Communication method and related equipment

Abstract

The invention provides a communication method and related equipment, which are used for reducing the overhead for indicating an AI model while enabling the computing power of a communication node to be applied to model processing of the AI model. In the method, after a first communication device sends first information used for indicating a corresponding relation between N communication parameters and M AI models, the first communication device can also send second information used for determining a first communication parameter in the N communication parameters; wherein in the corresponding relation, the first communication parameter corresponds to a first AI model in the M AI models. In other words, the first communication device can realize indication of the first AI model through the corresponding relationship indicated by the first information and the first communication parameter indicated by the second information, so that receivers of the first information and the second information can subsequently process based on the first AI model.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a communication method and related equipment. Background Art

[0002] Wireless communication can be the transmission communication between two or more communication nodes without propagation through conductors or cables. The communication nodes generally include network equipment and terminal equipment.

[0003] At present, in wireless communication systems, communication nodes generally have signal transceiving capabilities and computing capabilities. Taking network devices with computing capabilities as an example, the computing capabilities of network devices mainly provide computing power support for signal transceiving capabilities (for example: sending and receiving signals) to achieve communication between network devices and other communication nodes.

[0004] However, in a communication network, the computing power of communication nodes may have surplus computing power in addition to providing computing power support for the above communication tasks. Therefore, how to utilize this computing power is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a communication method and related equipment, which are used to enable the computing power of a communication node to be applied to model processing of an artificial intelligence (AI) model while also reducing the overhead of indicating the AI ​​model.

[0006] 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 partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the first communication device sends a 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 a 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.

[0007] 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 process 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.

[0008] 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.

[0009] It should be understood that the AI ​​model involved in this application (such as any AI model in the M AI models, any model in the Y AI models mentioned below, etc.) can be deployed in one or more communication devices. Optionally, when the AI ​​model is deployed in one communication device, the AI ​​model can be called a single-end deployed AI model, and when the AI ​​model is deployed in multiple communication devices, the AI ​​model can be called a dual-end / multi-end deployed AI model.

[0010] Exemplarily, the following will take the AI ​​model as the first AI model as an example to illustrate the processing process of the AI ​​model.

[0011] 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 may 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, and the processing 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.

[0012] 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.

[0013] 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, 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.

[0014] 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.

[0015] 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 of the AI ​​models for replacing one or more modules in a transmitter and / or receiver.

[0016] Optionally, the first information is used to indicate the correspondence between the N communication parameters and the M AI models, wherein the first information may include the model identifiers (Model ID) of the M AI models, the model indexes (ModelIndex) 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.

[0017] Optionally, the first information may include the N communication parameters and / or model parameters of the M AI models.

[0018] Optionally, the first communication parameter determined by the second information may include one or more parameters among the N communication parameters, and accordingly, the first AI model corresponding to the first communication parameter may include one or more AI models among the M AI models.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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 the corresponding relationship, 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 used for modulation and / or demodulation based on the parameters associated with modulation and demodulation.

[0025] Optionally, the AI ​​model used for modulation and / or demodulation may be referred to as an intelligent modulation model, an intelligent demodulation model, an intelligent modulation and demodulation model, etc.

[0026] 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 transmitted precoding matrix indication (TPMI).

[0027] 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.

[0028] 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 the corresponding relationship, 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 configuration information of the reference signal is associated with the process of channel prediction, 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 configuration information of the reference signal.

[0029] Optionally, the AI ​​model used for channel prediction can be called a channel estimation model, a channel prediction model, a channel simulation recovery model, a channel reconstruction model, a channel acquisition model, a channel inference model, etc.

[0030] 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.

[0031] 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.

[0032] Based on the above technical solution, the first communication parameter determined by the second information may include parameters associated with beam management. Accordingly, in the corresponding relationship, 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.

[0033] Optionally, the AI ​​model used for beam management may be referred to as a beam management model, a beam optimization model, etc.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] In a possible implementation manner of the first aspect, the first 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] In a possible implementation manner of the first aspect, before the first communication device sends the first information, the method also 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.

[0042] Optionally, at least one AI model among the M AI models is identical to at least one AI model among the K AI models.

[0043] 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 with the capabilities supported by the second communication device as much as possible to improve the success rate of model processing of the M AI models.

[0044] 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 a basis for determining the first information; alternatively, when 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 a basis for determining the first information.

[0045] In a possible implementation manner of the first aspect, the M AI models are included in the K AI models.

[0046] 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 be matched with the capabilities supported by the second communication device to improve the success rate of model processing of the M AI models.

[0047] In a possible implementation manner of the first aspect, 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.

[0048] 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.

[0049] Based on the above technical solution, P AI models out 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 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.

[0050] 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.

[0051] Optionally, when P AI models in 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.

[0052] In a possible implementation manner of the first aspect, after the first communication device sends the first information, the method further includes: the first communication device receives fourth information and / or fifth information, the fourth information includes model parameters of the trained AI model, and the fifth information includes auxiliary information; the first communication device sends sixth information, the sixth information is used to indicate the correspondence between X communication parameters and Y AI models, 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.

[0053] Based on the above technical solution, the first communication device can receive the fourth information including the model parameters of the trained AI model, and / or the first communication device can receive the fifth information including the 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 the 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.

[0054] 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, training data for the AI ​​model, auxiliary information during AI model training data collection, and auxiliary information during AI model reasoning.

[0055] Optionally, the sixth information includes model identifiers of the Y AI models. In this way, the overhead of the sixth information can be reduced.

[0056] 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.

[0057] Optionally, the trained AI model may be obtained by training based on the first AI model, or may be obtained by training based on other AI models (the other AI model may be one of the M AI models, or may not belong to 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 may continue to train (or retrain) according to the first AI model. For example, if the model performance of the AI ​​model obtained by continued training is better than a threshold, the trained first AI model may be used as a basis for determining the fourth information. For another example, if the model performance of the AI ​​model obtained by continued training is worse than a threshold, the trained first AI model may not be used as a basis for determining the fourth information, but may 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.

