Communication method and related device

Abstract

A communication method and a related device, for use in improving the flexibility of neural network deployment while applying the computing power of communication nodes to artificial intelligence (AI) processing of neural networks. In the method, second data sent by a first communication apparatus is obtained on the basis of first data, and subsequently, a receiver (such as a second communication apparatus) of the second data can process the second data to obtain third data, wherein an index of the first data in a first data set is used for determining fourth data from a second data set, and the fourth data is label data corresponding to the first data; and the second data and / or the third data are / is obtained on the basis of neural networks, that is, neural networks for AI processing can comprise a neural network deployed for the first communication apparatus and / or a neural network deployed for the second communication apparatus.

Description

A communication method and related equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on November 3, 2023, with application number 202311461212.5 and application name “A Communication Method and Related Equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communications, and in particular to a communication method and related equipment. Background Art

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

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

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

[0006] Summary of the Invention

[0007] The present application provides a communication method and related equipment for enabling the computing power of communication nodes to be applied to artificial intelligence (AI) processing of neural networks while also improving the flexibility of neural network deployment.

[0008] In a first aspect, the present application provides a communication method, which is performed by a first communication device. The first communication device may be a communication device (such as a terminal device or a network device), or the first communication device may be a component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can implement all or part of the functions of the communication device. In the first aspect and its possible implementation, the communication method is described as being performed by the first communication device. Exemplarily, the first communication device may be a terminal device or a network device. In this method, a first communication device processes first data to obtain second data; wherein the first data is data in a first data set; the first communication device sends the second data, and the second data is used to determine third data; wherein the index of the first data in the first data set is used to determine fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is label data corresponding to the first data; wherein the second data is obtained based on processing the first data by a first neural network, and / or the third data is obtained based on processing the second data by a second neural network; the index satisfies at least one of the following: the index is determined based on the resource carrying the second data; the index is determined based on the number of times the data in the first data set is processed.

[0009] Based on the above technical solution, the second data sent by the first communication device is obtained based on the first data, and the subsequent recipient of the second data (for example, the second communication device) can process the second data to obtain the third data. The index of the first data in the first data set is used to determine the fourth data in the second data set, and the fourth data is the label data corresponding to the first data. In other words, after receiving the second data, the recipient of the second data can determine the label data in the second data set and process the third data based on the label data. Moreover, the second data and / or the third data are obtained based on a neural network, that is, the neural network used for AI processing can include a neural network deployed on the first communication device and / or a neural network deployed on the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to the AI ​​processing of the neural network while also improving the flexibility of the neural network deployment.

[0010] In addition, the index of the first data in the first data set is used to determine the tag data (i.e., fourth data) corresponding to the first data in the second data set, and the index satisfies at least one of the above conditions. In other words, after receiving the second data, the second communication device can determine the tag data in the second data set based on the resource carrying the data or the number of times the data in the data set is processed. In this way, air interface overhead can be reduced to improve communication efficiency.

[0011] It should be understood that in the above technical solution, the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data. The first data set and the second data set can be contained in one data set. In this one data set, N input data and M label data can be included, where N and M are both positive integers; and the first data set can include the N input data, and the second data set can include the M label data. In other words, the first data set can be called an input data set, a neural network input data set, etc., and the second data set can be called a label data set, a neural network label data set, etc. In addition, the AI ​​neural network can be processed based on the same data set to achieve iteration, update, etc. of the AI ​​neural network.

[0012] Optionally, the first dataset may include N pieces of input data in a dataset. To this end, the first dataset may also be replaced with other descriptions, such as N pieces of input data, N pieces of input data in a dataset, etc. Correspondingly, the second dataset may include M pieces of labeled data in the dataset. To this end, the second dataset may also be replaced with other descriptions, such as M pieces of labeled data, M pieces of labeled data in a dataset, etc.

