Communication method and related device

Abstract

The present application discloses a communication method and a related device. The method comprises: generating a first artificial intelligence (AI) model; and sending first information to a second communication apparatus, the first information being used for determining the first AI model. After a first communication apparatus generates the first AI model, the first information used for determining the first AI model is sent to a receiver so as to deploy the first AI model in the receiver, thereby achieving deployment of the AI model and functions such as operation, generation, or updating of the model.

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 202311594529.6 and invention 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] With the advancement of communication technologies and the increasing maturity of artificial intelligence (AI), AI is becoming an indispensable component of wireless communication systems. Future wireless network architectures will need to support a large number of AI functions. Therefore, how to support model deployment in wireless networks is an urgent issue.

[0005] In the relevant standards, there are solutions for combining wireless networks with AI. However, these solutions are all in the early stages of discussion on the combination of wireless networks and AI and are not sufficient to support the deployment of AI.

[0006] Summary of the Invention

[0007] This application provides a communication method and related equipment that can realize the deployment of AI models and the operation of the 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 network device), or a component in a communication device (such as a processor, a chip, or a chip system, etc.), or a logic module or software that can implement all or part of the functions of the communication device. In this method, the first communication device generates a first artificial intelligence (AI) model and sends first information, which is used to determine the first AI model.

[0009] Based on the above technical solution, after the first communication device generates or updates the first AI model, it sends the first information used to determine the first AI model to the recipient to deploy the first AI model to the recipient, thereby realizing functions such as AI model deployment, model operation, generation or update.

[0010] Optionally, update can be replaced by other terms, such as modification, iteration, optimization, processing, or receiving from other devices.

[0011] 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.

[0012] 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.

[0013] It can be understood that the second communication device can be implemented in many ways.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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).

[0021] Optionally, the AI ​​model involved in this application (such as the first AI model, the second AI model, 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.

[0022] In a possible implementation manner of the first aspect, the first communication device is a functional entity for generating a first AI model.

[0023] Based on the above technical solution, the first communication device can be used to generate / obtain / determine / update one or more AI models.

[0024] In a possible implementation manner of the first aspect, the first communication device is a functional entity used to generate a first AI model and deploy the first AI model on the second communication device through the first information.

[0025] Based on the above technical solution, the first communication device can communicate with one or more second communication devices, and the first communication device can send information of one or more first communication devices (for example, one or more first information) to the second communication device to deploy the first AI model on one or more second communication devices.

[0026] In a possible implementation of the first aspect, the first AI model is a dedicated AI model.

[0027] Based on the above technical solution, the second communication device can be a terminal device. For this purpose, the AI ​​model deployed on the terminal device can be a dedicated AI model, and the first information sent by the first communication device to the second communication device can be used to determine the dedicated AI 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 AI model in the terminal device, the AI ​​model deployed on the terminal device can adapt to the end-side characteristics of the terminal device, in order to improve the model processing performance of the AI ​​model. In addition, compared with the large model, deploying a dedicated AI model on the second communication device can effectively improve the model accuracy of personalized scenarios.

[0028] It should be understood that a general AI model can be referred to as a basic model, a large model, or an L0 model, while a specialized AI model can be referred to as a small model, an L1 model, an L2 model, etc.

[0029] 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.

[0030] Alternatively, large models are typically built from deep neural networks, with billions or even hundreds of billions of parameters.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] In a possible implementation of the first aspect, before generating the first AI model, the first communication device includes: the first communication device receives second information from the second communication device; the first communication device generates the first AI model, including: the first communication device generates the first AI model based on the second information.

[0036] Based on the above technical solution, the first communication device can receive information (e.g., second information) from one or more second communication devices and generate / obtain / determine / update one or more dedicated AI models based on the information from the one or more second communication devices. Optionally, the information from the second communication device can be used to indicate user demand information, user data information, channel state information, etc., so that the first communication device can generate / obtain / determine / update a dedicated AI model based on the information from the second communication device, thereby effectively improving the model accuracy of personalized scenarios.

[0037] In a possible implementation manner of the first aspect, the second information includes at least one of the following: channel state information between the first communication device and the second communication device, and local computing power state of the second communication device.

[0038] Based on the above technical solution, when the communication bandwidth between the first communication device and the second communication device is certain, the first communication device can generate / obtain / determine / update one or more dedicated AI models based on the channel state information between the first communication device and the second communication device and / or the local computing power state of the second communication device, thereby generating a dedicated AI model in combination with actual scenario requirements and improving the model accuracy in personalized scenarios.

[0039] 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.

[0040] Optionally, the channel state information between the first communication device and the second communication device may be obtained based on a reference signal.

[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] For example, when 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.

[0043] In a possible implementation manner of the first aspect, the second information may further include at least one of the following: location information of the second communication device, behavior information of the second communication device, local data information and tag information of the second communication device.

[0044] Based on the above technical solution, the second information may include at least one of the above items. In other words, the first communication device may determine the first AI model group based on the above at least one item of information to improve the flexibility of the solution implementation.

[0045] In a possible implementation of the first aspect, the method further includes: the first communication device receives third information from the third communication device; the first communication device sends the third information to the second communication device, the third information is used to determine a third AI model, and the third AI model is a general AI model.

[0046] Based on the above technical solution, since the general AI model is mainly used to ensure the basic requirements of model accuracy in most scenarios, and the dedicated AI model is mainly used to ensure the model accuracy in personalized scenarios, the second communication device deploys the general AI model based on the third information of the first communication device before or after deploying the dedicated AI model. It can switch the dedicated AI model deployed on the second communication device to a general AI model, or switch the general AI model to a dedicated AI model, thereby realizing the coordinated deployment of the dedicated AI model and the general AI model. That is, the first communication device can generate or update a matching AI model based on the information feedback from itself, the second communication device or other communication devices, and deploy an AI model that meets the scenario requirements on itself, the second communication device or other communication devices based on the actual scenario, thereby realizing the joint deployment of the dedicated AI model and the general AI model, effectively reducing the application cost of the model and improving the model accuracy in personalized scenarios.

[0047] In a possible implementation of the first aspect, after the first communication device sends the first information to the second communication device, the method further includes: the first communication device receives fourth information from the second communication device, where the fourth information is used to indicate one of the AI ​​models in the list of one or more AI models.

[0048] Based on the above technical solution, on the basis that a dedicated AI model is deployed in the second communication device, the second communication device can send fourth information to the first communication device to feedback the recommended AI model.

[0049] Optionally, the AI ​​model list may include one or more AI models, and each AI model 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 list can also be replaced by an AI model group list, that is, the AI ​​model group list can include one or more AI models.

[0050] Optionally, list can be replaced with other terms such as set, dictionary, combination, space, etc.

[0051] In a possible implementation manner of the first aspect, the list of one or more AI models includes a list of one or more specialized AI models and / or a list of general AI models.

[0052] Based on the above technical solution, the list of one or more AI models contains list information of different AI models, so that the first communication device and / or the second communication device can select a matching AI model based on actual needs to meet the accuracy requirements of different scenarios.

[0053] In a possible implementation of the first aspect, the first AI model is a general AI model.

[0054] Based on the above technical solution, the first AI model can be either a dedicated AI model or a general AI model to adapt to the accuracy requirements of different scenarios.

[0055] In a possible implementation of the first aspect, a first communication device generates a first AI model, including: the first communication device receives fifth information from a third communication device, the fifth information is used to determine the first AI model, the first AI model is deployed on the first communication device and the second communication device, or the first AI model is deployed on the second communication device.

[0056] Based on the above technical solution, the first communication device can generate or update the first AI model through information from one or more third communication devices (for example, one or more fifth information), and subsequently multiple first communication devices and second communication devices connected to each first communication device can deploy a general AI model with higher generalization and better versatility.