[0058] The second aspect of the present application provides a communication method, which is performed by a second communication device, which may be a communication device (such as a terminal device), or the second communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the second communication device may also be a logic module or software that can 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.

[0059] 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.

[0060] 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.

[0061] 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 the corresponding relationship, 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 used for modulation and / or demodulation based on the parameters associated with modulation and demodulation.

[0062] Optionally, the AI ​​model used for modulation and / or demodulation may be referred to as an intelligent modulation model, an intelligent demodulation model, an intelligent modulation and demodulation model, etc.

[0063] 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 transmitted precoding matrix indication (TPMI).

[0064] 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.

[0065] 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 the corresponding relationship, 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 configuration information of the reference signal is associated with the process of channel prediction, 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 configuration information of the reference signal.

[0066] Optionally, the AI ​​model used for channel prediction can be called a channel estimation model, a channel prediction model, a channel simulation recovery model, a channel reconstruction model, a channel acquisition model, a channel inference model, etc.

[0067] 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.

[0068] 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.

[0069] Based on the above technical solution, the first communication parameter determined by the second information may include parameters associated with beam management. Accordingly, in the corresponding relationship, 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.

[0070] Optionally, the AI ​​model used for beam management may be referred to as a beam management model, a beam optimization model, etc.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] In a possible implementation manner 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.

[0075] Based on the above technical solution, the second 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.

[0076] 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.

[0077] 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.

[0078] In a possible implementation manner of the second aspect, before the second communication device receives the first information, the method also includes: the second communication device sends third information, where the third information is used to indicate K AI models supported by the second communication device.

[0079] 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.

[0080] 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 with the capabilities supported by the second communication device as much as possible to improve the success rate of model processing of the M AI models.

[0081] 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 a basis for determining the first information; alternatively, when 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 a basis for determining the first information.

[0082] In a possible implementation manner of the second aspect, the M AI models are included in the K AI models.

[0083] 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 be matched with the capabilities supported by the second communication device to improve the success rate of model processing of the M AI models.

[0084] In a possible implementation manner of the second aspect, 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.

[0085] 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.

[0086] Based on the above technical solution, P AI models out 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 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.

[0087] 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.

[0088] Optionally, when P AI models in 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.

[0089] In a possible implementation manner of the second aspect, after the second communication device receives the first information, the method further includes: the second communication device sends fourth information and / or fifth information, the fourth information includes model parameters of the trained AI model, and the fifth information includes auxiliary information; the second communication device receives sixth information, the sixth information is used to indicate the correspondence between X communication parameters and Y AI models, 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.

[0090] Based on the above technical solution, the first communication device can receive the fourth information including the model parameters of the trained AI model, and / or the first communication device can receive the fifth information including the 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 the 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.

[0091] 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, training data for the AI ​​model, auxiliary information during AI model training data collection, and auxiliary information during AI model reasoning.

[0092] Optionally, the sixth information includes model identifiers of the Y AI models. In this way, the overhead of the sixth information can be reduced.

[0093] 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.

[0094] Optionally, the trained AI model may be obtained by training based on the first AI model, or may be obtained by training based on other AI models (the other AI model may be one of the M AI models, or may not belong to 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 may continue to train (or retrain) according to the first AI model. For example, if the model performance of the AI ​​model obtained by continued training is better than a threshold, the trained first AI model may be used as a basis for determining the fourth information. For another example, if the model performance of the AI ​​model obtained by continued training is worse than a threshold, the trained first AI model may not be used as a basis for determining the fourth information, but may 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.

[0095] The third aspect of the present application provides a communication device, which is a first communication device, and includes a transceiver unit and a processing unit; the processing unit is used to determine first information and second information; the transceiver unit is used to send the first information, and the first information is used to indicate the correspondence between N communication parameters and M artificial intelligence AI models, N and M are both positive integers; the transceiver unit also sends the second information, and the second information 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.

[0096] 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.

[0097] In a fourth aspect, the present application provides a communication device, which is a second communication device, comprising a transceiver unit and a processing unit, wherein 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 a first AI model among the M AI models.

[0098] 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.

[0099] 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.

[0100] 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 method of any one of the first to second aspects above.

[0101] A seventh aspect of the present application provides a communication system, which includes the above-mentioned first communication device and second communication device.

[0102] In an eighth aspect, the present application provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes a method as described in any possible implementation of any one of the first to second aspects above.

[0103] A ninth aspect of the present application provides a computer program product (or computer program). When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of any one of the first to second aspects above.

[0104] In a tenth aspect, the present application provides a chip system, which includes at least one processor for supporting a communication device to implement the method described in any possible implementation of any one of the first to second aspects.

[0105] In a possible design, the chip system may also include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of a chip, or may include a chip and other discrete devices. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data for the at least one processor.

[0106] Among them, the technical effects brought about by any design method in the third aspect to the tenth aspect can refer to the technical effects brought about by the different design methods in the above-mentioned first aspect to the second aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1a to Figure 1c A schematic diagram of a communication system provided for this application;

[0108] Figure 2a to Figure 2g A schematic diagram of the AI ​​processing process involved in this application;

[0109] Figure 3 An interactive schematic diagram of the communication method provided by this application;

[0110] Figures 4a to 4c A schematic diagram of the AI ​​processing process provided for this application;

[0111] Figures 5 and 6 An interactive schematic diagram of the communication method provided by this application;

[0112] Figures 7 to 11 A schematic diagram of a communication device provided in this application. DETAILED DESCRIPTION

[0113] First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0114] (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 users, or a handheld device with wireless connection function, or other processing devices connected to a wireless modem.

[0115] The terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device can be a mobile terminal device, such as a mobile phone (or "cellular" phone, mobile phone), a computer and a data card, for example, a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device, which exchanges voice and / or data with the radio access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablet computers (Pads), computers with wireless transceiver functions, and other devices. The wireless terminal device can also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile station (MS), a remote station, an access point (AP), a remote terminal device (remote terminal), an access terminal device (access terminal), a user terminal device (user terminal), a user agent (user agent), a subscriber station (SS), a customer premises equipment (CPE), a terminal (terminal), a user equipment (UE), a mobile terminal (MT), etc.