[0013] Optionally, when each of the N pieces of input data corresponds to different label data in the M pieces of label data, the value of N is equal to the value of M. When at least two of the N pieces of input data correspond to the same piece of label data in the M pieces of label data, the value of N may be greater than or equal to the value of M. When one of the N pieces of input data corresponds to at least two pieces of label data in the M pieces of label data, the value of N may be less than or equal to the value of M.

[0014] In this application, terms such as AI, neural network, AI neural network, machine learning, AI processing, and AI neural network processing can be used interchangeably.

[0015] In this application, the data involved (such as first data, second data, third data, and fourth data, etc.) can be replaced by information, signals, etc.

[0016] Optionally, when the second data is obtained by processing the first data in the first data set based on the first neural network, since the second data is the send data obtained by the first communication device processing the first data, the first neural network can be referred to as a neural network deployed at the sending end, an encoding neural network, an AI encoding neural network, etc. Similarly, when the third data is obtained by processing the second data based on the second neural network, since the third data is data obtained by the second communication device processing the received second data based on the second neural network, the second neural network can be referred to as a neural network deployed at the receiving end, a decoding neural network, an AI decoding neural network, etc.

[0017] In a possible implementation of the first aspect, the index is determined based on the resource carrying the second data, including: the value of the index is determined by at least one of the time domain resource index of the resource, the frequency domain resource index of the resource, and the resource block size of the resource.

[0018] Based on the above technical solution, when the index of the first data in the first data set is determined based on the resource carrying the second data, the index can be specifically determined by at least one of the above items to improve the flexibility of the solution implementation.

[0019] In a possible implementation of the first aspect, when the index is determined based on the number of times data in the first data set is processed, the method further includes: the first communication device sends first information, which is used to indicate the index of the first data in the first data set.

[0020] Based on the above technical solution, if the index of the first data in the first data set is determined based on the number of times the data in the first data set is processed, since the second communication device may not be able to perceive the number of times the data in the first data set is processed, the second communication device can determine the index based on the number of times the data in the second data set is processed. Accordingly, the first communication device can also transmit first information, enabling the second communication device to determine the index of the first data in the first data set based on the first information, and subsequently determine the fourth data in the second data set based on the index.

[0021] It should be understood that the first communication device can perform multiple processing operations based on the data in the first data set to obtain and send processing results (for example, one of the processing results is the second data obtained based on the first data). Accordingly, among the multiple processing results, the first communication device can send corresponding indexes for some or all of the processing results (for example, sending first information for the second data processing result). Thereafter, sending corresponding indexes based on some or all of the processing results can achieve alignment of the understanding of the indexes by the data sender and receiver, thereby avoiding data processing errors caused by the misalignment of the understanding of the indexes by the data sender and receiver, thereby improving the robustness of the system.

[0022] In this application, "alignment" may mean that when there are interactive messages / data / information between different communication devices, the two devices have a consistent understanding of the meaning, configuration method, index in the data set, etc. of the interactive messages / data / information.

[0023] Optionally, the first communication device may send an index corresponding to a partial processing result, without sending an index corresponding to the entire processing result, thereby saving overhead.

[0024] In a possible implementation manner of the first aspect, the first information is one of multiple information transmitted based on a first period; the method also includes: the first communication device receives or sends configuration information, and the configuration information is used to configure the first period.

[0025] Based on the above technical solution, the first information can be one of the periodic information transmitted based on the first cycle. Prior to this, the first communication device can receive or send configuration information for configuring the first cycle, so that the first communication device can serve as both the configurator of the first cycle and the configured party of the first cycle. This can align the understanding of the first cycle between the data sender and receiver, and also improve the flexibility of the solution implementation.

[0026] In a possible implementation manner of the first aspect, the method further includes: the first communication device receiving indication information indicating the first data set; and / or the first communication device sending indication information indicating the second data set.

[0027] Based on the above technical solution, the first communication device can serve as the configured party of the first data set, and / or the first communication device can serve as the configured party of the second data set, so that the data sender and receiver can obtain the data set before exchanging data.