[0057] In a possible implementation of the first aspect, before the second communication device receives the first information from the first communication device, the method further includes: the first communication device sends a first message to the second communication device, where the first message is used to inform the second communication device to use the first AI model.

[0058] Based on the above technical solution, after receiving the fifth information, the first communication device can generate or update the first AI model based on the fifth information. Before the first communication device sends the first information to the second communication device, the first communication device can send a first message to the second communication device to inform the second communication device that it can use the first AI model (general AI model). It should be understood that the first message can also be used to inform the second communication device not to use the first AI model.

[0059] Optionally, the first message may be a broadcast, unicast or multicast message.

[0060] The first communication device sends the first message to one or more second communication devices by broadcast, unicast or multicast, so that the one or more second communication devices can obtain the first message by broadcast, unicast or multicast, thereby saving signaling overhead.

[0061] The second aspect of the present application provides a communication method, which is performed by a second communication device, which can be a communication device (such as a network device or a terminal device), or the second communication device can be a component in the communication device (such as a processor, chip or chip system, etc.), or the second communication device can also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the second communication device receives first information from the first communication device; the second communication device determines a first AI model based on the first information.

[0062] Based on the above technical solution, after the second communication device receives the first information, it can determine the first AI model based on the first information. In other words, as the recipient of the first information, the second communication device can determine the first AI model based on the first information from the first communication device to deploy the first AI model, thereby supporting the deployment and operation of the model.

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

[0064] In a possible implementation of the second aspect, before the second communication device receives the first information from the first communication device, the method further includes: the second communication device sends second information to the first communication device, where the second information is used to generate the first AI model.

[0065] In a possible implementation of the second aspect, the second information includes at least one of the following: channel state information between the first communication device and the second communication device, and local computing power state of the second communication device.

[0066] In a possible implementation of the second aspect, the second information further includes at least one of the following: location information of the second communication device, behavior information of the second communication device, local data information and tag information of the second communication device.

[0067] In a possible implementation of the second aspect, the method further includes: the second communication device receives third information from the first communication device, the third information is used to determine a third AI model, and the third AI model is a general AI model.

[0068] In a possible implementation of the second aspect, after the second communication device determines the first AI model based on the first information, the method further includes: the second communication device sends fourth information to the first communication device, where the fourth information is used to indicate one of the AI ​​models in the list of one or more AI models.

[0069] In a possible implementation of the second aspect, the list of one or more AI models includes a list of one or more specialized AI models and / or a list of general AI models.

[0070] In a possible implementation of the second aspect, the first AI model is a general AI model.

[0071] In a possible implementation of the second aspect, the method further includes: the second communication device receives fifth information from the first communication device, the fifth information is used to determine the first AI model, the first AI model is deployed on the first communication device and the second communication device, or the first AI model is deployed on the second communication device.

[0072] In a possible implementation of the second aspect, before the second communication device receives the first information from the first communication device, the method further includes: the second communication device receives a first message from the first communication device, where the first message is used to inform the second communication device to use the first AI model.

[0073] Optionally, the first message may be a broadcast, unicast or multicast message. For the description of various possible implementations of the second aspect of the embodiment of the present application, reference may be made to the description of various possible implementations in the first aspect, and no further details will be given here.

[0074] In a possible implementation manner of the first aspect or the second aspect, the first information is used to determine a category to which the second communication device belongs.

[0075] Based on the above technical solution, after receiving the second information from one or more second communication devices, the first communication device can classify the one or more second communication devices based on the second information, and include the relevant information of the category in the first information to determine the AI ​​model corresponding to each category according to the category. Subsequently, the first communication device will send the first information to the one or more second communication devices respectively. After the one or more second communication devices receive the first information from the first communication device, each second communication device can determine the category to which the second communication device belongs based on the first information, and filter out the AI ​​model corresponding to the category from the first information according to the category to which it belongs, thereby implementing the deployment of the AI ​​model and implementing the operation of the model in the second communication device. In a possible implementation of the first aspect or the second aspect, the first AI model is included in the first AI model group, the first AI model group also includes the 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; wherein, the input of the second AI model includes the output of the first AI model, or the input of the first AI model includes the output of the second AI model.

[0076] Based on the above technical solution, the first communication device can be a network device, and the second communication device can be a terminal device. Since different terminal devices may have different end-side characteristics (such as different local data, different local computing power, different channel characteristics, etc.), different network devices may have different edge-side characteristics. By deploying the second AI model in the terminal device and the first AI model in the network device, the second AI model deployed on the terminal device can be adapted to the end-side characteristics of the terminal device, and the first AI model deployed on the network device can be adapted to the edge-side characteristics of the network device, in order to improve the model processing performance of the AI ​​model.

[0077] It can be understood that the second communication device can be implemented in many ways.

[0078] 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.

[0079] 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.

[0080] The cloud can be understood as the central node in traditional cloud computing and the control end of edge computing. The edge can be understood as the edge side of cloud computing, which is divided into the infrastructure edge and the device edge. In wireless networks, it refers to edge servers or base stations. The end can be understood as terminal devices, such as mobile phones, tablets, sensors, and other terminals.

[0081] In a possible implementation of the first aspect or the second aspect, the first information is further used to determine the second AI model in the first AI model group.

[0082] Based on the above technical solution, under the premise that the first AI model is deployed on the first communication device and the second AI model is deployed on the second communication device, the second communication device determines the AI ​​models deployed on different devices respectively by receiving the first information from the first communication device.

[0083] In a possible implementation of the first aspect or the second aspect, the first information includes an identifier of the first AI model, where the identifier is used to determine the first AI model in a list including one or more dedicated AI models.

[0084] Based on the above technical solution, the first communication device can send the identifier of the first AI model to the second communication device to determine the first AI model deployed in the second communication device, thereby reducing the amount of data transmission between the first communication device and the second communication device.

[0085] In a possible implementation of the first aspect or the second aspect, before sending the first information to the second communication device, the method further includes: the first communication device sending a list of one or more dedicated AI models to the second communication device.

[0086] Based on the above technical solution, the prerequisite for the first communication device to issue the identifier of the first AI model is that the second communication device has already stored a list of one or more dedicated AI models. The first communication device sends a list of one or more dedicated AI models to the second communication device in advance so that when the model type needs to be changed later, the model identifier can be issued, thereby reducing the amount of data transmission.

[0087] In a possible implementation of the first aspect or the second aspect, the first information includes model parameters and / or model structure information of the first AI model.

[0088] In a possible implementation of the first aspect or the second aspect, the first information further includes an identifier of the first AI model.

[0089] Based on the above technical solution, in addition to the first information including the model parameters and / or model structure information of the first AI model, the first information may also include an identifier of the first AI model, so that the second communication device determines the deployed AI model based on the identifier.

[0090] In a possible implementation of the first aspect or the second aspect, the first information is periodically sent information.

[0091] Based on the above technical solution, the first information can be one of the bases for determining the AI ​​model. By periodically sending the first information between the first communication device and the second communication device, the periodic determination of the AI ​​model can be achieved, so as to achieve multiple iterative updates of the AI ​​model through a periodic process.

[0092] The third aspect of the present application provides a communication method, which is performed by a third communication device. The third communication device can be a network device (such as an access network device, a core network device, a cloud server, etc.), or the third communication device can be a component in the network device (such as a processor, a chip or a chip system, etc.), or the third communication device can also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the third communication device receives the sixth information from the first communication device; the third communication device generates the fifth information based on the sixth information; the third communication device sends the fifth information to the first communication device, and the fifth information is used to determine the general AI model, and the general AI model is deployed on the first communication device and the second communication device, or the general AI model is deployed on the second communication device.

[0093] Based on the above technical solution, the third communication device can generate a universal AI model based on the sixth information from the first communication device and send the fifth information to the first communication device to deploy the universal AI model on the first communication device. Furthermore, the universal AI model can also be deployed on the second communication device, thereby enabling the deployment and operation of the universal AI model on both the first and second communication devices.