[0116] As an example but not 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 the application of wearable technology to intelligently design and develop wearable devices 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 powerful functions achieved through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include full-featured, large-size, and independent of smartphones to achieve complete or partial functions, 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 types of smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0117] 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 a smart city, a wireless terminal in a smart home, etc.

[0118] In addition, the terminal device may also be a terminal device in a communication system that evolves after the fifth generation (5th generation, 5G) communication system (e.g., a sixth generation (6th generation, 6G) communication system, etc.) or a terminal device in a public land mobile network (PLMN) that evolves in the future, etc. Exemplarily, the 6G network can further expand the form and function of the 5G communication terminal, and the 6G terminal includes but is not limited to a car, a cellular network terminal (with integrated satellite terminal function), a drone, and an Internet of Things (IoT) device.

[0119] 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.

[0120] (2) Network equipment: It can be equipment 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. At present, some examples of RAN equipment are: 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 may include a centralized unit (CU) node, a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0121] Optionally, the RAN node may also be a macro base station, a micro base station or an indoor station, a relay node or a donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. The RAN node may also be a server, a wearable device, a vehicle or an onboard device, etc. For example, the access network device in the vehicle to everything (V2X) technology may be a road side unit (RSU).

[0122] 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 can also be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU) or a remote radio head (RRH).

[0123] 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, CU, CU-CP, CU-UP, DU and RU are described as examples in this application. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0124] 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.

[0125] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, refer to Table 1 below.

[0126] Table 1

[0127] ORAN network elements 3GPP protocol layer functions O-CU-CP RRC+PCDP-Control Plane (PDCP-C) O-CU-UP SDAP+PCDP-User Plane (PDCP-U) O-DU RLC+MAC+PHY-high O-RU PHY-low

[0128] The network device may be any other device that provides wireless communication functions for the terminal device. The embodiments of the present application do not limit the specific technology and specific device form used by the network device. For the convenience of description, the embodiments of the present application do not limit.

[0129] The network equipment may also include core network equipment, such as mobility management entity (MME), home subscriber server (HSS), serving gateway (S-GW), policy and charging rules function (PCRF), public data network gateway (PDN gateway, P-GW) in the fourth generation (4G) network; access and mobility management function (AMF), user plane function (UPF) or session management function (SMF) and other network elements in the 5G network. In addition, the core network equipment may also include other core network equipment in the 5G network and the next generation network of the 5G network.

[0130] 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 may be an AI node on the network side (access network or core network), a computing node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.

[0131] In the embodiment of the present application, the device for realizing the function of the network device may be a network device, or may be a device capable of supporting the network device to realize the function, such as a chip system, which may be installed in the network device. In the technical solution provided in the embodiment of the present application, the technical solution provided in the embodiment of the present application is described by taking the device for realizing the function of the network device as an example that the network device is used as the device.

[0132] (3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used at the same time. 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 specified by 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.

[0133] Furthermore, these values ​​and parameters can be changed or updated.

[0134] (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 may exist. For example, A and / or B can represent: 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 objects associated with each other 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 items or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. And, 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.

[0135] (5) "Send" and "receive" in the embodiments of the present application indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information is XX, which can include direct sending through the air interface, and also include indirect sending through the air interface by other units or modules. "Receive information from YY" can be understood as the source of the information is YY, which can include direct receiving from YY through the air interface, and also include indirect receiving from YY through the air interface from other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

[0136] 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.

[0137] 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.

[0138] (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 realized 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 may be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information may be used to determine the information to be indicated.

[0139] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments in this application, and the various methods / designs / implementations in each embodiment, if there is no special description 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 implementation methods of this application described below do not constitute a limitation on the scope of protection of this application.

[0140] 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.

[0141] See also Figure 1a , which is a schematic diagram of a communication system in this application. Figure 1a In the example, a network device and six terminal devices are shown, and the six terminal devices are terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. Figure 1a In the example shown, terminal device 1 is a smart tea cup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas station, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

[0142] like Figure 1a As shown, the AI ​​configuration information sending entity may be a network device. The AI ​​configuration information receiving entity may be terminal devices 1-6. At this time, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 may 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 may send configuration information to terminal devices 1-6.

[0143] For example, in Figure 1a In the communication system, terminal device 4-terminal device 6 can also form a communication system. Among them, terminal device 5 acts as a network device, that is, an AI configuration information sending entity; terminal device 4 and terminal device 6 act as terminal devices, that is, AI configuration information receiving entities. For example, in the Internet of Vehicles system, terminal device 5 sends AI configuration information to terminal device 4 and terminal device 6 respectively, and receives data sent by terminal device 4 and terminal device 6; correspondingly, terminal device 4 and terminal device 6 receive AI configuration information sent by terminal device 5, and send data to terminal device 5.

[0144] by Figure 1a Taking the communication system shown as an example, in addition to executing communication-related services, different devices (including between network devices and network devices, between network devices and terminal devices, and / or between terminal devices and terminal devices) may also execute AI-related services.

[0145] like Figure 1b As shown, 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 communication-related services and AI-related services can also be performed between different terminal devices.

[0146] like Figure 1c As shown, 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.

[0147] The technical solution provided by this application can be applied to wireless communication systems (such as Figure 1a , Figure 1b or Figure 1cThe system shown in the figure), for example, the communication system provided in the present application can introduce an AI network element to implement some or all AI-related operations. The AI ​​network element may also be referred to as an AI node, an AI device, an AI entity, an AI module, an AI model, or an AI unit, etc. The AI ​​network element may be a network element built into a communication system. For example, the AI ​​network element may be an AI module built into: an access network device, a core network device, a cloud server, or a network management (operation, administration and maintenance, OAM) to implement AI-related functions. The OAM may 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 may also be an independently set network element in the communication system. Optionally, the terminal or the chip built into the terminal may also include an AI entity to implement AI-related functions.