[0028] Optionally, the first data set may be preconfigured for the first communication device, and / or the second data set may be preconfigured for the second communication device. In this way, overhead can be reduced.

[0029] In a possible implementation manner of the first aspect, the method further includes: the first communication device receiving gradient information and / or a result of a loss function determined based on the third data and the fourth data.

[0030] Based on the above technical solution, a receiver of the second data (e.g., a second communication device) can process the second data to obtain third data. Furthermore, the receiver can determine and send corresponding gradient information and / or loss function results based on the third and fourth data. This allows the first communication device to update or iterate the first neural network based on the gradient information and / or loss function results after receiving them.

[0031] In a possible implementation manner of the first aspect, the method further includes: the first communication device receiving or sending indication information indicating that the index satisfies the at least one item.

[0032] Based on the above technical solution, the first communication device can also receive or send indication information indicating that the index satisfies at least one item, so that the data sender and receiver can align their understanding of the index of the data in the data set based on the indication information, so as to avoid data processing errors caused by the misalignment of the understanding of the index between the data sender and receiver, thereby improving the robustness of the system.

[0033] In a second aspect of the present application, a communication method is provided, which is performed by a second communication device. The second communication device may be a communication device (e.g., a terminal device or a network device), or the second communication device may be a component of the communication device (e.g., a processor, a chip, or a chip system), or the second communication device may be a logic module or software that can implement all or part of the functions of the communication device. In the second aspect and its possible implementations, the communication method is described as being performed by the second communication device, where the second communication device may be a terminal device or a network device. In this method, a second communication device receives second data, which is obtained by processing first data, and the first data is data in the first data set; wherein the index of the first data in the first data set is used to determine fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is label data corresponding to the first data; the second communication device determines third data based on the second data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network; the index satisfies at least one of the following: the index is determined based on the resources carrying the second data; the index is determined based on the number of times the data in the second data set is processed.

[0034] Based on the above technical solution, the second data received by the second communication device is obtained based on the first data, and the second communication device can subsequently process the second data to obtain the third data. The index of the first data in the first data set is used to determine the fourth data in the second data set, and the fourth data is the label data corresponding to the first data. In other words, after receiving the second data, the second communication device can determine the label data in the second data set and process the third data based on the label data. Moreover, the second data and / or the third data are obtained based on a neural network, that is, the neural network used for AI processing can include a neural network deployed in the first communication device and / or a neural network deployed in the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to the AI ​​processing of the neural network while also improving the flexibility of the neural network deployment.

[0035] In addition, the index of the first data in the first data set is used to determine the tag data (i.e., fourth data) corresponding to the first data in the second data set, and the index satisfies at least one of the above conditions. In other words, after receiving the second data, the second communication device can determine the tag data in the second data set based on the resource carrying the data or the number of times the data in the data set is processed. In this way, air interface overhead can be reduced to improve communication efficiency.

[0036] In a possible implementation of the second aspect, the index is determined based on the resource carrying the second data, including: the value of the index is determined by at least one of the time domain resource index of the resource, the frequency domain resource index of the resource, and the resource block size of the resource.

[0037] Based on the above technical solution, when the index of the first data in the first data set is determined based on the resource carrying the second data, the index can be specifically determined by at least one of the above items to improve the flexibility of the solution implementation.

[0038] In a possible implementation of the second aspect, when the index is determined based on the number of times data in the second data set is processed, the method further includes: the second communication device sends first information, and the first information is used to indicate the index of the first data in the first data set.

[0039] Based on the above technical solution, when the index of the first data in the first data set is determined based on the number of times the data in the first data set is processed, since the second communication device may not be able to perceive the number of times the data in the first data set is processed, the second communication device can determine the index based on the number of times the data in the second data set is processed. Accordingly, the second communication device can also receive first information, enabling the second communication device to determine the index of the first data in the first data set based on the first information, and subsequently determine the fourth data in the second data set based on the index.

