Communication method and related device

Abstract

Provided in the present application are a communication method and a related device, which are used for enabling the computing power of communication nodes to be applied to model processing of AI models and also reducing overheads for instructing the AI models. In the method, after a first communication apparatus sends first information used for indicating the corresponding relationship between N communication parameters and M AI models, the first communication apparatus can also send second information used for determining a first communication parameter among the N communication parameters, wherein in the corresponding relationship, the first communication parameter corresponds to a first AI model among the M AI models. That is to say, the first communication apparatus can instruct the first AI model on the basis of the corresponding relationship indicated by the first information and the first communication parameter indicated by the second information, so that a receiver of the first information and the second information can subsequently perform processing on the basis of the first AI 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 15, 2023, with application number 202311547635.9 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 communications, and in particular to a communication method and related equipment. Background Art

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

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

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

[0006] Summary of the Invention

[0007] The present application provides a communication method and related equipment for enabling the computing power of communication nodes to be applied to model processing of artificial intelligence (AI) models while also reducing the overhead of instructing the AI ​​models.

[0008] The first aspect of the present application provides a communication method, which is performed by a first communication device, which may be a communication device (such as a network device or a terminal device), or the first communication device may be a component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the first communication device sends first information, which is used to indicate the correspondence between N communication parameters and M AI models, where N and M are both positive integers; the first communication device sends second information, which is used to determine the first communication parameter among the N communication parameters; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models.

[0009] Based on the above technical solution, after the first communication device sends the first information for indicating the correspondence between N communication parameters and M AI models, the first communication device may also send the second information for determining the first communication parameter among the N communication parameters; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models. In other words, the first communication device can implement the indication of the first AI model through the correspondence indicated by the first information and the first communication parameter indicated by the second information, so that the recipients of the first information and the second information can subsequently perform processing based on the first AI model. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to AI processing, and the implementation method of implementing the indication of the AI ​​model by multiplexing the indication of the communication parameters can also reduce the overhead of indicating the AI ​​model.

[0010] In this application, terms such as AI model, neural network model, AI neural network model, machine learning model, and AI processing model can be used interchangeably.

[0011] It should be understood that the AI ​​models involved in this application (e.g., any of the M AI models, any of the Y AI models mentioned below, etc.) can be deployed on one or more communication devices. Optionally, when an AI model is deployed on one communication device, the AI ​​model can be referred to as a single-end deployed AI model; when an AI model is deployed on multiple communication devices, the AI ​​model can be referred to as a dual-end / multi-end deployed AI model.

[0012] For example, the following will take the AI ​​model as the first AI model as an example to illustrate the processing process of the AI ​​model.

[0013] In an implementation example, the first AI model can be deployed on a first communication device, and the input data of the first AI model may include data from a second communication device. Accordingly, after the second communication device receives the first information and the second information sent by the first communication device, the second communication device can send data associated with the first AI model to the first communication device, so that the first communication device can implement model processing of the first AI model based on the data associated with the first AI model. Among them, the model processing involved in this application can be understood as processing the model, which includes updating the model, training the model, inferring the model, selecting the model, switching the model, activating the model, deactivating the model, and identifying the model. One or more of the following.

[0014] In another implementation example, the first AI model may be deployed on a second communication device. Accordingly, after the second communication device receives the first information and the second information sent by the first communication device, the second communication device may perform model processing on the first AI model.

[0015] In another implementation example, the first AI model may include a first AI sub-model and a second AI sub-model, that is, the first AI sub-model and the second AI sub-model may be different parts of the same AI model. For example, the input data of the second AI sub-model may include the output data of the first AI sub-model, or the input data of the first AI sub-model may include the output data of the second AI sub-model. Taking the case where the first AI sub-model is deployed on the first communication device and the second AI sub-model is deployed on the second communication device, and the input data of the second AI sub-model includes the output data of the first AI sub-model as an example, after the first communication device indicates the first AI model through the first information and the second information, the first communication device may send the output data of the first AI sub-model to the second communication device, and the second communication device may use the output data of the first AI sub-model as one of the input data of the second AI sub-model, and perform model processing on the second AI sub-model.

[0016] Optionally, the first AI model processing includes not only the first AI sub-model and the second AI sub-model, but also other AI sub-models. The other AI sub-models can be deployed in other communication devices different from the first communication device and the second communication device, which is not limited here.

[0017] 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 (first communication device and second communication device). Among them, the AI ​​model involved in this application (such as any AI model of the M AI models, any model of the Y AI models mentioned later, 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, and one or more of the AI ​​models for replacing one or more modules in a transmitter and / or receiver.

[0018] Optionally, the first information is used to indicate the correspondence between N communication parameters and M AI models, wherein the first information may include model identifiers (Model IDs) of the M AI models, model indexes (Model Indexes) of the M AI models, etc. In addition, the first information may include identifiers of the N communication parameters, indexes of the N communication parameters, etc. In this way, the overhead of the first information can be reduced.

[0019] Optionally, the first information may include the N communication parameters and / or model parameters of the M AI models.

[0020] Optionally, the first communication parameter determined by the second information may include one or more parameters among the N communication parameters. Accordingly, the first AI model corresponding to the first communication parameter may include one or more AI models among the M AI models.

[0021] It should be noted that both N and M are positive integers, and the size relationship between N and M can be realized in many different ways.

[0022] For example, when N and M are equal, there can be a one-to-one correspondence between the N communication parameters and the M AI models.

[0023] For another example, when N is less than M, at least two AI models among the M AI models can be indicated by one of the N communication parameters. That is, the first AI model corresponding to the first communication parameter determined by the second information can be the at least two AI models (or any one of the at least two AI models). Optionally, the at least two AI models can be AI models with the same or similar model structures, the at least two AI models can be AI models with the same or similar model functions, etc.

[0024] For another example, when N is greater than M, one of the M AI models can be indicated by at least two communication parameters among the N communication parameters.

[0025] In a possible implementation manner of the first aspect, the first communication parameter includes parameters associated with modulation and demodulation, and the first AI model includes an AI model for modulation and / or demodulation.

[0026] Based on the above technical solution, the first communication parameter determined by the second information may include parameters associated with modulation and demodulation. Accordingly, in this correspondence, the first AI model corresponding to the first communication parameter may include an AI model for modulation and / or demodulation, so as to indicate the parameters associated with modulation and demodulation while also being able to indicate the AI ​​model used for modulation and / or demodulation, so that the subsequent second communication device can participate in the model processing of the AI ​​model for modulation and / or demodulation based on the parameters associated with modulation and demodulation.

[0027] Optionally, the AI ​​model used for modulation and / or demodulation can be called an intelligent modulation model, an intelligent demodulation model, an intelligent modulation and demodulation model, etc.

[0028] Optionally, the parameters associated with modulation and demodulation include one or more of the following: modulation and coding scheme (MCS), frequency domain resource indication, transmit power control command (TPC command), and transmit precoding matrix indicator (TPMI).

[0029] In a possible implementation manner of the first aspect, the first communication parameter includes configuration information of a reference signal, and the first AI model includes an AI model for channel prediction.

[0030] Based on the above technical solution, the first communication parameter determined by the second information may include the configuration information of the reference signal. Accordingly, in this correspondence, the first AI model corresponding to the first communication parameter may include an AI model for channel prediction, so as to indicate the configuration information of the reference signal while also being able to indicate the AI ​​model for channel prediction. Since the transmission and reception of the reference signal configured by the reference signal configuration information is associated with the channel prediction process, in this way, the subsequent second communication device can participate in the model processing of the AI ​​model for channel prediction based on the reference signal transmitted by the reference signal configuration information.

[0031] Optionally, the AI ​​model used for channel prediction can be called a channel estimation model, a channel pre-estimation model, a channel simulation recovery model, a channel reconstruction model, a channel acquisition model, a channel inference model, etc.

[0032] Optionally, the configuration information of the reference signal includes at least one of the following: time domain configuration information, frequency domain configuration information, spatial domain configuration information, port information, period information, and codebook configuration information.

[0033] In a possible implementation manner of the first aspect, the first communication parameter includes a parameter associated with beam management, and the first AI model includes an AI model for beam management.

[0034] Based on the above technical solution, the first communication parameter determined by the second information may include parameters associated with beam management. Accordingly, in this correspondence, the first AI model corresponding to the first communication parameter may include an AI model for beam management, so as to indicate the parameters associated with beam management while also being able to indicate the AI ​​model used for beam management, so that the subsequent second communication device can participate in the model processing of the AI ​​model used for beam management based on the parameters associated with beam management.