[0148] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0149] Artificial intelligence (AI) can give machines human intelligence, for example, it can allow machines to use computer hardware and software to simulate certain intelligent behaviors of humans. In order to realize artificial intelligence, machine learning methods can be used. In machine learning methods, the machine uses training data to learn (or train) to obtain a model. The model represents the mapping from input to output. The learned model can be used for reasoning (or prediction), that is, the model can be used to predict the output corresponding to a given input. Among them, the output can also be called an inference result (or prediction result).

[0150] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0151] Supervised learning uses machine learning algorithms to learn the mapping relationship from sample values ​​to sample labels based on the collected sample values ​​and sample labels, and uses AI models to express the learned mapping relationship. The process of training a machine learning model is the process of learning this mapping relationship. During the training process, the sample values ​​are input into the model to obtain the model's predicted values, and the model parameters are optimized by calculating the error between the model's predicted values ​​and the sample labels (ideal values). After 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 mapping or nonlinear mapping. According to the type of label, the learning task can be divided into classification task and regression task.

[0152] Unsupervised learning uses algorithms to discover the inherent patterns of samples based on the collected sample values. One type of algorithm in unsupervised learning uses the samples themselves as supervisory signals, that is, the model learns the mapping relationship from sample to sample, which is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predicted value and the sample itself. Self-supervised learning can be used in applications such as signal compression and decompression recovery. Common algorithms include autoencoders and adversarial generative networks.

[0153] Reinforcement learning is different from supervised learning. It is a type of algorithm that learns problem-solving strategies by interacting with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have clear "correct" action label data. The algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust the decision-making actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user according to the total system throughput fed back by the wireless network, and then expects to obtain a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the state of the environment and the better (e.g., optimal) decision action. However, because the label of the "correct action" cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action". Reinforcement learning training is achieved through iterative interaction with the environment.

[0154] Neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, neural network can theoretically approximate any continuous function, so that neural network has the ability to learn any mapping. Traditional communication systems require rich expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover implicit pattern structures from large data sets, establish mapping relationships between data, and obtain performance that is superior to traditional modeling methods.

[0155] The idea of ​​neural networks comes from the neuron structure of the brain. For example, each neuron performs a weighted sum operation on its input values ​​and outputs the operation result through an activation function.

[0156] like Figure 2a As shown in Figure 1, 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 xi Weighted. The bias of weighted summation of 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), the output of the neuron is: For another example, 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.

[0157] In addition, a neural network generally includes multiple layers, each of which may include one or more neurons. By increasing the depth and / or width of a neural network, the expressive power of the neural network can be improved, providing a more powerful information extraction and abstract modeling capability for complex systems. Among them, the depth of a neural network may refer to the number of layers included in the neural network, and the number of neurons included in each layer may be referred to as the width of the layer. In one implementation, the neural network includes an input layer and an output layer. The input layer of the neural network processes the received input information through neurons, passes the processing results to the output layer, and the output layer obtains the output result of the neural network. In another implementation, the neural network includes an input layer, a hidden layer, and an output layer. The input layer of the neural network processes the received input information through neurons, passes the processing results to the middle hidden layer, the hidden layer calculates the received processing results, obtains the calculation results, and the hidden layer passes the calculation results to the output layer or the next adjacent hidden layer, and finally the output layer obtains the output result of the neural network. Among them, a neural network may include one hidden layer, or include multiple hidden layers connected in sequence, without limitation.

[0158] The neural network is, for example, a deep neural network (DNN). Depending on how the network is constructed, DNN may include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

[0159] Figure 2b This is a schematic diagram of an FNN network. The characteristic of the FNN network is that the neurons in adjacent layers are fully connected to each other. This characteristic makes FNN usually require a large amount of storage space and leads to high computational complexity.

[0160] CNN is a neural network that is specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling on the time axis) and image data (discrete sampling on two dimensions) can be considered to be data with a grid-like structure. CNN does not use all the input information for calculations at once, but uses a fixed-size window to intercept part of the information for convolution operations, which greatly reduces the amount of calculation of model parameters. In addition, depending on the type of information intercepted by the window (for example, people and objects in a picture are different types of information), each window can use different convolution kernel operations, which enables CNN to better extract the features of the input data.

[0161] RNN is a type of DNN network that uses feedback time series information. Its input includes the new input value at the current moment and its own output value at the previous moment. RNN is suitable for obtaining sequence features that are correlated in time, and is particularly suitable for applications such as speech recognition and channel coding.

[0162] In the above machine learning model training process, a loss function can be defined. The loss function describes the gap or difference between the output value of the model and the ideal target value. The loss function can be expressed in many forms, and there is no restriction on the specific form of the loss function. The model training process can be regarded as the following process: by adjusting some or all parameters of the model, the value of the loss function is less than the threshold value or meets the target requirements.

[0163] Models can also be referred to as AI models, rules or other names. AI models can be considered as specific methods for implementing AI functions. AI models characterize the 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 publishing, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model verification, or reasoning result publishing, etc. AI functions can also be referred to as AI (related) operations, or AI-related functions.

[0164] The implementation process of the neural network will be described exemplarily below with reference to the accompanying drawings.

[0165] 1. Fully connected neural network, also called multilayer perceptron (MLP).

[0166] like Figure 2c As shown in the figure, an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (middle). Each layer of the MLP contains several nodes, called neurons. The neurons in two adjacent layers are connected to each other.

[0167] Optionally, considering the neurons of two adjacent layers, the output h of the neurons in the next layer is the weighted sum of all the neurons x in the previous layer connected to it and passes through the activation function, which can be expressed as:

[0168] 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:

[0171] y=f n (w n f n-1 (…)+b n ).

[0172] Among them, n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0173] In other words, a neural network can be understood as a mapping relationship from an input data set to an output data set. Usually, neural networks are randomly initialized, and the process of obtaining this mapping relationship from random w and b using existing data is called neural network training.

