Communication method and related apparatus

Abstract

A communication method and a related apparatus. In the method, on the basis of a request carried in first information, a first communication apparatus can determine M models matching the request, and sends second information on the basis of P piece(s) of output data obtained by processing using the M models. In this way, on the basis of an AI processing request, the first communication apparatus can provide a processing result obtained by processing using an AI model matching the request, so that an AI processing requester (e.g., a sender of the first information) can obtain a target output matching a personalized request, thereby improving user experience. In addition, when the P piece(s) of output data include multiple pieces of output data (e.g., P being greater than 1), a first model can obtain diversified output data by means of the M models, and process the diversified output data to obtain the second information used for determining a personalized target output, thereby improving the performance of the personalized target output.

Description

Communication method and related apparatus

[0001] This application claims priority from the Chinese patent application No. 202411081322.3 filed on August 6, 2024, and entitled "A communication method and related apparatus", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the field of communication, and in particular, to a communication method and related apparatus. BACKGROUND

[0003] With the development of communication technology, in a communication system, in addition to the traditional communication service, the service performed by the communication device can also include other new services, such as artificial intelligence (AI) services. Generally, a communication system capable of processing AI services can also be referred to as an AI system.

[0004] Currently, a communication device can serve as a participating node of an AI system, and the computing power of the communication device is applied to a certain link of the AI system. Generally, the AI function introduced in the communication network needs to rely on a model to be implemented. However, in the above process, how to provide personalized model output is a technical problem to be solved. SUMMARY

[0005] The present application provides a communication method and related apparatus for providing personalized model output to improve user experience.

[0006] The first aspect of the present application provides a communication method, which is performed by a first communication apparatus. The first communication apparatus can be a communication device (such as a terminal device or a network device), or the first communication apparatus can be a part of the communication device (such as a circuit or a chip responsible for communication functions (such as a Modem chip, also known as a baseband chip, or a system on chip (SoC) chip or a system in package (SIP) chip containing a modem core), etc.), or the first communication apparatus can also be a logic module or software that can realize all or part of the functions of the communication device. In the method, the first communication apparatus receives first information, the first information being used to request AI processing on first input data; wherein the first input data is processed by M models to obtain P output data, the M models being determined based on the first information, M being a positive integer, and P being greater than or equal to M; the first communication apparatus sends second information, the second information being used to determine a target output corresponding to the first input data; wherein the second information is determined based on the P output data.

[0007] Based on the above scheme, the first information received by the first communication device is used to request AI processing of the first input data, and after the first input data is processed by the M models to obtain P output data, the first communication device can determine and send second information based on the P output data, so that the receiver (e.g., the second communication device) of the second information can determine the target output corresponding to the first input data based on the second information. Wherein, the M models are determined based on the first information, that is, the first communication device can determine the M models suitable for the request based on the request of the first information (for example, the model processing of the M models can meet part or all of the request), and send the second information based on the P output data obtained by processing the M models. In this way, the first communication device can provide the processing result of the AI model matched with the request based on the AI processing request, so that the AI processing requester (e.g., the sender of the first information) can obtain the target output matched with the personalized request, to improve the user experience.

[0008] In this application, the model can be replaced by other terms, such as AI model, neural network, neural network model, AI neural network model, machine learning model, or AI processing model, etc.

[0009] In this application, the target output can be replaced by other terms, such as target, expected output, ideal output, personalized output, or label, etc.

[0010] As an example, the second information is determined based on the P output data, including: the second information is obtained based on the processing of the P output data by Q models, Q is a positive integer. In other words, the first communication device can determine the second information by processing the P output data through Q models. In the above scheme, in the case that the P output data is multiple output data (e.g., P is greater than 1), the Q models can obtain diversified output data through the M models, and process the diversified output data to obtain the second information for determining the personalized target output, to improve the performance of the personalized target output.

[0011] Optionally, the second information is obtained based on the processing of the P output data by Q models, which can be understood that each model in the Q models processes part or all of the P output data, and each model obtains one or more output data. The output data obtained by the Q models can be represented as T output data (T is an integer greater than or equal to Q); the second information can be obtained based on the processing of the T output data (e.g., sum average, weighted average, etc.).

[0012] It should be understood that any of the T output data can be a model output and / or an intermediate result of one of the Q models. In other words, the one or more output data corresponding to each of the Q models can include a model output and / or an intermediate result of the each of the Q models, etc. For example, the intermediate result can include one or more of logits, features, gradients, or weights.

[0013] For example, one of the models can include one or more layers of neural networks, and the model output of the one of the models can be understood as a partial or full output of a last layer of the one or more layers of neural networks. For example, the model output can include a partial or full output of the last layer of the one or more layers of neural networks. In addition, the intermediate result of the one of the models can be understood as a partial or full output of at least one layer of the one or more layers of neural networks other than the last layer of the one or more layers of neural networks. For example, the intermediate result can include a partial or full output of each of the at least one layer of the one or more layers of neural networks.

[0014] For example, each of the Q models can be configured to process the P output data to obtain one or more of the T output data, and the T output data can be used to determine the target output corresponding to the first input data. In other words, the Q models can be configured to determine the target output corresponding to the first input data based on the P output data, and the model processing performed by the Q models can include evaluation, ranking, optimization, screening, selection, or filtering, etc. Accordingly, any of the Q models can be referred to as an evaluation model, a personalized model, a personalized evaluation model, a reward model, or a scoring model, etc.

[0015] It should be understood that the P output data is obtained by processing the first input data by the M models. In other words, each of the M models can obtain one or more output data after processing the first input data, and the P output data can include the one or more output data corresponding to each of the M models.

[0016] It should be understood that any of the P output data can be a model output and / or an intermediate result of one of the M models. In other words, the one or more output data corresponding to each of the M models can include a model output and / or an intermediate result of the each of the M models, etc. For example, the intermediate result can include one or more of logits, features, gradients, or weights.

[0017] As an example, in the above scheme, any model in the M models is used to process the first input data to obtain one or more output data, that is, the model processing performed by the any model can include one or more of inference, prediction, derivation, identification, decision-making. Correspondingly, the any model can be referred to as an inference model, a prediction model, a pre-trained model for the above model processing, or a pre-trained large model for the above model processing, etc.

[0018] It should be noted that there is an association relationship between the first communication device and the M models, which can be implemented in various ways.

[0019] Example one, the M models can be deployed in the first communication device. In example one, after receiving the first information, the first communication device can process the first input data based on the locally deployed M models to obtain P output data.

[0020] Example two, the M models can be deployed in one or more third communication devices different from the first communication device. In example two, after receiving the first information, the first communication device can send a request to the one or more third communication devices, so that the one or more third communication devices send P output data to the first communication device based on the request.

[0021] Example three, a part of the M models are deployed in the first communication device, and another part of the M models are deployed in one or more third communication devices different from the first communication device. Wherein, the first communication device can determine the output data corresponding to the part of the models through example one, and determine the output data corresponding to the other part of the models through example two, and determine P output data through the two parts of output data.

[0022] Optionally, the first input data can be included in the first information, or the first input data can be included in other messages / signaling different from the first information, which is not limited here.

[0023] In a possible implementation manner of the first aspect, the second information includes at least one of:

[0024] The first indication information indicates the order of the P output data; wherein the target output is one of the P output data;

[0025] The second indication information indicates the first index, and the first index is used to determine the target output in the P output data; wherein the target output is one of the P output data;

[0026] The third indication information indicates the target output; wherein the target output is obtained based on the P output data (for example, sum average, weighted average, etc.);

[0027] The fourth indication information indicates the P output data (or contains the P output data).

[0028] Based on the above scheme, the second information can be implemented in the above-mentioned multiple ways, so that the receiver of the second information can determine the target output corresponding to the first input data in multiple ways, so as to improve the flexibility of the implementation of the scheme.

[0029] In a possible implementation manner of the first aspect, the M models are contained in N models, N is an integer greater than or equal to M; the method further comprises: the first communication device receives third information (for example, the third information comes from a third communication device), the third information is used to indicate model information of the N models, and the first information is used to determine the M models by the model information of the N models, N is greater than or equal to M.

[0030] Based on the above scheme, the first communication device can receive the third information, so that the first communication device can determine the model information of the N models through the third information, and determine the M models matched with the request of the first information based on the model information of the N models.

[0031] In a possible implementation manner of the first aspect, the model information indicates at least one of the following: model function, task corresponding to the model, applicable scene of the model, or reference performance of the model.

[0032] Based on the above scheme, the model information indicated by the third information can indicate at least one of the above, so as to improve the flexibility of the implementation of the scheme.

[0033] In a possible implementation manner of the first aspect, the first information indicates any one of the following: requested model function, requested task, requested scene, or requested target performance.

[0034] Based on the above scheme, the request of the first information can indicate at least one of the above, so as to improve the flexibility of the implementation of the scheme.

[0035] In a possible implementation manner of the first aspect, the second information is carried in a multicast message containing a first multicast identifier; the method further comprises: the first communication device sends fourth information to the second communication device, the fourth information indicating the first multicast identifier.

[0036] Based on the above scheme, the first communication device can send fourth information indicating the first multicast identifier to the second communication device, and the first communication device can send a multicast message carrying the second information and the first multicast identifier, so that one or more second communication devices can obtain the corresponding second information through the multicast mode, and the transmission overhead of the output data can be reduced.

[0037] Optionally, the first multicast identifier can be determined based on the communication state information corresponding to the second communication device and / or the first information.

[0038] In a possible implementation of the first aspect, the first information and the first multicast identifier are used to determine the M models.

[0039] Based on the above scheme, after the first communication device determines the first multicast identifier corresponding to the second communication device based on the communication state information corresponding to the second communication device and / or the first information, the first communication device can determine the M models for processing the first input data based on the first information and the first multicast identifier, so that the M models not only match the request corresponding to the first information, but also match the first multicast identifier.

[0040] In a possible implementation of the first aspect, the method further includes: the first communication device sends fifth information to a third communication device, the fifth information being used to request the M models to perform AI processing on the first input data, the third communication device being used to manage part or all of the M models; and the first communication device receives sixth information from the third communication device, the sixth information indicating the first multicast identifier; wherein the first multicast identifier is determined based on the fifth information.

[0041] Alternatively, in the case that the second information is carried in a multicast message containing a second multicast identifier; the method further includes: the third communication device receives fifth information from the first communication device, the fifth information being used to request the M models to perform AI processing on the first input data, the third communication device being used to manage part or all of the M models; and the third communication device sends sixth information to the first communication device, the sixth information indicating the first multicast identifier; wherein the first multicast identifier is determined based on the fifth information.

[0042] Based on the above scheme, the first communication device can send the multicast message corresponding to the second communication device to the second communication device through the multicast mode based on the first multicast identifier provided by the third communication device, so that one or more second communication devices can obtain the corresponding second information through the multicast mode, and the transmission overhead of the output data can be reduced.

[0043] Further, in the above solution, the third communication device providing the first communication device with the first groupcast identifier is configured to manage part or all of the M models, such that the grouping of the one or more second communication devices by the third communication device is able to match the characteristics of the respective models.

[0044] In a possible implementation of the first aspect, the first information is from the second communication device, and the fifth information satisfies at least one of the following: the fifth information is determined based on the communication state information of the one or more second communication devices; or, the fifth information is further used to indicate a second groupcast identifier; wherein the second groupcast identifier is determined based on the corresponding communication state information of the second communication device and / or the first information.

[0045] Based on the above solution, the first communication device is able to group the one or more second communication devices based on the corresponding communication state information of the one or more second communication devices and / or the first information, and send the fifth information to the third communication device based on the grouping result, so as to reduce the communication overhead between the first communication device and the third communication device.