[0094] Optionally, the sixth information includes the second information.

[0095] Optionally, the sixth information is determined based on the processed second information.

[0096] In a possible implementation of the third aspect, the sixth information includes at least one of the following:

[0097] Channel state information between the first communication device and the second communication device, and local computing power status of the second communication device.

[0098] In a possible implementation of the third aspect, the sixth information further includes at least one of the following:

[0099] location information of the second communication device, behavior information of the second communication device, local data information and tag information of the second communication device.

[0100] For the description of various possible implementations of the third aspect of the embodiment of the present application, reference can be made to the description of various possible implementations in the first and second aspects, and no further details will be given here.

[0101] In a fourth aspect, the present application provides a communication device, which is a first communication device and includes a processing unit and a transceiver unit; the processing unit is used to generate a first artificial intelligence (AI) model; the transceiver unit is used to send first information, and the first information is used to determine the first AI model.

[0102] In the fourth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects. For details, please refer to the first aspect and will not be repeated here.

[0103] In a fifth aspect, the present application provides a communication device, which is a second communication device. The device includes a transceiver unit and a processing unit. The transceiver unit is used to receive first information from a first communication device; the processing unit is used to determine a first AI model based on the first information.

[0104] 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 second aspect and achieve corresponding technical effects. For details, please refer to the second aspect and will not be repeated here.

[0105] In the sixth aspect of the present application, a communication device is provided, which is a third communication device, and includes a transceiver unit and a processing unit; the transceiver unit is used to receive sixth information from the first communication device; the processing unit is used to generate fifth information based on the sixth information; the transceiver unit is used to send fifth information to the first communication device, and the fifth information is used to determine a general AI model, and the general AI model is deployed in the first communication device and the second communication device, or the general AI model is deployed in the second communication device.

[0106] 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 third aspect and achieve corresponding technical effects. For details, please refer to the third aspect and will not be repeated here.

[0107] In the seventh aspect of the present application, a communication device is provided, comprising at least one processor, wherein the at least one processor is coupled to a memory; the memory is used to store programs or instructions; and the at least one processor is used to execute the program or instructions so that the device implements a method of any possible implementation method of any aspect of the first to third aspects.

[0108] In a possible implementation, the communication device further includes a memory. Optionally, the processor and the memory are integrated together.

[0109] In an eighth 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 a method as any possible implementation method in any of the first to third aspects mentioned above.

[0110] A ninth aspect of the present application provides a communication system, which includes the first communication device and the second communication device. Alternatively, the communication system includes the first communication device, the second communication device, and the third communication device.

[0111] In the tenth aspect of the present application, a computer-readable storage medium is provided, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes a method as any possible implementation method of any aspect of the first to third aspects mentioned above.

[0112] In the eleventh aspect of the present application, a computer program product (or computer program) is provided. When the computer program in the computer program product is executed by the processor, the processor executes a method of any possible implementation of any aspect of the first to third aspects above.

[0113] The twelfth aspect of the present application provides a chip system, which includes at least one processor, and is used to support a communication device to implement any possible implementation method of any aspect of the first to third aspects above.

[0114] 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 also include an interface circuit that provides program instructions and / or data to at least one processor.

[0115] Among them, the technical effects brought about by any design method in the fourth to twelfth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to third aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figures 1a to 1d are schematic diagrams of a communication system provided by this application;

[0117] Figures 2a to 2g are schematic diagrams of the AI ​​processing process involved in this application;

[0118] FIG3 is a schematic diagram of an implementation of a communication method provided in an embodiment of the present application;

[0119] FIG4 is a schematic diagram of another implementation of the communication method provided in an embodiment of the present application;

[0120] Figures 5a and 5b are interactive schematic diagrams of the communication method provided by this application;

[0121] FIG6a is a schematic diagram of a collaborative deployment of a dedicated AI model and a general AI model provided in an embodiment of the present application;

[0122] Figures 6b to 6d are schematic diagrams of another implementation of the communication method provided in an embodiment of the present application;

[0123] 7 to 11 are schematic diagrams of the communication device provided in this application. DETAILED DESCRIPTION

[0124] First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0125] (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.

[0126] 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.

[0127] 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.

[0128] The terminal may also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle-to-everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc.

[0129] 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.

[0130] 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.

[0131] (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.

[0132] Alternatively, a RAN node can be a macro base station, micro base station, indoor base station, relay node, donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. A RAN node can also be a server, wearable device, vehicle, or vehicle-mounted device. For example, the access network device in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

[0133] 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).

[0134] 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.

[0135] 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.

[0136] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

[0137] Table 1

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] (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.

[0143] Furthermore, these values ​​and parameters can be changed or updated.

[0144] (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.

[0145] (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.

[0146] 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.

[0147] 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.

[0148] (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.

[0149] 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.

[0150] This 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 Beyond 5G (B5G), 6G, etc.). The communication system includes at least one network device and / or at least one terminal device.

[0151] 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 radio access network (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 connected to the RAN node 110 wirelessly, and the RAN node 110 is connected to the core network 200 wirelessly or by wire. 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.

[0152] RAN100 may be an evolved universal terrestrial radio access (E-UTRA) system, a new radio (NR) system, or a future radio access system defined in the 3rd Generation Partnership Project (3GPP). RAN100 may also include two or more of the aforementioned different radio access systems. RAN100 may also be an open RAN (O-RAN).

[0153] For ease of description, a base station is taken as an example of a RAN node for description below.

[0154] 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.

[0155] 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.

[0156] Communication between base stations and terminals, between base stations, and between terminals can be carried out using licensed spectrum, unlicensed spectrum, or both. Communication can be carried out using spectrum below 6 gigahertz (GHz), spectrum above 6 GHz, or both. The embodiments of this application do not limit the spectrum resources used for wireless communication.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Exemplarily, the sidelink supports broadcast, unicast, and multicast.

[0161] Broadcast communication is similar to network device broadcasting system information, that is, the terminal device sends broadcast service data to the outside without encryption. Any other terminal device within the effective receiving range can receive the broadcast service data if it is interested in the broadcast service.

[0162] Unicast communication is similar to data communication that occurs after an RRC connection is established between a terminal device and a network device. It requires a unicast connection to be established between the two devices. After the unicast connection is established, the two devices can communicate data based on a negotiated identifier. This data can be encrypted or unencrypted. Unlike broadcasting, unicast communication is only possible between two devices that have established a unicast connection.

[0163] Optionally, a unicast communication on the sidelink corresponds to a pair of a source layer-2 identifier (denoted as source L2 ID) and a destination layer-2 identifier (denoted as destination L2 ID). Optionally, the source L2 ID and the destination L2 ID are included in a subheader of a media access control protocol data unit (MAC PDU) in the sidelink to ensure that the data is transmitted to the correct receiving end.

[0164] Multicast communication refers to communication between all terminal devices in a communication group. Any terminal device in the group can send and receive data of the multicast service.

[0165] As shown in Figure 1c, when a terminal device (denoted as UE1) communicates directly with another terminal device (denoted as UE2) without going through a network device, the communication link between the two terminal devices can be called a sidelink, or the two terminal devices are said to communicate based on the proximity-based services communication 5 (PC5) port.

[0166] As shown in Figure 1d, V2X communication technology, a typical application of sidelinks, leverages and enhances current cellular network features and elements to enable low-latency and high-reliability communications between various nodes in a vehicle network, including vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), vehicle-to-infrastructure (V2I), and vehicle-to-network (V2N). As cellular systems evolve from 4G Long Term Evolution (LTE) to 5G, C-V2X is evolving from LTE-V2X to NR-V2X (New Radio V2X).