[0174] Optionally, a specific method of training is to use a loss function to evaluate the output results of the neural network.

[0175] like Figure 2d As shown, the error can be back-propagated, and the neural network parameters (including w and b) can be iteratively optimized by the gradient descent method until the loss function reaches the minimum value, that is, Figure 2d The term "better point (e.g., optimal point)" is used in the context of Figure 2d The neural network parameters corresponding to the “better point (e.g., optimal point)” in the training can be used as the neural network parameters in the trained AI model information.

[0176] Alternatively, the gradient descent process can be expressed as:

[0177]

[0178] Among them, θ is the parameter to be optimized (including w and b), L is the loss function, η is the learning rate, which controls the step size of gradient descent. represents the derivative operation, It means taking the derivative of θ with respect to L.

[0179] Optionally, the back-propagation process utilizes the chain rule for partial derivatives.

[0180] like Figure 2e As shown, the gradient of the previous layer parameters can be recursively calculated by the gradient of the next layer parameters, which can be expressed as:

[0181]

[0182] Among them, w ij is the weight of node j connecting node i, s i is the weighted sum of the inputs to node i.

[0183] 2. Federated Learning (FL)

[0184] 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 tasks by promoting the collaboration of various edge devices and central servers.

[0185] like Figure 2f As shown in the figure, 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 as follows:

[0186] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0187] (2) In 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.

[0188] (3) The central node aggregates and collects 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.

[0189] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0190] 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.

[0191] As you can see, in the FL framework, the data set exists in the distributed nodes, that is, the distributed nodes collect local data sets, perform local training, and report the local results (models or gradients) obtained from the training to the central node. The central node itself does not have a data set, and is only responsible for fusing the training results of the distributed nodes to obtain the global model and send it to the distributed nodes.

[0192] 3. Decentralized learning: Different from federated learning, there is another distributed learning architecture - decentralized learning.

[0193] like Figure 2g As shown in Figure 2, 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:

[0194]

[0195] 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.

[0196] The technical solution provided by this application can be applied to wireless communication systems (such as Figure 1a or Figure 1b In the wireless communication system, communication nodes generally have signal transceiving capabilities and computing capabilities. Taking network devices with computing capabilities as an example, the computing capabilities of network devices mainly provide computing support for signal transceiving capabilities (for example, sending and receiving signals) to achieve communication tasks between network devices and other communication nodes.

[0197] In a communication network, the computing power of communication nodes may have surplus computing power in addition to providing computing power support for the above communication tasks. Therefore, how to utilize this computing power is a technical problem that needs to be solved urgently.

[0198] In one possible implementation, the communication node can be used as a participating node of the AI ​​learning system, and the computing power of the communication node is applied to a certain link of the AI ​​learning 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 transformer (GPT-2), can complete more and more complex tasks and achieve better performance. However, for large models, even the reasoning process of the model will be limited by the device capacity, so generally large models are stored on cloud central servers. At the same time, each device in the network generates a huge amount of raw data every day, which requires multiple calls to the large model for reasoning. Generally speaking, the device (such as a communication node) can send data to the central server, the central server uses the data for reasoning, and then the central server returns the reasoning result to the device. This process will consume a lot of communication resources for data transmission, and the privacy of device data will also be at risk.

[0199] In order to better save communication overhead and protect the privacy of user data, scholars have proposed distributed reasoning technology for deep neural networks. The approach is to distribute the model to devices and use the local computing power of the devices to infer the model, thereby reducing communication overhead and obtaining data privacy protection.

[0200] For example, Figure 2f In the federated learning scenario, the communication process between the central node and any distributed node is taken 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.

[0201] For example, Figure 2gIn the decentralized learning scenario, take the communication process between a distributed node and another distributed node as an example. The AI ​​model includes an AI sub-model deployed on a 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 another 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 another node. In this way, the AI ​​processing process involving multiple nodes can also be realized.

[0202] However, in the above implementation process, in order to improve the flexibility of AI model deployment, one or more AI models can be deployed in the same node. Accordingly, in a certain communication process, how the node and other nodes determine which AI model is currently used for AI processing is an urgent problem to be solved.

[0203] 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.

[0204] See also Figure 3 , is a schematic diagram of an implementation of the communication method provided in this application, and the method includes the following steps.

[0205] It should be noted that in Figure 3 In the example, the first communication device and the second communication device are used as the execution subjects of the interaction indication to illustrate the method, but the present application does not limit the execution subjects of the interaction indication. Figure 3 and later Figure 6 In the method, the execution subject can be replaced by a chip, a chip system, a processor, a logic module or software in a communication device. The first communication device can be a network device and the second communication device can be a terminal device, or both the first communication device and the second communication device are terminal devices (for example, the method can be applied to the communication process of different terminal devices in a sidelink communication scenario).

[0206] S301. A first communication device sends first information, and correspondingly, a second communication device receives the first information, wherein the first information is used to indicate a correspondence between N communication parameters and M AI models, where N and M are both positive integers.

[0207] 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.

[0208] S302. The first communication device sends second information, and correspondingly, the second communication device receives the second information. 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.

[0209] 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. Wherein, 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.

[0210] 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.

[0211] 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, and accordingly, the first AI model corresponding to the first communication parameter may include one or more AI models among the M AI models.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] It should be understood that the AI ​​model involved in this application (such as any AI model in the M AI models, any model in the Y AI models mentioned below, etc.) can be deployed in one or more communication devices. Optionally, when the AI ​​model is deployed in one communication device, the AI ​​model can be called a single-end deployed AI model, and when the AI ​​model is deployed in multiple communication devices, the AI ​​model can be called a dual-end / multi-end deployed AI model.

[0217] Exemplarily, the following will take the AI ​​model as the first AI model as an example to illustrate the processing process of the AI ​​model.

[0218] Implementation example 1, such as Figure 4a As shown, 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 may 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, and the processing 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.