[0046] Further, in the case that the groupcast identifier (e.g. the first groupcast identifier, the second groupcast identifier, the third groupcast identifier described later, etc.) is determined based on the corresponding communication state information of the second communication device, the first communication device is able to group the one or more second communication devices based on the corresponding communication state information of the one or more second communication devices, and assign the same groupcast identifier to the second communication devices with the same or similar communication state, and assign different groupcast identifiers to the second communication devices with different or dissimilar communication state, so as to improve the groupcast efficiency.

[0047] Further, in the case that the groupcast identifier (e.g. the first groupcast identifier, the second groupcast identifier, the third groupcast identifier described later, etc.) is determined based on the first information of the second communication device, the first communication device is able to group the one or more second communication devices based on the AI processing request of the one or more second communication devices, and assign the same groupcast identifier to the second communication devices with the same or similar AI processing request, and assign different groupcast identifiers to the second communication devices with different AI processing request, so as to improve the groupcast efficiency.

[0048] Optionally, the communication state information can indicate one or more of the following: latency, data transmission rate, packet loss rate, channel eigenvector, channel multi-path component information, or load.

[0049] In a possible implementation of the first aspect, the first information and the second groupcast identifier are used to determine the M models.

[0050] Based on the above scheme, after the first communication device determines the second groupcast identifier corresponding to the second communication device based on the communication state information corresponding to the second communication device and / or the first information, the third communication device can determine the M models for processing the first input data based on the first information and the second groupcast identifier, so that the M models not only match the request corresponding to the first information, but also match the third groupcast identifier.

[0051] The second aspect of the present application provides a communication method, which is performed by a second communication device. The second communication device can be a communication device (such as a terminal device or a network device), or the second communication device can be a part of the communication device (for example, a circuit or a chip responsible for communication functions (such as a Modem chip, also known as a baseband chip, or a SoC chip or a SIP chip containing a modem core, etc.), or the second communication device can also be a logic module or software that can realize all or part of the functions of the communication device. In the method, the second communication device sends first information, the first information being used to request AI processing of first input data; wherein the first input data is AI-processed by M models to obtain P output data, the M models being determined based on the first information, M being a positive integer, and P being greater than or equal to M; and the second communication device receives second information, the second information being used to determine a target output corresponding to the first input data; wherein the second information is determined based on the P output data.

[0052] Based on the above scheme, the first information sent by the second communication device to the first communication device is used to request AI processing of the first input data, and after the first input data is AI-processed by M models to obtain P output data, the first communication device can determine and send second information based on the P output data, so that the second communication device can determine a target output corresponding to the first input data based on the second information. Wherein the M models are determined based on the first information, that is, the first communication device can determine M models that match the request based on the request of the first information, and send the second information based on the P output data obtained by processing the M models. In this way, the first communication device can provide a processing result of AI models that match the request based on the AI processing request, so that the AI processing requester (for example, the sender of the first information) can obtain a target output that matches the personalized request, thereby improving the user experience.

[0053] In a possible implementation manner of the second aspect, the first information indicates any of the following: a requested model function, a requested task, a requested scene, or a requested target performance.

[0054] Based on the above scheme, the request of the first information can indicate at least one of the above, to improve the flexibility of the implementation of the scheme.

[0055] In a possible implementation manner of the second aspect, the second information is carried in a multicast message comprising the first multicast identifier; and the method further includes: receiving, by the second communication device, fourth information from the first communication device, the fourth information indicating the first multicast identifier.

[0056] Based on the above scheme, the second communication device can receive the fourth information indicating the first multicast identifier sent by the first communication device, and the second communication device can receive the multicast message carrying the second information and the first multicast identifier, so that one or more second communication devices can obtain the corresponding second information through the multicast manner, and the transmission overhead of the output data can be reduced.

[0057] Optionally, the first multicast identifier is determined based on the corresponding communication state information of the second communication device and / or the first information.

[0058] In a possible implementation manner of the second aspect, the first information and the first multicast identifier are used to determine the M models.

[0059] Based on the above scheme, after the first communication device determines the first multicast identifier corresponding to the second communication device based on the corresponding communication state information of the second communication device and / or the first information, the first communication device can determine the M models for processing the first input data based on the first information and the first multicast identifier, so that the M models not only match the request corresponding to the first information, but also match the first multicast identifier.

[0060] In a possible implementation manner of the second aspect, the second information is carried in a multicast message comprising a third multicast identifier; and the method further includes: receiving, by the second communication device, seventh information from a third communication device, the seventh information indicating the third multicast identifier.

[0061] Or, the second information is carried in a multicast message comprising a third multicast identifier; and the method further includes: sending, by the third communication device, seventh information to the second communication device, the seventh information indicating the third multicast identifier.

[0062] Optionally, the third communication device can determine the third multicast identifier based on the fifth information sent by the first communication device.

[0063] Optionally, the third communication device sends the seventh information to the second communication device through the first communication device. For example, the first communication device is an access network device, the second communication device is a terminal device, and the third communication device can be a core network device (or an over the top (OTT) server or an application server of the terminal device, etc.), and in the above process, the seventh information can be carried in a non access stratum (NAS) message.

[0064] Based on the above scheme, the second communication device can receive the groupcast message corresponding to the second communication device through the groupcast mode based on the third groupcast identifier provided by the third communication device, so that one or more second communication devices can obtain the corresponding second information through the groupcast mode, and the transmission overhead of the output data can be reduced.

[0065] In a possible implementation of the second aspect, the second information is determined based on the P output data, including: the second information is obtained based on processing of the P output data by Q models, Q being a positive integer. In other words, the first communication device can process the P output data through the Q models to determine the second information. In the above scheme, in the case that the P output data is a plurality of output data (for example, P is greater than 1), the Q models can obtain diversified output data through the M models, and process the diversified output data to obtain the second information for determining the personalized target output, so as to improve the performance of the personalized target output.

[0066] In a possible implementation of the second aspect, the second information includes at least one of the following:

[0067] first indication information, indicating an order of the P output data; wherein the target output is one of the P output data;

[0068] second indication information, indicating a first index, the first index being used to determine the target output in the P output data; wherein the target output is one of the P output data;

[0069] third indication information, indicating the target output; wherein the target output is obtained based on the P output data;

[0070] fourth indication information, indicating the P output data (or containing the P output data).

[0071] Based on the above scheme, the second information can be implemented in the above-mentioned various ways to improve the flexibility of the scheme implementation.

[0072] The third aspect of the present application provides a communication method, which is executed by a third communication device. The third communication device can be a communication device (e.g., a terminal device or a network device or a server (e.g., an application server of a terminal device)), or the third communication device can be a part of a communication device (e.g., a circuit or a chip responsible for communication functions (e.g., a Modem chip, also known as a baseband chip, or a SoC chip containing a modem core or a SIP chip, etc.), or the third communication device can also be a logic module or software capable of implementing all or part of the functions of a communication device.

[0073] In the method provided in the third aspect, the third communication device receives fifth information, the fifth information being used to request M models to perform AI processing on the first input data, M being a positive integer; the third communication device sends sixth information, the sixth information indicating a second multicast identifier; wherein the second multicast identifier is determined based on the fifth information.

[0074] Based on the above scheme, after receiving the fifth information used to request M models to perform AI processing on the first input data, the third communication device can determine the second multicast identifier based on the fifth information, and indicate the second multicast identifier to the first communication device through the sixth information. Thereafter, the first communication device can send a multicast message corresponding to the second communication device to the second communication device in a multicast manner based on the second multicast identifier provided by the third communication device, so that one or more second communication devices can obtain corresponding second information in a multicast manner, and the transmission overhead of the output data can be reduced.

[0075] Alternatively, in the method provided in the third aspect, the third communication device receives fifth information, the fifth information being used to request M models to perform AI processing on the first input data, M being a positive integer; the third communication device sends seventh information, the seventh information indicating a third multicast identifier; wherein the third multicast identifier is determined based on the fifth information.

[0076] Based on the above scheme, after receiving the fifth information used to request M models to perform AI processing on the first input data, the third communication device can determine the third multicast identifier based on the fifth information, and indicate the third multicast identifier to the second communication device through the seventh information. Thereafter, the second communication device can receive a corresponding multicast message in a multicast manner based on the third multicast identifier provided by the third communication device, so that one or more second communication devices can obtain corresponding second information in a multicast manner, and the transmission overhead of the output data can be reduced.

[0077] It should be understood that the process executed by the third communication device can refer to the foregoing first aspect, the second aspect, and the related description.

[0078] The fourth aspect of the present application provides a communication device, which is a first communication device, comprising a processing unit; the transceiver is configured to receive first information, the first information being used for requesting AI processing on first input data; wherein the first input data is processed by M models to obtain P output data, the M models being determined based on the first information, M being a positive integer, and P being greater than or equal to M; the processing unit is configured to determine second information; the transceiver is configured to send the second information, the second information being used for determining a target output corresponding to the first input data; wherein the second information is determined based on the P output data.

[0079] In the fourth aspect of the present application, the component modules of the communication device can also be used to perform the steps performed in the various possible implementation manners of the first aspect and achieve the corresponding technical effects, which can be referred to the first aspect for details and will not be described here.

[0080] The fifth aspect of the present application provides a communication device, which is a second communication device, comprising a transceiver and a processing unit, the processing unit being configured to determine first information; the transceiver is configured to send the first information, the first information being used for requesting AI processing on first input data; wherein the first input data is processed by M models to obtain P output data, the M models being determined based on the first information, M being a positive integer, and P being greater than or equal to M; the transceiver is also configured to receive second information, the second information being used for determining a target output corresponding to the first input data; wherein the second information is determined based on the P output data.

[0081] In the fifth aspect of the present application, the component modules of the communication device can also be used to perform the steps performed in the various possible implementation manners of the second aspect and achieve the corresponding technical effects, which can be referred to the second aspect for details and will not be described here.

[0082] The sixth aspect of the present application provides a communication device, which is a second communication device, comprising a transceiver and a processing unit, the transceiver being configured to receive fifth information, the fifth information being used for requesting M models to perform AI processing on the first input data, M being a positive integer; the processing unit is configured to determine sixth information; the transceiver is also configured to send the sixth information, the sixth information indicating a second multicast identifier; wherein the second multicast identifier is determined based on the fifth information. Alternatively, the transceiver is configured to receive fifth information, the fifth information being used for requesting M models to perform AI processing on the first input data, M being a positive integer; the processing unit is configured to determine seventh information; the transceiver is also configured to send the seventh information, the seventh information indicating a third multicast identifier; wherein the second multicast identifier is determined based on the fifth information.

[0083] In the sixth aspect of the present application, the constituent modules of the communication device can also be used to perform the steps performed in the various possible implementation manners of the third aspect and achieve the corresponding technical effects. For details, please refer to the third aspect, which will not be described here again.

[0084] The seventh aspect of the present application provides a communication device, comprising at least one processor configured to execute computer programs or instructions to enable the communication device to implement the method described in any one of the possible implementation manners of the first aspect to the third aspect. Optionally, the communication device can comprise a memory (or the communication device can be externally connected to a memory), which is configured to store the computer programs or instructions.

[0085] The eighth aspect of the present application provides a communication device, comprising at least one logic circuit and an input / output interface; the logic circuit is configured to execute the method described in any one of the possible implementation manners of the first aspect to the third aspect.

[0086] The ninth aspect of the present application provides a communication system, comprising the first communication device and the second communication device.

[0087] The tenth aspect of the present application provides a computer readable storage medium, which is configured to store one or more computer execution instructions, when the computer execution instructions are executed by a processor, the processor executes the method described in any one of the possible implementation manners of the first aspect to the third aspect.

[0088] The eleventh 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 a processor, the processor executes the method described in any one of the possible implementation manners of the first aspect to the third aspect.

[0089] The twelfth aspect of the present application provides a chip system, comprising at least one processor configured to support the communication device to implement the method described in any one of the possible implementation manners of the first aspect to the third aspect.