[0167] Furthermore, V2X communication has significant potential to reduce vehicle collisions, thereby reducing the number of casualties. The advantages of V2X extend beyond safety. Vehicles capable of V2X communication contribute to better traffic management, further promoting green transportation and lowering energy consumption. The Intelligent Transportation System (ITS) is an application that integrates V2X. Based on V2X technology, vehicle users (V-UEs) can transmit information such as their location, speed, and intentions (turns, lane changes, and reversing) to surrounding V-UEs periodically, as well as information triggered by aperiodic events. Similarly, V-UEs receive real-time information from surrounding users. 5G NR V2X supports lower transmission latency, more reliable communication, higher throughput, and a better user experience, meeting the needs of a wider range of application scenarios. Furthermore, the vehicle-to-vehicle communication technology supported by V2X can be extended to device-to-device (D2D) communication in any system.

[0168] The technical solution provided in this application can be applied to a wireless communication system (e.g., the system shown in FIG. 1a , FIG. 1b , FIG. 1c , or FIG. 1d ). 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.

[0169] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0170] 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).

[0171] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 for weighted summation of input values ​​according to the weights 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The following is an exemplary description of the implementation process of the neural network with reference to the accompanying drawings.

[0186] 1. Fully connected neural network, also known as multilayer perceptron (MLP).

[0187] 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.

[0188] Optionally, considering neurons in two adjacent layers, the output h of a neuron in the next layer is the weighted sum of all neurons x connected to it in the previous layer and passes through an activation function, which can be expressed as: h=f(wx+b).

[0189] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0190] Alternatively, the output of the neural network can be recursively expressed as: y = f n (w n f n-1 (…)+b n ).

[0191] Where n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0192] 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.

[0193] Optionally, a specific training method is to use a loss function to evaluate the output results of the neural network.

[0194] 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.

[0195] Alternatively, the gradient descent process can be expressed as:

[0196] 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.

[0197] Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

[0198] 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:

[0199] 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.

[0200] 2. Federated Learning (FL)

[0201] 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.

[0202] 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:

[0203] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0204] (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.

[0205] (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.

[0206] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0207] 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.

[0208] 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.

[0209] 3. Decentralized learning: Different from federated learning, decentralized learning is another distributed learning architecture.

[0210] 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:

[0211] 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.

[0212] The technical solutions provided in this application can be applied to wireless communication systems (e.g., the systems shown in Figure 1a or Figure 1b). With the development of communication technology and the increasing maturity of artificial intelligence (AI), AI has gradually become an indispensable part of wireless communication systems. Future wireless network architectures will need to support a large number of AI functions. Therefore, how to support the deployment of models in wireless networks is an urgent problem that needs to be solved.

[0213] Future intelligent wireless networks will need to support a wide range of AI / machine learning (ML) capabilities, including the deployment and updating of general-purpose and specialized AI models. However, there are no mature and recognized technical solutions for wireless networks to support the deployment of both general-purpose and specialized AI models, and this gap urgently needs to be filled.

[0214] Currently, relevant standards include solutions that combine wireless networks with AI. The first type of deployment solution is:

[0215] (1) AI / ML model training is located in OAM (operation, administrator and maintenance), and AL / ML model inference is located in gNB.

[0216] (2) Both AI / ML model training and AI / ML model inference are located in the gNB.

[0217] For the first type of deployment, model training is located in OAM and model inference is located in the next generation radio access network (NG-RAN).

[0218] If the 5G base station adopts a split structure, that is, the gNB can adopt a split structure. The gNB consists of a control unit (CU) and one or more distributed units (DU). The interface between the gNB CU and gNB DU is called F1. The second type of deployment solution is:

[0219] (1) AI / ML model training is located in OAM, and AI / ML model inference is located in gNB-CU.

[0220] (2) Both AI / ML model training and AI / ML model inference are located in the gNB-CU.

[0221] For the second type of deployment, both model training and model inference are located in NG-RAN.

[0222] It should be understood that in future 6G-oriented intelligent wireless networks, there should be scenarios where general-purpose AI models and specialized AI models coexist, and the wireless network architecture needs to support the deployment, generation, or update of general-purpose AI models as well as specialized AI models. However, the above-mentioned solutions (such as the first and second types of deployment solutions) are all in the early stages of discussing the integration of wireless networks and AI. Wireless air interface signaling for the purpose of supporting connections or sessions is not sufficient to support model deployment, and the signaling process between model training and inference does not provide a deployment solution for specialized AI models and / or general AI models.

[0223] In order to solve the above problems, an embodiment of the present application provides a communication method. Please refer to Figure 3, which is a schematic diagram of an implementation of the communication method provided by an embodiment of the present application. The method includes the following steps.

[0224] It should be noted that in Figure 3, the method is illustrated by taking the first communication device and the second communication device as the execution subjects of the interaction diagram as an example, but the present application does not limit the execution subjects of the interaction diagram. For example, in Figure 3, the execution subject of the method can be replaced by a chip, a chip system, a processor, a logic module or software in the communication device. 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 sidelink communication scenario).

[0225] S301. The first communication device generates a first AI model.

[0226] S302: The first communication device sends first information, where the first information is used to determine a first AI model. Correspondingly, the second communication device receives the first information.

[0227] S303. The second communication device determines a first AI model based on the first information.

[0228] 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.

[0229] 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).

[0230] Optionally, the AI ​​models involved in this application (such as the first AI model, the second AI model, and the third AI model and the fourth AI model mentioned later) 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, and 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] In one possible implementation, the first communication device is a functional entity for generating or updating a first AI model, that is, generating / obtaining / determining / updating one or more AI models.

[0235] In one possible implementation, the first communication device is a functional entity for generating or updating a first AI model and deploying the first AI model on a second communication device through first information. Specifically, the first communication device can communicate with one or more second communication devices, and the first communication device can send information of one or more first communication devices (e.g., one or more first information) to the second communication device to deploy the first AI model on the one or more second communication devices.

[0236] For the sake of convenience, the first AI model is taken as an example, namely a dedicated AI model and a general AI model.

[0237] It should be understood that a general AI model can be referred to as a basic model, a large model, or an L0 model, while a specialized AI model can be referred to as a small model, an L1 model, an L2 model, etc.

[0238] 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.

[0239] Alternatively, large models are typically built from deep neural networks, with billions or even hundreds of billions of parameters.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 1. In one possible implementation, the first AI model is a dedicated AI model.

[0245] Correspondingly, the first communication device generates a dedicated AI model in step S301, and the second communication device determines the dedicated AI model according to the first information in step S303. Specifically, the second communication device may be a terminal device, and the AI ​​model deployed on the terminal device may be a dedicated AI model, and the first information sent by the first communication device to the second communication device may be used to determine the dedicated AI 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 AI 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. In addition, compared with the large model, deploying a dedicated AI model in the second communication device can effectively improve the model accuracy of personalized scenarios.

[0246] In one possible implementation, the first communication device generates a dedicated AI model in step S301, including: receiving second information from a second communication device; generating a dedicated AI model based on the second information. Specifically, the first communication device can receive second information from one or more second communication devices, and generate a dedicated AI model based on the second information. For ease of explanation, taking the first communication device communicating with two second communication devices as an example, the deployment mechanism of the dedicated AI model is specifically illustrated with Figure 4. Please refer to Figure 4, which is another implementation diagram of the communication method provided in an embodiment of the present application, including the following steps:

[0247] S401. A first communication device receives second information from a first second communication device.

[0248] S402. The first communication device receives second information from a second communication device.

[0249] After completing the initial access, the first second communication device and the second communication device may respectively feed back the second information to the first communication device.

[0250] S403. The first communication device generates corresponding dedicated AI models according to the second information of the two second communication devices.

[0251] S404. The first communication device sends first information to the first second communication device, where the first information is used to determine a dedicated AI model.