[0219] Implementation example 2, such as Figure 4b As shown, the first AI model can be deployed in 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 can perform model processing on the first AI model.

[0220] 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.

[0221] like Figure 4cAs shown, 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.

[0222] Optionally, in implementation example three, the first AI model processing includes, in addition to the first AI sub-model and the second AI sub-model, other AI sub-models may also be included. The other AI sub-models may be deployed in other communication devices different from the first communication device and the second communication device, which is not limited here.

[0223] 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 of the 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.

[0224] 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.

[0225] 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 used for modulation and / or demodulation based on the parameters associated with modulation and demodulation.

[0226] Optionally, in implementation A, in an AI model 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 bit stream is processed by the AI ​​model, and the output data obtained may include modulation symbols. In addition, if the AI ​​model is used for demodulation, the input data of the AI ​​model may include noisy symbols, and the bit stream is processed by the AI ​​model, and the output data obtained may include log-likelihood ratios.

[0227] Optionally, the AI ​​model used for modulation and / or demodulation may be referred to as an intelligent modulation model, an intelligent demodulation model, an intelligent modulation and demodulation model, etc.

[0228] 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 transmitted precoding matrix indication (TPMI).

[0229] The following will take the example that the parameters associated with modulation and demodulation include MCS, and N and M are equal, to exemplify implementation method A.

[0230] Example 1. As mentioned above Figure 4a In the implementation example 1, 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.

[0231] Table 2

[0232]

[0233] 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.

[0234] Example 2. As mentioned above Figure 4b In the second implementation example, 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.

[0235] Table 3

[0236]

[0237] 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.

[0238] Example 3. As mentioned above Figure 4c In the implementation example three, 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 the case, the first information in step S301 can indicate the correspondence between N communication parameters and M AI models in the manner of Table 4.

[0239] Table 4

[0240]

[0241] 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 of the first AI sub-model and the AI ​​model identifier of the second AI sub-model corresponding to the one of the indexes based on Table 4, and further determines the first AI sub-model and the second AI sub-model based on the two identifiers.

[0242] Optionally, in Table 4, it is taken that the number of the first AI sub-model and the number of the second AI sub-model are both M. 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.

[0243] Optionally, in practical 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.

[0244] Take the case where the number of first AI sub-models is M and the number of second AI sub-models is K as an example. When K is greater than M, for the second communication device, the first AI sub-model can be determined based on Table 4, and the two or more second AI sub-models corresponding to the first AI sub-model can be determined 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, for the second communication device, the first AI sub-model can be determined based on Table 4, and the second AI sub-model corresponding to the first AI sub-model can be determined based on Table 4.

[0245] It can be understood that, through the implementation process of implementation method A, the first communication device can execute step S302 multiple times, so that the AI ​​model of the model processing executed by the second communication device switches following the change of MCS.

[0246] 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.

[0247] Implementation method B: The first communication parameter includes configuration information of a reference signal, and the first AI model includes an AI model for channel prediction.

[0248] In implementation B, the first communication parameter determined by the second information may include configuration information of the reference signal. Accordingly, in the corresponding relationship, 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 configuration information of the reference signal is associated with the process of channel prediction, 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 configuration information of the reference signal.

[0249] 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.

[0250] 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.

[0251] Optionally, the AI ​​model used for channel prediction can be called a channel estimation model, a channel prediction model, a channel simulation recovery model, a channel reconstruction model, a channel acquisition model, a channel inference model, etc.

[0252] 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.

[0253] 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).

[0254] Implementation method C: The first communication parameter includes parameters associated with beam management, and the first AI model includes an AI model for beam management.

[0255] 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.

[0256] Optionally, in implementation method C, in the AI ​​model used for beam management, the input data of the AI ​​model may include measurement results of A beams. The measurement results of the A measurement beams are processed by the AI ​​model, and the obtained output data may include indexes or identifiers of B beams, where B is a positive integer and A is an integer greater than B.

[0257] Optionally, the AI ​​model used for beam management may be referred to as a beam management model, a beam optimization model, etc.

[0258] 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").

[0259] 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, so 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 transmit / receive beam configuration, the first communication device / second communication device antenna configuration, the first communication device / second communication device antenna pattern, interference information, signal-to-interference-noise ratio, and RSRP distribution.

[0260] It should be noted that in the above-mentioned implementation method B and implementation method C, the correspondence between the first communication parameter and the first AI model can refer to the description of implementation method A above.

[0261] Optionally, any two or three of the above implementation manner A, implementation manner B and implementation manner C may be integrated for implementation. The following will take the integrated implementation of these three items as an example and introduce them in combination with the implementation manner shown in Table 5.

[0262] Example 4. As mentioned above Figure 4a In the implementation example 1, 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.

[0263] Table 5

[0264]

[0265] In other words, in step S302, the second information may include one of the N indexes. And the N indexes include the index of the parameter in implementation mode A (i.e., the index of MCS 0 and the index of MCS1 in Table 5), and the index of the parameter in implementation mode 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 mode 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 one of the indexes based on Table 5, and determines the AI ​​model indicated by the AI ​​model identifier as the first AI model.

[0266] It can be understood that Table 5 can be an extended implementation of the aforementioned Table 2. Similarly, the implementation methods of the aforementioned Tables 3 and 4 can also be extended with reference to the implementation of Table 5.

[0267] In one possible implementation, Figure 3 In the method shown, after step S302, the method further includes: the first communication device receives or sends AI data of the first AI model. Specifically, after the first communication device sends the second information to indicate the first AI model, the first communication device and the second communication device can also exchange the AI ​​data of the first AI model to implement model processing of the first AI model through the AI ​​data.

[0268] 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.

[0269] based on Figure 3Technical solution, after the first communication device sends the first information for indicating the correspondence between N communication parameters and M AI models in step S301, the first communication device may also send the second information for determining the 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 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 process 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.

[0270] exist Figure 3 In a possible implementation of the technical solution shown, before step S301, the second communication device may also send capability information to the first communication device. Figure 5 The scheme shown is described.