[0035] Optionally, the AI ​​model used for beam management can be called a beam management model, a beam optimization model, etc.

[0036] Optionally, the parameters associated with beam management include at least one of the following: channel characteristic information of the cell, and information on the number of beams associated with the AI ​​model used for beam management.

[0037] In a possible implementation manner of the first aspect, the method further includes: the first communication device receiving or sending AI data of the first AI model.

[0038] Based on the above technical solution, after the first communication device sends the second information to indicate the first AI model, the first communication device can also receive or send AI data of the first AI model to implement model processing of the first AI model through the AI ​​data.

[0039] In a possible implementation manner of the first aspect, the first communication device receiving or sending the AI ​​data of the first AI model includes: the first communication device receiving or sending the AI ​​data of the first AI model based on the first communication parameter.

[0040] Based on the above technical solution, the first communication device can receive or send AI data of the first AI model based on the first communication parameters determined by the second information. In this way, the second information can be used to implement the first communication parameters and the indication of the first AI model, and the model processing of the AI ​​model can also be realized through the communication parameters indicated by the second information.

[0041] In a possible implementation manner of the first aspect, the second information includes a first communication parameter among the N communication parameters, or the second information includes an index of the first communication parameter among the N communication parameters.

[0042] Based on the above technical solution, the second information can be implemented in any of the above methods to improve the flexibility of the solution implementation. In addition, when the second information includes the index of the first communication parameter among the N communication parameters, the overhead of indicating the first communication parameter can be reduced.

[0043] In a possible implementation of the first aspect, before the first communication device sends the first information, the method further includes: the first communication device receives third information, where the third information is used to indicate K AI models supported by the second communication device.

[0044] Optionally, at least one AI model among the M AI models is the same as at least one AI model among the K AI models.

[0045] Based on the above technical solution, the first communication device may also receive third information indicating the K AI models supported by the second communication device, and the first communication device may subsequently determine the first information based on the third information. In this way, the M AI models indicated by the first information can be matched to the capabilities supported by the second communication device as much as possible, thereby improving the success rate of model processing of the M AI models.

[0046] Optionally, for the first communication device, after receiving the third information, the first communication device may use at least one AI model among the K AI models indicated by the third information as the basis for determining the first information, or, if the first communication device determines that the K AI models are not applicable, the first communication device may not need to use the third information as the basis for determining the first information.

[0047] In a possible implementation of the first aspect, the M AI models are included in the K AI models.

[0048] Based on the above technical solution, the M AI models indicated by the first information can be included in the K AI models indicated by the third information, so that the M AI models indicated by the first information can match the capabilities supported by the second communication device to improve the success rate of model processing of the M AI models.

[0049] In a possible implementation of the first aspect, P AI models among the M AI models are different from the K AI models, where P is a positive integer; wherein the first information includes model parameters of the P AI models.

[0050] Optionally, the size relationship between P and K is not limited, for example, P is less than K, P is equal to K, or P is greater than K.

[0051] Based on the above technical solution, P AI models among the M AI models indicated by the first information may be different from the K AI models indicated by the third information, and the first information may include model parameters of the P AI models, so that the second communication device can obtain the model parameters of other P AI models other than the K AI models supported by the third information through the first information, thereby improving the implementation flexibility of the indication of the M AI models through the second information.

[0052] Optionally, the third information includes identifiers or indexes of the K AI models. In this way, the overhead of the third information can be reduced.

[0053] Optionally, when P of the M AI models are different from the K AI models, the model parameters of the P AI models may be carried in other information in addition to the first information, which is not limited here. In other words, the first information may not include the model parameters of the P AI models.

[0054] In a possible implementation of the first aspect, after the first communication device sends the first information, the method further includes: the first communication device receiving fourth information and / or fifth information, the fourth information including model parameters of the trained AI model, and the fifth information including auxiliary information; and the first communication device sending sixth information, the sixth information being used to indicate a correspondence between X communication parameters and Y AI models, where the correspondence between the X communication parameters and the Y AI models is determined based on the fourth information and / or the fifth information, where X and Y are both positive integers.

[0055] Based on the above technical solution, the first communication device can receive fourth information including model parameters of the trained AI model, and / or the first communication device can receive fifth information including auxiliary information, so that the first communication device can determine the correspondence between X communication parameters and Y AI models based on the fourth information and / or the fifth information, and indicate the correspondence through sixth information. In other words, the first communication device can update the correspondence between the communication parameters and the AI ​​model and indicate the updated correspondence through the fourth information and / or the fifth information.

[0056] Optionally, the auxiliary information is used to generate / train / strengthen / select / switch / update the AI ​​model to obtain Y AI models. In other words, the auxiliary information may include at least one of auxiliary information for AI model training, AI model training data, auxiliary information during AI model training data collection, and auxiliary information during AI model inference.

[0057] Optionally, the sixth information includes model identifiers of the Y AI models. In this way, the overhead of the sixth information can be reduced.

[0058] Optionally, the auxiliary information includes at least one of the following: cell information, cell configuration, distribution of the first communication device and / or the second communication device, speed of the first communication device and / or the second communication device, channel power delay spectrum, configuration of the first communication device and / or the second communication device, antenna configuration of the first communication device and / or the second communication device, beam configuration of the first communication device and / or the second communication device, antenna pattern of the first communication device and / or the second communication device, interference information, signal-to-noise ratio, MCS, channel rank, data error, data quality, resource granularity, and location information.

[0059] Optionally, the trained AI model can be obtained by training based on the first AI model, or can be obtained by training based on other AI models (the other AI model can be one of the M AI models, or can be not one of the M AI models) by the second communication device, which is not limited here. Exemplarily, the first communication device and / or the second communication device can continue to train (or retrain) according to the first AI model. If the model performance of the AI ​​model obtained by continued training is better than the threshold, the trained first AI model can be used as the basis for determining the fourth information. For example, if the model performance of the AI ​​model obtained by continued training is worse than the threshold, the trained first AI model can be used instead of as the basis for determining the fourth information. Instead, it can be trained based on other models or retrained with random parameters to obtain the model parameters of the trained AI model contained in the fourth information.

[0060] 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 terminal device), or the second communication device can be a partial 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, which is used to indicate the correspondence between N communication parameters and M AI models, where N and M are both positive integers; the second communication device receives second information, which is used to indicate the first communication parameter among the N communication parameters; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models.

[0061] Based on the above technical solution, after the second communication device receives the first information indicating the correspondence between N communication parameters and M AI models, the first communication device may also receive the second information for determining the first communication parameter among the N communication parameters; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models. In other words, the first communication device can implement the indication of the first AI model through the correspondence indicated by the first information and the first communication parameter indicated by the second information, so that the second communication device can subsequently perform processing based on the first AI model. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to AI processing, and the implementation method of implementing the indication of the AI ​​model by multiplexing the indication of the communication parameters can also reduce the overhead of indicating the AI ​​model.

[0062] In a possible implementation manner of the second aspect, the first communication parameter includes parameters associated with modulation and demodulation, and the first AI model includes an AI model for modulation and / or demodulation.

[0063] Based on the above technical solution, the first communication parameter determined by the second information may include parameters associated with modulation and demodulation. Accordingly, in this correspondence, the first AI model corresponding to the first communication parameter may include an AI model for modulation and / or demodulation, so as to indicate the parameters associated with modulation and demodulation while also being able to indicate the AI ​​model used for modulation and / or demodulation, so that the subsequent second communication device can participate in the model processing of the AI ​​model for modulation and / or demodulation based on the parameters associated with modulation and demodulation.

[0064] Optionally, the AI ​​model used for modulation and / or demodulation can be called an intelligent modulation model, an intelligent demodulation model, an intelligent modulation and demodulation model, etc.

[0065] Optionally, the parameters associated with modulation and demodulation include one or more of the following: modulation and coding scheme (MCS), frequency domain resource indication, transmit power control command (TPC command), and transmit precoding matrix indicator (TPMI).

[0066] In a possible implementation manner of the second aspect, the first communication parameter includes configuration information of a reference signal, and the first AI model includes an AI model for channel prediction.