[0090] In a possible design, the chip system can further comprise a memory configured to store necessary program instructions and data of the communication device. The chip system can be composed of a chip, or can comprise a chip and other discrete devices. Optionally, the chip system further comprises an interface circuit configured to provide program instructions and / or data for the at least one processor.

[0091] The technical effects brought by any one of the design manners in the fourth aspect to the twelfth aspect can be referred to the technical effects brought by the different design manners in the first aspect to the third aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0092] FIGS. 1a to 1c are schematic diagrams of a communication system provided by the present application;

[0093] FIGS. 2a to 2g are schematic diagrams of an AI processing process related to the present application;

[0094] FIG. 3 is an interaction schematic diagram of a communication method provided by the present application;

[0095] FIGS. 4a to 4c are some schematic diagrams of a model provided by the present application;

[0096] FIG. 5 is another interaction schematic diagram of a communication method provided by the present application;

[0097] FIGS. 6 to 10 are schematic diagrams of a communication device provided by the present application. DETAILED DESCRIPTION

[0098] First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0099] (1) Terminal device: can be a wireless terminal device capable of receiving network device scheduling and indication information, the wireless terminal device can be a device that provides voice and / or data connectivity to a user, or a handheld device with wireless connection function, or other processing devices connected to a wireless modem.

[0100] A terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN), and the terminal device can be a mobile terminal device, such as a mobile phone (or known as "cellular" phone, mobile phone), a computer, and a data card, for example, which can be a portable, pocket, hand-held, computer- built-in, or vehicle-mounted mobile device that exchanges voice and / or data with a radio access network. For example, a personal communication service (PCS) phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a tablet, a computer with wireless transceiver, and the like. The wireless terminal device can also be referred to as a subscriber unit, a subscriber station, a mobile station / mobile terminal (MS), a remote station, an access point (AP), a remote terminal, an access terminal, a user terminal, a user agent, a subscriber station (SS), customer premises equipment (CPE), a terminal, user equipment (UE), a mobile terminal (MT), and the like.

[0101] By way of example and not limitation, in embodiments of the present application, the terminal device can also be a wearable device. The wearable device can also be referred to as a smart wearable device or a smart wearable device, etc., which is a general term for devices that apply wearable technology to the intelligent design and development of daily wear, such as glasses, gloves, watches, clothing, and shoes, etc. The wearable device is a portable device that can be directly worn on the body or integrated into the user's clothes or accessories. The wearable device is not just a hardware device, but also has powerful functions through software support and data interaction, cloud interaction. The broad sense of wearable smart devices includes devices with full functions, large sizes, and the ability to realize complete or partial functions without relying on smart phones, such as smart watches or smart glasses, etc., and devices that focus only on a certain application function and need to be used with other devices such as smart phones, such as various smart wristbands, smart helmets, smart jewelry, etc.

[0102] The terminal can 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 telemedicine or telehealth services, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc.

[0103] In addition, the terminal device can also be a terminal device in a future communication system (such as a 5G Advanced communication system, etc.) after the 5th generation (5G) communication system or a terminal device in a future evolved public land mobile network (PLMN), etc. For example, the 5G Advanced network can further expand the form and function of the 5G communication terminal, and the 5G Advanced terminal includes but is not limited to vehicles, cellular network terminals (with satellite terminal functions), drones, and internet of things (IoT) devices.

[0104] In the embodiments of the present application, the terminal device can also obtain the AI service provided by the network device. Optionally, the terminal device can also have AI processing capability.

[0105] (2) Network device: can be a device in a wireless network, for example, the network device can be a RAN node (or device) for accessing the terminal device to the wireless network, which can also be referred to as a base station. At present, some examples of RAN devices are: base station (base station), evolved NodeB (eNodeB), base station gNB (gNodeB) in 5G communication system, transmission reception point (transmission reception point or transmit / receive point, TRP), evolved Node B (eNB), radio network controller (radio network controller, RNC), Node B (Node B, NB), home base station (for example, home evolved Node B, or home Node B, HNB), baseband unit (baseband unit, BBU) or wireless fidelity (wireless fidelity, Wi-Fi) access point (AP) and the like. In addition, in one network structure, the network device can include a central unit (central unit, CU) node, or a distributed unit (distributed unit, DU) node, or a RAN device including a CU node and a DU node.

[0106] Optionally, the RAN node can 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 (cloud radio access network, CRAN) scenario. The RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, etc. For example, the access network device in vehicle external connection (V2X) technology can be a road side unit (road side unit, RSU).

[0107] In another possible scenario, a terminal is assisted by multiple RAN nodes to implement wireless access, and different RAN nodes respectively implement part of functions of a base station. For example, a RAN node can be a CU, a DU, a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. The CU and the DU can be separately arranged, or can be included in the same network element, for example, in a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, for example, in a remote radio unit (RRU), an active antenna unit (AAU), a radio head (RH), or a remote radio head (RRH).

[0108] In different systems, the CU (or CU-CP and CU-UP), the DU, or the RU can also have different names, but those skilled in the art can understand their meanings. For example, in an open RAN (O-RAN or ORAN) system, the CU can also be referred to as an O-CU (open CU), the DU can also be referred to as an O-DU, the CU-CP can also be referred to as an O-CU-CP, the CU-UP can also be referred to as an O-CU-UP, and the RU can also be referred to as an O-RU. For the convenience of description, the CU, the CU-CP, the CU-UP, the DU, and the RU are taken as examples for description in this application. Any one of the CU (or the CU-CP, the CU-UP), the DU, and the RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0109] Communication between the access network device and the terminal device complies with a certain protocol layer structure. The protocol layer can include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer can 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 / medium access control (MAC) layer, or a physical (PHY) layer, etc. The user plane protocol layer can 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, etc.

[0110] For the correspondence between the network elements in the ORAN system and the protocol layer functions that can be implemented by the network elements, refer to Table 1 below.

[0111] Table 1

[0112] The network device can be another device that provides a wireless communication function for the terminal device. Embodiments of the present application do not limit the specific technology and specific device form adopted by the network device. For the convenience of description, embodiments of the present application do not limit.

[0113] The network device can also include a core network device, for example, a mobility management entity (MME) in a fourth generation (4G) network, a home subscriber server (HSS), a serving gateway (S-GW), a policy and charging rules function (PCRF), a public data network gateway (PDN gateway or P-GW), a network element such as an access and mobility management function (AMF) in a 5G network, a user plane function (UPF), or a session management function (SMF). In addition, the core network device can also include other core network devices in the 5G network and the next generation network of the 5G network.

[0114] In the embodiments of the present application, the network device mentioned above can also be an AI-capable network node, which can provide AI services for terminals or other network devices, for example, AI nodes, computing power nodes, AI-capable RAN nodes, AI-capable core network elements, etc. on the network side (access network or core network).

[0115] In the embodiments of the present application, the device for implementing the function of the network device can be a network device or a device capable of supporting the network device to implement the function, such as a chip system, which can be arranged in the network device. In the technical solutions provided in the embodiments of the present application, the device for implementing the function of the network device is taken as an example to describe the technical solutions provided in the embodiments of the present application.

[0116] (3) Configuration and pre-configuration: in this application, configuration and pre-configuration will be used simultaneously. Among them, configuration refers to that the network device / server sends some parameter configuration information or parameter values to the terminal through messages or signaling, so that the terminal determines the communication parameters or resource in transmission according to the values or information. Pre-configuration is similar to configuration, which can be parameter information or parameter values agreed by the network device / server and the terminal device in advance, or parameter information or parameter values adopted by the base station / network device or the terminal device according to the standard protocol, or parameter information or parameter values pre-stored in the base station / server or the terminal device. This application does not limit this.

[0117] Further, these values and parameters can be changed or updated.

[0118] (4) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" means two or more. "And / or" describes the association between the associated objects, which means that there can be three relationships, for example, A and / or B, which means that A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single item or multiple items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. In addition, unless otherwise specified, the ordinal numbers "first", "second", etc. mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, time sequence, priority or importance of the multiple objects.

[0119] (5) In the embodiments of the present application, "sending" and "receiving" represent the direction of signal transmission. For example, "sending information to XX" can be understood as that the destination of the information is XX, which can include direct sending through the air interface, or indirect sending through the air interface by other units or modules. "Receiving information from YY" can be understood as that the source of the information is YY, which can include direct receiving from YY through the air interface, or indirect receiving from YY through the air interface by other units or modules. "Sending" can also be understood as the "output" of the chip interface, and "receiving" can also be understood as the "input" of the chip interface.

[0120] In other words, sending and receiving can be carried out between devices, such as between network devices and terminal devices, or within devices, such as between components, modules, chips, software modules or hardware modules within a device through buses, wires or interfaces.

[0121] It can be understood that the information can be processed, such as encoding and modulation, between the source end and the destination end of the information transmission, but the destination end can understand the effective information from the source end. Similar expressions in this application can be similarly understood, and will not be repeated here.

[0122] (6) In the embodiments of the present application, “indication” can include direct indication and indirect indication, and can also include explicit indication and implicit indication. The information indicated by certain information (indication information described below) is referred to as to-be-indicated information. In the implementation process, there are many ways to indicate the to-be-indicated information, for example, but not limited to, the to-be-indicated information can be directly indicated, such as the to-be-indicated information itself or the index of the to-be-indicated information. The to-be-indicated information can also be indirectly indicated by indicating other information, where the other information and the to-be-indicated information have an association relationship. The to-be-indicated information can also be indicated only by a part, and the other part of the to-be-indicated information is known or agreed in advance. For example, the indication of a specific information can be achieved by means of the arrangement order of each information agreed in advance (for example, protocol predefined), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific manner of indication. It can be understood that the indication information can be used to indicate the to-be-indicated information for the sender of the indication information, and the indication information can be used to determine the to-be-indicated information for the receiver of the indication information.

[0123] In the present application, the same or similar parts of each embodiment can be mutually referred to, unless otherwise specified. In the various embodiments of the present application, and the various methods / designs / implementation manners in each embodiment, the terms and / or descriptions of different embodiments, and the various methods / designs / implementation manners in each embodiment are consistent and can be mutually referred to, unless otherwise specified and logically conflicted. The technical features of different embodiments, and the various methods / designs / implementation manners in each embodiment can be combined to form new embodiments, methods, or implementation manners according to their inherent logical relationship. The implementation manners of the present application described below do not constitute a limitation on the protection scope of the present application.

[0124] The present application can be applied to a long term evolution (LTE) system, a new radio (NR) system, or a future communication system after 5G. The communication system includes at least one network device and / or at least one terminal device.

[0125] Referring to FIG. 1a, an architecture diagram of a communication system 1000 to which embodiments of the present application are applied is shown. As shown in FIG. 1a, the communication system can include a radio access network (RAN) 100, and optionally, the communication system 1000 can further include a core network 200 and an Internet 300. The RAN 100 includes at least one RAN node (e.g., 110a and 110b in FIG. 1a, collectively referred to as 110), and can further include at least one terminal (e.g., 120a-120j in FIG. 1a, collectively referred to as 120). The RAN 100 can further include other RAN nodes, such as a wireless relay device and / or a wireless backhaul device (not shown in FIG. 1a). The terminal 120 is connected to the RAN node 110 in a wireless manner, and the RAN node 110 is connected to the core network 200 in a wireless or wired manner. The core network device in the core network 200 and the RAN node 110 in the RAN 100 can be independent and different physical devices, or can be the same physical device integrated with the logical functions of the core network device and the logical functions of the RAN node. Terminals and terminals, and RAN nodes and RAN nodes can be connected to each other in a wired or wireless manner.

[0126] Taking the communication system shown in FIG. 1a as an example, different devices (including network devices and network devices, network devices and terminal devices, and / or terminal devices and terminal devices) can perform AI-related services in addition to performing communication-related services.

[0127] As shown in FIG. 1b, taking a base station as an example of a network device, the base station can perform communication-related services and AI-related services between one or more terminal devices, and communication-related services and AI-related services can also be performed between different terminal devices.

