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.

[0185] Optionally, a specific training method is to use a loss function to evaluate the output results of the neural network.

[0186] 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.

[0187] Alternatively, the gradient descent process can be expressed as:

[0188] 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.

[0189] Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

[0190] 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:

[0191] 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.

[0192] 2. Federated Learning (FL)

[0193] 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.

[0194] As shown in Figure 2f, the FL architecture is a training architecture currently used in the FL field. For example, the FedAvg algorithm is the basic algorithm of FL, and its algorithm flow is roughly as follows:

[0195] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0196] (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.

[0197] (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.

[0198] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0199] 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.

[0200] 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.

[0201] 3. Decentralized learning: Different from federated learning, decentralized learning is another distributed learning architecture.

[0202] 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:

[0203] 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.

[0204] The technical solutions provided in this application can be applied to wireless communication systems (e.g., the systems shown in Figures 1a and 1b). 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.

[0205] In communication networks, communication nodes may have excess computing power beyond supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue.

[0206] In one possible implementation, a communication node can act as a participating node in an AI learning system, applying its computing power to a specific component of the system. With the advent of the era of large models, deep learning models with massive parameters, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT), can accomplish increasingly complex tasks and achieve superior performance. However, for large models, even the inference process is limited by device capacity, so large models are typically stored on central cloud servers. Furthermore, each device in the network generates a massive amount of raw data daily, which requires multiple inference calls on the large model. Typically, a device (such as a communication node) sends data to a central server, which then performs inference using the data and returns the inference results to the device. This process consumes significant communication resources for data transmission and also risks the privacy of device data.

[0207] To better reduce communication overhead and protect user data privacy, researchers have proposed distributed inference technology for deep neural networks. This approach involves distributing models to devices and leveraging their local computing power to infer the models, thereby reducing communication overhead and ensuring data privacy. However, in communication systems, how to determine the AI ​​models used by communication nodes (for example, how to generate and update them) remains a topic of debate.

[0208] 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.

[0209] It should be noted that, in Figure 3, the first communication device and the second communication device (in Figure 6, the second communication device and the third communication device) are used as the execution subjects of the interaction diagram to illustrate the method, 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, chip system, processor, logic module or software in the communication device. In Figure 3, the first communication device can be a network device and the second communication device can be a terminal device, or the first communication device and the second communication device are both terminal devices (for example, the method can be applied to the communication process of different terminal devices in a side link communication scenario).

[0210] S301. A first communication device sends first information, and a second communication device receives the first information. The first information is used to determine a first AI model group, where the first AI model group includes the first AI model and a second AI model; the first AI model is deployed on the first communication device, and the second AI model is deployed on the second communication device; the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model.

[0211] S302. The second communication device sends second information, and the first communication device receives the second information accordingly. The second information includes model parameters of the first AI model group or model parameters of the first AI model.

[0212] 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.

[0213] It should be understood that the first information sent by the first communication device in step S301 is used to determine the first AI model group. The first AI model group includes a first AI model and a second AI model. It can be understood that the function of the first AI model group is implemented at least through the model processing of the first AI model and the model processing of the second AI model. In other words, after the first communication device receives the second information, the first communication device can deploy the first AI model on the first communication device through the model parameters of the first AI model group or the model parameters of the first AI model contained in the second information, and perform model processing on the first AI model; accordingly, the second communication device can perform model processing on the second AI model deployed on the second communication device. Optionally, the model processing may include one or more of model update processing, model training processing, and model inference processing.

[0214] It should be understood that wireless communication signals (such as the transmission and reception of configuration information of communication resources, the transmission and reception of reference signals, etc.) can be transmitted between different communication devices (such as the first communication device and the second communication device).

[0215] Optionally, the AI ​​model involved in this application (such as the first AI model, the second AI model, the third to sixth AI models mentioned below, etc.) can be used to manage the wireless communication signal (including at least one of 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, or one or more AI models for replacing one or more modules in a transmitter and / or receiver. Alternatively, the AI ​​model involved in this 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.

[0216] Optionally, when the first AI model group is regarded as one AI model, the first AI model and the second AI model can be understood as two AI sub-models in the one AI model.

[0217] In this application, an AI model is deployed on a communication device (for example, a first AI model is deployed on a first communication device, a second AI model is deployed on a second communication device, etc.). It can be understood that after the communication device obtains the model parameters of the AI ​​model, it obtains / generates / constructs the AI ​​model based on the model parameters of the AI ​​model, and subsequently the communication device can perform model processing on the AI ​​model.

[0218] Optionally, the model parameters may include one or more of the model's hyperparameters, the model's dataset (including the model's input data and label data corresponding to the input data), and the model's structural parameters.

[0219] Optionally, an AI model group may include two or more AI models. For example, in addition to the first AI model and the second AI model, the first AI model group may also include other AI models. The other AI models can be deployed on other communication devices different from the first communication device and the second communication device. This is not limited here.

[0220] In one possible implementation, the second communication device is a functional entity that determines an AI model group list based on the first information, and the AI ​​model group list includes one or more AI model groups, and the one or more AI model groups include the first AI model group. Specifically, the second communication device can communicate with one or more first communication devices, and the second communication device can receive information (e.g., one or more first information) from the one or more first communication devices to generate / obtain / determine one or more AI model groups. In other words, the second communication device can perform information collection and perform model generation based on the collected information, and subsequently the second communication device can deploy the AI ​​model on one or more first communication devices.

[0221] Optionally, the AI ​​model group list may include one or more AI model groups, and each AI model group may include two or more AI models. As mentioned above, the relationship between an AI model group and an AI model can also be understood as the relationship between an AI model and an AI sub-model. To this end, the AI ​​model group list may also be replaced by an AI model list, that is, the AI ​​model list may include one or more AI models.

[0222] Optionally, list can be replaced with other terms such as set, dictionary, combination, space, etc.