[0040] It should be understood that the first communication device can perform multiple processing operations based on the data in the first data set to obtain and send processing results (for example, one of the processing results is the second data obtained based on the first data). Accordingly, among the multiple processing results, the first communication device can send corresponding indexes for some or all of the processing results (for example, sending first information for the second data processing result). Thereafter, sending corresponding indexes based on some or all of the processing results can achieve alignment of the understanding of the indexes by the data sender and receiver, thereby avoiding data processing errors caused by the misalignment of the understanding of the indexes by the data sender and receiver, thereby improving the robustness of the system.

[0041] Optionally, the first communication device may send an index corresponding to a partial processing result, without sending an index corresponding to the entire processing result, thereby saving overhead.

[0042] In a possible implementation of the second aspect, the first information is one of multiple information transmitted based on a first period; the method also includes: the second communication device receives or sends configuration information, and the configuration information is used to configure the first period.

[0043] Based on the above technical solution, the first information can be one of the periodic information transmitted based on the first cycle. Prior to this, the second communication device can receive or send configuration information for configuring the first cycle, so that the second communication device can serve as both the configurator of the first cycle and the configured party of the first cycle. This can align the understanding of the first cycle between the data sender and receiver, and also improve the flexibility of the solution implementation.

[0044] In a possible implementation manner of the second aspect, the method further includes: the second communication device sending indication information indicating the first data set; and / or, the second communication device receiving indication information indicating the second data set.

[0045] Based on the above technical solution, the second communication device can serve as the configurator of the first data set, and / or the configured party of the second data set, so that the data sender and receiver can obtain the data set before exchanging data.

[0046] Optionally, the first data set may be preconfigured for the first communication device, and / or the second data set may be preconfigured for the second communication device. In this way, overhead can be reduced.

[0047] In a possible implementation manner of the second aspect, the method further includes: the second communication device sending gradient information and / or a result of a loss function determined based on the third data and the fourth data.

[0048] Based on the above technical solution, the second communication device can process the second data to obtain third data, and the second communication device can also determine and transmit corresponding gradient information and / or loss function results based on the third data and the fourth data. This enables the first communication device to update or iterate the first neural network based on the gradient information and / or loss function results after receiving them.

[0049] In a possible implementation manner of the second aspect, the method further includes: the second communication device receiving or sending indication information indicating that the index satisfies the at least one item.

[0050] Based on the above technical solution, the second communication device can also receive or send indication information indicating that the index satisfies at least one item, so that the data sender and receiver can align their understanding of the index of the data in the data set based on the indication information, so as to avoid data processing errors caused by the misalignment of the understanding of the index between the data sender and receiver, thereby improving the robustness of the system.

[0051] The third aspect of the present application provides a communication method, wherein the first communication device may be a communication device (such as a terminal device or a network device), or the first communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can implement all or part of the functions of the communication device. In the third aspect and its possible implementation, the communication method is described as being performed by the first communication device, wherein the first communication device may be a terminal device or a network device. In the method, the first communication device processes the first data to obtain the second data; the first communication device sends the second data and the fourth data, wherein the second data is used to determine the third data, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data.

[0052] Based on the above technical solution, the second data sent by the first communication device is obtained based on the first data, and the subsequent recipient of the second data (such as the second communication device) can process the second data to obtain the third data. Among them, the first communication device can also send fourth data, and the fourth data is the label data corresponding to the first data. Accordingly, after receiving the second data, the recipient of the second data and the fourth data can process the third data based on the label data. Moreover, the second data and / or the third data are obtained based on a neural network, that is, the neural network used for AI processing can include a neural network deployed on the first communication device and / or a neural network deployed on the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to the AI ​​processing of the neural network, while also improving the flexibility of the neural network deployment.