[0128] As shown in FIG. 1c, taking a television and a mobile phone as examples of terminal devices, the television and the mobile phone can also perform communication-related services and AI-related services.

[0129] The technical solutions provided in the present application can be applied to a wireless communication system (for example, the system shown in FIG. 1a, FIG. 1b or FIG. 1c), for example, an AI network element can be introduced in the communication system provided in the present application to implement part or all of the AI related operations. The AI network element can 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 can be built-in in a network element of the communication system. For example, the AI network element can be an AI module built-in in an access network device, a core network device, a cloud server, or an operation, administration and maintenance (OAM) to implement AI related functions. The OAM can be a network management of the core network device and / or a network management of the access network device. Alternatively, the AI network element can also be a network element independently arranged in the communication system. Optionally, an AI entity can also be included in a terminal or a chip built-in in the terminal to implement AI related functions.

[0130] Optionally, in the communication system, the AI application cases can include but are not limited to: channel-state information (CSI) feedback enhancement, beam management enhancement, positioning accuracy enhancement, network energy saving, load balancing, and mobility optimization. The following will be described respectively.

[0131] 1. CSI feedback enhancement.

[0132] CSI is the channel property of the communication link, and is the channel quality information reported by the terminal device to the network device. The terminal device reports the channel quality information to the network device, so as to select a suitable modulation and coding scheme (MCS) for the terminal device, so that the wireless channel can be adapted to the change. For example, the terminal device performs channel estimation according to the received channel state information-reference signal (CSI-RS), and then feeds back the channel quality information to the network device. The information is used as the input of the model of the network device, so that the network device can implement AI model training. By applying AI to CSI feedback enhancement, the overhead can be reduced, the accuracy can be improved, and prediction can be realized.

[0133] CSI-RS feedback enhancement can include at least one sub-function, such as: CSI compression, CSI prediction, and CSI-RS configuration signaling reduction, respectively. CSI compression can further include CSI compression in at least one of spatial, time, and frequency domains.

[0134] 2. Beam management enhancement.

[0135] Beam management enhancement is mainly to find the strongest transmit / receive beam pair. AI-based sparse beam prediction can improve accuracy. According to AI training and inference, it can include AI sparse beam prediction on the network side and AI sparse beam prediction on the terminal device side. Taking AI sparse beam prediction on the terminal device side as an example, the pre-trained AI model on the terminal device side can be delivered by the network side or pre-stored on the terminal device side. In the training phase, the network device scans all possible beams, and then the network reports the transmit beam pattern to the terminal device. When the model training is completed, the network device only needs to scan a small part of the beam, and then the terminal device feeds back the inference result to the network device. AI-based beam management can realize, for example, beam prediction in time and / or spatial domain to reduce overhead and delay and improve beam selection accuracy.

[0136] Beam management enhancement can include at least one sub-function, such as: beam scanning matrix prediction, and optimal (or better) beam prediction, respectively.

[0137] 3. Positioning accuracy enhancement.

[0138] In line of sight (LoS) or non-line of sight (NLoS) scenarios, AI-based positioning can improve positioning accuracy with a smaller number of TRP antennas. Positioning enhancement can include at least one sub-function, such as: access network device-based positioning enhancement, positioning management function network element-based positioning enhancement, and terminal device-based positioning enhancement, respectively.

[0139] 4. Network energy saving.

[0140] Network energy saving can be achieved through cell activation / deactivation, load reduction, improved coverage, or other RAN setting adjustments. AI technology can be used to optimize energy saving decisions by utilizing data collected in the RAN network. AI algorithms can predict the energy efficiency and load status of the next period, which can be used to assist in decision-making for cell activation / deactivation to save energy. Based on the predicted load, the system can dynamically configure energy saving strategies to maintain a balance between system performance and energy efficiency and reduce energy consumption.

[0141] 5. Load balancing.

[0142] Load balancing can make the load evenly distributed among cells and among areas within a cell, or divert part of the traffic from congested cells, or split users among cells, carriers or access technologies to improve network performance. AI model-based load balancing can provide higher quality of user experience and improve system capacity.

[0143] 6. Mobility management.

[0144] Mobility management is a solution to ensure service continuity during terminal device movement by minimizing dropped calls, radio link failure (RLF), unnecessary handover and ping-pong effect. AI can enhance mobility management, such as reducing the probability of unexpected events, predicting terminal device location / mobility / performance, and traffic steering, etc.

[0145] It should be understood that the definitions of the above technical terms are only examples. For example, as technology continues to evolve, the scope of the above definitions can also change, and the embodiments of the present application are not limited.

[0146] For example, an AI function can include multiple AI sub-functions.

[0147] Optionally, the AI application case is also referred to as an AI application scenario or an AI function.

[0148] As described above, AI can be widely used in CSI feedback enhancement, beam management, positioning accuracy enhancement, energy saving, mobility enhancement, load balancing and other aspects to improve network performance. AI models can usually be deployed on the network side and / or the terminal device side, and the training of AI models depends on the collection of training data, which can come from terminal device measurement and feedback.

[0149] The concepts that may be involved in the present application will be briefly introduced below.

[0150] AI can give machines human-like intelligence, such as allowing machines to apply computer hardware and software to simulate some intelligent behavior of humans. To achieve artificial intelligence, machine learning methods can be used. In machine learning methods, machines learn (or train) models using training data. The model represents the mapping between input and output. The learned model can be used for inference (or prediction), i.e., the model can be used to predict the output corresponding to a given input. The output can also be referred to as the inference result (or prediction result).

[0151] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be referred to as non-supervised learning.

[0152] Supervised learning learns the mapping relationship from sample values to sample labels according to the collected sample values and sample labels, and uses an AI model to express the learned mapping relationship. The process of training a machine learning model is the process of learning such a mapping relationship. In the training process, the sample values are input into the model to obtain the predicted values of the model, and the model parameters are optimized by calculating the error between the predicted values of the model and the sample labels (ideal values). After the mapping relationship is learned, the learned mapping can be used to predict new sample labels. The learned mapping relationship of supervised learning can include linear mapping or nonlinear mapping. According to the type of label, the learned task can be divided into classification tasks and regression tasks.

[0153] Unsupervised learning uses algorithms to discover the internal patterns of samples according to the collected sample values. In unsupervised learning, a class of algorithms uses the sample itself as a supervision signal, that is, the model learns the mapping relationship from the sample to the sample, which is called self-supervised learning. In training, the model parameters are optimized by calculating the error between the predicted values of the model and the sample itself. Self-supervised learning can be used for signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

[0154] Reinforcement learning is different from supervised learning, and is a class of algorithms that learn strategies to solve problems 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 the reward signal of the environment feedback, and then adjust the decision action to obtain a larger reward signal value. In the following power control, the reinforcement learning model adjusts the downlink transmission power of each user according to the system total throughput rate feedback by the wireless network, and then expects to obtain a higher system throughput rate. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and the optimal (for example, the optimal) decision action. However, because the "correct action" label 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.

[0155] A neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, a neural network can theoretically approximate any continuous function, so that the neural network has the ability to learn any mapping. Traditional communication systems need to use rich expert knowledge to design communication modules, while a deep learning communication system based on a neural network can automatically discover the implicit pattern structure from a large amount of data set, establish the mapping relationship between the data, and obtain better performance than traditional modeling methods.

[0156] The idea behind neural networks comes from the neuronal structure of the brain. For example, each neuron performs a weighted summation of its input values ​​and outputs the result through an activation function.

[0157] Figure 2a shows a schematic diagram of a neuron structure. Assume the neuron's input is x = [x0, x1, ..., x...]. n The weights corresponding to each input are w = [w0, w1, ..., w] n ], where n is a positive integer, w i and x i It can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. i As x i The weights are used to assign weights to x. i Weighting is applied. The bias for the weighted sum of the input values ​​is, for example, b. Activation functions can take many forms. Suppose the activation function of a neuron is: y = f(z) = max(0, z), then the output of that neuron is: For example, if the activation function of a neuron is y = f(z) = z, then the output of that neuron is: Here, b can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. The activation functions of different neurons in a neural network can be the same or different.

[0158] Furthermore, neural networks generally consist of multiple layers, each of which may include one or more neurons. Increasing the depth and / or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it includes, and the number of neurons in each layer can be called the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the processing result to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, hidden layers, and an output layer. The input layer processes the received input information through neurons and passes the processing result to the hidden layer. The hidden layer calculates the received processing result and passes the calculation result to the output layer or the next adjacent hidden layer, ultimately obtaining the output of the neural network. A neural network may include one hidden layer or multiple sequentially connected hidden layers, without limitation.

[0159] The neural network is, for example, a deep neural network (DNN). According to the construction manner of the network, the DNN can include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

[0160] FIG. 2b is a schematic diagram of a FNN network. The FNN network is characterized by that the neurons in adjacent layers are fully connected to each other. This feature makes the FNN usually need a large amount of storage space and lead to a high computational complexity.

[0161] The CNN is a neural network specially designed to process data with a similar grid structure. For example, time series data (e.g., time axis discrete sampling) and image data (e.g., two-dimensional discrete sampling) can be considered as data with a similar grid structure. The CNN does not use all the input information for operation at one time, but uses a fixed size window to extract part of the information for convolution operation, which greatly reduces the calculation amount of model parameters. In addition, according to the different types of information extracted by the window (such as people and objects in the same image are different types of information), each window can use different convolution kernel operations, which makes the CNN better extract the features of the input data.

[0162] The RNN is a kind of neural network that uses feedback time series information. The input of the RNN includes the new input value at the current time and the output value of itself at the previous time. The RNN is suitable for obtaining sequence features with temporal correlation, such as speech recognition, channel coding and decoding, etc.

[0163] In the above model training process of machine learning, 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 embodied in various forms, and the specific form of the loss function is not limited. The model training process can be regarded as the following process: by adjusting part or all of the parameters of the model, the value of the loss function is less than the threshold value or meets the target requirement.

[0164] The model can also be referred to as an AI model, a rule, or other names, etc. The AI model can be considered as a specific method to implement an AI function. The AI model represents a mapping relationship or a function between the input and the output of the model. The AI function can include one or more of the following: data collection, model training (or model learning), model information publishing, model inference (or model reasoning, reasoning, or prediction, etc.), model monitoring or model verification, or inference result publishing, etc. The AI function can also be referred to as an AI (related) operation, or an AI-related function.

[0165] The implementation process of the neural network will be described below with reference to the accompanying drawings.

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

[0167] As shown in FIG. 2c, an MLP includes an input layer (left side), an output layer (right side), and multiple hidden layers (middle). Each layer of the MLP includes a number of nodes, referred to as neurons. The neurons of adjacent two layers are connected to each other.

[0168] Optionally, considering the neurons of adjacent two layers, the output h of the neuron of the next layer is the weighted sum of all the neurons x of the previous layer connected to it and is processed by an activation function, which can be represented as: h = f(wx + b).

[0169] where w is a weight matrix, b is a bias vector, and f is an activation function.

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

[0171] where z is the index of the layer of the neural network, z is greater than or equal to 1, and z is less than or equal to Z, where Z is the total number of layers of the neural network.

[0172] In other words, the neural network can be understood as a mapping relationship from a set of input data to a set of output data. Usually, the neural network is randomly initialized, and the process of obtaining this mapping relationship from the random w and b with the existing data is called training of the neural network.

[0173] Optionally, the specific way of training is to evaluate the output result of the neural network by using a loss function.

[0174] As shown in FIG. 2d, the error can be back-propagated, and the neural network parameters (including w and b) can be iteratively optimized by the method of gradient descent until the output of the loss function reaches a minimum value, i.e., the "better point (e.g., optimal point)" in FIG. 2d. It can be understood that the neural network parameters corresponding to the "better point (e.g., optimal point)" in FIG. 2d can be used as the neural network parameters in the trained AI model information.