[0067] Based on the above technical solution, the first communication parameter determined by the second information may include the configuration information of the reference signal. Accordingly, in this correspondence, the first AI model corresponding to the first communication parameter may include an AI model for channel prediction, so as to indicate the configuration information of the reference signal while also being able to indicate the AI ​​model for channel prediction. Since the transmission and reception of the reference signal configured by the reference signal configuration information is associated with the channel prediction process, in this way, the subsequent second communication device can participate in the model processing of the AI ​​model for channel prediction based on the reference signal transmitted by the reference signal configuration information.

[0068] Optionally, the AI ​​model used for channel prediction can be called a channel estimation model, a channel pre-estimation model, a channel simulation recovery model, a channel reconstruction model, a channel acquisition model, a channel inference model, etc.

[0069] Optionally, the configuration information of the reference signal includes at least one of the following: time domain configuration information, frequency domain configuration information, spatial domain configuration information, port information, period information, and codebook configuration information.

[0070] In a possible implementation manner of the second aspect, the first communication parameter includes a parameter associated with beam management, and the first AI model includes an AI model for beam management.

[0071] Based on the above technical solution, the first communication parameter determined by the second information may include parameters associated with beam management. Accordingly, in this correspondence, the first AI model corresponding to the first communication parameter may include an AI model for beam management, so as to indicate the parameters associated with beam management while also being able to indicate the AI ​​model used for beam management, so that the subsequent second communication device can participate in the model processing of the AI ​​model used for beam management based on the parameters associated with beam management.

[0072] Optionally, the AI ​​model used for beam management can be called a beam management model, a beam optimization model, etc.

[0073] Optionally, the parameters associated with beam management include at least one of the following: channel characteristic information of the cell, and information on the number of beams associated with the AI ​​model used for beam management.

[0074] In a possible implementation manner of the second aspect, the method further includes: the second communication device receiving or sending AI data of the first AI model.

[0075] Based on the above technical solution, after the second communication device receives the second information to determine the first AI model, the second communication device can also receive or send AI data of the first AI model to implement model processing of the first AI model through the AI ​​data.

[0076] In a possible implementation of the second aspect, the second communication device receives or sends the AI ​​data of the first AI model, including: the second communication device receives or sends the AI ​​data of the first AI model based on the first communication parameter.

[0077] Based on the above technical solution, the second communication device can receive or send the AI ​​data of the first AI model based on the first communication parameters determined by the second information. In this way, the second information can be used to implement the first communication parameters and the indication of the first AI model, and the model processing of the AI ​​model can also be realized through the communication parameters indicated by the second information.

[0078] In a possible implementation manner of the second aspect, the second information includes a first communication parameter among the N communication parameters, or the second information includes an index of the first communication parameter among the N communication parameters.

[0079] Based on the above technical solution, the second information can be implemented in any of the above methods to improve the flexibility of the solution implementation. In addition, when the second information includes the index of the first communication parameter among the N communication parameters, the overhead of indicating the first communication parameter can be reduced.

[0080] In a possible implementation of the second aspect, before the second communication device receives the first information, the method further includes: the second communication device sending third information, where the third information is used to indicate K AI models supported by the second communication device.

[0081] Optionally, at least one AI model among the M AI models is the same as at least one AI model among the K AI models.

[0082] Based on the above technical solution, the second communication device may also send third information indicating the K AI models supported by the second communication device, and the first communication device may subsequently determine the first information based on the third information. In this way, the M AI models indicated by the first information can be matched to the capabilities supported by the second communication device as much as possible, thereby improving the success rate of model processing of the M AI models.

[0083] Optionally, for the first communication device, after receiving the third information, the first communication device may use at least one AI model among the K AI models indicated by the third information as the basis for determining the first information, or, if the first communication device determines that the K AI models are not applicable, the first communication device may not need to use the third information as the basis for determining the first information.

[0084] In a possible implementation of the second aspect, the M AI models are included in the K AI models.

[0085] Based on the above technical solution, the M AI models indicated by the first information can be included in the K AI models indicated by the third information, so that the M AI models indicated by the first information can match the capabilities supported by the second communication device to improve the success rate of model processing of the M AI models.

[0086] In a possible implementation of the second aspect, P AI models among the M AI models are different from the K AI models, where P is a positive integer; wherein the first information includes model parameters of the P AI models.

[0087] Optionally, the size relationship between P and K is not limited, for example, P is less than K, P is equal to K, or P is greater than K.

[0088] Based on the above technical solution, P AI models among the M AI models indicated by the first information may be different from the K AI models indicated by the third information, and the first information may include model parameters of the P AI models, so that the second communication device can obtain the model parameters of other P AI models other than the K AI models supported by the third information through the first information, thereby improving the implementation flexibility of the indication of the M AI models through the second information.

[0089] Optionally, the third information includes identifiers or indexes of the K AI models. In this way, the overhead of the third information can be reduced.

[0090] Optionally, when P of the M AI models are different from the K AI models, the model parameters of the P AI models may be carried in other information in addition to the first information, which is not limited here. In other words, the first information may not include the model parameters of the P AI models.

[0091] In a possible implementation of the second aspect, after the second communication device receives the first information, the method further includes: the second communication device sending fourth information and / or fifth information, the fourth information including model parameters of the trained AI model, and the fifth information including auxiliary information; and the second communication device receiving sixth information, the sixth information being used to indicate a correspondence between X communication parameters and Y AI models, where the correspondence between the X communication parameters and the Y AI models is determined based on the fourth information and / or the fifth information, where X and Y are both positive integers.

[0092] Based on the above technical solution, the first communication device can receive fourth information including model parameters of the trained AI model, and / or the first communication device can receive fifth information including auxiliary information, so that the first communication device can determine the correspondence between X communication parameters and Y AI models based on the fourth information and / or the fifth information, and indicate the correspondence through sixth information. In other words, the first communication device can update the correspondence between the communication parameters and the AI ​​model and indicate the updated correspondence through the fourth information and / or the fifth information.

[0093] Optionally, the auxiliary information is used to generate / train / strengthen / select / switch / update the AI ​​model to obtain Y AI models. In other words, the auxiliary information may include at least one of auxiliary information for AI model training, AI model training data, auxiliary information during AI model training data collection, and auxiliary information during AI model inference.

[0094] Optionally, the sixth information includes model identifiers of the Y AI models. In this way, the overhead of the sixth information can be reduced.

[0095] Optionally, the auxiliary information includes at least one of the following: cell information, cell configuration, distribution of the first communication device and / or the second communication device, speed of the first communication device and / or the second communication device, channel power delay spectrum, configuration of the first communication device and / or the second communication device, antenna configuration of the first communication device and / or the second communication device, beam configuration of the first communication device and / or the second communication device, antenna pattern of the first communication device and / or the second communication device, interference information, signal-to-noise ratio, MCS, channel rank, data error, data quality, resource granularity, and location information.

[0096] Optionally, the trained AI model can be obtained by training based on the first AI model, or can be obtained by training based on other AI models (the other AI model can be one of the M AI models, or can be not one of the M AI models) by the second communication device, which is not limited here. Exemplarily, the first communication device and / or the second communication device can continue to train (or retrain) according to the first AI model. If the model performance of the AI ​​model obtained by continued training is better than the threshold, the trained first AI model can be used as the basis for determining the fourth information. For example, if the model performance of the AI ​​model obtained by continued training is worse than the threshold, the trained first AI model can be used instead of as the basis for determining the fourth information. Instead, it can be trained based on other models or retrained with random parameters to obtain the model parameters of the trained AI model contained in the fourth information.

[0097] A third aspect of the present application provides a communication device, which is a first communication device and includes a transceiver unit and a processing unit; the processing unit is used to determine first information and second information; the transceiver unit is used to send first information, and the first information is used to indicate the correspondence between N communication parameters and M artificial intelligence AI models, where N and M are both positive integers; the transceiver unit also sends second information, and the second information is used to determine a first communication parameter among the N communication parameters; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models.

[0098] In the third aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects. For details, please refer to the first aspect and will not be repeated here.

[0099] In a fourth aspect, the present application provides a communication device, which is a second communication device, and includes a transceiver unit and a processing unit. The transceiver unit is used to receive first information and second information; the processing unit is used to determine the correspondence between N communication parameters and M AI models based on the first information, where N and M are both positive integers; the processing unit is also used to determine a first communication parameter among the N communication parameters based on the second information; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models.