[0053] In addition, the second data sent by the first communication device is the processing result of the first data, and the fourth data sent by the first communication device is the label data corresponding to the first data. Thus, by sending the processing result of the first data and the label data corresponding to the first data, the recipient can further process based on the label data, and the solution can also be applied to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0054] In a possible implementation manner of the third aspect, the method further includes: the first communication device receiving gradient information and / or a result of a loss function determined based on the third data and the fourth data.

[0055] Based on the above technical solution, a receiver of the second data (e.g., a second communication device) can process the second data to obtain third data. Furthermore, the receiver can determine and send corresponding gradient information and / or loss function results based on the third and fourth data. This allows the first communication device to update or iterate the first neural network based on the gradient information and / or loss function results after receiving them.

[0056] In a fourth aspect of the present application, a communication method is provided, which is performed by a second communication device, which may be a communication device (such as a terminal device or a network device), or the second communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the second communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In the fourth aspect and its possible implementation, the communication method is described as being performed by a second communication device, wherein the second communication device may be a terminal device or a network device. In the method, the second communication device receives second data and fourth data, wherein the second data is obtained based on the first data, and the fourth data is the label data corresponding to the first data; the second communication device determines third data based on the second data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data.

[0057] Based on the above technical solution, the second data received by the second communication device is obtained based on the first data, and the second communication device can subsequently process the second data to obtain third data. Among them, the second communication device can also receive fourth data, and the fourth data is the label data corresponding to the first data. Accordingly, after receiving the second data, the second communication device can process the third data based on the label data. Moreover, the second data and / or the third data are obtained based on a neural network, that is, the neural network used for AI processing can include a neural network deployed on the first communication device and / or a neural network deployed on the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to the AI ​​processing of the neural network while also improving the flexibility of the neural network deployment.

[0058] In addition, the second data received by the second communication device is the processing result of the first data, and the fourth data sent by the second communication device is the label data corresponding to the first data. Thus, by sending the processing result of the first data and the label data corresponding to the first data, the second communication device can further process based on the label data, and can also make the solution applicable to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0059] In a possible implementation manner of the fourth aspect, the method further includes: the second communication device sending gradient information and / or a result of a loss function determined based on the third data and the fourth data.

[0060] Based on the above technical solution, the second communication device can process the second data to obtain third data, and the second communication device can also determine and transmit corresponding gradient information and / or loss function results based on the third data and the fourth data. This enables the first communication device to update or iterate the first neural network based on the gradient information and / or loss function results after receiving them.

[0061] In a fifth aspect of the present application, a communication method is provided, which is performed by a first communication device, which may be a communication device (such as a terminal device or a network device), or the first communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can implement all or part of the functions of the communication device. In the fifth aspect and its possible implementation, the communication method is described as being performed by a first communication device, wherein the first communication device may be a terminal device or a network device. In this method, the first communication device processes the first data to obtain the second data; wherein the first data is the data in the first data set; the first communication device sends the second data and a first index, and the second data is used to determine the third data; wherein the first index is used to determine the fourth data in the second data set, and the second data set includes the label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained based on the first neural network processing the first data, and / or the fourth data is obtained based on the neural network processing the second data.

[0062] Based on the above technical solution, the second data sent by the first communication device is obtained based on the first data, and the subsequent recipient of the second data (such as the second communication device) can process the second data to obtain the third data. Among them, the first communication device can also send a first index. Correspondingly, after receiving the second data, the recipient of the second data and the fourth data can process the third data based on the label data. Moreover, the second data and / or the third data are obtained based on a neural network, that is, the neural network used for AI processing may include a neural network deployed on the first communication device and / or a neural network deployed on the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to the AI ​​processing of the neural network while also improving the flexibility of the neural network deployment.