[0223] Optionally, the second communication device is a functional entity that determines the AI ​​model group list based on the first information, including: the second communication device is a functional entity that determines the AI ​​model group list based on the first information, and selects some or all of the AI ​​model groups used by the first communication device in the AI ​​model group list. Specifically, after the second communication device determines the AI ​​model group list based on the first information, the function implemented by the second communication device may also include selecting some or all of the AI ​​model groups used by the first communication device in the AI ​​model group list. In other words, in addition to performing information collection and performing model generation based on the collected information, the second communication device can also perform model selection so that the second communication device can subsequently deploy an AI model that is compatible with the one or more first communication devices in one or more first communication devices.

[0224] For ease of understanding, the AI ​​models deployed by the first communication device and the second communication device will be described below through the examples shown in Figures 4 and 5.

[0225] In the example shown in Figure 4, the first AI model is deployed on the first communication device, and the second AI model is deployed on the second communication device. In addition, the input of the first AI model deployed on the first communication device includes the output of the second AI model deployed on the second communication device. In this example, taking the input data of the second AI model as X, after processing by the second AI model, the second communication device can obtain and send data Z; after transmission through the wireless channel, the data received by the first communication device is represented as (It is understandable that due to the transmission loss and noise interference on the wireless channel, and Z may not be the same, It can be understood as an estimate of Z or a measured value of Z, etc.). Thereafter, the first communication device can As the input of the first AI model, the data is processed by the first AI model

[0226] As shown in Figure 5, the first AI model is deployed on the first communication device, the second AI model is deployed on the second communication device, and the input of the second AI model includes the output of the first AI model. In this example, taking the input data of the first AI model as X, after processing by the first AI model, the first communication device can obtain and send data Z; after transmission through the wireless channel, the data received by the second communication device is represented as Afterwards, the second communication device can As the input of the second AI model, the data is processed by the second AI model

[0227] It can be understood that the first communication device and the second communication device can be implemented in many ways.

[0228] For example, the second communication device may be a terminal device, and accordingly, the first communication device and the second communication device may communicate on a sidelink (SL). In this case, the first AI model and the second AI model may be referred to as an end-to-end model, or an end-to-end collaborative model, etc.

[0229] For another example, the second communication device may be a network device (such as an access network device), and accordingly, the first communication device and the second communication device may communicate on the uplink and downlink communication links. In this case, the first AI model and the second AI model may be referred to as an edge-end model, an edge-end collaboration model, an end-edge model, an end-edge collaboration model, and the like. Exemplarily, when the first communication device is a terminal device and the second communication device is an access network device, the scenario shown in FIG4 can be understood as end-edge collaboration based on a downlink scenario, and the scenario shown in FIG5 can be understood as end-edge collaboration based on an uplink scenario.

[0230] It should be noted that in Figures 4 and 5, data Y may be the label data corresponding to data X, and the label data Y and the processing results of the first AI model and the second AI model are The correlation relationship between the first and second AI models can be used to detect or determine the processing performance of the first AI model and the second AI model. For example, the correlation relationship can be determined by gradient information, loss function, etc.

[0231] As can be seen from the implementation process shown in Figure 3, for the second communication device, after receiving the first information in step S301, the second communication device can perform a model generation process based on the first information to obtain a first AI model group. The following is an exemplary description of the process by which the second communication device determines the first AI model group based on the first information.

[0232] First, the theoretical basis for the generation of the AI ​​model is described through the implementation process of the following method A.

[0233] Generally speaking, in networks oriented towards traditional connections or sessions, the design goal between different communication devices (i.e., transceivers) is to enable the receiver to accurately reply to all data sent by the transmitter, that is, to pursue lossless transmission of data. However, when facing future intelligent networks, due to the existence of massive data and the different purposes of different AI tasks, it may no longer be necessary to transmit all data, but rather to transmit data that is valuable to the AI ​​task. Therefore, it is possible for the network to optimize the performance of the AI ​​model (e.g., maximizing the accuracy of the AI ​​model) by transmitting the minimum amount of wireless data. To achieve this goal, in the examples shown in Figures 4 and 5, the performance of the AI ​​model can be characterized based on the mutual information between different data.

[0234] As an implementation example, in FIG4 and FIG5, the input data X of the AI ​​model, the label data Y corresponding to the input data X, and the data received by the receiving party (for example, the first communication device in FIG4 or the second communication device in FIG5) are Satisfaction method A:

[0235] Where I(a;b) represents the mutual information between variables a and b. Specifically, Represents data transmitted over a wireless channel The mutual information between the label data Y (label data Y can be understood as the correct result / expected result of the AI ​​task) and the label data Y (label data Y can be understood as the correct result / expected result of the AI ​​task). The larger the value of the mutual information, the better the data transmitted through the wireless channel. The more information of the label data Y contained in , the higher the accuracy of the AI ​​model can be understood as, that is, the better the model performance of the AI ​​model. Represents the original input data X and wireless link data The mutual information between them is smaller, which means the amount of data transmitted in the wireless link is smaller, that is, the wireless communication overhead is smaller. The configurable parameter β (β can be in the range of [0, 1]) can control the ratio between the two mutual information. Therefore, minimizing the above formula This means: while ensuring that wireless communication overhead is as low as possible, ensure that the correctness of the AI ​​model's processing results is maximized. The subscript "IB" in stands for information bottleneck (IB) theory (or distributed information bottleneck, deterministic information bottleneck, other information theories, etc.). In other words, Other symbols can be used instead. This is just an implementation example.

[0236] Exemplarily, based on the implementation of method A, during the AI ​​model generation process, the model generation basis may include one or more of the following data A to data C, that is, an AI model group can be generated based on the following data A to data C (for example, the second communication device can generate the first AI model group). The data A to data C are described exemplarily below.

[0237] Data AM for data and labels As input data (where M is the batch size, x m Represents the input data of the mth pair of data, y m represents the label data of the mth pair of data).

[0238] Data B. Dimensions of data transmitted in a wireless link (e.g., in FIG. 4 or FIG. 5 ) Or the dimension of Z). Exemplarily, the dimension of the data can be expressed as the number of tokens / words / tags (hereinafter uniformly expressed as tokens), or the dimension of the data can be expressed as the dimension of the embedding vector. It can be understood that Token: a concept similar to "word", can be understood as the basic unit for splitting intermediate transmission volume; Embedding: a vector representation of intermediate transmission volume. A token can be understood as each component unit in the Embedding vector, that is, the number of components in the Embedding can be the dimension of the token.