[0100] In the fourth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the second aspect and achieve corresponding technical effects. For details, please refer to the second aspect and will not be repeated here.

[0101] In a fifth aspect, the present application provides a communication device, comprising at least one processor, wherein the at least one processor is coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the program or instructions so that the device implements the method described in any possible implementation method of any one of the first to second aspects.

[0102] In a sixth aspect, the present application provides a communication device comprising at least one logic circuit and an input / output interface; the logic circuit is used to execute the method described in any possible implementation of any one of the first to second aspects.

[0103] In a seventh aspect, the present application provides a communication system, which includes the above-mentioned first communication device and second communication device.

[0104] In an eighth aspect, the present application provides a computer-readable storage medium for storing one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in any possible implementation of any one of the first to second aspects above.

[0105] In a ninth aspect, the present application provides a computer program product (or computer program). When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of any one of the first to second aspects above.

[0106] In a tenth aspect, the present application provides a chip system comprising at least one processor for supporting a communication device to implement the method described in any possible implementation of any one of the first to second aspects.

[0107] In one possible design, the chip system may further include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of a chip or may include a chip and other discrete components. Optionally, the chip system may further include an interface circuit for providing program instructions and / or data to the at least one processor.

[0108] Among them, the technical effects brought about by any design method in the third to tenth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to second aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figures 1a to 1c are schematic diagrams of a communication system provided by this application;

[0110] Figures 2a to 2g are schematic diagrams of the AI ​​processing process involved in this application;

[0111] FIG3 is an interactive schematic diagram of the communication method provided by this application;

[0112] Figures 4a to 4c are schematic diagrams of the AI ​​processing process provided by this application;

[0113] Figures 5 and 6 are interactive diagrams of the communication method provided by this application;

[0114] 7 to 11 are schematic diagrams of the communication device provided in this application. DETAILED DESCRIPTION

[0115] First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0116] (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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] (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.

[0123] 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).

[0124] 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).

[0125] 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.

[0126] 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.

[0127] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

[0128] Table 1

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] (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.

[0134] Furthermore, these values ​​and parameters can be changed or updated.

[0135] (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.

[0136] (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.

[0137] 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.

[0138] 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.

[0139] (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.

[0140] 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.

[0141] The present application can be applied to a long term evolution (LTE) system, a new radio (NR) system, or a communication system evolved after 5G (such as 6G, etc.). The communication system includes at least one network device and / or at least one terminal device.

[0142] Please refer to Figure 1a, which is a schematic diagram of a communication system in this application. Figure 1a exemplarily illustrates a network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. In the example shown in Figure 1a, terminal device 1 is a smart teacup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas pump, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

[0143] As shown in Figure 1a, the AI ​​configuration information sending entity can be a network device. The AI ​​configuration information receiving entity can be terminal devices 1-6. In this case, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 can send data to the network device, and the network device needs to receive data sent by terminal devices 1-6. At the same time, the network device can send configuration information to terminal devices 1-6.

[0144] For example, in Figure 1a, terminal devices 4 and 6 can also form a communication system. Terminal device 5 serves as a network device, i.e., the AI ​​configuration information sending entity; terminal devices 4 and 6 serve as terminal devices, i.e., the AI ​​configuration information receiving entities. For example, in a connected vehicle system, terminal device 5 sends AI configuration information to terminal devices 4 and 6, respectively, and receives data from them. Correspondingly, terminal devices 4 and 6 receive AI configuration information from terminal device 5 and send data to terminal device 5.

[0145] Taking the communication system shown in Figure 1a as an example, in addition to executing communication-related services, different devices (including between network devices, between network devices and terminal devices, and / or between terminal devices) may also execute AI-related services.

[0146] As shown in Figure 1b, taking the network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

[0147] As shown in Figure 1c, taking the terminal devices including a TV and a mobile phone as an example, communication-related services and AI-related services can also be performed between the TV and the mobile phone.

[0148] The technical solution provided in this application can be applied to a wireless communication system (e.g., the system shown in FIG. 1a , FIG. 1b , or FIG. 1c ). For example, an AI network element can be introduced into the communication system provided in this application to implement some or all AI-related operations. The AI ​​network element can also be referred to as an AI node, AI device, AI entity, AI module, AI model, or AI unit, etc. The AI ​​network element can be a network element built into the communication system. For example, the AI ​​network element can be an AI module built into: an access network device, a core network device, a cloud server, or a network management (OAM) to implement AI-related functions. The OAM can be a network management device for a core network device and / or a network management device for an access network device. Alternatively, the AI ​​network element can also be an independently set network element in the communication system. Optionally, the terminal or the chip built into the terminal can also include an AI entity to implement AI-related functions.

[0149] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0150] 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).

[0151] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] As shown in Figure 2a, it is a schematic diagram of the neuron structure. Assume that the input of the neuron is x=[x0,x1,…,x n ], and the weights corresponding to each input are w=[w,w1,…,w n ], where n is a positive integer, w i and x i It can be a decimal, an integer (such as 0, a positive integer or a negative integer, etc.), or a complex number. i As x i The weight of x i Weighted. The bias of the weighted sum of the input values ​​according to the weight is, for example, b. The activation function can take many forms. Assuming that the activation function of a neuron is: y = f(z) = max(0,z), then the output of the neuron is: For another example, if the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: b can be a decimal, an integer (eg, 0, a positive integer, or a negative integer), or a complex number, etc. The activation functions of different neurons in a neural network can be the same or different.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The following is an exemplary description of the implementation process of the neural network with reference to the accompanying drawings.

[0166] 1. Fully connected neural network, also known as multilayer perceptron (MLP).

[0167] 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.

[0168] 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).

[0169] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0170] Alternatively, the output of the neural network can be recursively expressed as: y = f n (w n f n-1 (…)+b n ).

[0171] Where n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0172] 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.

[0173] Optionally, a specific training method is to use a loss function to evaluate the output results of the neural network.

[0174] 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.

[0175] Alternatively, the gradient descent process can be expressed as:

[0176] 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.

[0177] Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

[0178] 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:

[0179] 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.

[0180] 2. Federated Learning (FL)

[0181] 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.

[0182] As shown in Figure 2f, the FL architecture is the most widely used training architecture in the current FL field. The FedAvg algorithm is the basic algorithm of FL. Its algorithm flow is roughly as follows:

[0183] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0184] (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.

[0185] (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.

[0186] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0187] 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.

[0188] 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.

[0189] 3. Decentralized learning: Different from federated learning, decentralized learning is another distributed learning architecture.

[0190] 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:

[0191] 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.

[0192] The technical solutions provided in this application can be applied to wireless communication systems (e.g., the systems shown in Figures 1a and 1b). In wireless communication systems, communication nodes generally have both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., performing signal transmission and reception processing) to enable communication between the network device and other communication nodes.

[0193] In communication networks, communication nodes may have excess computing power beyond supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue.

[0194] In one possible implementation, a communication node can act as a participating node in an AI learning system, applying its computing power to a specific component of the system. With the advent of the era of large models, deep learning models with massive parameters, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT-2), can accomplish increasingly complex tasks and achieve superior performance. However, for large models, even the inference process is limited by device capacity, so large models are typically stored on central cloud servers. Furthermore, each device in the network generates a massive amount of raw data daily, which requires multiple inference calls on the large model. Typically, a device (such as a communication node) sends data to a central server, which then performs inference using the data and returns the inference results to the device. This process consumes significant communication resources for data transmission and also risks the privacy of device data.

[0195] To better save communication costs and protect user data privacy, scholars have proposed distributed inference technology for deep neural networks. This approach distributes models to devices and uses the local computing power of the devices to infer the models, thereby reducing communication costs and ensuring data privacy.

[0196] For example, take the communication process between the central node and any distributed node in the federated learning scenario of Figure 2f as an example. The AI ​​model includes an AI sub-model deployed on the central node and an AI sub-model deployed on any distributed node. Accordingly, through the communication process between the two nodes, the output data of one AI sub-model can be used as the input data of another AI sub-model to realize the AI ​​processing process involving multiple nodes. Alternatively, the AI ​​model can be deployed only on the central node or one of the distributed nodes, and the input data of the AI ​​model deployed on one of the nodes can include the communication data of another node. In this way, the AI ​​processing process involving multiple nodes can also be realized.