[0252] S405. The first communication device sends first information to the second communication device, where the first information is used to determine a dedicated AI model.

[0253] In this step, after the first communication device generates first information corresponding to two second communication devices respectively based on the second information, it sends the corresponding first information to one or more second communication devices respectively to deploy the dedicated AI model in the corresponding second communication devices respectively.

[0254] Step S404 and step S405 can be understood as an implementation example of the aforementioned step S302.

[0255] In one possible implementation, the first communication device generates a dedicated AI model based on the second information in step S403, including: the first communication device performs at least one of migration, fine-tuning, distillation, and pruning on the general AI model.

[0256] Alternatively, a large model can learn complex patterns and features by training on massive amounts of data, possessing stronger generalization capabilities and enabling accurate predictions on unprocessed data. In contrast, a small model can refer to a model with fewer parameters and a shallower number of layers. The first communication device can obtain a lightweight, specialized AI model with fewer parameters by migrating, fine-tuning, distilling, and pruning the general AI model.

[0257] In one possible implementation, the first communication device may classify the second communication devices based on one or more second information in step S402, and generate a dedicated AI model (e.g., a single model or a group of edge-end models) corresponding to each type of second communication device. Specifically, the first communication device may classify the second communication devices based on the second information, and add a type identifier (e.g., type 1, type 2, etc.) to each type of dedicated AI model, thereby generating the first information, i.e., a list of dedicated AI models.

[0258] Optionally, list can be replaced with other terms such as set, dictionary, combination, space, etc.

[0259] Optionally, the first communication device may generate a list of dedicated AI models based on the information bottleneck (IB) theory.

[0260] In a possible implementation, the second information may include one or more of the following information A to information E.

[0261] Information A: Channel state information between the first communication device and the second communication device.

[0262] Information B: Local computing power status of the second communication device.

[0263] Information C. Location information of the second communication device.

[0264] Information D. Behavior information of the second communication device.

[0265] Information E. Local data information and tag information of the second communication device.

[0266] Regarding the information A, optionally, the channel state information between the first communication device and the second communication device may be obtained based on a reference signal.

[0267] 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.

[0268] For example, when 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.

[0269] 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.

[0270] With respect to information B, when the second information includes the local computing power status information of the second 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 second communication device, for this reason, the first communication device can adapt the dedicated AI model determined by the first communication device based on the local computing power status information to the local computing power status of the second communication device, thereby providing the second communication device with a dedicated AI model that satisfies the local computing power status, thereby improving the processing performance of the model processing performed by the first communication device based on the dedicated AI model.

[0271] Regarding information C, if the second information includes the location information of the second communication device, the category to which the second communication device belongs may change when the location of the second communication device changes. Therefore, for the first communication device, the dedicated AI model determined by the first communication device based on the location information can be adapted to the location of the second communication device to generate a dedicated AI model that meets the accuracy requirements.

[0272] Regarding information D, if the second information includes the behavior information of the second communication device, the parameters or structure of the dedicated AI model may change when the behavior of the second communication device changes. Therefore, for the first communication device, the first communication device determines the dedicated AI model based on the location information to generate a dedicated AI model that meets the accuracy requirements.

[0273] Optionally, the behavior information includes one or more of the user's movement trajectory, RRC status, and access or handover related behaviors.

[0274] For information E, if the second information includes local data information and tag information of the second communication device, to ensure data security or prevent privacy leakage of the second communication device, for AI tasks that require the second communication device to upload original local data, the data can be encrypted, such as through homomorphic encryption. For AI tasks that do not require the second communication device to upload original local data, the second communication device can send the local data to the first communication device using compression, embedding, or other non-one-to-one mapping methods to ensure data security.

[0275] In one possible implementation, the second communication device may determine the category to which the second communication device belongs based on the first information in step S303. Specifically, the first information includes the category to which the second communication device belongs, and the first communication device sends the first information to the second communication device so that the second communication device determines the category to which it belongs based on the first information.

[0276] It should be understood that as the second communication device moves, the channel and position of the second communication device will change, and the category to which it belongs may change. However, the second communication device periodically reports the second information to the first communication device, so the first communication device periodically updates the category to which the second communication device belongs, and sends the type identifier and dedicated AI model of the category to the second communication device. Since the category of the second communication device changes, the first communication device needs to send a new model category (and / or) model to the second communication device. Therefore, the solution described in Figure 4 is suitable for scenarios where the second communication device is relatively fixed or moves slowly. In this scenario, the frequency of the first communication device sending the model is low, so the overhead is small.

[0277] The following describes the content of the list of one or more AI models included in the first information, wherein the content of the first information is related to the first AI model, wherein the first AI model is a single AI model or the first AI model is included in the first AI model group.

[0278] (1) The first AI model is a single AI model

[0279] As previously described, the first information can be used to determine the category to which the second communication device belongs. In one possible implementation, the first information includes an identifier of a first AI model, where the identifier type is used to identify the first AI model in a list of one or more specialized AI models. For example, the contents of the list of specialized AI models are shown in Table 2 below.

[0280] Table 2

[0281] In a possible implementation manner, the first information includes an identifier, that is, the first communication device sends an identifier type to the second communication device.

[0282] Optionally, before the first communication device sends the first information to the second communication device, it sends a list of one or more dedicated AI models to the second communication device. Specifically, by sending the list of one or more dedicated AI models to the second communication device in advance, the first communication device can send the model identifier "type" when the model type needs to be changed later, thereby reducing the amount of data transmission. The second communication device can determine the AI ​​model corresponding to the identifier from Table 2 by looking up the table or other means.

[0283] It should be understood that the scenario where the first communication device sends the identifier of the second communication device's category after the second communication device's category changes is more suitable for scenarios where the second communication device moves at a relatively high speed. In this scenario, although the second communication device's category changes rapidly, because the first communication device only needs to send the updated identifier to the second communication device instead of the updated model, signaling overhead is reduced.

[0284] Optionally, list can be replaced with other terms such as set, dictionary, combination, space, etc.

[0285] In another possible implementation, the first information includes model parameters and / or model structure information of the first AI model, i.e., the first communication device sends the model parameters and / or model structure information of the first AI model to the second communication device. Specifically, the second communication device can directly determine the deployed first AI model based on the model parameters and / or model structure information of the first AI model, without performing the aforementioned table lookup operation.

[0286] In this implementation, the first information optionally also includes an identifier of the first AI model. Specifically, in addition to sending the model parameters and / or model structure information of the first AI model to the second communication device, the first communication device may also send the identifier of the first AI model to the second communication device, so that the second communication device can determine the first AI model based on the identifier or model parameters.

[0287] (2) The first AI model is included in the first AI model group.

[0288] In one possible implementation, the first AI model is included in a first AI model group, the first AI model group also includes a second AI model, the first AI model is deployed on a first communication device, and the second AI model is deployed on a second communication device; wherein the input of the second AI model includes the output of the first AI model, or the input of the first AI model includes the output of the second AI model.

[0289] In this implementation, it is assumed that the first communication device can be a network device and the second communication device can be a terminal device. Since different terminal devices may have different terminal-side characteristics (such as different local data, different local computing power, different channel characteristics, etc.), different network devices may have different edge characteristics. By deploying the second AI model in the terminal device and the first AI model in the network device, the second AI model deployed in the terminal device can be adapted to the terminal-side characteristics of the terminal device, and the first AI model deployed in the network device can be adapted to the edge characteristics of the network device, so as to improve the model processing performance of the AI ​​model.

[0290] 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.

[0291] 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.

[0292] Optionally, list can be replaced with other terms such as set, dictionary, combination, space, etc.

[0293] 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.

[0294] Optionally, the first AI model and the second AI model included in the first AI model group may be general AI models or dedicated AI models to enable updates of different types of models.