[0239] Data C. Channel state information.

[0240] Optionally, the process of generating an AI model group based on one or more items of data A to data C can be implemented by a neural network. That is, the input data of the neural network includes one or more items of data A to data C, and after processing by the neural network, the model parameters of the AI ​​model group are obtained.

[0241] As an implementation example, the loss function of the neural network can be expressed as method B:

[0242] As another implementation example, in order to reduce To solve the complexity of , we can introduce variational processing to calculate the mutual information. For example, the loss function of the neural network can be expressed as C:

[0243] The subscript “VIB” stands for variational information bottleneck;

[0244] φ: neural network or model parameters configured on the user side (e.g., the first communication device);

[0245] θ: neural network or model parameters deployed on the base station side (e.g., the second communication device);

[0246] β: Lagrange multiplier, which balances the accuracy of AI processing results and wireless communication overhead;

[0247] P: Given x, The conditional probability density of ;

[0248] q represents The variational probability density of ;

[0249] p and q actually represent different probability density functions.

[0250] Given a known probability density function p(x,y), find the expected / mean value of the part in {·}.

[0251] Represents a known probability density function Under the premise of , find the expectation / mean of the part in {·}.

[0252] Represents a conditional probability distribution with parameter θ, which can approximate the conditional probability The variational distribution form of .

[0253] represents a conditional probability distribution with parameter φ.

[0254] Represents an approximate probability distribution The variational distribution form of .

[0255] Represents a probability distribution and probability distribution The Coulomb-Leibler (KL) divergence between .

[0256] As another implementation example, under the premise of constraining the tokens / embedding dimension according to the wireless link bandwidth, the loss function of the neural network can be expressed as form D:

[0257] in, The constraints are Th_1 represents the maximum threshold of wireless link bandwidth, and the constraint condition The physical meaning of D is that the amount of data transmitted over the wireless link satisfies the air interface bandwidth constraint. In other words, using method D, we can maximize the accuracy of AI tasks while ensuring that information transmitted over the wireless channel satisfies the bandwidth constraint.

[0258] As another implementation example, under the premise of constraining the tokens / embedding dimension according to the wireless link bandwidth, the loss function of the neural network can be expressed as E:

[0259] in, The constraints are Th_2 represents the minimum threshold value of AI task accuracy, and the constraint condition The physical meaning of is that the accuracy of the AI ​​task is greater than the minimum threshold. In other words, through this method E, the amount of information transmitted by the wireless channel can be minimized under the premise that the accuracy of the AI ​​task is greater than the minimum threshold.

[0260] Based on the above implementation process, under the premise of bandwidth constraints on the tokens / embedding dimensions, the resulting AI model has high inference accuracy. For example, the simulation results of the above method C based on different datasets are as follows: Tested on the Canadian Institute for Advanced Research (CIFAR) dataset: training cycle (epoch) = 319, intermediate output dimension (intermediate dim) = 20, accuracy (accuracy) = 92.37%.

[0261] Tested on the MNIST (Mixed National Institute of Standards and Technology) dataset: epoch = 400, intermediate dim = 64, accuracy = 97.62%.

[0262] Among them, the intermediate output latitude is the above The latitude is the latitude, and the precision is the accuracy of the AI ​​task.

[0263] As can be seen from the foregoing description, the first information sent by the first communication device in step S301 can be used to determine the first AI model group. Based on the parameters involved in the processes shown in the above-mentioned methods A to E, the first information may include one or more of the following information A to E. In other words, the second communication device can obtain the parameters required by methods A to D through one or more of the information A to E contained in the following first information, and generate the first AI model group according to one of the methods A to E.

[0264] Information A. First dimensional information. When the input of the first AI model includes the output of the second AI model, the first dimensional information is used to determine the dimensional information of the input data of the first AI model or the dimensional information of the output data of the second AI model.

[0265] Information B. Second dimensional information. When the input of the second AI model includes the output of the first AI model, the second dimensional information is used to determine the dimensional information of the output data of the first AI model or the dimensional information of the input data of the second AI model.

[0266] Information C: input data of the first AI model and label data of the input data of the first AI model.

[0267] Information D: local computing power status information of the first communication device.

[0268] Information E. Channel state information.

[0269] For information A or information B, the second communication device can determine the dimensional information of the data transmitted on the communication link through the first information. For example, the dimensional information can be the number of tokens or the dimension of the embedding vector. When the communication bandwidth between the first communication device and the second communication device is constant, since the dimension of the data transmitted on the communication link is related to the processing performance of the AI ​​model, this method can simplify the implementation process of the second communication device determining the first AI model group, reducing the complexity of the second communication device while also improving the processing performance of the AI ​​models included in the first AI model group.

[0270] Optionally, the dimension information includes at least one of the following items: an upper limit value of the dimension, a lower limit value of the dimension, the dimension expected by the first communication device (or the dimension not expected by the first communication device), and a value range of the dimension expected by the first communication device (or the value range of the dimension not expected by the first communication device).

[0271] For example, when the above-mentioned dimensional information includes the upper limit value and / or the lower limit value of the dimension, the second communication device can use the range indicated by the upper limit value and / or the lower limit value as one of the bases for determining the AI ​​model, which can improve the flexibility of the implementation of the solution.

[0272] For example, when the above-mentioned dimensional information includes the dimension expected by the first communication device and / or the value range of the dimension expected by the first communication device, the AI ​​model determined by the second communication device based on the dimensional information can meet the expectations of the first communication device.

[0273] Optionally, in information A or information B, the first dimension information or the second dimension information is determined based on channel state information (CSI). Specifically, the first dimension information or the second dimension information contained in the first information can be determined based on the channel state information, so that the first dimension information or the second dimension information can reflect the channel characteristics of the wireless channel between the first communication device and the second communication device to a certain extent, so that the AI ​​model that can be subsequently obtained based on the first information can be adapted to the channel characteristics of the wireless channel, in order to improve the transmission performance of the AI ​​data corresponding to the AI ​​model. Moreover, when the AI ​​model obtained based on the first information can be adapted to the channel characteristics of the wireless channel, the transmission data of the wireless link can also meet the channel bandwidth requirements as much as possible, thereby improving the model performance of the AI ​​model included in the first AI model group.