[0197] For another example, take the communication process between a distributed node and another distributed node in the decentralized learning scenario in Figure 2g as an example. The AI ​​model includes an AI sub-model deployed on one distributed node and an AI sub-model deployed on the other distributed node. Accordingly, through the communication process between the two nodes, the output data of one AI sub-model can be used as the input data of the other AI sub-model to realize the AI ​​processing process involving multiple nodes. Alternatively, the AI ​​model can be deployed only on one of the nodes, and the input data of the AI ​​model deployed on one of the nodes can include the communication data of the other node. In this way, the AI ​​processing process involving multiple nodes can also be realized.

[0198] However, in the above implementation process, to improve the flexibility of AI model deployment, one or more AI models can be deployed on the same node. Accordingly, how this node and other nodes determine which AI model to use for AI processing during a communication process is an urgent problem that needs to be solved.

[0199] In order to solve the above problems, the present application provides a communication method and related equipment, which are used to enable the computing power of the communication node to be applied to the model processing of the AI ​​model while reducing the overhead of indicating the AI ​​model.

[0200] Please refer to FIG3 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0201] 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 and Figure 6 below, the execution subject of the method can be replaced by a chip, a chip system, a processor, a logic module or software in the communication device. 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).

[0202] S301. A first communication device sends first information, and a second communication device receives the first information. The first information indicates a correspondence between N communication parameters and M AI models, where N and M are both positive integers.

[0203] It should be noted that the first information sent by the first communication device in step S301 is used to indicate the correspondence between the N communication parameters and the M AI models. For example, the first information may include the model identifiers of the M AI models, the model indexes of the M AI models, etc. In addition, the first information may include the identifiers of the N communication parameters, the indexes of the N communication parameters, etc. In this way, the overhead of the first information can be reduced. Alternatively, the first information sent by the first communication device in step S301 may include the N communication parameters and / or the model parameters of the M AI models.

[0204] S302. The first communication device sends second information, and the second communication device receives the second information accordingly. The second information is used to determine a first communication parameter among the N communication parameters; wherein, in the corresponding relationship, the first communication parameter corresponds to the first AI model among the M AI models.

[0205] It should be noted that the second information includes the first communication parameter among the N communication parameters, or the second information includes the index of the first communication parameter among the N communication parameters. In the case where the second information includes the index of the first communication parameter among the N communication parameters, the overhead of indicating the first communication parameter can be reduced.

[0206] 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.

[0207] It should be noted that the first communication parameter used to determine the second information sent by the first communication device in step S302 may include one or more parameters among the N communication parameters. Accordingly, the first AI model corresponding to the first communication parameter may include one or more AI models among the M AI models.

[0208] It should be noted that both N and M are positive integers, and the size relationship between N and M can be realized in many different ways.

[0209] For example, when N and M are equal, there can be a one-to-one correspondence between the N communication parameters and the M AI models.

[0210] For another example, when N is less than M, at least two AI models among the M AI models can be indicated by one of the N communication parameters. That is, the first AI model corresponding to the first communication parameter determined by the second information can be the at least two AI models (or any one of the at least two AI models). Optionally, the at least two AI models can be AI models with the same or similar model structures, the at least two AI models can be AI models with the same or similar model functions, etc.

[0211] For another example, when N is greater than M, one of the M AI models can be indicated by at least two communication parameters among the N communication parameters.

[0212] It should be understood that the AI ​​models involved in this application (e.g., any of the M AI models, any of the Y AI models mentioned below, etc.) can be deployed on one or more communication devices. Optionally, when an AI model is deployed on one communication device, the AI ​​model can be referred to as a single-end deployed AI model; when an AI model is deployed on multiple communication devices, the AI ​​model can be referred to as a dual-end / multi-end deployed AI model.

[0213] For example, the following will take the AI ​​model as the first AI model as an example to illustrate the processing process of the AI ​​model.

[0214] Implementation example 1, as shown in Figure 4a, the first AI model can be deployed on a first communication device, and the input data of the first AI model may include data from a second communication device. Accordingly, after the second communication device receives the first information and the second information sent by the first communication device, the second communication device can send data associated with the first AI model to the first communication device, so that the first communication device can implement model processing of the first AI model based on the data associated with the first AI model. Among them, the model processing involved in this application can be understood as processing the model, which includes updating the model, training the model, inferring the model, selecting the model, switching the model, activating the model, deactivating the model, and identifying the model. One or more of the following.

[0215] In implementation example 2, as shown in FIG4b , the first AI model may be deployed on the second communication device. Accordingly, after the second communication device receives the first information and the second information sent by the first communication device, the second communication device may perform model processing on the first AI model.

[0216] In implementation example three, the first AI model may include a first AI sub-model and a second AI sub-model. That is, the first AI sub-model and the second AI sub-model may be different parts of the same AI model. For example, the input data of the second AI sub-model may include the output data of the first AI sub-model, or the input data of the first AI sub-model may include the output data of the second AI sub-model.

[0217] As shown in Figure 4c, in implementation example three, taking the case where the first AI sub-model is deployed on the first communication device and the second AI sub-model is deployed on the second communication device, and the input data of the second AI sub-model includes the output data of the first AI sub-model, after the first communication device indicates the first AI model through the first information and the second information, the first communication device can send the output data of the first AI sub-model to the second communication device, and the second communication device can use the output data of the first AI sub-model as one of the input data of the second AI sub-model, and perform model processing on the second AI sub-model.

[0218] Optionally, in implementation example three, the first AI model processing includes not only the first AI sub-model and the second AI sub-model, but also other AI sub-models. The other AI sub-models can be deployed in other communication devices different from the first communication device and the second communication device, which is not limited here.

[0219] 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 (first communication device and second communication device). Among them, the AI ​​model involved in this application (such as any AI model among the M AI models, any model among the Y AI models mentioned later, 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, and one or more AI models for replacing one or more modules in a transmitter and / or receiver. The following will exemplify the various implementation methods of the first AI model in combination with some implementation examples.

[0220] Implementation A: The first communication parameters include parameters associated with modulation and demodulation, and the first AI model includes an AI model for modulation and / or demodulation.

[0221] In implementation method A, the first communication parameter determined by the second information may include parameters associated with modulation and demodulation. Accordingly, in this correspondence, the first AI model corresponding to the first communication parameter may include an AI model for modulation and / or demodulation, so as to indicate the parameters associated with modulation and demodulation while also being able to indicate the AI ​​model used for modulation and / or demodulation, so that the subsequent second communication device can participate in the model processing of the AI ​​model for modulation and / or demodulation based on the parameters associated with modulation and demodulation.

[0222] Optionally, in implementation A, in an AI model used for modulation and / or demodulation, if the AI ​​model is used for modulation, the input data of the AI ​​model may include a bit stream, and the output data obtained by processing the bit stream by the AI ​​model may include modulation symbols. Furthermore, if the AI ​​model is used for demodulation, the input data of the AI ​​model may include noisy symbols, and the output data obtained by processing the bit stream by the AI ​​model may include log-likelihood ratios.

[0223] Optionally, the AI ​​model used for modulation and / or demodulation can be called an intelligent modulation model, an intelligent demodulation model, an intelligent modulation and demodulation model, etc.

[0224] Optionally, the parameters associated with modulation and demodulation include one or more of the following: modulation and coding scheme (MCS), frequency domain resource indication, transmit power control command (TPC command), and transmit precoding matrix indicator (TPMI).

[0225] The following will take the example where the parameters associated with modulation and demodulation include MCS, and N and M are equal, to exemplify implementation method A.

[0226] Example 1. As in the implementation example 1 of Figure 4a above, when the first AI model is deployed in the first communication device, the first information in step S301 can indicate the correspondence between N communication parameters and M AI models in the form of Table 2.

[0227] Table 2

[0228] In other words, in step S302, the second information may include one of the indexes of the N MCSs, so that the second communication device determines the AI ​​model identifier corresponding to the one of the indexes based on Table 2, and determines the AI ​​model indicated by the AI ​​model identifier as the first AI model.

[0229] Example 2. As in the implementation example 2 of Figure 4b above, when the first AI model is deployed in the second communication device, the first information in step S301 can indicate the correspondence between N communication parameters and M AI models in the form of Table 3.

[0230] Table 3

[0231] Similarly, in step S302, the second information may include one of the indexes of the N MCSs, so that the second communication device determines the AI ​​model identifier corresponding to the one of the indexes based on Table 3, and determines the AI ​​model indicated by the AI ​​model identifier as the first AI model.