[0295] In a possible implementation, the first information includes an identifier of the first AI model, where the identifier is used to determine the first AI model in a list including one or more dedicated AI models.

[0296] In this implementation, optionally, before sending the first information to the second communication device, the method further includes: sending a list of one or more dedicated AI models to the second communication device.

[0297] It should be understood that, similar to the case where the first AI model is a single model, in this implementation, the content of the first information may include an identifier (type). Before receiving the first information, the second communication device stores a list of one or more dedicated AI models in advance. After receiving the first information from the first communication device, the second communication device can search the list for the corresponding AI model based on the identifier in the first information.

[0298] It should be understood that the scenario where the first communication device sends the identifier of the second communication device's category after the second communication device's category changes is more suitable for scenarios where the second communication device moves at a relatively high speed. In this scenario, although the second communication device's category changes rapidly, because the first communication device only needs to send the updated identifier to the second communication device instead of the updated model, signaling overhead is reduced.

[0299] In one possible implementation, the first information includes model parameters and / or model structure information of the first AI model.

[0300] In a possible implementation, the first information further includes an identifier (type) of the first AI model.

[0301] In the above two implementation methods, the content of the first information may be the model parameters and / or model structure information of the first AI model, or the content of the first information may be the model parameters and / or model structure information of the first AI model and the identifier corresponding to the first AI model (type and AI model).

[0302] In the case where the first AI model is included in the first AI model group, the first information sent by the first communication device to the second communication device may include information of most models in the first model group.

[0303] In another possible implementation, the first information is also used to determine the second AI model in the first AI model group.

[0304] Exemplarily, when the first AI model is included in the first AI model group, the content of the first information may be as shown in Table 3 below.

[0305] Table 3

[0306] Based on the contents of Table 2 and Table 3, it should be understood that the content of the first information includes the following forms:

[0307] 1. Type (applicable to the case where a list has been saved, the type can be sent so that the first communication device can find the corresponding AI model from the list according to the type to reduce signaling overhead).

[0308] 2. Types and individual AI models.

[0309] 3. Type and AI model group.

[0310] 4. Type and end-side AI model.

[0311] For ease of understanding, the following will use the examples shown in Figures 5a and 5b to describe the first AI model deployed by the first communication device and the second communication device.

[0312] In the example shown in Figure 5a, 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

[0313] In the example shown in Figure 5b, 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 Thereafter, the second communication device may transmit the data As the input of the second AI model, the data is processed by the second AI model

[0314] It can be understood that the first communication device and the second communication device can be implemented in many ways.

[0315] 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.

[0316] 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 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 FIG5a can be understood as end-edge collaboration based on a downlink scenario, and the scenario shown in FIG5b can be understood as end-edge collaboration based on an uplink scenario.

[0317] It should be noted that in FIG5a and FIG5b, 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.

[0318] Based on the introduction of dedicated AI models, the coordinated deployment of dedicated AI models and general AI models is explained below.

[0319] Please refer to Figure 6a, which is a schematic diagram of the collaborative deployment of a dedicated AI model and a general AI model provided in an embodiment of the present application.

[0320] Assuming the first communication device is a base station and the second communication device is a terminal device, corresponding to Figure 6a, the cloud can be understood as the central node of traditional cloud computing, the control end of edge computing, and the cloud / core network can correspond to the third communication device described in this embodiment. The edge can be understood as the edge side of cloud computing, which is divided into the infrastructure edge and the device edge. In wireless networks, it refers to edge servers or base stations, which can correspond to the first communication device of this embodiment. The end can be understood as terminal devices, such as mobile phones, tablets, sensors, and other types of terminals, which can correspond to the second communication device of this embodiment.

[0321] As shown in Figure 6a, the general AI model is mainly generated by the cloud or core network, or pre-configured by the first communication device, and the dedicated AI model is generated by the first communication device. The above implementation method can achieve end-edge-cloud AI model collaboration.

[0322] In one possible implementation, please refer to FIG6b, which is another schematic diagram of another implementation of the communication method provided in an embodiment of the present application. FIG6b is mainly used to introduce the deployment scheme of the general AI model. FIG6b is mainly used to illustrate that the user adopts the latest AI model issued by the base station, including the following steps:

[0323] S601. The first communication device receives third information from the third communication device.

[0324] After receiving the third information from the third communication device, the first communication device may determine a universal AI model based on the third information and store the universal AI model.

[0325] Optionally, the general AI model can also be pre-configured locally by the first communication device without receiving third information generated and sent from the cloud / core network.

[0326] S602. The first communication device sends third information to the second communication device, where the third information is used to determine a third AI model, which is a general AI model.

[0327] S603. The second communication device determines a third AI model based on the third information.

[0328] S604: The second communication device determines a first AI model according to the first information.

[0329] For simplicity, step S604 only illustrates the second communication device determining the first AI model based on the first information. In actual implementation, after step S603, the second communication device may provide the first communication device with additional requirements. The first communication device responds to the requirements and sends the first information corresponding to the requirements to the second communication device. Alternatively, the first communication device may send the first information to the second communication device, causing the second communication device to determine the first AI model based on the first information and switch the deployed AI model from the third AI model to the first AI model.

[0330] Optionally, before step S601 shown in Figure 6b, steps S301 to S303 of Figure 3 can also be executed, that is, the second communication device can first deploy or store a dedicated AI model, and then deploy or store a general AI model, so as to deploy a matching AI model according to actual needs and realize the joint deployment of the dedicated AI model and the general AI model.

[0331] Optionally, after step S604 shown in Figure 6b, steps S301 to S303 of Figure 3 can also be executed, that is, the second communication device can first deploy or store a common AI model, and then deploy or store a dedicated AI model, so as to deploy a matching AI model according to actual needs and realize the joint deployment of a dedicated AI model and a general AI model.

[0332] For example, please refer to Figure 6c, which is a schematic diagram of another implementation of the communication method provided in an embodiment of the present application. The first communication device is a base station (BS) and the second communication device is a user equipment (UE). The steps are as follows:

[0333] 1. The base station sends the general AI model category (for example, type 0) and the terminal-side model to the UE (the UE has been informed through the SIB that the general AI model must be used after access).

[0334] 2. After the UE initially accesses, the type 0 end-side model is used.

[0335] 3. The UE feeds back channel state information (ie, second information) to the base station.

[0336] 4. The base station classifies the UE and determines the category to which the UE belongs based on the list of dedicated AI models.

[0337] 5. The base station sends the corresponding category (for example, type 1) and the terminal-side dedicated AI model to the UE.

[0338] 6. The UE uses the latest type 1 end-side model.

[0339] It should be understood that for the sake of simplicity, the specific source of the general AI model is omitted. The base station can receive third information sent from the cloud / core network to determine the general AI model, or the base station can obtain the general AI model based on configuration information. Step 1 corresponds to step S602 in Figure 6b, step 2 corresponds to step S603 in Figure 6b, and the UE uses a type 0 terminal-side model. Step 5 corresponds to step S604 in Figure 6b.

[0340] It should be understood that steps 3, 4, and 6 are all optional steps. For steps 3 and 4, the base station can send the category corresponding to the UE and the end-side dedicated AI model to the UE based on the second information fed back by the UE, or it can send the category corresponding to the UE and the end-side dedicated AI model to the UE based on its own needs or the needs fed back by other communication devices. For step 6, after receiving the category corresponding to the UE and the end-side dedicated AI model from the base station, the UE can choose to adopt and deploy the dedicated AI model, or it can first store the category (for example: type 1) and the dedicated AI model corresponding to the category, and deploy it when there is a demand for the dedicated AI model.

[0341] Optionally, after the base station sends the entire list of dedicated AI models to the UE, as the UE moves, the base station can notify the UE of updates to its category.

[0342] Optionally, the third communication device can generate a third AI model based on simulator pre-training or live network data, and send third information to the second communication device, where the third information is used to determine the third AI model.