[0274] Optionally, the channel state information may include channel information between the first communication device and the second communication device, and / or channel information between the second communication device and the first communication device. Where the first communication device is a terminal device and the second communication device is a network device, the channel information between the first communication device and the second communication device may be understood as uplink channel information, and the channel information between the second communication device and the first communication device may be understood as downlink channel information.

[0275] Optionally, the channel state information may be obtained based on a reference signal.

[0276] For example, in the case where the first communication device and the second communication device communicate through a sidelink, the reference signal may include a sidelink synchronization signal / physical broadcast channel block (sidelink synchronization signal / physical broadcast channel block, sidelink SSB, SL-SSB, or S-SS / PSBCH block), a sidelink channel state information reference signal (sidelink channel state information reference signal, SL-CSI-RS), etc.

[0277] For example, when the first communication device and the second communication device communicate via uplink and downlink, the reference signal may include a channel state information reference signal (CSI-RS), a sounding reference signal (SRS), etc.

[0278] For information C, when the first information includes the input data of the first AI model and the label data of the input data of the first AI model, since the input data can be used as the input of the first AI model, the label data can be used as one of the bases for determining the model processing performance of the first AI model. Therefore, for the second communication device, the second communication device can obtain an AI model with better performance based on these two pieces of information.

[0279] In addition, for the second communication device, the second communication device can perform mathematical calculations based on mutual information based on these two pieces of information and the AI ​​data sent and received by the second communication device on the wireless link (such as the input data of the second AI model or the output data of the second AI model, etc.), and determine the first AI model group based on the result of the mathematical calculation, so as to improve the model performance of the AI ​​models included in the first AI model group under the premise that the wireless link data meets the bandwidth.

[0280] With respect to information D, when the first information includes the local computing power status information of the first communication device, since the complexity requirement of the model processing of the AI ​​model may be related to the local computing power status of the first communication device, for this reason, the first AI model group determined by the second communication device based on the local computing power status information can be adapted to the local computing power status of the first communication device to provide a first AI model that satisfies the local computing power status, thereby improving the success rate of the model processing performed by the first communication device based on the first AI model.

[0281] For information E, when the first information includes channel state information, since the channel state information can reflect the channel characteristics of the wireless channel between the first communication device and the second communication device, for this reason, the second communication device determines a first AI model group based on the channel state information to adapt to the channel characteristics, so that the transmission data of the wireless link meets the channel bandwidth requirements as much as possible, in order to improve the transmission performance of the AI ​​data corresponding to the AI ​​model.

[0282] It can be understood that when the first information includes two or more of the above-mentioned information A to information E, based on the technical gain brought by any one of the above-mentioned items, further superposition gain can be obtained through the two or more items of information.

[0283] In one possible implementation, the first communication device sends first information in step S301, and the first information is used to determine the first AI model group, including: the first information is used to update the second AI model group to obtain the first AI model group; the second AI model group includes a third AI model and a fourth AI model, the third AI model is deployed on the first communication device, and the fourth AI model is deployed on the second communication device, and the input of the third AI model includes the output of the fourth AI model, or the input of the fourth AI includes the output of the third AI model. Specifically, for the second communication device, after receiving the first information, the second communication device can update the second AI model group based on the first information to obtain the first AI model group. In other words, the first information sent by the first communication device can be used to update other AI models, so that the solution can be applicable to AI model update scenarios.

[0284] Optionally, update can be replaced by other terms such as modification, iteration, optimization, processing, etc.

[0285] Optionally, when the second AI model group is regarded as one AI model, the third AI model and the fourth AI model can be understood as two AI sub-models in the one AI model.

[0286] Optionally, the third AI model and the second AI model included in the second AI model group may be general models or dedicated models to enable updates of different types of models.

[0287] It should be understood that the general model can be called the basic model, large model or L0 model, and the specialized model can be called the small model, L1 model, L2 model, etc.

[0288] Taking large models as an example, large models can refer to machine learning models with a large number of parameters and complex structures, which can process massive data and complete various complex tasks, such as natural language processing, computer vision, speech recognition, etc.

[0289] Alternatively, large models are typically built from deep neural networks, with billions or even hundreds of billions of parameters.

[0290] Optionally, the purpose of designing a large model can be to improve the model's expressiveness and predictive performance, and to be able to handle more complex tasks and data.

[0291] Alternatively, large models can learn complex patterns and features by training on massive amounts of data, have stronger generalization capabilities, and can make accurate predictions on unprocessed data.

[0292] In contrast, a small model can refer to a model with fewer parameters and fewer layers. Generally speaking, compared to small models, large models usually have more parameters and deeper layers, and have stronger expressive power and higher accuracy, but also require more computing resources and time for training and inference. They are suitable for scenarios with large data volumes and abundant computing resources, such as cloud computing, high-performance computing, and artificial intelligence.

[0293] Alternatively, small models have the advantages of being lightweight, efficient, and easy to deploy, and are suitable for scenarios with small data volumes and limited computing resources, such as mobile applications, embedded devices, and the Internet of Things.

[0294] In one possible implementation, the first information sent by the first communication device in step S301 is periodically sent information, and / or the second information sent by the second communication device in step S302 is periodically sent information. Specifically, the first information may be one of the bases for determining the AI ​​model, and the second information may be used to deploy the AI ​​model. The periodic transmission of the first information and / or the second information between the first communication device and the second communication device enables periodic determination and / or periodic deployment of the AI ​​model, thereby enabling multiple iterative updates of the AI ​​model through a periodic process.

[0295] In one possible implementation, the first information sent by the first communication device in step S301 is used to determine the first AI model group, and the AI ​​model of the first AI model group is a dedicated model. Specifically, the first communication device can be a terminal device, and for this purpose, the AI ​​model deployed on the terminal device can be a dedicated model, and the first information sent by the terminal device can be used to determine the dedicated model. Since different terminal devices may have different end-side characteristics (such as different local data, different local computing power, different channel characteristics, etc.), by deploying a dedicated model in the terminal device, the AI ​​model deployed on the terminal device can be adapted to the end-side characteristics of the terminal device, in order to improve the model processing performance of the AI ​​model.