[0232] Example 3. As in the implementation example three of Figure 4c above, the first AI model includes a first AI sub-model and a second AI sub-model, and the first AI sub-model is deployed in the first communication device and the second AI sub-model is deployed in the second communication device. In this case, the first information in step S301 can indicate the correspondence between N communication parameters and M AI models in the form of Table 4.

[0233] Table 4

[0234] Similarly, in step S302, the second information may include one of the indices of the N MCSs, so that the second communication device determines the AI ​​model identifier of the first AI sub-model and the AI ​​model identifier of the second AI sub-model corresponding to the one of the indices based on Table 4, and further determines the first AI sub-model and the second AI sub-model based on these two identifiers.

[0235] Optionally, in Table 4, the case where the number of the first AI sub-model and the second AI sub-model are both M is taken as an example. In this case, the AI ​​model identifier of the first AI sub-model and the AI ​​model identifier of the second AI sub-model may be different. For example, in Table 4, when the index of N MCSs is 0, the AI ​​model identifier of the first AI sub-model is "0", and the AI ​​model identifier of the second AI sub-model is "10". It should be noted that Table 4 is only an implementation example. When the number of the first AI sub-model and the second AI sub-model are both M, the AI ​​model identifier of the first AI sub-model and the AI ​​model identifier of the second AI sub-model may be the same, or the AI ​​model identifier of the first AI sub-model and the AI ​​model identifier of the second AI sub-model may be represented by the same field, which is not limited here.

[0236] Optionally, in actual applications, one of the number of the first AI sub-models and the number of the second AI sub-models is M, and the other may be K, and K may be different from M.

[0237] Take the example where the number of first AI sub-models is M and the number of second AI sub-models is K. When K is greater than M, the second communication device can determine the first AI sub-model based on Table 4, and determine the two or more second AI sub-models corresponding to the first AI sub-model based on Table 4. Thereafter, the second communication device can select one of the two or more second AI sub-models to perform subsequent model processing. When K is less than M, the second communication device can determine the first AI sub-model based on Table 4, and determine the second AI sub-model corresponding to the first AI sub-model based on Table 4.

[0238] It can be understood that, through the implementation process of implementation method A, the first communication device can perform step S302 multiple times, so that the AI ​​model used by the second communication device to perform model processing switches as the MCS changes.

[0239] Optionally, the MCS change may be based on a change in a channel measurement result of the first communication device, or the MCS change may be based on an adjusted modulation and coding (AMC) adjustment, which is not limited here.

[0240] Implementation B: The first communication parameter includes configuration information of a reference signal, and the first AI model includes an AI model for channel prediction.

[0241] In implementation B, the first communication parameter determined by the second information may include reference signal configuration information. Accordingly, in this correspondence, the first AI model corresponding to the first communication parameter may include an AI model for channel prediction, so that the reference signal configuration information can be indicated while also indicating the AI ​​model for channel prediction. Because the transmission and reception of the reference signal configured by the reference signal configuration information is associated with the channel prediction process, in this manner, the subsequent second communication device can participate in the model processing of the AI ​​model for channel prediction based on the reference signal transmitted by the reference signal configuration information.

[0242] Optionally, in implementation method B, in the AI ​​model used for channel prediction, the input data of the AI ​​model may include low-dimensional channel information, and the low-dimensional channel information is processed by the AI ​​model, and the output data obtained may include high-dimensional channel information, wherein the channel dimension may include at least one of the time domain dimension, the spatial domain dimension, and the frequency domain dimension.

[0243] Optionally, in implementation method B, in the AI ​​model used for channel prediction, the input data of the AI ​​model may include a pilot signal (or reference signal), and the pilot signal is processed by the AI ​​model, and the output data obtained may include channel information.

[0244] Optionally, the AI ​​model used for channel prediction can be called a channel estimation model, a channel pre-estimation model, a channel simulation recovery model, a channel reconstruction model, a channel acquisition model, a channel inference model, etc.

[0245] Optionally, the configuration information of the reference signal includes at least one of the following: time domain configuration information, frequency domain configuration information, spatial domain configuration information, port information, period information, and codebook configuration information.

[0246] It can be understood that through the implementation process of implementation method B, the first communication device can execute step S302 multiple times, so that the AI ​​model executed by the second communication device for model processing switches following the change of the configuration information of the reference signal (or the reconfiguration of the reference signal).

[0247] Implementation C: The first communication parameter includes parameters associated with beam management, and the first AI model includes an AI model for beam management.

[0248] In implementation method C, the first communication parameter determined by the second information may include parameters associated with beam management. Accordingly, in this correspondence, the first AI model corresponding to the first communication parameter may include an AI model for beam management, so as to indicate the parameters associated with beam management while also being able to indicate the AI ​​model used for beam management, so that the subsequent second communication device can participate in the model processing of the AI ​​model used for beam management based on the parameters associated with beam management.

[0249] Optionally, in implementation method C, in the AI ​​model used for beam management, the input data of the AI ​​model may include the measurement results of A beams. The measurement results of the A measurement beams are processed by the AI ​​model, and the output data obtained may include the indexes or identifiers of B beams, where B is a positive integer and A is an integer greater than B.

[0250] Optionally, the AI ​​model used for beam management can be called a beam management model, a beam optimization model, etc.

[0251] Optionally, the parameters associated with beam management include at least one of the following: channel characteristic information of the cell, and information on the number of beams associated with the AI ​​model for beam management. The information on the number of beams associated with the AI ​​model for beam management may include the number of input measurement beams (e.g., the number of measurement beams input to the AI ​​model in the above example, "A") and / or the number of output candidate beams (e.g., the number of indexes or identifiers of beams output by the AI ​​model in the above example, "B").

[0252] It should be understood that when the second communication device is a terminal device, the terminal device can communicate with different cells. Generally, the communication beams between different cells and the terminal device are different. Therefore, the channel characteristic information of the cell can be used for beam management. The channel characteristic information of the cell may include one or more of the following: transmit / receive beam configuration, first communication device / second communication device antenna configuration, first communication device / second communication device antenna pattern, interference information, signal-to-interference-and-noise ratio, and RSRP distribution.

[0253] It should be noted that, in the above implementation methods B and C, the correspondence between the first communication parameter and the first AI model can refer to the description of the implementation method A above.

[0254] Optionally, any two or three of the above implementations A, B, and C can be implemented in a fusion. The following takes the fusion of these three as an example and introduces the implementation shown in Table 5.

[0255] Example 4. As in the implementation example 1 of Figure 4a above, when the first AI model is deployed in the first communication device, the first information in step S301 can indicate the correspondence between N communication parameters and M AI models in the form of Table 5.

[0256] Table 5

[0257] In other words, in step S302, the second information may include one of the N indices. Furthermore, the N indices include the index of the parameter in implementation A (i.e., the index of MCS 0 and the index of MCS 1 in Table 5), the index of the parameter in implementation B (the index of reference signal configuration information 0 and the index of reference signal configuration information 1), and the index of the parameter in implementation B (the index of input measurement result quantity information 0 and the index of input measurement result quantity information 1). Thus, the second communication device determines the AI ​​model identifier corresponding to the one of the indices based on Table 5, and determines the AI ​​model indicated by the AI ​​model identifier as the first AI model.

[0258] It can be understood that Table 5 can be an extended implementation of the aforementioned Table 2. Similarly, the implementation methods of the aforementioned Table 3 and Table 4 can also be extended with reference to the implementation of Table 5.

[0259] In one possible implementation, in the method shown in FIG3 , after step S302, the method further includes: the first communication device receiving or sending AI data of the first AI model. Specifically, after the first communication device sends the second information indicating the first AI model, the first communication device and the second communication device may further exchange the AI ​​data of the first AI model to implement model processing of the first AI model using the AI ​​data.

[0260] Optionally, the first communication device (or the second communication device) receives or sends the AI ​​data of the first AI model, including: the first communication device receives or sends the AI ​​data of the first AI model based on the first communication parameter. Specifically, the first communication device (or the second communication device) can receive or send the AI ​​data of the first AI model based on the first communication parameter determined by the second information. In this way, the second information can be used to implement the first communication parameter and the indication of the first AI model, and the model processing of the AI ​​model can also be implemented through the communication parameter indicated by the second information.