[0175] Further optionally, the process of gradient descent can be represented as:

[0176] wherein θ is the parameter to be optimized (including w and b), L is the loss function, η is the learning rate, and controls the step size of gradient descent, represents the derivation operation, represents the derivative of L with respect to θ.

[0177] Further optionally, the process of back-propagation utilizes the chain rule of partial derivative.

[0178] As shown in FIG. 2e, the gradient of the parameters of the previous layer can be recursively calculated from the gradient of the parameters of the next layer, which can be expressed as:

[0179] wherein w ij is the weight of node j connected to node i, and s i is the input weighted sum on node i.

[0180] 2. Federated Learning (FL).

[0181] The concept of federated learning effectively solves the difficulties faced by the current development of artificial intelligence. Under the premise of fully guaranteeing the privacy and security of user data, the learning task of the model is efficiently completed by promoting the cooperation of various edge devices and central servers.

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

[0183] (1) The central end initializes the model to be trained and broadcasts it to all client ends.

[0184] (2) In the t-th round t∈[1, T], the client end k∈[1, K] trains the received global model based on the local data set for E epochs to obtain the local training result Report it to the center node. In the example shown in Figure 2f, the local training results sent by the distributed nodes n, k, and m are respectively denoted as G n , k , m .

[0185] (3) The center node collects the local training results from all (or part) of the clients, assuming that the set of clients uploading the local model in the tthround is The center end will obtain a new global model by weighted averaging with the sample number of the corresponding client as the weight, and the specific updating rule is After that, the center end broadcasts the latest version of the global model to all clients for a new round of training.

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

[0187] Optionally, in addition to reporting the local model , the client can also report the trained local gradient , and the center node will average all the local gradients reported by the clients and update the global model according to the average gradient.

[0188] As can be seen, in the FL framework, the data set exists in the distributed nodes (such as the client), that is, the distributed nodes collect the local data set and perform local training, and report the local results (model or gradient) obtained by training to the center node. The center node itself may not have a data set, and can be responsible for fusing the training results of the distributed nodes to obtain a global model and issuing it to the distributed nodes.

[0189] 3. Decentralized learning.

[0190] As shown in Figure 2g, it is a completely distributed system without a center node. The design goal f(x) of the decentralized learning system is generally the average of the goals f i (x) of each node, that is, where n is the number of distributed nodes, and 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 calculates the local gradient i using the local data and the local goal f , and then sends it to the neighbor nodes that are communicatively reachable. After receiving the gradient information sent by the neighbor nodes, any node can update the parameter x of the local model according to the following formula:

[0191] where, denotes the parameters of the local model of the i-th node after the k+1-th (k is a natural number) update, denotes the parameters of the local model of the i-th node after the k-th update (if k is 0, denotes the parameters of the local model of the i-th node before the update) denotes the parameters of the local model of the i-th node that does not participate in the update), and a denotes a tuning coefficient k denotes a tuning coefficient, and N denotes the number of nodes in the communication system i denotes the set of neighbor nodes of node i, and |N denotes the number of elements in the set of neighbor nodes of node i, i.e., the number of neighbor nodes of node i. Through information interaction between nodes, the decentralized learning system will eventually learn a unified model. i

[0192] The technical solutions provided by the present application can be applied in a communication system (such as the system shown in FIG. 1a or FIG. 1b or FIG. 1c). In the communication system, the communication nodes generally have signal transceiving capability and computing capability. Taking a network device with computing capability as an example, the computing capability of the network device is mainly to provide computing power support for the signal transceiving capability (such as signal sending processing and receiving processing) to realize the communication task of the network device and other communication nodes.

[0193] With the development of communication technology, in the communication system, the services performed by the communication devices can include not only traditional communication services but also other new services such as AI services. Generally, a system capable of processing AI services, such as a communication system, can also be referred to as an AI system. However, in the AI system, how to improve user experience is a technical problem to be solved.

[0194] In a possible implementation manner, the complexity of the AI model deployed in the communication device is improved (such as increasing the number of parameters of the model, increasing the number of neural network layers contained in the model, etc.) by using the scaling law, which can effectively improve the performance of the model to improve the user experience.

[0195] As an example of the scaling law, the AI model deployed in the communication device can learn the knowledge of a pre-trained large model with high complexity deployed in the cloud. The former can be referred to as a student model and the latter can be referred to as a teacher model, so as to improve the performance of the student model (such as improving the inference accuracy and precision of the student model) by means of knowledge distillation (or knowledge transfer) to improve the user experience.

[0196] As another example of the scaling law, the AI model deployed in the communication device can be trained by a large amount of personalized data, which can improve the performance of the AI model (such as improving the inference accuracy and precision of the student model) to improve the user experience.

[0197] ​However, in an AI system, the use of the scaling law will inevitably increase the cost and power consumption, resulting in limited application scenarios.

[0198] To solve the above problems, the present application provides a communication method and related devices, which will be described in detail below in conjunction with the accompanying drawings.

[0199] Please refer to FIG. 3, which is an implementation schematic diagram of the communication method provided by the present application. The method includes the following steps.

[0200] It should be noted that in FIG. 3 and the related implementation examples below, the first communication device and other communication devices (such as the second communication device) are taken as examples of the execution subject of the interaction schematic to illustrate the method, but the present application does not limit the execution subject of the interaction schematic. For example, the communication device can be a communication equipment, or a chip, a baseband chip, a modem chip, a system on chip (SoC) chip containing a modem core, a system in package (SIP) chip, a communication module, a chip system, a processor, a logic module or software in the communication equipment, etc. Optionally, the communication equipment can be a terminal equipment or a network equipment (such as an access network equipment, an access network network element, a core network network element, or a core network equipment, etc.).

[0201] As an example, the first communication device can be a terminal equipment and the second communication device can be a network equipment, or both the first communication device and the second communication device are network equipments. For example, the network equipment can be an access network equipment, a communication equipment (such as at least one of a CU, a DU, and a RU) in an ORAN system.

[0202] As another example, both the first communication device and the second communication device are terminal equipments, i.e., the scheme shown in FIG. 3 can be applied to a sidelink communication scenario.

[0203] As an example, the third communication device can be a device deploying a model (such as part or all of M models, or part or all of N models), for example, the third communication device can be a terminal equipment, a network equipment, an OTT server, or an application server of a terminal equipment, etc. For example, the second communication device is a terminal equipment, the first communication device is a network equipment (such as an access network equipment), and the third communication device is an OTT server or an application server of a terminal equipment, etc.

[0204] S301. The second communication device sends first information, and correspondingly, the first communication device receives the first information. The first information is used to request AI processing of first input data. The first input data is processed by M models to obtain P output data. The M models are determined based on the first information. M is a positive integer, and P is greater than or equal to M.

[0205] 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 target output corresponding to the first input data. The second information is determined based on the P output data.

[0206] In this application, the model can be replaced by other terms, such as AI model, neural network, neural network model, AI neural network model, machine learning model, or AI processing model, etc. For example, the model can include a model for one or more model functions involved in communication processes such as CSI feedback enhancement, beam management enhancement, positioning accuracy enhancement, network energy saving, load balancing, mobility optimization, etc., and can also include a model for one or more model functions such as translation, image recognition, speech recognition, etc.

[0207] In this application, the target output can be replaced by other terms, such as target, desired output, ideal output, personalized output, or label, etc.

[0208] It should be understood that the P output data is obtained by processing the first input data by the M models. It can be understood that each of the M models processes the first input data to obtain one or more output data. The P output data can include one or more output data corresponding to each of the M models.

[0209] It should be understood that any of the P output data can be a model output and / or intermediate result of one of the M models. In other words, one or more output data corresponding to each of the M models can include a model output and / or intermediate result of the each model, etc. For example, the intermediate result can include one or more of scores, features, gradients, or weights.

[0210] For example, one model can include one or more neural networks, and the model output of the one model can be understood as part or all of the output of the last neural network in the one or more neural networks, for example, the model output can include part or all of the output of the last neural network. In addition, the intermediate result of the one model can be understood as part or all of the output of at least one neural network other than the last neural network in the one or more neural networks, for example, the intermediate result can include part or all of the output of each neural network in the at least one neural network.

[0211] As an example, in the above scheme, any model in the M models is used to process the first input data to obtain one or more output data, that is, the model processing performed by the any model can include one or more of inference, prediction, derivation, identification, decision. Correspondingly, the any model can be referred to as an inference model, a prediction model, a pre-trained model for the above model processing, or a pre-trained large model for the above model processing, etc.

[0212] It should be noted that there is an association relationship between the first communication device and the M models, which can be implemented in various ways.

[0213] Example one, the M models can be deployed in the first communication device. In example one, after receiving the first information, the first communication device can process the first input data based on the locally deployed M models to obtain the P output data.

[0214] Example two, the M models can be deployed in one or more third communication devices different from the first communication device. In example two, after receiving the first information, the first communication device can send a request (for example, the request can include the first input data, and the request can be the fifth information described below) to the one or more third communication devices, so that the one or more third communication devices send the P output data to the first communication device based on the request.

[0215] Example three, a part of the M models are deployed in the first communication device, and another part of the M models are deployed in one or more third communication devices different from the first communication device. Wherein, the first communication device can determine the output data corresponding to the part of the models through example one, and determine the output data corresponding to the other part of the models through example two, and determine the P output data through the two parts of the output data.

[0216] Optionally, the first input data can be included in the first information, or the first input data can be included in other messages / signaling different from the first information, which is not limited here.

[0217] Based on the scheme shown in FIG. 3, the first information received by the first communication device in step S301 is used to request AI processing of the first input data, and after the first input data is processed by the M models to obtain P output data, the first communication device can determine and send the second information based on the P output data in step S302, so that the receiver (for example, the second communication device) of the second information can determine the target output corresponding to the first input data based on the second information. Wherein, the M models are determined based on the first information, that is, the first communication device can determine the M models suitable for the request based on the request of the first information (for example, the model processing of the M models can meet part or all of the request), and send the second information based on the P output data obtained by processing the M models. In this way, the first communication device can provide the processing result of the AI model matched with the request based on the AI processing request, so that the AI processing requester (for example, the sender of the first information) can obtain the target output matched with the personalized request to improve the user experience.

[0218] In a possible implementation, the second information is determined based on the P output data, including: the second information is obtained based on processing of the P output data by Q models, Q being a positive integer. In other words, the first communication device can determine the second information by processing the P output data by the Q models. In the above scheme, in the case that the P output data is multiple output data (for example, P is greater than 1), the Q models can obtain diversified output data through the M models, and process the diversified output data to obtain the second information used to determine the personalized target output, so as to improve the performance of the personalized target output.

[0219] Optionally, the second information is obtained based on processing of the P output data by the Q models, which can be understood as that each model of the Q models processes part or all of the P output data, and the each model obtains one or more output data. The output data obtained by the Q models can be represented as T output data (T being an integer greater than or equal to Q). In other words, the second information can be obtained based on processing of the T output data (for example, sum average, weighted average, etc.).

[0220] It should be understood that any output data in the T output data can be a model output and / or intermediate result of one of the Q models. In other words, the one or more output data corresponding to each model of the Q models can include the model output and / or intermediate result of the each model, etc. For example, the intermediate result can include one or more of logits, features, gradients, or weights.

[0221] In a possible implementation, the second information comprises at least one of the following:

[0222] first indication information, indicating an order of the P output data; wherein the target output is one of the P output data;

[0223] second indication information, indicating a first index, the first index being used to determine the target output from the P output data; wherein the target output is one of the P output data;

[0224] third indication information, indicating the target output; wherein the target output is obtained (for example, sum average, weighted average, etc.) based on the P output data;

[0225] fourth indication information, indicating the P output data (or containing the P output data).

[0226] Therefore, the second information can be implemented in the above-mentioned multiple ways, so that the receiver of the second information can determine the target output corresponding to the first input data in multiple ways, to improve the flexibility of the scheme implementation.