[0296] Based on the technical solution shown in Figure 3, after the first communication device sends the first information for determining the first AI model group in step S301, the first communication device can receive the second information in step S302, where the second information includes the model parameters of the first AI model group or the model parameters of the first AI model in the first AI model group. In other words, the second communication device, as the recipient of the first information, can determine the first AI model group based on the first information from the first communication device, and deploy the first AI model to the first communication device through the second information. Thus, when the communication device in the communication system acts as an AI participating node, while enabling the computing power of the communication device to be applied to the processing of the AI ​​model, the first information from the first communication device can also serve as one of the bases for the second communication device to determine the AI ​​model, so that the AI ​​model determined by the second communication device can be adapted to the first communication device as much as possible, thereby improving the processing performance of the subsequent model processing based on the AI ​​model by the first communication device.

[0297] Please refer to FIG6 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0298] S601. The second communication device sends third information, and the third communication device receives the third information accordingly. The third information is used to determine a third AI model group, where the third AI model group includes the fifth AI model and a sixth AI model; the fifth AI model is deployed on the first communication device, the sixth AI model is deployed on the second communication device, and the input of the fifth AI model includes the output of the sixth AI model, or the input of the sixth AI model includes the output of the fifth AI model.

[0299] S602: The third communication device sends fourth information, and the second communication device receives the fourth information accordingly, wherein the fourth information includes model parameters of the third AI model group.

[0300] It should be noted that the second communication device and the third communication device can be implemented in multiple ways, wherein the second communication device can be a terminal device or an access network device, and the third communication device can be a cloud server or a core network device. For example, when the third communication device is a cloud server, the second communication device can communicate with the cloud server through the core network device. For another example, when the third communication device is a core network device, the second communication device can be a terminal device, and the terminal device can communicate with the core network device through the access network device. For another example, when the third communication device is a core network device, the second communication device can be an access network device, and the access network device can communicate through the communication interface between the access network device and the core network device.

[0301] It should be understood that the third AI model group includes the fifth AI model and the sixth AI model. It can be understood that the functions of the third AI model group are implemented at least through the model processing of the fifth AI model and the model processing of the sixth AI model. In other words, after the second communication device receives the fourth information, the second communication device can determine the model parameters of the fifth AI model and the model parameters of the sixth AI model, and the second communication device can deploy the fifth AI model in the first communication device and deploy the sixth AI model in the second communication device to implement model processing of the fifth AI model and the sixth AI model. Optionally, the model processing can include one or more of model update processing, model training processing, and model inference processing.

[0302] Optionally, when the third AI model group is regarded as one AI model, the fifth AI model and the sixth AI model can be understood as two AI sub-models in the one AI model.

[0303] In one possible implementation, the third communication device is a functional entity that determines the third AI model group based on the third information. Specifically, the third communication device can communicate with one or more second communication devices, and the third communication device can receive information (e.g., one or more third information) from the one or more second communication devices to generate / obtain / determine the third AI model group. In other words, the second communication device can perform information collection and perform model generation based on the collected information, and subsequently the second communication device can deploy the AI ​​model on one or more second communication devices (and the corresponding first communication device).

[0304] Optionally, the AI ​​model of the third AI model group is a universal model. In this way, the third communication device can determine the universal model through information from the one or more second communication devices (e.g., one or more third information), and subsequently multiple second communication devices and the first communication devices connected to each second communication device can deploy a universal model with high generalization and good universality.

[0305] As can be seen from the foregoing description, the third information sent by the second communication device in step S601 can be used to determine the third AI model group. Based on the processes shown in the above methods A to E, it can be seen that the third information includes one or more of the following information 1 to 5. In other words, the third communication device can obtain the parameters required by methods A to E through one or more of the information 1 to 5 contained in the third information, and generate the third AI model group according to one of the methods A to E.

[0306] Information 1. Third Dimension Information: When the input of the fifth AI model includes the output of the sixth AI model, the third dimensional information is used to determine the dimensional information of the input data of the fifth AI model or the dimensional information of the output data of the sixth AI model.

[0307] Information 2. Fourth dimensional information. When the input of the sixth AI model includes the output of the fifth AI model, the fourth dimensional information is used to determine the dimensional information of the output data of the fifth AI model or the dimensional information of the input data of the sixth AI model.

[0308] Information 3. Model parameters of the AI ​​models in one or more AI model groups; wherein each AI model group in the one or more AI model groups includes a dedicated model deployed on the first communication device and a dedicated model deployed on the second communication device.

[0309] Information 4. Data from one or more first communication devices to which the second communication device is connected.

[0310] Information 5. Input data of the AI ​​model deployed on the second communication device and label data of the input data of the AI ​​model deployed on the second communication device.

[0311] For information 1 and information 2, the third communication device can determine the dimension information of the data transmitted on the communication link between the first communication device and the second communication device through the third information. For example, the dimension information can be the number of tokens or the dimension of the embedding vector. When the communication bandwidth between the first communication device and the second communication device is constant, since the dimension of the data transmitted on the communication link is related to the processing performance of the AI ​​model, this method can simplify the implementation process of the third communication device determining the first AI model group, reducing the complexity of the third communication device while also improving the processing performance of the AI ​​models included in the first AI model group.

[0312] Optionally, the dimension information includes at least one of the following: an upper limit value of the dimension, a lower limit value of the dimension, a dimension desired by the second communication device (or a dimension not desired by the first communication device), and a value range of the dimension desired by the second communication device (or a value range of the dimension not desired by the first communication device). Specifically, the dimension information determined by the third dimension information or the fourth dimension information may include at least one of the above items to enhance the flexibility of the solution implementation.

[0313] Optionally, the dimensional information may include the value of at least one of the above items, or the quantized value of the value of at least one of the above items, or the index of the value of at least one of the above items, the index of the quantized value of the value of at least one of the above items, etc., or may be implemented in other ways, which are not limited here.