[0261] Based on the technical solution of Figure 3, after the first communication device sends first information indicating the correspondence between N communication parameters and M AI models in step S301, the first communication device may also send second information for determining a first communication parameter among the N communication parameters in step S302; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models. In other words, the first communication device can indicate the first AI model through the correspondence indicated by the first information and the first communication parameter indicated by the second information, so that the recipients of the first information and the second information can subsequently perform processing based on the first AI model. Therefore, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to AI processing, and the implementation method of indicating the AI ​​model by multiplexing the indication of the communication parameters can also reduce the overhead of indicating the AI ​​model.

[0262] In a possible implementation of the technical solution shown in FIG3 , before step S301 , the second communication device may further send capability information to the first communication device, which will be described below based on the solution shown in FIG5 .

[0263] As shown in FIG5 , compared to the technical solution shown in FIG3 , before the first communication device sends the first information, the method further includes:

[0264] Step A: The second communication device sends third information, and the first communication device receives the third information accordingly. The third information is used to indicate K AI models supported by the second communication device.

[0265] Optionally, at least one AI model among the M AI models is the same as at least one AI model among the K AI models.

[0266] Optionally, the third information includes identifiers or indexes of the K AI models. In this way, the overhead of the third information can be reduced.

[0267] Based on the implementation process of step A, the first communication device may also receive third information indicating the K AI models supported by the second communication device. The first communication device may subsequently determine the first information based on the third information. In this way, the M AI models indicated by the first information can be matched to the capabilities supported by the second communication device as much as possible, thereby improving the success rate of model processing of the M AI models.

[0268] Optionally, for the first communication device, after receiving the third information, the first communication device may use at least one AI model among the K AI models indicated by the third information as the basis for determining the first information, or, if the first communication device determines that the K AI models are not applicable, the first communication device may not need to use the third information as the basis for determining the first information.

[0269] In one possible implementation, the M AI models are included in the K AI models. In other words, the M AI models indicated by the first information may be included in the K AI models indicated by the third information, enabling the M AI models indicated by the first information to match the capabilities supported by the second communication device, thereby improving the success rate of model processing of the M AI models.

[0270] In one possible implementation, P of the M AI models are different from the K AI models, where P is a positive integer; wherein the first information includes model parameters of the P AI models. Specifically, the P of the M AI models indicated by the first information may be different from the K AI models indicated by the third information, and the first information may include the model parameters of the P AI models. This allows the second communication device to obtain, through the first information, the model parameters of the P AI models other than the K supported AI models indicated by the third information, thereby increasing the flexibility of the second information in indicating the M AI models.

[0271] Optionally, the size relationship between P and K is not limited, for example, P is less than K, P is equal to K, or P is greater than K.

[0272] Optionally, when P of the M AI models are different from the K AI models, the model parameters of the P AI models may be carried in other information in addition to the first information, which is not limited here. In other words, the first information may not include the model parameters of the P AI models.

[0273] In one possible implementation of the technical solution shown in Figure 3, after the first communication device sends the indication of the correspondence between the communication parameters and the AI ​​model in step S301, the second communication device may also send some information to the first communication device, so that the first communication device can update / optimize the correspondence based on the information, etc. The following will be described based on the solution shown in Figure 6.

[0274] As shown in FIG6 , compared to the technical solution shown in FIG3 , after the first communication device sends the first information, the method further includes:

[0275] Step B: The second communication device sends the fourth information and / or the fifth information, and the first communication device receives the fourth information and / or the fifth information accordingly. The fourth information includes model parameters of the trained AI model, and the fifth information includes auxiliary information.

[0276] Step C: The first communication device sends sixth information, and the second communication device receives the sixth information accordingly. The sixth information indicates a correspondence between X communication parameters and Y AI models, where the correspondence between the X communication parameters and the Y AI models is determined based on the fourth information and / or the fifth information, where X and Y are both positive integers.

[0277] It should be noted that both X and Y are positive integers, and the magnitude relationship between X and Y can be realized in many different ways.

[0278] For example, when X and Y are equal, there may be a one-to-one correspondence between the X communication parameters and the Y AI models.

[0279] For another example, when X is less than Y, at least two AI models among the Y AI models can be indicated by one of the X communication parameters. That is, the first AI model corresponding to the first communication parameter determined by the second information can be the at least two AI models (or any one of the at least two AI models). Optionally, the at least two AI models can be AI models with the same or similar model structures, the at least two AI models can be AI models with the same or similar model functions, etc.

[0280] For another example, when X is greater than Y, one of the Y AI models may be indicated by at least two of the X communication parameters.

[0281] It should be understood that the sixth information indicates the correspondence between X communication parameters and Y AI models, and the first information indicates the correspondence between N communication parameters and M AI models. The implementation process of the sixth information can refer to the implementation process of the first information. For example, the correspondence indicated by the sixth information can refer to the implementation process of any of Tables 2 to 5 above.

[0282] Optionally, the Y AI models and the M AI models may be partially or completely identical AI models. For example, when the Y AI models and the M AI models are all identical, the difference between the sixth information and the first information is that the first information indicates the correspondence between N communication parameters and the AI ​​model, while the sixth information indicates the correspondence between X communication parameters and the AI ​​model, that is, the X communication parameters and the N communication parameters may be different. For another example, when the Y AI models and the M AI models are partially identical, the Z AI models in the Y AI module may be different from the M AI models, and the Z AI models may be obtained based on the fourth information and / or the fifth information, or the Z AI models may be generated based on the local data of the first communication device, which is not limited here.

[0283] Based on the technical solution shown in Figure 6, the first communication device can receive fourth information including model parameters of the trained AI model, and / or the first communication device can receive fifth information including auxiliary information, so that the first communication device can determine the correspondence between X communication parameters and Y AI models based on the fourth information and / or the fifth information, and indicate the correspondence through sixth information. In other words, the first communication device can update the correspondence between the communication parameters and the AI ​​model and indicate the updated correspondence through the fourth information and / or the fifth information.

[0284] Optionally, the auxiliary information is used to generate / train / strengthen / select / switch / update the AI ​​model to obtain Y AI models. In other words, the auxiliary information may include at least one of auxiliary information for AI model training, AI model training data, auxiliary information during AI model training data collection, and auxiliary information during AI model inference.

[0285] Optionally, the sixth information includes model identifiers of the Y AI models. In this way, the overhead of the sixth information can be reduced.

[0286] Optionally, the auxiliary information includes at least one of the following: cell information, cell configuration, distribution of the first communication device and / or the second communication device, speed of the first communication device and / or the second communication device, channel power delay spectrum, configuration information of the first communication device and / or the second communication device, antenna configuration of the first communication device and / or the second communication device, beam configuration of the first communication device and / or the second communication device, antenna pattern of the first communication device and / or the second communication device, interference information, signal-to-noise ratio, MCS, channel rank, data error, data quality, resource granularity, maximum signal quality information (for example, the signal quality information may include one or more of received signal strength indication (RSSI), reference signal received power (RSRP), and reference signal received quality (RSRQ)), and location information.

[0287] Optionally, the trained AI model can be obtained by training based on the first AI model, or can be obtained by training based on other AI models (the other AI model can be one of the M AI models, or can be not one of the M AI models) by the second communication device, which is not limited here. Exemplarily, the first communication device and / or the second communication device can continue to train (or retrain) according to the first AI model. If the model performance of the AI ​​model obtained by continued training is better than the threshold, the trained first AI model can be used as the basis for determining the fourth information. For example, if the model performance of the AI ​​model obtained by continued training is worse than the threshold, the trained first AI model can be used instead of as the basis for determining the fourth information. Instead, it can be trained based on other models or retrained with random parameters to obtain the model parameters of the trained AI model contained in the fourth information.

[0288] It can be understood that, as described above, the AI ​​model involved in this application (for example, any AI model among the M AI models, any model among the Y AI models mentioned later, etc.) can be used to manage wireless communication signals (including at least one of configuration, update, and optimization). Accordingly, the auxiliary information may include parameters involved in the management of the wireless communication signal. Some examples will be provided below for illustration.

[0289] As an implementation example, if the M AI models indicated by the first information include a model for modulation and / or demodulation (for ease of reference, denoted as the second AI model), the Y AI models indicated by the sixth information may include a third AI model, which is also used for modulation and / or demodulation. The third AI model can be obtained by processing the second AI model based on the fourth information and / or the fifth information (e.g., model update processing, model optimization processing, etc.). For example, in this case, the auxiliary information included in the fifth information may include one or more of the following: beam configuration, MCS, signal-to-interference-and-noise ratio, channel power delay spectrum, capability information of the second communication device, and hardware configuration information of the second communication device.