[0343] Optionally, the third AI model is included in the second AI model group, the second AI model group also includes 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; wherein, the input of the third AI model includes the output of the fourth AI model, or the input of the fourth AI model includes the output of the third AI model.

[0344] In addition to obtaining the third information from the third communication device in step S601, the first communication device may optionally obtain the third information by default from the first communication device. For example, the first communication device obtains the third information based on a pre-configuration (such as a default configuration), stores the third information locally or in the cloud, and obtains it from the local or cloud when the first communication device needs to deploy a general AI model. After the first communication device receives the third information from the third communication device, the first communication device may receive the third information sent from the second communication device to determine the third AI model, i.e., the general AI model, and deploy the general AI model on the second communication device.

[0345] In addition to obtaining the third information from the first communication device, the second communication device may also optionally provide the third information as a default feature of the second communication device. For example, the second communication device may obtain the third information based on a pre-configured configuration (e.g., a default configuration), store the third information locally or in the cloud, and then obtain the third information from the local computer or cloud when the second communication device needs to deploy a general AI model.

[0346] Optionally, the first communication device and / or the second communication device itself carries or stores (for example, default configuration) a general AI model, and the general AI model can be stored locally or in the cloud of the first communication device and / or the second communication device.

[0347] Optionally, the third information can be understood as a list of general AI models. The list of general AI models can be shown in Table 4 below, taking the general AI model as a single model as an example. The content and delivery method of the list of general AI models are similar to those of the list of specific AI models. For details, please refer to the above description of the list of specific AI models and will not be repeated here.

[0348] Table 4

[0349] In this embodiment, type 0 represents a general AI model, and type 1 represents a specialized AI model. In actual implementation, the types corresponding to different AI models can be set according to specific needs. This is only an example and not a limitation.

[0350] Optionally, after the second communication device determines the third AI model based on the third information in step S603, if the second communication device receives the first information from the first communication device, the second communication device determines the first AI model based on the first information and uses the first AI model, i.e., the dedicated AI model, for communication or transmission.

[0351] In one possible implementation, after the first communication device sends the first information to the second communication device, the method further includes: the first communication device receives fourth information from the second communication device, where the fourth information is used to indicate one of the AI ​​models in the list of one or more AI models.

[0352] In this implementation, based on the deployment of a dedicated AI model in the second communication device, the second communication device can actively send fourth information to the first communication device to feedback the recommended AI model (e.g., type 3).

[0353] There are two categories:

[0354] Category 1: The first communication device adopts the suggestion of the second communication device.

[0355] 1. The second communication device feeds back to the first communication device the model type currently recommended by the second communication device.

[0356] 2. The first communication device makes a decision and provides feedback to the second communication device: this type is optional.

[0357] 3. The second communication device runs the terminal-side model of this type.

[0358] Category 2: The first communication device rejects the suggestion of the second communication device and provides a type selection for the first communication device.

[0359] The second communication device feeds back to the first communication device the model type currently recommended by the second communication device.

[0360] The first communication device decides not to recommend selecting the type and feeds back the type recommended by the UE (eg, type 2).

[0361] The second communication device runs the terminal-side model of this type as instructed by the first communication device.

[0362] For example, please refer to Figure 6d, which is a schematic diagram of another implementation of the communication method provided in an embodiment of the present application. This example illustrates a first communication device being a base station (BS) and a second communication device being a user equipment (UE). Figure 6d primarily illustrates that the UE has the right to provide feedback to the base station on the model type of local preference selection, and that the UE's suggestions must interact with the base station's decision.

[0363] 1. The base station sends the general AI model category (for example, type 0) and the terminal-side model to the UE (the UE has been informed through the SIB that the general AI model must be used after access).

[0364] 2. After the UE initially accesses, the type 0 end-side model is used.

[0365] 3. The UE feeds back channel state information (ie, second information) to the base station.

[0366] 4. The base station generates a list of dedicated AI models.

[0367] 5. The base station sends a list of dedicated AI models to the UE (if it is a device-side collaborative model group, you can choose to send only the category and device-side models).

[0368] 6. UE selects a model based on a list of dedicated AI models.

[0369] 7. The UE feeds back the recommended model selection (e.g., type 3) to the base station.

[0370] 8. The base station determines the feasibility of the model selection recommended by the UE.

[0371] 9. If the base station determines that the model selection is feasible, it will feedback a model selection confirmation (ACK) message to the UE. Otherwise, it will feedback a model selection rejection (NACK) message to the UE and provide the base station's selection (for example, type 2).

[0372] 10. If the UE receives confirmation information from the base station, it uses the selection type previously suggested to the base station (for example, type 3). If the UE receives rejection information from the base station, it uses the selection type suggested by the base station (for example, type 2).

[0373] It should be understood that for the sake of simplicity, the specific source of the general AI model is omitted. The base station can receive third information sent from the cloud / core network to determine the general AI model, or the base station can obtain the general AI model based on configuration information. Step 1 corresponds to step S602 in Figure 6b, step 2 corresponds to step S603 in Figure 6b, and the UE uses a type 0 terminal-side model. Step 5 corresponds to step S604 in Figure 6b.

[0374] It should be understood that steps 3, 4, and 6 through 10 are all optional. For steps 3 and 4, the base station may send the UE's corresponding category and device-side dedicated AI model to the UE based on the second information fed back by the UE, or may send the UE's corresponding category and device-side dedicated AI model to the UE based on its own needs or those fed back by other communication devices. For step 6, after receiving the UE's corresponding category and device-side dedicated AI model from the base station, the UE may choose to adopt and deploy the dedicated AI model, or may first store the category (e.g., type 1) and the corresponding dedicated AI model and deploy the dedicated AI model when needed. For steps 7 through 10, whether the UE feeds back a recommended model selection to the base station is determined by the UE's actual needs during execution and is optional. The UE may feed back a recommended model selection (e.g., type 3) to the base station. If the base station determines that the recommended model selection is feasible, the UE may adopt the recommended model selection (e.g., type 3). If the base station rejects the recommendation, the UE adopts the model selection recommended by the base station (e.g., type 2).

[0375] It should be understood that since the base station has more local information, the priority of the base station selection is higher than the UE recommendation selection.

[0376] Optionally, in this implementation, the list of one or more AI models includes a list of one or more specialized AI models and / or a list of general AI models.

[0377] It should be understood that the list of one or more AI models may include the content of a dedicated AI model and / or the content of a general AI model, that is, any of the contents in Table 2, Table 3, and Table 4 above, or the contents in Table 2 and Table 4 above, Table 3 and Table 4 above, or a combination of the contents shown in Table 2, Table 3, and Table 4 above. Specifically, the settings can be made according to actual needs and are not limited here.

[0378] 2. In one possible implementation, the first AI model is a general AI model.

[0379] In one possible implementation, the universal AI model is generated by a third communication device. Specifically, the third communication device receives sixth information from the first communication device and generates fifth information based on the sixth information. The third communication device then sends the fifth information to the first communication device. The fifth information is used to determine the universal AI model. The universal AI model is deployed on the first communication device and the second communication device, or the universal AI model is deployed on the second communication device.

[0380] Based on the above technical solution, the third communication device generates a universal AI model based on the sixth information from the first communication device and sends the fifth information to the first communication device to deploy the universal AI model on the first communication device. The first communication device can generate or update the universal AI model based on the information (e.g., one or more fifth information) from one or more third communication devices. Subsequently, multiple first communication devices and second communication devices connected to each first communication device can deploy a universal AI model with high generalization and good versatility.

[0381] Optionally, the sixth information includes the second information. Specifically, the first communication device receives the second information from the second communication device and sends the sixth information to the third communication device, where the sixth information includes all the content of the second information, or the sixth information includes part of the content of the second information, for example, only sending data required by the third communication device and omitting other data to reduce the amount of data transmission.