[0227] For example, M is equal to P, that is, each of the M models can obtain an output data based on the first input data, satisfying:

[0228] wherein x represents the first input data, P(x) represents the processing of the M models on the first input data, y0, y1, …, y P-1 respectively represent the output data (i.e., P output data) that each of the M models can obtain based on the first input data.

[0229] In the above example, Q and T are both 1, that is, the Q models are one model (referred to as the first model), and the first model processes the P output data to obtain one output data. In this case, the one output data output by the first model can be understood as the second information in the step S302 in the foregoing, that is, the Q models (for example, the first model) process the P output data to obtain the second information. For example, the model processing performed by the first model is scoring, the first model can process the i-th output data y i in the P output data, satisfying:

[0230] wherein, represents the processing of the first model on the i-th output data y i , and s i represents the score obtained by the processing.

[0231] For example, in the case that the value of the score is positively correlated with the performance, the greater the value of the score s i indicates the higher the performance of y i , and vice versa, the greater the value of the score s i indicates the lower the performance of y i . For another example, in the case that the value of the score is negatively correlated with the performance, the smaller the value of the score s i indicates the higher the performance of y i , and vice versa, the greater the value of the score s i indicates the lower the performance of y i . Hereinafter, the case that the value of the score is positively correlated with the performance is taken as an example for illustration.

[0232] Optionally, the optimal score y best satisfies:

[0233] wherein, indicates that the score y is selected to be the maximum.

[0234] It should be noted that the second information sent by the first communication device in step S302 can be implemented in various ways.

[0235] In a first way, the second information includes first indication information, and the first indication information indicates the ranking of the P output data; wherein the target output corresponding to the first input data is one of the P output data.

[0236] For example, in the above process, the first communication device can determine the P scores corresponding to the P output data based on the processing process of , and determine the ranking of the P output data indicated by the first indication information based on the P scores. For example, the ranking of the P scores from high to low can indicate the ranking of the performances of the P output data corresponding to the P scores from high to low. For another example, the ranking of the P scores from low to high can indicate the ranking of the performances of the P output data corresponding to the P scores from low to high.

[0237] As shown in FIG. 4a, it is an example of the first way. In FIG. 4a, taking the value of P as 3 as an example, the first model can calculate scores for three output data (output data 1, output data 2 and output data 3 respectively) to obtain three scores (i.e. score 1, score 2 and score 3), and rank the three scores to obtain the first indication information.

[0238] Thus, in the first mode, the first communication device can determine the performance of the output data corresponding to the higher (or highest) performance among the P output data through the first indication information, and determine the output data as the target output (i.e., the personalized target output) corresponding to the first input data.

[0239] In the second mode, the second information includes second indication information, the second indication information indicating a first index, the first index being used to determine the target output corresponding to the first input data among the P output data; and the target output is one of the P output data.

[0240] For example, in the above process, the first communication device can determine the P scores corresponding to the P output data based on a processing procedure of , and determine the index of the output data corresponding to the highest score among the P scores as the first index indicated by the second indication information.

[0241] As shown in FIG. 4b, it is an example of the second mode. In FIG. 4b, taking the value of P as 3 as an example, the first model can process the three output data (output data 1, output data 2 and output data 3) to obtain the second indication information.

[0242] Thus, in the second mode, the first communication device can determine the output data corresponding to the higher (or highest) performance among the P output data through the second indication information, and determine the output data as the target output (i.e., the personalized target output) corresponding to the first input data.

[0243] In the third mode, the second information includes third indication information, the third indication information indicating the target output corresponding to the first input data; and the target output is determined based on the P output data.

[0244] For example, in the above process, the first communication device can process (e.g., sum average, weighted average, etc.) the P output data to determine the target output corresponding to the first input data. For example, the processing procedure can be that the first communication device determines the P scores corresponding to the P output data based on , and processes based on the P scores to determine the target output corresponding to the first input data.

[0245] Optionally, taking the first communication device processing the P output data as weighted average as an example, the target output corresponding to the first input data can be obtained based on at least one of the following:

[0246] X weighted results corresponding to X scores obtained by processing X output data among the P output data through the first model, X being less than or equal to P; or

[0247] a score corresponding to a result of processing Y output data in the P output data by the first model, Y being less than or equal to P;

[0248] The X output data and the Y output data can be completely different output data, or can be output data that partially or completely overlap, which is not limited here.

[0249] As shown in FIG. 4c, it is an example of the third mode. In FIG. 4c, taking 3 as the value of P, the first model can process 3 output data (output data 1, output data 2 and output data 3) to obtain the third indication information.

[0250] Thus, in the third mode, the first communication device can obtain the target output (i.e., personalized target output) corresponding to the first input data based on the P output data through the third indication information.

[0251] Optionally, in addition to the above-mentioned first mode to the third mode, the second information can also be implemented in other modes. For example, the second information can include fourth indication information indicating the P output data (or containing the P output data). In this way, the second communication device can obtain the P output data based on the second information, and subsequently determine the target data corresponding to the first input data based on the P output data (for example, by the Q models described above).

[0252] In a possible implementation, the M models are included in N models, N being an integer greater than or equal to M.

[0253] As shown in FIG. 5, the method shown in FIG. 3 further includes:

[0254] Step A. The third communication device sends third information, and correspondingly, the first communication device receives the third information. The third information is used to indicate the model information of the N models, and the first information is used to determine the M models based on the model information of the N models, N being greater than or equal to M.

[0255] In step A, the first communication device can receive the third information, so that the first communication device can determine the model information of the N models through the third information, and determine the M models that match the request of the first information based on the model information of the N models.

[0256] Optionally, the model information indicated by the third information can indicate at least one of the following: a model function, a task corresponding to the model, a scenario applicable to the model, or a reference performance of the model. Thus, the model information indicated by the third information can indicate at least one of the above, so as to improve the flexibility of the scheme implementation. Optionally, in addition to the at least one of the above, the model information can further include, but is not limited to, a model identifier (or index), a model structure, a model inference delay, or other information, etc.

[0257] For example, in a case where the third information indicates the model information, the first communication device can determine the model information of the N models based on the third information, so as to implement the model registration process of the N models in the first communication device. Subsequently, the first communication device can provide model information suitable for a request of another communication device (for example, the request indicated by the first information by the second communication device) based on the request, so as to implement corresponding data processing, so as to improve the data processing performance.

[0258] For example, the first communication device determines the model function, the task corresponding to the model, the scenario applicable to the model, or the reference performance of the model of the M models based on the model information indicated by the third information, so as to be suitable for the request indicated by the first information, so as to improve the processing performance of the first input data requested by the first information based on the M models.

[0259] Similarly, the first information received by the first communication device in step S301 indicates any one of the following: a requested model function, a requested task, a requested scenario, or a requested target performance. Thus, the request of the first information can indicate at least one of the above, so as to improve the flexibility of the scheme implementation. Moreover, the first communication device can determine the model function, the task corresponding to the model, the scenario applicable to the model, or the reference performance of the model requested by the second communication device based on the first information, and the first communication device can provide M models (for example, the first communication device determines (or selects) the M models suitable for the request based on the N models in the model registration process) suitable for the request.

[0260] Optionally, in addition to indicating at least one of the above, the request of the first information can further include, but is not limited to, a requested model index, a requested model structure, a requested model inference delay, or other information, etc.

[0261] Optionally, if the model information of the N models cannot satisfy the request of the first information, the first communication device can determine that the request of the first communication device cannot be satisfied at present, and for this purpose, the first communication device can not send the second information of step S302. For example, the first communication device can send indication information to the second communication device, the indication information being used to indicate that the current model cannot satisfy the request of the second communication device, or the indication information being used to indicate that the request of the second communication device is rejected, or other implementations, which are not limited herein.

[0262] As an example, taking N=4 as an example, the third information in step A can indicate the model information of 4 (N=4) models, and the model information can include the implementation shown in Table 2.

[0263] Table 2

[0264] As shown in Table 2, taking each of the 4 model information as an example, the model identifier, the task corresponding to the model, the scene used by the model, the reference performance of the model, and the model inference delay are included.

[0265] For example, taking the request indicated by the request of the first information as the model index as an example, the first communication device can select one or more models with the same model index as the request indicated by the first information as the M models in Table 2.

[0266] For another example, taking the request indicated by the request of the first information as the model inference delay as an example, the first communication device can select one or more models satisfying the model inference delay requested by the first information as the M models in Table 2.

[0267] As another example, taking N=5 as an example, the third information in step A can indicate the model information of 5 (N=5) models, and the model information can include the implementation shown in Table 3.

[0268] Table 3

[0269] As shown in Table 3, taking each of the 5 model information as an example, the model identifier, the task corresponding to the model, the scene used by the model, the reference performance of the model, and the model inference delay are included.

[0270] For example, taking the request indicated by the request of the first information as the task corresponding to the requested model as “picture classification” and the target performance as “90%” as an example, the first communication device can select two models with model identifier 0 and model identifier 1 satisfying the request as the M models in Table 3.

[0271] For example, the first communication device can select two models with model identifier 1 and model identifier 2 as the M models in Table 3, if the request indicates that the task corresponding to the requested model is "picture classification" and the inference delay of the requested model is "12 ms".

[0272] For example, the first communication device can select two models with model identifier 3 and model identifier 4 as the M models in Table 3, if the request indicates that the task corresponding to the requested model is "picture generation" and the inference delay of the requested model is "400 ms".

[0273] Optionally, if the model information of the N models cannot meet the request of the first information, the first communication device can determine that the request of the first communication device cannot be met at present, and therefore, the first communication device can not send the second information in step S302. The implementation process of Table 3 will be taken as an example below.

[0274] For example, the first communication device cannot select the M models that meet the request in Table 3, if the request indicates that the task corresponding to the requested model is "picture classification" and the inference delay of the requested model is "3 ms". Accordingly, the first communication device can send the indication information to the second communication device, the indication information being used to indicate that the current model cannot meet the request of the second communication device, or the indication information being used to indicate that the request of the second communication device is rejected, or other implementations, which are not limited herein.

[0275] In a possible implementation, the second information received by the second communication device in step S302 can be carried in a multicast message, which will be described below in combination with some implementation examples.

[0276] Example A: The second information is carried in a multicast message containing a first multicast identifier, wherein the first multicast identifier can be determined by the first communication device.

[0277] For example, as shown in FIG. 5, the method shown in FIG. 3 further includes:

[0278] Step B: The first communication device sends fourth information, and accordingly, the second communication device receives the fourth information. The fourth information indicates the first multicast identifier, wherein the first multicast identifier is determined based on the communication state information corresponding to the second communication device and / or the first information.

[0279] In step B, the first communication device can send fourth information indicating the first groupcast identity to the second communication device, and the first communication device can send a groupcast message carrying the second information and the first groupcast identity, so that one or more second communication devices can obtain the corresponding second information through groupcast, and the transmission overhead of output data can be reduced.

[0280] As an example, the first groupcast identity can be determined by the first communication device.

[0281] As another example, the first groupcast identity can be determined by the first communication device based on the received information, as shown in FIG. 5, the method shown in FIG. 3 further comprises:

[0282] Step C. The first communication device sends fifth information, and the third communication device receives the fifth information accordingly. The fifth information is used to request the M models to perform AI processing on the first input data, and the third communication device is used to manage part or all of the M models.

[0283] Optionally, the fifth information can include the first input data. Alternatively, the first input data can be preconfigured data.

[0284] Step D. The third communication device sends sixth information, and the first communication device receives the sixth information accordingly. The sixth information indicates the first groupcast identity, and the first groupcast identity is determined based on the fifth information.

[0285] Therefore, the first communication device can send the groupcast message corresponding to the second communication device to the second communication device through groupcast based on the first groupcast identity, so that one or more second communication devices can obtain the corresponding second information through groupcast, and the transmission overhead of output data can be reduced.