[0290] As an implementation example, if the M AI models indicated by the first information include a model for channel prediction (for ease of reference, denoted as the second AI model), the Y AI models indicated by the sixth information may include a third AI model, which is also used for channel prediction. The third AI model can be obtained by processing the second AI model based on the fourth information and / or the fifth information (e.g., model update processing, model optimization processing, etc.). For example, in this case, the auxiliary information contained in the fifth information may include one or more of beam configuration, MCS, channel power delay spectrum, antenna configuration, antenna pattern, signal to interference and noise ratio, interference information, channel rank, data quality, and resource granularity.

[0291] As an implementation example, if the M AI models indicated by the first information include a model for beam management (for ease of reference, recorded as the second AI model), the Y AI models indicated by the sixth information may include a third AI model, which is also used for beam management. The third AI model can be obtained by processing the second AI model based on the fourth information and / or the fifth information (for example, model update processing, model optimization processing, etc.). For example, in this case, the auxiliary information contained in the fifth information may include one or more of beam configuration, antenna configuration, antenna pattern, interference information, signal-to-interference-and-noise ratio, maximum signal quality information, beam sampling times, location information, and map information.

[0292] 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).

[0293] 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.

[0294] In one possible implementation, when the device 700 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the processing unit 701 is used to determine first information and second information; the transceiver unit 702 is used to send first information, where the first information is used to indicate the correspondence between N communication parameters and M AI models, where N and M are both positive integers; the transceiver unit 702 also sends second information, where the second information is used to determine a first communication parameter among the N communication parameters; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models.

[0295] In one possible implementation, when the device 700 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive first information and second information; the processing unit 701 is used to determine the correspondence between N communication parameters and M AI models based on the first information, where N and M are both positive integers; the processing unit 701 is also used to determine the first communication parameter among the N communication parameters based on the second information; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models.

[0296] 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.

[0297] 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.

[0298] 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.

[0299] Optionally, the logic circuit 801 is used to determine first information and second information; the input-output interface 802 is used to send first information, wherein the first information is used to indicate the correspondence between N communication parameters and M artificial intelligence AI models, where N and M are both positive integers; the input-output interface 802 also sends second information, wherein the second information is used to determine a first communication parameter among the N communication parameters; wherein, in the correspondence, the first communication parameter corresponds to a first AI model among the M AI models.

[0300] Optionally, the input-output interface 802 is used to receive first information and second information; the logic circuit 801 is used to determine the correspondence between N communication parameters and M AI models based on the first information, where N and M are both positive integers; the logic circuit 801 is also used to determine the first communication parameter among the N communication parameters based on the second information; wherein, in the correspondence, the first communication parameter corresponds to the first AI model among the M AI models.

[0301] 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.

[0302] In a possible implementation, the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .

[0303] 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.

[0304] 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.

[0305] Alternatively, the processing device may include only a processor. A memory for storing the computer program is located outside the processing device, and the processor is connected to the memory via circuits / wires to read and execute the computer program stored in the memory. The memory and processor may be integrated or physically separate.

[0306] 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.

[0307] 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).

[0308] 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 .

[0309] 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.

[0310] 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.

[0311] 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.

[0312] 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.

[0313] 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.

[0314] 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.

[0315] 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.

[0316] 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.

[0317] The memory is primarily used to store software programs and data. Memory 1012 can exist independently and be connected to processor 1011. Alternatively, memory 1012 and processor 1011 can be integrated together, for example, within a single chip. Memory 1012 can store program code for executing the technical solutions of the embodiments of the present application, and execution is controlled by processor 1011. The various computer program codes executed can also be considered drivers for processor 1011.

[0318] 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.

[0319] 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.

[0320] 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.

[0321] 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.

[0322] 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.

[0323] 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.

[0324] 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 ).

[0325] 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.

[0326] Optionally, the processor 111 and / or the memory 112 may include AI modules 117 and 118, each configured to implement AI-related functions. The AI ​​module may be implemented using 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.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] In addition, the functional units in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the contributing part or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Claims

1. A communication method, characterized in that: include: Sending first information, where the first information is used to indicate a correspondence between N communication parameters and M artificial intelligence AI models, where N and M are both positive integers; Sending second information, where the second information is used to determine a first communication parameter among the N communication parameters; wherein, in the corresponding relationship, the first communication parameter corresponds to a first AI model among the M AI models.

2. The method according to claim 1, characterized in that The method further comprises: Receive or send AI data of the first AI model.

3. The method according to claim 1 or 2, characterized in that: Before sending the first information, the method further includes: receiving third information, where the third information is used to indicate K AI models supported by the second communication device; The M AI models are included in the K AI models; or, P AI models among the M AI models are different from the K AI models, and P is a positive integer; wherein the first information includes model parameters of the P AI models.

4. The method according to claim 3, characterized in that The third information includes identifiers or indexes of the K AI models.

5. The method according to any one of claims 1 to 4, characterized in that: After sending the first information, the method further includes: receiving fourth information and / or fifth information, wherein the fourth information includes model parameters of the trained AI model, and the fifth information includes auxiliary information; Send sixth information, where the sixth information is used to indicate a correspondence between X communication parameters and Y AI models, where the correspondence between the X communication parameters and the Y AI models is determined based on the fourth information and / or the fifth information, and X and Y are both positive integers.

6. A communication method, characterized in that: include: Receive first information, where the first information is used to indicate a correspondence between N communication parameters and M AI models, where N and M are both positive integers; Receive second information, where the second information is used to indicate a first communication parameter among the N communication parameters; wherein, in the corresponding relationship, the first communication parameter corresponds to a first AI model among the M AI models.

7. The method according to claim 6, characterized in that The method further comprises: Receive or send AI data of the first AI model.

8. The method according to claim 6 or 7, characterized in that: Before receiving the first information, the method further includes: Sending third information, where the third information is used to indicate K AI models supported by the second communication device; The M AI models are included in the K AI models; or, P AI models among the M AI models are different from the K AI models, and P is a positive integer; wherein the first information includes model parameters of the P AI models.

9. The method according to claim 8, characterized in that The third information includes identifiers or indexes of the K AI models.

10. The method according to any one of claims 6 to 9, characterized in that: After receiving the first information, the method further includes: Sending fourth information and / or fifth information, wherein the fourth information includes model parameters of the trained AI model, and the fifth information includes auxiliary information; Receive sixth information, where the sixth information is used to indicate a correspondence between X communication parameters and Y AI models, where the correspondence between the X communication parameters and the Y AI models is determined based on the fourth information and / or the fifth information, and X and Y are both positive integers.

11. The method according to any one of claims 1 to 10, characterized in that: The first communication parameters include parameters associated with modulation and demodulation, and the first AI model includes an AI model for modulation and / or demodulation.

12. The method according to claim 11, characterized in that The parameters associated with the modem include one or more of the following: Modulation and coding strategy MCS, frequency domain resource indication, transmission power control command TPC command, transmission precoding matrix indication TPMI.

13. The method according to any one of claims 1 to 12, characterized in that: The first communication parameter includes configuration information of a reference signal, and the first AI model includes an AI model for channel prediction.

14. The method according to claim 13, characterized in that The configuration information of the reference signal includes at least one of the following: Time domain configuration information, frequency domain configuration information, spatial domain configuration information, port information, cycle information, codebook configuration information.

15. The method according to any one of claims 1 to 10, characterized in that The first communication parameters include parameters associated with beam management, and the first AI model includes an AI model for beam management.

16. The method according to claim 15, characterized in that The parameters associated with beam management include at least one of the following: The channel characteristic information of the cell, and the number of beams associated with the AI ​​model for beam management.

17. The method according to any one of claims 1 to 16, characterized in that: The second information includes a first communication parameter among the N communication parameters, or the second information includes an index of the first communication parameter among the N communication parameters.

18. The method according to any one of claims 1 to 17, characterized in that The first AI model is deployed on the first communication device and / or the second communication device.

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

20. A communication device, characterized in that: The method comprises at least one processor coupled to a memory; the at least one processor is configured to execute the method according to any one of claims 1 to 18.

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

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

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