[0314] For example, when the above-mentioned dimensional information includes the upper limit value and / or the lower limit value of the dimension, the third communication device can use the range indicated by the upper limit value and / or the lower limit value as one of the bases for determining the AI ​​model, which can improve the flexibility of the implementation of the solution.

[0315] For example, when the above-mentioned dimensional information includes the dimension expected by the second communication device and / or the value range of the dimension expected by the second communication device, the AI ​​model determined by the third communication device based on the dimensional information can meet the expectations of the second communication device.

[0316] Optionally, the third dimension information or the fourth dimension information is determined based on channel state information. Specifically, the third dimension information or the fourth dimension information contained in the third information can be determined based on the channel state information, so that the third dimension information or the fourth dimension information can reflect the channel characteristics of the wireless channel between the first communication device and the second communication device to a certain extent, so that the AI ​​model that can be subsequently obtained based on the third information can be adapted to the channel characteristics of the wireless channel, in order to improve the transmission performance of the AI ​​data corresponding to the AI ​​model. Moreover, in the case that the AI ​​model obtained based on the first information can be adapted to the channel characteristics of the wireless channel, the transmission data of the wireless link can also meet the channel bandwidth requirements as much as possible, thereby improving the model performance of the AI ​​model included in the first AI model group.

[0317] For information 3, when the third information includes model parameters of AI models in one or more AI model groups, the third communication device can obtain one or more dedicated models deployed in the first communication device and the second communication device based on the third information. In this way, the third communication device can obtain the model characteristics of the one or more dedicated models and embody the obtained characteristics in the third AI model group to improve the generalization (or universality) of the general model contained in the third AI model group.

[0318] For information 4, when the third information includes data from one or more first communication devices (such as terminal devices) connected to the second communication device, since different terminal devices may have different data characteristics (for example, different data may be collected at different geographical locations, different data may be collected at different times, different data may correspond to different wireless channels where users are located, etc.), in this way, the third communication device obtains a third AI model group based on these data characteristics to improve the generalization (or universality) of the general model contained in the third AI model group.

[0319] For information 5, when the third information includes the input data of the AI ​​model deployed on the second communication device and the label data of the input data of the AI ​​model deployed on the second communication device, since the input data can be used as the input of the sixth AI model, the label data can be used as one of the bases for determining the model processing performance of the sixth AI model. Therefore, for the third communication device, the third communication device can obtain an AI model with better performance based on these two pieces of information.

[0320] In addition, for the second communication device, the second communication device can perform mathematical calculations based on mutual information based on these two information and the AI ​​data sent and received by the second communication device on the wireless link (for example, the input data of the sixth AI model or the output data of the sixth AI model, etc.), and determine the third AI model group based on the result of the mathematical calculation, so as to improve the model performance of the AI ​​models included in the third AI model group under the premise that the wireless link data meets the bandwidth.

[0321] It can be understood that when the third information includes two or more of the above-mentioned information 1 to information 5, based on the technical gain brought by any one of the above-mentioned items, further superposition gain can be obtained through the two or more items of information.

[0322] Optionally, each item of information included in the third information may be partial information obtained by the second communication device screening multiple pieces of information. The following will be described illustratively using the example where the third information includes information 3.

[0323] As an implementation example, when the third information includes information 3, the first communication device and the second communication device can deploy N (N is greater than or equal to 2) AI model groups, each AI model group includes a dedicated model deployed on the first communication device and a dedicated model deployed on the second communication device, and the information 3 may include model parameters of k (k is a positive integer) AI model groups among the N AI model groups.

[0324] As an example of information 3, the second communication device can determine k AI model groups from the N AI model groups based on target information (the target information can be used to determine a general AI model group, a general AI model, a benchmark AI model, etc.; or the target information can be a general AI model group itself, a general AI model itself, a benchmark AI model itself, etc.). For example, the second communication device can determine the difference between the characterization parameters (such as Logits) of the N AI model groups and the characterization parameters of the AI ​​model / AI model group determined by the target information (such as the cosine (cos) value corresponding to Logits), that is, the second communication device can determine N difference values, and determine the corresponding k AI model groups based on the larger k difference values ​​among the N difference values, and carry the model parameters of the k AI model groups in information 3.

[0325] As another example of information 3, the N AI model groups may include 2N dedicated models. For example, N of the 2N dedicated models are deployed on a first communication device, and the remaining N of the 2N dedicated models are deployed on a second communication device. For ease of reference, the N dedicated models deployed on the first communication device will be referred to as N1 dedicated models, and the N dedicated models deployed on the second communication device will be referred to as N2 dedicated models. Thereafter, the second communication device may determine k AI model groups from the 2N AI models based on target information (the target information may be used to determine a general AI model, a baseline AI model, etc.; or the target information may be the general AI model itself, the baseline AI model itself, etc.).

[0326] In addition, for any one of the 2N dedicated models, the second communication device can determine the difference between the characterization parameters (such as Logits) of the any one AI model and the characterization parameters of the AI ​​model determined by the target information (such as the cosine (cos) value between the Logits), that is, the second communication device can determine the 2N difference values ​​corresponding to the 2N dedicated models. Subsequently, the second communication device can determine the k AI model groups corresponding to the larger k difference values ​​among the N difference values ​​corresponding to the N1 dedicated models based on the 2N difference values, and carry the model parameters of the k AI model groups in information 3; or, the second communication device can determine the k AI model groups corresponding to the larger k difference values ​​among the N difference values ​​corresponding to the N2 dedicated models based on the 2N difference values, and carry the model parameters of the k AI model groups in information 3.

[0327] Optionally, the target information may be pre-configured in the second communication device, or may be configured by a third communication device (or other equipment) to the second communication device, which is not limited here.

[0328] Similarly, when the third information includes information 1, information 2, information 4 or information 5, the above implementation can also be referred to and will not be repeated here.