[0286] In addition, in the implementation process of step C and step D, the third communication device providing the first groupcast identity for the first communication device is used to manage part or all of the M models, so that the grouping of the one or more second communication devices can match the characteristics of each model.

[0287] Optionally, the fifth information satisfies at least one of the following: the fifth information is determined based on the communication state information of the one or more second communication devices; or the fifth information is further used to indicate a second groupcast identity; wherein the second groupcast identity is determined based on the corresponding communication state information of the second communication device and / or the first information.

[0288] In other words, the first communication device can group one or more second communication devices based on the corresponding communication state information of the one or more second communication devices and / or the first information, and send fifth information to a third communication device based on the grouping result, so as to reduce the communication overhead between the first communication device and the third communication device.

[0289] In example B, the second information is carried in a groupcast message comprising a second groupcast identifier, wherein the first groupcast identifier can be determined by the third communication device.

[0290] For example, as shown in FIG. 5, the method shown in FIG. 3 further comprises:

[0291] In step E, the third communication device sends seventh information, and the second communication device receives the seventh information. The seventh information indicates the third groupcast identifier.

[0292] Optionally, the third communication device can determine the third groupcast identifier based on the fifth information sent by the first communication device in step C.

[0293] Optionally, in step E, the third communication device can send the seventh information to the second communication device through the first communication device. For example, the first communication device is an access network device, the second communication device is a terminal device, and the third communication device can be a core network device (or an OTT server or an application server of a terminal device, etc.). In the above process, the seventh information can be carried in a NAS message.

[0294] Therefore, the second communication device can receive the groupcast message corresponding to the second communication device through groupcast based on the third groupcast identifier provided by the third communication device, so that one or more second communication devices can obtain corresponding second information through groupcast, and the transmission overhead of output data can be reduced.

[0295] In addition, when the groupcast identifier (e.g., the first groupcast identifier, the second groupcast identifier, or the third groupcast identifier, etc.) is determined based on the communication state information corresponding to the second communication device, the first communication device (or the third communication device) can group one or more second communication devices based on the communication state information corresponding to the one or more second communication devices, and the same groupcast identifier can be allocated to the second communication devices with the same or relatively small difference in communication state, and different groupcast identifiers can be allocated to the second communication devices with different or relatively large difference in communication state, so as to improve the groupcast efficiency.

[0296] Further, in a case that the groupcast identifier (e.g., the first groupcast identifier, the second groupcast identifier, or the third groupcast identifier, etc.) is determined based on the first information corresponding to the second communication device, the first communication device (or the third communication device) can group the AI processing requests based on one or more second communication devices, can assign the same groupcast identifier to the second communication devices with the same or similar AI processing requests, and can assign different groupcast identifiers to the second communication devices with different AI processing requests, so as to improve the groupcast efficiency.

[0297] Optionally, the communication state information can indicate one or more of a time delay, a data transmission rate, a packet loss rate, a channel eigenvector, channel multi-path component information, or a load.

[0298] Optionally, in the above process, the first information and the groupcast identifier (e.g., the first groupcast identifier, the second groupcast identifier, or the third groupcast identifier, etc.) are used to determine the M models. In other words, after the first communication device determines the groupcast identifier corresponding to the second communication device based on the communication state information corresponding to the second communication device and / or the first information, the first communication device can determine the M models for processing the first input data based on the first information and the groupcast identifier, so that the M models not only match the request corresponding to the first information, but also match the groupcast identifier.

[0299] Referring to FIG. 6, the embodiment of the present application provides a communication device 600, which can implement the functions of the second communication device or the first communication device in the above method embodiments, and thus can also implement the beneficial effects possessed by the above method embodiments. In the embodiment of the present application, the communication device 600 can be the first communication device (or the second communication device), or an integrated circuit or element inside the first communication device (or the second communication device), such as a chip.

[0300] It should be noted that the transceiver unit 602 can include a sending unit and a receiving unit, which are respectively used for sending and receiving.

[0301] In a possible implementation, when the apparatus 600 is configured to perform the method performed by the first communication device in the foregoing embodiments, the apparatus 600 includes a processing unit 601, and a transceiver 602. The transceiver 602 is configured to receive first information, where the first information is used to request AI processing on first input data. The first input data is processed by M models to obtain P output data, where the M models are determined based on the first information, M is a positive integer, and P is greater than or equal to M. The processing unit 601 is configured to determine second information. The transceiver 602 is configured to send the second information, where the second information is used to determine a target output corresponding to the first input data. The second information is determined based on the P output data.

[0302] In a possible implementation, when the apparatus 600 is configured to perform the method performed by the second communication device in the foregoing embodiments, the apparatus 600 includes a processing unit 601 and a transceiver 602. The processing unit 601 is configured to determine first information. The transceiver 602 is configured to send the first information, where the first information is used to request AI processing on first input data. The first input data is processed by M models to obtain P output data, where the M models are determined based on the first information, M is a positive integer, and P is greater than or equal to M. The transceiver 602 is further configured to receive second information, where the second information is used to determine a target output corresponding to the first input data. The second information is determined based on the P output data.

[0303] In a possible implementation, when the apparatus 600 is configured to perform the method performed by the third communication device in the foregoing embodiments, the apparatus 600 includes a processing unit 601 and a transceiver 602. The transceiver 602 is configured to receive fifth information, where the fifth information is used to request M models to perform AI processing on the first input data, and M is a positive integer. The processing unit 601 is configured to determine sixth information. The transceiver 602 is further configured to send the sixth information, where the sixth information indicates a second multicast identifier. The second multicast identifier is determined based on the fifth information. Alternatively, the transceiver 602 is configured to receive fifth information, where the fifth information is used to request M models to perform AI processing on the first input data, and M is a positive integer. The processing unit 601 is configured to determine seventh information. The transceiver 602 is further configured to send the seventh information, where the seventh information indicates a third multicast identifier. The second multicast identifier is determined based on the fifth information.

[0304] It should be noted that the information execution process and the like of the units of the communication device 600 are described in the foregoing method embodiments of the present application, and will not be described here.

[0305] Please refer to FIG. 7, which is another schematic structural diagram of a communication apparatus 700 provided in the present application, the communication apparatus 700 comprising a logic circuit 701 and an input-output interface 702. The communication apparatus 700 can be a chip or an integrated circuit.

[0306] The transceiver unit 602 shown in FIG. 6 can be a communication interface, which can be the input-output interface 702 in FIG. 7, and the input-output interface 702 can comprise an input interface and an output interface. Alternatively, the communication interface can also be a transceiver circuit, which can comprise an input interface circuit and an output interface circuit.

[0307] Optionally, the input-output interface 702 is configured to receive first information, the first information being used to request AI processing on first input data; wherein the first input data is processed by M models to obtain P output data, the M models being determined based on the first information, M being a positive integer, and P being greater than or equal to M; the logic circuit 701 is configured to determine second information; and the input-output interface 702 is configured to send the second information, the second information being used to determine a target output corresponding to the first input data; wherein the second information is determined based on the P output data.

[0308] Optionally, the logic circuit 701 is configured to determine first information, the first information being used to request AI processing on first input data; wherein the first input data is processed by M models to obtain P output data, the M models being determined based on the first information, M being a positive integer, and P being greater than or equal to M; the input-output interface 702 is configured to send the first information; and the input-output interface 702 is further configured to receive second information, the second information being used to determine a target output corresponding to the first input data; wherein the second information is determined based on the P output data.

[0309] Optionally, the input-output interface 702 is configured to receive fifth information, the fifth information being used to request M models to perform AI processing on the first input data, M being a positive integer; the logic circuit 701 is configured to determine sixth information; and the input-output interface 702 is further configured to send the sixth information, the sixth information indicating a second multicast identifier; wherein the second multicast identifier is determined based on the fifth information. Alternatively, the input-output interface 702 is configured to receive fifth information, the fifth information being used to request M models to perform AI processing on the first input data, M being a positive integer; the logic circuit 701 is configured to determine seventh information; and the input-output interface 702 is further configured to send the seventh information, the seventh information indicating a third multicast identifier; wherein the second multicast identifier is determined based on the fifth information.

[0310] The logic circuit 701 and the input / output interface 702 can also perform other steps and achieve corresponding beneficial effects performed by the first communication device or the second communication device in any embodiment, which will not be repeated here.

[0311] In a possible implementation, the processing unit 601 shown in FIG. 6 can be the logic circuit 701 in FIG. 7.

[0312] Optionally, the logic circuit 701 can be a processing device, and the functions of the processing device can be partially or entirely implemented by software.

[0313] Optionally, the processing device can include a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to read and execute the computer program stored in the memory to perform the corresponding processing and / or steps in any one of the method embodiments.

[0314] Optionally, the processing device can only include the processor. The memory for storing the computer program is located outside the processing device, and the processor is connected with the memory through a circuit / wire to read and execute the computer program stored in the memory. The memory and the processor can be integrated together or can be physically independent of each other.

[0315] Optionally, the processing device can be one or more chips or one or more integrated circuits. For example, the processing device can be one or more field-programmable gate arrays (FPGA), application specific integrated circuits (ASIC), system on chips (SoC), central processing units (CPU), network processors (NP), digital signal processors (DSP), micro controller units (MCU), programmable logic devices (PLD), or other integrated chips, or any combination of the above chips or processors, etc.

[0316] Referring to FIG. 8, the communication device 800 involved in the above embodiments is provided by an embodiment of the present application, and the communication device 800 can be specifically a communication device as a terminal device in the above embodiments, and the communication device in the example shown in FIG. 8 is implemented by a terminal device (or a component in the terminal device).

[0317] Wherein, a possible logical structure diagram of the communication device 800 can include but not limited to at least one processor 801 and a communication port 802.

[0318] Wherein, the transceiver unit 602 shown in Fig. 6 can be a communication interface, which can be the communication port 802 in Fig. 8, and the communication port 802 can include an input interface and an output interface. Alternatively, the communication port 802 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0319] Further optionally, the device can further include at least one of a memory 803, a bus 804, and in the embodiments of the present application, the at least one processor 801 is configured to control and process the actions of the communication device 800.

[0320] In addition, the processor 801 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, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logical blocks, modules and circuits described in connection with the disclosure. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, digital signal processor and microprocessor combinations, etc. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0321] It should be noted that the communication device 800 shown in Fig. 8 can be used to implement the steps implemented by the terminal device in the foregoing method embodiments, and achieve the corresponding technical effects of the terminal device. The specific implementation of the communication device shown in Fig. 8 can refer to the description in the foregoing method embodiments, which will not be described here.

[0322] Please refer to Fig. 9, which is a structure diagram of a communication device 900 involved in the above embodiments according to an embodiment of the present application. The communication device 900 can be a communication device as a network device in the above embodiments, and the communication device shown in the example of Fig. 9 is implemented by a network device (or a component in the network device), wherein the structure of the communication device can refer to the structure shown in Fig. 9.

[0323] The communication device 900 comprises at least one processor 911 and at least one network interface 914. Further optionally, the communication device further comprises at least one memory 912, at least one transceiver 913 and one or more antennas 915. The processor 911, the memory 912, the transceiver 913 and the network interface 914 are connected, for example, through a bus, which may, in embodiments of the present application, comprise various types of interfaces, transmission lines or buses, etc., and the present embodiments do not limit the same. The antenna 915 is connected to the transceiver 913. The network interface 914 is configured to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 914 can comprise a network interface between the communication device and a core network device, such as an S1 interface, and the network interface can comprise 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.

[0324] The transceiving unit 602 shown in FIG. 6 can be a communication interface, which can be the network interface 914 in FIG. 9, and the network interface 914 can comprise an input interface and an output interface. Alternatively, the network interface 914 can be a transceiving circuit, which can comprise an input interface circuit and an output interface circuit.