[0271] like Figure 5 As shown, compared to Figure 3 In the technical solution shown, before the first communication device sends the first information, the method further includes:

[0272] Step A: The second communication device sends third information, and correspondingly, the first communication device receives the third information, wherein the third information is used to indicate K AI models supported by the second communication device.

[0273] 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.

[0274] 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.

[0275] 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, 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 with the capabilities supported by the second communication device as much as possible to improve the success rate of model processing of the M AI models.

[0276] 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 a basis for determining the first information; alternatively, when 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 a basis for determining the first information.

[0277] In a 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, 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.

[0278] In a possible implementation, P of 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. 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 model parameters of the P AI models, so that the second communication device can obtain model parameters of other P AI models other than the K AI models supported by the third information through the first information, and can improve the implementation flexibility of the indication of the M AI models through the second information.

[0279] 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.

[0280] Optionally, when P AI models in 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.

[0281] exist Figure 3 In a possible implementation of the technical solution shown, after the first communication device sends the corresponding relationship between the communication parameter 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 corresponding relationship based on the information. Figure 6 The scheme shown is described.

[0282] like Figure 6 As shown, compared to Figure 3 In the technical solution shown, after the first communication device sends the first information, the method further includes:

[0283] Step B. The second communication device sends the fourth information and / or the fifth information, and correspondingly, the first communication device receives the fourth information and / or the fifth information. The fourth information includes the model parameters of the trained AI model, and the fifth information includes the auxiliary information.

[0284] Step C. The first communication device sends sixth information, and correspondingly, the second communication device receives the sixth information. The sixth information is used to indicate the correspondence between X communication parameters and Y AI models, and 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.

[0285] 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.

[0286] 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.

[0287] 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.

[0288] 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.

[0289] It should be understood that the sixth information is used to indicate the correspondence between X communication parameters and Y AI models, and the first information in the preceding text is used to indicate the correspondence between N communication parameters and M AI models, wherein the implementation process of the sixth information can refer to the implementation process of the first information in the preceding text. For example, the correspondence indicated by the sixth information can refer to the implementation process of any one of Tables 2 to 5 in the preceding text.

[0290] 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 completely 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.

[0291] based on Figure 6 In the technical solution shown, the first communication device may receive fourth information including model parameters of the trained AI model, and / or the first communication device may receive fifth information including auxiliary information, so that the first communication device may 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 the sixth information. In other words, the first communication device may 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.

[0292] 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, training data for the AI ​​model, auxiliary information during AI model training data collection, and auxiliary information during AI model reasoning.

[0293] Optionally, the sixth information includes model identifiers of the Y AI models. In this way, the overhead of the sixth information can be reduced.

[0294] 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.

[0295] Optionally, the trained AI model may be obtained by training based on the first AI model, or may be obtained by training based on other AI models (the other AI model may be one of the M AI models, or may not belong to 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 may continue to train (or retrain) according to the first AI model. For example, if the model performance of the AI ​​model obtained by continued training is better than a threshold, the trained first AI model may be used as a basis for determining the fourth information. For another example, if the model performance of the AI ​​model obtained by continued training is worse than a threshold, the trained first AI model may not be used as a basis for determining the fourth information, but may 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.

[0296] It can be understood that, as described above, the AI ​​model involved in the present 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.

[0297] 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 may 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 the 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.

[0298] As an implementation example, if the M AI models indicated by the first information include a model for channel prediction (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 channel prediction. The third AI model may 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.

[0299] 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 may 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, antenna configuration, antenna pattern, interference information, signal-to-interference-noise ratio, maximum signal quality information, beam sampling times, location information, and map information.

[0300] See also Figure 7 , the embodiment of the present application provides a communication device 700, which can implement the functions of the second communication device or the first communication device in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment. In the embodiment of the present application, the communication device 700 can be the first communication device (or the second communication device), or it can be an integrated circuit or component inside the first communication device (or the second communication device), such as a chip.

[0301] 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.

[0302] In a 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, wherein the first information is used to indicate the correspondence between N communication parameters and M AI models, and N and M are both positive integers; the transceiver unit 702 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.

[0303] In a 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, and 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.

[0304] It should be noted that the information execution process and other contents of the units of the above-mentioned communication device 700 can be specifically referred to the description in the method embodiment shown in the above-mentioned application, and will not be repeated here.

[0305] See also Figure 8 , is another schematic structural diagram of a communication device 800 provided in the present application, wherein 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.

[0306] in, Figure 7 The transceiver unit 702 shown may be a communication interface, which may be Figure 8 The input / output interface 802 in the communication interface may include an input interface and an output interface. Alternatively, the communication interface may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0307] 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.

[0308] 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.

[0309] 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.

[0310] In one possible implementation, Figure 7 The processing unit 701 shown can be Figure 8 The logic circuit 801 in.

[0311] Optionally, the logic circuit 801 may be a processing device, and the functions of the processing device may be partially or completely implemented by software. The functions of the processing device may be partially or completely implemented by software.

[0312] 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.

[0313] Alternatively, the processing device may include only a processor. A memory for storing a computer program is located outside the processing device, and the processor is connected to the memory via a circuit / wire to read and execute the computer program stored in the memory. The memory and the processor may be integrated together, or may be physically independent of each other.

[0314] 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 (FPGA), application specific integrated circuits (ASIC), system on chip (SoC), central processor unit (CPU), network processor (NP), digital signal processor (DSP), microcontroller unit (MCU), programmable logic device (PLD) or other integrated chips, or any combination of the above chips or processors.

[0315] See also Fig. 9 , is a communication device 900 involved in the above embodiment provided in an embodiment of the present application, and the communication device 900 may specifically be a communication device as a terminal device in the above embodiment, Fig. 9 The example shown is implemented by a terminal device (or a component in the terminal device).

[0316] Among them, a possible logical structure diagram of the communication device 900 is shown, and the communication device 900 may include but is not limited to at least one processor 901 and a communication port 902.