[0329] In one possible implementation, the second communication device may send third information in step S601. The third information is used to determine a third AI model group, including: the third information is used to update the fourth AI model group to obtain the third AI model group; the fourth AI model group includes a seventh AI model and an eighth AI model, the seventh AI model is deployed on the first communication device, and the eighth AI model is deployed on the second communication device; the input of the seventh AI model includes the output of the eighth AI model, or the input of the eighth AI model includes the output of the seventh AI model. Specifically, after receiving the third information, the third communication device may update the fourth AI model group based on the third information to obtain the third AI model group. In other words, the third information sent by the second communication device can be used to update other AI models, making the solution applicable to AI model update scenarios. Optionally, if the fourth AI model group is considered as one AI model, the seventh AI model and the eighth AI model can be understood as two AI sub-models within the one AI model.

[0330] In one possible implementation, the third information sent by the second communication device in step S601 is periodically transmitted information, and / or the fourth information sent by the third communication device in step S602 is periodically transmitted information. Specifically, the third information may be one of the bases for determining the AI ​​model, and the fourth information may be used to deploy the AI ​​model. The periodic transmission of the third information and / or the fourth information between the second communication device and the third communication device enables periodic determination and / or periodic deployment of the AI ​​model, thereby achieving multiple iterative updates of the AI ​​model through a periodic process.

[0331] Based on the technical solution shown in Figure 6, after the second communication device sends the third information for determining the third AI model group in step S601, the second communication device can receive fourth information in step S602, where the third information includes model parameters for the third AI model group. In other words, as the recipient of the third information, the third communication device can determine the third AI model group based on the third information from the second communication device, and through the fourth information, enable the second communication device to subsequently deploy the fifth AI model in the first communication device and the sixth AI model in the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, while enabling the computing power of the communication device to be applied to the processing of the AI ​​model, the third information from the second communication device can also serve as one of the bases for the third communication device to determine the AI ​​model, so that the AI ​​model determined by the third communication device can be adapted to the second communication device as much as possible, thereby improving the success rate of the subsequent model processing of the AI ​​model by the second communication device.

[0332] Referring to Figure 7, an embodiment of the present application provides a communication device 700. This communication device 700 can implement the functions of the second communication device or the first communication device in the above-mentioned method embodiment, thereby also achieving the beneficial effects of the above-mentioned method embodiment. In this embodiment of the present application, the communication device 700 can be the first communication device (or the second communication device), or it can be an integrated circuit or component, such as a chip, within the first communication device (or the second communication device).

[0333] 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.

[0334] In one possible implementation, when the device 700 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the processing unit 701 is used to determine first information; the transceiver unit 702 is used to send first information, and the first information is used to determine a first artificial intelligence AI model group, and the first AI model group includes the first AI model and the second AI model; wherein the first AI model is deployed on the first communication device and the second AI model is deployed on the second communication device; the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; the transceiver unit 702 is also used to receive second information from the second communication device, and the second information includes model parameters of the first AI model group or model parameters of the first AI model.

[0335] In one possible implementation, when the device 700 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 receives first information; the processing unit 701 is used to determine a first AI model group based on the first information, and the first AI model group includes the first AI model and the second AI model; wherein the first AI model is deployed in the first communication device, and the second AI model is deployed in the second communication device, and the input of the first AI model includes the output of the second AI model, or the input of the second AI includes the output of the first AI model; the transceiver unit 702 is also used to send second information, and the second information includes the model parameters of the first AI model group or the model parameters of the first AI model.

[0336] In one possible implementation, when the device 700 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the processing unit 701 is used to determine third information; the transceiver unit 702 is used to send third information, and the third information is used to determine a third AI model group, and the third AI model group includes the fifth AI model and the sixth AI model; wherein the fifth AI model is deployed on the first communication device, and the sixth AI model is deployed on the second communication device, and the input of the fifth AI model includes the output of the sixth AI model, or the input of the sixth AI includes the output of the fifth AI model; the transceiver unit 702 is also used to receive fourth information from the third communication device, and the fourth information includes the model parameters of the third AI model group.

[0337] In one possible implementation, when the device 700 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive third information; the processing unit 701 is used to determine a third AI model group based on the third information, and the third AI model group includes the fifth AI model and the sixth AI model; wherein the fifth AI model is deployed on the first communication device and the sixth AI model is deployed on the second communication device; the input of the fifth AI model includes the output of the sixth AI model, or the input of the sixth AI model includes the output of the fifth AI model; the transceiver unit 702 is further used to send fourth information, and the fourth information includes model parameters of the third AI model group.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] Optionally, the logic circuit 801 is used to determine first information; the input-output interface 802 is used to send first information, the first information is used to determine a first artificial intelligence (AI) model group, the first AI model group including the first AI model and the second AI model; wherein the first AI model is deployed on the first communication device, and the second AI model is deployed on the second communication device; the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; the input-output interface 802 is also used to receive second information from the second communication device, the second information including model parameters of the first AI model group or model parameters of the first AI model.

[0342] Optionally, the input-output interface 802 receives first information; the logic circuit 801 is used to determine a first AI model group based on the first information, and the first AI model group includes the first AI model and the second AI model; wherein the first AI model is deployed on the first communication device, and the second AI model is deployed on the second communication device, and the input of the first AI model includes the output of the second AI model, or the input of the second AI includes the output of the first AI model; the input-output interface 802 is also used to send second information, and the second information includes the model parameters of the first AI model group or the model parameters of the first AI model.

[0343] Optionally, the logic circuit 801 is used to determine third information; the input-output interface 802 is used to send third information, the third information is used to determine a third AI model group, the third AI model group including the fifth AI model and the sixth AI model; wherein the fifth AI model is deployed on the first communication device, the sixth AI model is deployed on the second communication device, the input of the fifth AI model includes the output of the sixth AI model, or the input of the sixth AI includes the output of the fifth AI model; the input-output interface 802 is also used to receive fourth information from the third communication device, the fourth information including the model parameters of the third AI model group.

[0344] Optionally, the input-output interface 802 is used to receive third information; the logic circuit 801 is used to determine a third AI model group based on the third information, and the third AI model group includes the fifth AI model and the sixth AI model; wherein the fifth AI model is deployed on the first communication device and the sixth AI model is deployed on the second communication device; the input of the fifth AI model includes the output of the sixth AI model, or the input of the sixth AI model includes the output of the fifth AI model; the input-output interface 802 is also used to send fourth information, and the fourth information includes the model parameters of the third AI model group.