[0325] The processor 911 is mainly configured to process communication protocols and communication data, and control the whole communication device, execute software programs, process data of the software programs, for example, to support the communication device to perform actions described in embodiments. The communication device can comprise a baseband processor and a central processor, the baseband processor is mainly configured to process communication protocols and communication data, and the central processor is mainly configured to control the whole terminal device, execute software programs, and process data of the software programs. The processor 911 in FIG. 9 can integrate the functions of the baseband processor and the central processor, and those skilled in the art can understand that the baseband processor and the central processor can also be independent processors interconnected through a bus. Those skilled in the art can understand that the terminal device can comprise a plurality of baseband processors to adapt to different network modes, and the terminal device can comprise a plurality of central processors to enhance the processing capability, and various components of the terminal device can be connected through various buses. The baseband processor can also be referred to as a baseband processing circuit or a baseband processing chip. The central processor can also be referred to as a central processing circuit or a central processing chip. The function of processing communication protocols and communication data can be built in the processor, or stored in the memory in the form of software programs, and the processor executes the software programs to realize the baseband processing function.

[0326] The memory is mainly used for storing software programs and data. The memory 912 can exist independently of the processor 911. Alternatively, the memory 912 can be integrated with the processor 911, for example, integrated in a chip. The memory 912 can store program codes for implementing the technical solutions of the embodiments of the present application, and the processor 911 controls the execution. The executed computer programs of various types can also be regarded as the driver of the processor 911.

[0327] Figure 9 only shows one memory and one processor. In actual terminal equipment, there can be multiple processors and multiple memories. The memory can also be referred to as a storage medium or a storage device, etc. The memory can 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.

[0328] The transceiver 913 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal. The transceiver 913 can be connected to the antenna 915. The transceiver 913 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 915 can receive radio frequency signals, the receiver Rx of the transceiver 913 is used to receive the radio frequency signals from the antenna and convert the radio frequency signals into digital baseband signals or digital intermediate frequency signals, and provide the digital baseband signals or digital intermediate frequency signals to the processor 911 for further processing of the digital baseband signals or digital intermediate frequency signals by the processor 911, such as demodulation processing and decoding processing. In addition, the transmitter Tx in the transceiver 913 is also used to receive the modulated digital baseband signals or digital intermediate frequency signals from the processor 911, and convert the modulated digital baseband signals or digital intermediate frequency signals into radio frequency signals, and transmit the radio frequency signals through one or more antennas 915. Specifically, the receiver Rx can selectively perform one or more levels of down-mixing and analog-to-digital conversion to obtain digital baseband signals or digital intermediate frequency signals, and the order of the down-mixing and analog-to-digital conversion can be adjusted. The transmitter Tx can selectively perform one or more levels of up-mixing and digital-to-analog conversion on the modulated digital baseband signals or digital intermediate frequency signals to obtain radio frequency signals, and the order of the up-mixing and digital-to-analog conversion can be adjusted. The digital baseband signals and the digital intermediate frequency signals can be collectively referred to as digital signals.

[0329] The transceiver 913 can also be referred to as a transceiving unit, a transceiver, a transceiving device, etc. Alternatively, the devices in the transceiving unit for implementing the receiving function can be regarded as a receiving unit, and the devices in the transceiving unit for implementing the transmitting function can be regarded as a transmitting unit, i.e. the transceiving unit includes a receiving unit and a transmitting unit, the receiving unit can also be referred to as a receiver, an input port, a receiving circuit, etc., and the transmitting unit can be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0330] It should be noted that the communication apparatus 900 shown in FIG. 9 can be specifically used to implement the steps implemented by the network device in the foregoing method embodiments, and achieve the corresponding technical effects of the network device. The specific implementation of the communication apparatus 900 shown in FIG. 9 can be referred to the description in the foregoing method embodiments, which will not be repeated here.

[0331] Please refer to FIG. 10, which is a structural schematic diagram of the communication apparatus involved in the foregoing embodiments provided by the embodiments of the present application.

[0332] It can be understood that the communication apparatus 10 includes, for example, modules, units, elements, circuits, or interfaces, etc., which are properly configured together to execute the technical solutions provided by the present application. The communication apparatus 10 can be the terminal device or the network device described above, or can be a component (such as a chip) of these devices, to implement the methods described in the following method embodiments. The communication apparatus 10 includes one or more processors 101. The processor 101 can be a general-purpose processor or a special-purpose processor, etc. For example, it can be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication apparatus (such as a RAN node, a terminal, or a chip, etc.), execute software programs, and process data of the software programs.

[0333] Optionally, in one design, the processor 101 can include a program 103 (which can also be referred to as code or instructions sometimes), which can be run on the processor 101, so that the communication apparatus 10 executes the methods described in the following embodiments. In another possible design, the communication apparatus 10 includes a circuit (not shown in FIG. 10).

[0334] Optionally, the communication apparatus 10 can include one or more memories 102, which have a program 104 (which can also be referred to as code or instructions sometimes) stored thereon, and the program 104 can be run on the processor 101, so that the communication apparatus 10 executes the methods described in the foregoing method embodiments.

[0335] Optionally, the processor 101 and / or the memory 102 can include an AI module 107, 108, which is used to implement AI-related functions. The AI module can be implemented in software, hardware, or a combination of software and hardware. For example, the AI module can include a radio intelligence control (RIC) module. For example, the AI module can be a near-real-time RIC or a non-real-time RIC.

[0336] Optionally, the processor 101 and / or the memory 102 can also store data. The processor and the memory can be separately arranged, or integrated together.

[0337] Optionally, the communication device 10 can also include a transceiver 105 and / or an antenna 106. The processor 101 can also be referred to as a processing unit, which controls the communication device (e.g., a RAN node or a terminal). The transceiver 105 can also be referred to as a transceiving unit, a transceiver, a transceiving circuit, or a transceiver, etc., which is used to realize the transceiving function of the communication device through the antenna 106.

[0338] The processing unit 601 shown in FIG. 6 can be the processor 101. The transceiving unit 602 shown in FIG. 6 can be a communication interface, which can be the transceiver 105 in FIG. 10, and the transceiver 105 can include an input interface and an output interface. Alternatively, the transceiver 105 can also be a transceiving circuit, which can include an input interface circuit and an output interface circuit.

[0339] The embodiments of the present application also provide a computer readable storage medium for storing one or more computer-executable instructions, which, when executed by a processor, cause the processor to perform the method described in the possible implementation manners of the first communication device or the second communication device.

[0340] The embodiments of the present application also provide a computer program product (or computer program), which, when executed by a processor, causes the processor to perform the method described in the possible implementation manners of the first communication device or the second communication device.

[0341] The embodiments of the present application also provide a chip system, which includes at least one processor for supporting the communication device to implement the functions involved in the possible implementation manners of the 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 a possible design, the chip system can also include a memory, which is used to store the necessary program instructions and data of the communication device. The chip system can be composed of a chip, or can include a chip and other discrete devices, and the communication device can be the first communication device or the second communication device in the foregoing method embodiments.

[0342] The embodiments of the present application also provide a communication system, which includes the first communication device and the second communication device in any of the foregoing embodiments.

[0343] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely illustrative. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0344] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0345] In addition, each functional unit in the embodiments of the present application can be integrated in one processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of software function unit. When the integrated unit is implemented in the form of software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions of the present application essentially, or the part contributing to the prior art, or all or a part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and various other media that can store program codes.

Claims

1. A communication method characterized by comprising: The method comprises: receiving first information, the first information being used for requesting artificial intelligence (AI) processing on first input data; wherein the first input data is processed by M models to obtain P output data, the M models being determined based on the first information, M being a positive integer, and P being greater than or equal to M; sending second information, the second information being used for determining a target output corresponding to the first input data; wherein the second information is determined based on the P output data.

2. The method of claim 1, wherein, The M models are included in N models, N being an integer greater than or equal to M; the method further comprises: receiving third information, the third information being used for indicating model information of the N models, and the first information being used for determining the M models by using the model information of the N models, N being greater than or equal to M.

3. The method of claim 2, wherein, The model information indicates at least one of the following: model function, task corresponding to the model, scenario applicable to the model, or reference performance of the model.

4. The method according to any one of claims 1 to 3, characterized in that, The first information indicates any one of the following: requested model function, requested task, requested scenario, or requested target performance.

5. The method according to any one of claims 1 to 4, characterized in that, The second information is carried in a multicast message comprising a first multicast identifier; the method further comprises: sending fourth information to a second communication device, the fourth information indicating the first multicast identifier.

6. The method of claim 5, wherein, The first multicast identifier is determined based on communication state information corresponding to the second communication device and / or the first information.

7. The method of claim 6, wherein, The first information and the first multicast identifier are used for determining the M models.

8. The method of claim 5, wherein, The method further comprises: sending fifth information to a third communication device, the fifth information being used for requesting the M models to perform AI processing on the first input data, the third communication device being used for managing part or all of the M models; receiving sixth information from the third communication device, the sixth information indicating the first multicast identifier; wherein the first multicast identifier is determined based on the fifth information.

9. The method of claim 5, wherein, The method further comprises: The third communication device receives fifth information from the first communication device, the fifth information being used for requesting the M models to perform AI processing on the first input data, the third communication device being used for managing part or all of the M models; The third communication device sends sixth information to the first communication device, the sixth information indicating the first multicast identifier; wherein the first multicast identifier is determined based on the fifth information.

10. The method according to claim 8 or 9, characterized in that, The first information is from a second communication device, and the fifth information satisfies at least one of the following: The fifth information is determined based on communication state information of the second communication device; or The fifth information is further used for indicating a second multicast identifier; wherein the second multicast identifier is determined based on communication state information corresponding to the second communication device and / or the first information.

11. The method of claim 10, wherein, The first information and the second multicast identifier are used for determining the M models.

12. A communication method characterized by comprising: The method comprises: transmitting first information, the first information being used for requesting artificial intelligence (AI) processing on first input data; wherein the first input data is processed by M models to obtain P output data, the M models being determined based on the first information, M being a positive integer, and P being greater than or equal to M; receiving second information, the second information being used for determining a target output corresponding to the first input data; wherein the second information is determined based on the P output data.

13. The method of claim 12, wherein, The first information indicates any of the following: a requested model function, a requested task, a requested scenario, or a requested target performance.

14. The method according to claim 12 or 13, characterized in that, The second information is carried in a multicast message containing a first multicast identifier; the method further comprises: receiving fourth information from a first communication device, the fourth information indicating the first multicast identifier.

15. The method of claim 14, wherein, The first multicast identifier is determined based on communication state information corresponding to a second communication device and / or the first information.

16. The method of claim 12 or 13, wherein, The second information is carried in a multicast message containing a third multicast identifier; the method further comprises: receiving seventh information from a third communication device, the seventh information indicating the third multicast identifier.

17. The method of claim 12 or 13, wherein, The second information is carried in a multicast message containing a third multicast identifier; the method further comprises: a third communication device transmits seventh information to a second communication device, the seventh information indicating the third multicast identifier.

18. The method according to any one of claims 1 to 17, characterized in that, The second information is determined based on the P output data, including: The second information is obtained based on processing of the P output data by Q models, Q being a positive integer.

19. The method according to any one of claims 1 to 18, characterized in that, The second information includes at least one of the following: first indication information indicating an order of the P output data; wherein the target output is one of the P output data; second indication information indicating a first index, the first index being used for determining the target output in the P output data; wherein the target output is one of the P output data; third indication information indicating the target output; wherein the target output is obtained based on the P output data; or fourth indication information indicating the P output data.

20. A communications device, characterized by A module for performing the method of any of claims 1 to 19.

21. A communications device, characterized by At least one processor for performing the method of any of claims 1 to 19.

22. The communication apparatus according to claim 21, wherein, The communication device is a chip or a chip system.

23. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program or instructions, which, when executed by the communication device, implement the method of any of claims 1 to 19.

24. A computer program product, characterised in that, A computer program or instructions, which, when executed by a computer, implement the method of any of claims 1 to 19.

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