[0382] Optionally, the sixth information is determined based on the processed second information. Specifically, after the first communication device receives the second information from the second communication device, the first communication device processes the second information based on its own AI requirements or those from the third communication device and sends the processed information (i.e., the sixth information) to the third communication device. Alternatively, the first communication device extracts a list of general AI models from the second information and sends the list of general AI models to the third communication device.

[0383] Optionally, the third communication device is a cloud side or a core network.

[0384] In one possible implementation, before the first communication device receives the first information from the first communication device, the method further includes: the first communication device sending a broadcast message to the second communication device, the broadcast message being used to notify the second communication device to use the first AI model. In response, the second communication device receives the broadcast message from the first communication device.

[0385] It should be understood that the first communication device can inform the second communication device whether it needs to use the general AI model after it accesses the second communication device by sending a broadcast message (e.g., a system information block (SIB)). If so, the first communication device can send the first information to the second communication device after the second communication device accesses the second communication device. If not, the first communication device does not send the first information to the second communication device, but instead sends the first information based on a request from the second communication device when the second communication device has relevant needs.

[0386] In a possible implementation, one or more of the first information, second information, third information, fourth information, fifth information, and sixth information are periodically sent information.

[0387] Specifically, the first information can be one of the bases for determining the AI ​​model, the second information can deploy a dedicated AI model, the third information can deploy a general AI model, the fourth information can provide model suggestions, and the fifth information can deploy a general AI model. Among them, the first communication device and the second communication device periodically send the first information and / or the second information and / or the third information and / or the fourth information, which can achieve periodic determination and / or periodic deployment and / or periodic suggestions of the AI ​​model, so as to achieve multiple iterative updates of the AI ​​model through a periodic process. The first communication device and the third communication device periodically send the fifth information and / or the sixth information, which can achieve periodic deployment of the AI ​​model, so as to achieve multiple iterative updates of the AI ​​model through a periodic process.

[0388] It should be understood that the specific implementation method of the general AI model is similar to that of the dedicated AI model. For the specific implementation content of the general AI model, please refer to the description in the dedicated AI model and will not be repeated here.

[0389] 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).

[0390] 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.

[0391] 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 generate a first artificial intelligence AI model; the transceiver unit 702 is used to send first information, and the first information is used to determine the first AI model.

[0392] In one possible implementation, when the device 700 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive first information from the first communication device; and the processing unit 701 is used to determine a first AI model based on the first information.

[0393] In one possible implementation, when the device 700 is used to execute the method executed by the third 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 sixth information from the first communication device; the processing unit 701 is used to generate fifth information based on the sixth information; the transceiver unit 702 is used to send fifth information to the first communication device, and the fifth information is used to determine a general AI model, and the general AI model is deployed in the first communication device and the second communication device, or the general AI model is deployed in the second communication device.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] Optionally, the logic circuit 801 is used to generate a first artificial intelligence AI model; the input and output interface 802 is used to send first information, and the first information is used to determine the first AI model.

[0398] Optionally, the input / output interface 802 is used to receive first information from a first communication device; and the logic circuit 801 is used to determine a first AI model according to the first information.

[0399] Optionally, the input-output interface 802 is used to receive sixth information from the first communication device; the logic circuit 801 is used to generate fifth information based on the sixth information; the input-output interface 802 is used to send fifth information to the first communication device, and the fifth information is used to determine a common AI model, and the common AI model is deployed in the first communication device and the second communication device, or the common AI model is deployed in the second communication device.

[0400] 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.

[0401] In a possible implementation, the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .

[0402] 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.

[0403] 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.

[0404] Optionally, the processing device may include 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.

[0405] 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.

[0406] 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).

[0407] Herein, a possible logical structure diagram of the communication device 900 is shown. The communication device 900 may include but is not limited to at least one processor 901 and a communication port 902 .

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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, for example, within a single chip. Memory 1012 can store program code for implementing the technical solutions of the embodiments of this application, and its execution is controlled by processor 1011. The various computer program codes executed can also be considered drivers for processor 1011.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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 ).

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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: Generate the first artificial intelligence AI model; Sending first information to a second communication device, where the first information is used to determine the first AI model.

2. The method according to claim 1, characterized in that The first AI model is a dedicated AI model.

3. The method according to claim 2, characterized in that Before generating the first AI model, the process includes: receiving second information from the second communication device; Generating the first AI model includes: Generate the first AI model according to the second information.

4. The method according to claim 3, characterized in that The second information includes at least one of the following: The channel state information between the first communication device and the second communication device, and the local computing power state of the second communication device.

5. The method according to claim 4, characterized in that The second information may further include at least one of the following: The location information of the second communication device, the behavior information of the second communication device, the local data information and the tag information of the second communication device.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: receiving third information from a third communication device; The third information is sent to the second communication device, where the third information is used to determine a third AI model, and the third AI model is a general AI model.

7. The method according to any one of claims 1 to 6, characterized in that After sending the first information to the second communication device, the method further includes: Fourth information is received from the second communication device, where the fourth information is used to indicate one of the AI ​​models in the list of one or more AI models.

8. The method according to claim 7, characterized in that The list of one or more AI models includes a list of one or more specialized AI models and / or a list of general AI models.

9. A communication method, characterized in that: The method is applied to a second communication device, and the method includes: receiving first information from a first communication device; A first AI model is determined according to the first information.

10. The method according to claim 9, characterized in that The first AI model is a dedicated AI model.

11. The method according to claim 10, characterized in that Before receiving the first information from the first communication device, the method further includes: Sending second information to the first communication device, where the second information is used to generate the first AI model.

12. The method according to claim 11, characterized in that The second information includes at least one of the following: The channel state information between the first communication device and the second communication device, and the local computing power state of the second communication device.

13. The method according to claim 12, characterized in that The second information also includes at least one of the following: The location information of the second communication device, the behavior information of the second communication device, the local data information and the tag information of the second communication device.

14. The method according to any one of claims 10 to 13, characterized in that The method further comprises: Receive third information from the first communication device, where the third information is used to determine a third AI model, where the third AI model is a general AI model.

15. The method according to any one of claims 10 to 14, characterized in that After determining the first AI model according to the first information, the method further includes: Send fourth information to the first communication device, where the fourth information is used to indicate one of the AI ​​models in the list of one or more AI models.

16. The method according to claim 15, characterized in that The list of one or more AI models includes a list of one or more specialized AI models and / or a list of general AI models.

17. The method according to any one of claims 1 to 16, characterized in that The first information is used to determine the category to which the second communication device belongs.

18. The method according to any one of claims 1 to 17, characterized in that The first AI model is included in a first AI model group, the first AI model group also includes the 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; wherein the input of the second AI model includes the output of the first AI model, or the input of the first AI model includes the output of the second AI model.

19. The method according to claim 18, characterized in that The first information is also used to determine a second AI model in the first AI model group.

20. The method according to any one of claims 1 to 19, characterized in that The first information includes an identifier of the first AI model, and the identifier is used to determine the first AI model in a list including one or more dedicated AI models.

21. The method according to claim 20, characterized in that Before sending the first information to the second communication device, the method further includes: The list of the one or more specialized AI models is sent to the second communication device.

22. The method according to any one of claims 1 to 19, characterized in that The first information includes model parameters and / or model structure information of the first AI model.

23. The method according to claim 22, characterized in that The first information also includes an identifier of the first AI model.

24. The method according to any one of claims 1 to 23, characterized in that The first information is information sent periodically.

25. A communication device, characterized in that: Comprising means for performing the method as claimed in any one of claims 1 to 24.

26. 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 24.

27. The communication device according to claim 26, characterized in that The communication device is a chip or a chip system.

28. 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 24 is implemented.

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