[0345] The logic circuit 801 and the input / output interface 802 may also execute other steps executed by the first communication device or the second communication device in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

[0346] In a possible implementation, the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .

[0347] 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.

[0348] 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.

[0349] 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.

[0350] 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.

[0351] 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).

[0352] 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 .

[0353] 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.

[0354] 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.

[0355] 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.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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 ).

[0369] 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.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] An embodiment of the present application further provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in the possible implementation methods of the first communication device or the second communication device in the aforementioned embodiment.

[0375] An embodiment of the present application also provides a computer program product (or computer program). When the computer program product is executed by the processor, the processor executes the method that may be implemented by the above-mentioned first communication device or second communication device.

[0376] An embodiment of the present application also provides a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation methods of the above-mentioned communication device. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data to the at least one processor. In one possible design, the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device. The chip system can be composed of chips, or it can include chips and other discrete devices, wherein the communication device can specifically be the first communication device or the second communication device in the aforementioned method embodiment.

[0377] An embodiment of the present application further provides a communication system, wherein the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

[0378] 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.

[0379] 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.

[0380] 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: The method is applied to a first communication device, and the method includes: Sending first information, where the first information is used to determine a first artificial intelligence (AI) model group, where the first AI model group includes the first AI model and a second AI model; wherein the first AI model is deployed on the first communication device, and the second AI model is deployed on the second communication device; the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; Second information is received from the second communication device, where the second information includes model parameters of the first AI model group or model parameters of the first AI model.

2. The method according to claim 1, characterized in that The first information includes first dimension information or second dimension information; When the input of the first AI model includes the output of the second AI model, the first dimension information is used to determine the dimension information of the input data of the first AI model or the dimension information of the output data of the second AI model; In a case where the input of the second AI model includes the output of the first AI model, the second dimensional information is used to determine dimensional information of output data of the first AI model or dimensional information of input data of the second AI model.

3. The method according to claim 2, characterized in that The dimension information includes at least one of the following: The upper limit value of the dimension, the lower limit value of the dimension, the dimension expected by the first communication device, and the value range of the dimension expected by the first communication device.

4. The method according to claim 2 or 3, characterized in that: The first dimension information or the second dimension information is determined based on channel state information.

5. The method according to any one of claims 1 to 4, characterized in that: The first information includes at least one of the following: The input data of the first AI model and the label data of the input data of the first AI model, the local computing power status information of the first communication device, and the channel status information.

6. The method according to any one of claims 1 to 5, characterized in that: The first information is used to determine the first AI model group, including: The first information is used to update the second AI model group to obtain the first AI model group; the second AI model group includes a third AI model and a fourth AI model, the third AI model is deployed on the first communication device, the fourth AI model is deployed on the second communication device, the input of the third AI model includes the output of the fourth AI model, or the input of the fourth AI includes the output of the third AI model.

7. The method according to any one of claims 1 to 6, further characterized in that the first information is information sent periodically, and / or the second information is information sent periodically.

8. The method according to any one of claims 1 to 7, characterized in that: The AI ​​model of the first AI model group is a dedicated model.

9. A communication method, characterized in that: Applied to a second communication device, the method comprises: receiving a first message; Determine a first AI model group based on the first information, the first AI model group including the first AI model and the second AI model; wherein the first AI model is deployed on a first communication device, the second AI model is deployed on the second communication device, the input of the first AI model includes the output of the second AI model, or the input of the second AI includes the output of the first AI model; Send second information, where the second information includes model parameters of the first AI model group or model parameters of the first AI model.

10. The method according to claim 9, characterized in that The second communication device is a functional entity that determines an AI model group list based on the first information, where the AI ​​model group list includes one or more AI model groups, and the one or more AI model groups include the first AI model group.

11. The method according to claim 10, characterized in that The second communication device is a functional entity that determines an AI model group list based on the first information, and selects part or all of the AI ​​model groups used by the first communication device from the AI ​​model group list.

12. The method according to any one of claims 9 to 11, characterized in that: The first information includes first dimension information or second dimension information; When the input of the first AI model includes the output of the second AI model, the first dimension information is used to determine the dimension information of the input data of the first AI model or the dimension information of the output data of the second AI model; In a case where the input of the second AI model includes the output of the first AI model, the second dimensional information is used to determine dimensional information of output data of the first AI model or dimensional information of input data of the second AI model.

13. The method according to claim 12, characterized in that The dimension information includes at least one of the following: The upper limit value of the dimension, the lower limit value of the dimension, the dimension expected by the first communication device, and the value range of the dimension expected by the first communication device.

14. The method according to claim 12 or 13, characterized in that The first dimension information or the second dimension information is determined based on channel state information.

15. The method according to any one of claims 11 to 14, characterized in that The first information includes at least one of the following: The input data of the first AI model and the label data of the input data of the first AI model, the local computing power status information of the first communication device, and the channel status information.

16. The method according to any one of claims 11 to 15, characterized in that The determining a first AI model group based on the first information includes: The second AI model group is updated based on the first information to obtain the first AI model group; wherein the second AI model group includes a third AI model and a fourth AI model, the third AI model is deployed on the first communication device, and the fourth AI model is deployed on the second communication device; the input of the third AI model includes the output of the fourth AI model, or the input of the fourth AI includes the output of the third AI model.

17. The method according to any one of claims 11 to 16, further characterized in that the first information is information sent periodically, and / or the second information is information sent periodically.

18. The method according to any one of claims 11 to 17, characterized in that The AI ​​model of the first AI model group is a dedicated model.

19. A communication device, characterized in that: Comprising means for performing the method as claimed in any one of claims 1 to 18.

20. 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 18.

21. The communication device according to claim 20, characterized in that: The communication device is a chip or a chip system.

22. A readable storage medium, characterized in that: The storage medium stores a computer program or an instruction, and when the computer program or the instruction is executed by the communication device, the method according to any one of claims 1 to 18 is implemented.

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