[0317] in, Figure 7 The transceiver unit 702 shown may be a communication interface, which may be Fig. 9 The communication port 902 in the embodiment may include an input interface and an output interface. Alternatively, the communication port 902 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0318] 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 .

[0319] 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 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 a computing function, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and the like. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0320] It should be noted that Fig. 9 The communication device 900 shown 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. Fig. 9 The specific implementation methods of the communication device shown can all refer to the description in the aforementioned method embodiment, and will not be described in detail here.

[0321] See also Fig.10 , is a schematic diagram of the structure of the communication device 1000 involved in the above embodiment provided in the embodiment of the present application, and the communication device 1000 may specifically be the communication device as the network device in the above embodiment, Fig.10 The example shown is that the network device is implemented by the network device (or a component in the network device), wherein the structure of the communication device can refer to Fig.10 The structure shown.

[0322] 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, through 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.

[0323] in, Figure 7 The transceiver unit 702 shown may be a communication interface, which may be Fig.10 The network interface 1014 in the embodiment may include an input interface and an output interface. Alternatively, the network interface 1014 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0324] The processor 1011 is mainly used to process the communication protocol and communication data, and to control the entire communication device, execute the software program, and process the data of the software program, for example, to support the communication device to perform the actions described in the embodiment. The communication device may include a baseband processor and a central processing unit. The baseband processor is mainly used to process the communication protocol and communication data, and the central processing unit is mainly used to control the entire terminal device, execute the software program, and process the data of the software program. Fig.10 The processor 1011 in the embodiment can integrate the functions of the baseband processor and the central processor. It can be understood by those skilled in the art that the baseband processor and the central processor can also be independent processors, which are interconnected through technologies such as buses. It can be understood by those skilled in the art that the terminal device can include multiple baseband processors to adapt to different network formats, and the terminal device can include multiple central processors to enhance its processing capabilities. The various components of the terminal device can be connected through various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The central processor can also be described as a central processing circuit or a central processing chip. The function of processing the communication protocol and the communication data can be built into the processor, or it can be stored in the memory in the form of a software program, and the processor executes the software program to realize the baseband processing function.

[0325] The memory is mainly used to store software programs and data. The memory 1012 can be independent and connected to the processor 1011. Optionally, the memory 1012 can be integrated with the processor 1011, for example, integrated into a chip. Among them, the memory 1012 can store program codes for executing the technical solutions of the embodiments of the present application, and the execution is controlled by the processor 1011. The various types of computer program codes executed can also be regarded as drivers of the processor 1011.

[0326] Fig.10 Only one memory and one processor are shown. 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, etc. 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 embodiments of the present application.

[0327] The transceiver 1013 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal, and 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, and 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 the digital intermediate frequency signal to the processor 1011, so that the processor 1011 further processes the digital baseband signal or the 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 a digital intermediate frequency signal from the processor 1011, and convert the modulated digital baseband signal or the digital intermediate frequency signal into a radio frequency signal, and send 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 processing on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal, and the order of the down-mixing and analog-to-digital conversion processing is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion processing on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal, and the order of the up-mixing and digital-to-analog conversion processing is adjustable. The digital baseband signal and the digital intermediate frequency signal can be collectively referred to as a digital signal.

[0328] 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 for implementing a receiving function may be regarded as a receiving unit, and a device in the transceiver unit for implementing a sending function may be regarded as a sending unit, that is, the transceiver unit includes a receiving unit and a sending unit, the receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the sending unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0329] It should be noted that Fig.10 The communication device 1000 shown can be specifically used to implement the steps implemented by the network device in the aforementioned method embodiment, and achieve the corresponding technical effects of the network device. Fig.10 The specific implementation methods of the communication device 1000 shown can all refer to the description in the aforementioned method embodiment, and will not be repeated here.

[0330] See also Fig.11 , which is a structural diagram of the communication device involved in the above-mentioned embodiments provided in the embodiments of the present application.

[0331] It can be understood that the communication device 110 includes, for example, modules, units, elements, circuits, or interfaces, etc., which are appropriately configured together to perform the technical solutions provided in the present application. The communication device 110 may be the terminal device or network device described above, or a component (such as a chip) in these devices, to implement the method described in the following method embodiment. The communication device 110 includes one or more processors 111. The processor 111 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control the communication device (such as a RAN node, a terminal, or a chip, etc.), execute software programs, and process data of software programs.

[0332] Optionally, in one design, the processor 111 may include a program 113 (sometimes also referred to as code or instruction), and the program 113 may be executed on the processor 111 to enable the communication device 110 to perform the method described in the following embodiments. In another possible design, the communication device 110 includes a circuit ( Fig.11 not shown).

[0333] 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 executed on the processor 111 so that the communication device 110 executes the method described in the above method embodiment.

[0334] Optionally, the processor 111 and / or the memory 112 may include an AI module 117, 118, and the AI ​​module is used to implement AI-related functions. The AI ​​module may be implemented by software, hardware, or a combination of software and hardware. For example, the AI ​​module may include a radio intelligence control (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0335] Optionally, data may also be stored in the processor 111 and / or the memory 112. The processor and the memory may be provided separately or integrated together.

[0336] Optionally, the communication device 110 may further include a transceiver 115 and / or an antenna 116. The processor 111 may also be sometimes referred to as a processing unit, which controls the communication device (e.g., a RAN node or a terminal). The transceiver 115 may also be sometimes referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, etc., which is used to implement the transceiver function of the communication device through the antenna 116.

[0337] in, Figure 7 The processing unit 701 shown may be the processor 111 . Figure 7 The transceiver unit 702 shown may be a communication interface, which may be Fig.11 The transceiver 115 in the embodiment may include an input interface and an output interface. Alternatively, the transceiver 115 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0338] 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.

[0339] 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.

[0340] 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 for 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, and may also 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.

[0341] An embodiment of the present application also provides a communication system, and the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

[0342] In the several embodiments provided in the present 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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.

[0343] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0344] In addition, each functional unit in each embodiment 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 part that contributes or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

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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