[0063] In addition, the second data sent by the first communication device is the processing result of the first data, and the fourth data sent by the first communication device is the label data corresponding to the first data. Thus, by sending the processing result of the first data and the label data corresponding to the first data, the recipient can further process based on the label data, and the solution can also be applied to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0064] It should be understood that in the above technical solution, the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data. The first data set and the second data set can be contained in one data set. In this one data set, N input data and M label data can be included, where N and M are both positive integers; and the first data set can include the N input data, and the second data set can include the M label data. In other words, the first data set can be called an input data set, a neural network input data set, etc., and the second data set can be called a label data set, a neural network label data set, etc. In addition, the AI ​​neural network can be processed based on the same data set to achieve iteration, update, etc. of the AI ​​neural network.

[0065] In a possible implementation, the first index is determined by the second index of the first data in the first data set.

[0066] In an implementation example, when each of N pieces of input data corresponds to different label data in M ​​pieces of label data, the value of N is equal to the value of M.

[0067] For example, the first index can be the same as the second index of the first data in the first data set. In other words, the label data of the i-th piece of input data in the first data set is the j-th piece of data in the second data set, i is the first index, j is the second index, and i and j are equal. That is, the label data of the first piece of N pieces of input data is the first piece of data in the M pieces of labeled data, the label data of the second piece of N pieces of input data is the second piece of data in the M pieces of labeled data, and so on. The label data of the N-th piece of N pieces of input data is the M-th piece of data in the M pieces of labeled data (N and M are equal).

[0068] For another example, the first index may be partially or completely different from the second index of the first data in the first dataset. In other words, the label data of the i-th piece of input data in the first dataset is the j-th piece of data in the second dataset, i is the first index, j is the second index, and i and j may be partially or completely different. The mapping relationship between i and j can be preconfigured.

[0069] As an example of a preconfigured mapping relationship between i and j, i can be traversed from 1 to N, and j can be traversed from N (N equals M) to 1. For example, taking the values ​​of N and M as 3, the label data of the first copy of N input data is the third copy of the M label data, the label data of the second copy of N input data is the second copy of the M label data, and the label data of the third copy of N input data is the first copy of the M label data. In this example, the first index can be different from the second index portion of the first data in the first data set.

[0070] As another example in which the mapping relationship between i and j can be preconfigured, the mapping relationship between i and j can be configured or preconfigured to align the data sender and receiver. For example, still taking the value of N and M as 3 as an example, the label data of the first copy of N input data is the third copy of the M label data, the label data of the second copy of N input data is the first copy of the M label data, and the label data of the third copy of N input data is the second copy of the M label data. In this example, the first index can be completely different from the second index of the first data in the first data set.

[0071] In another implementation example, the mapping relationship between the first index and the second index of the first data in the first data set is preconfigured.

[0072] For example, when at least two of the N pieces of input data correspond to the same label data in the M pieces of label data, the value of N may be greater than or equal to the value of M. Accordingly, in this case, the first index may be less than or equal to the second index of the first data in the first data set.

[0073] For another example, when one of the N pieces of input data corresponds to at least two pieces of label data in the M pieces of label data, the value of N may be less than or equal to the value of M. Accordingly, in this case, the first index may be greater than or equal to the second index of the first data in the first data set.

[0074] In a possible implementation manner of the fifth aspect, the method further includes: the first communication device receiving gradient information and / or a result of a loss function determined based on the third data and the fourth data.

[0075] Based on the above technical solution, a receiver of the second data (e.g., a second communication device) can process the second data to obtain third data. Furthermore, the receiver can determine and send corresponding gradient information and / or loss function results based on the third and fourth data. This allows the first communication device to update or iterate the first neural network based on the gradient information and / or loss function results after receiving them.

[0076] In a sixth aspect of the present application, a communication method is provided, which is performed by a second communication device, which may be a communication device (such as a terminal device or a network device), or the second communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the second communication device may also be a logic module or software that can implement all or part of the functions of the communication device. In the sixth aspect and its possible implementation, the communication method is described as being performed by a second communication device, wherein the second communication device may be a terminal device or a network device. In this method, the second communication device receives second data and a first index, the second data is obtained based on the first data, and the first data is data in the first data set; wherein the first index is used to determine fourth data in the second data set, the second data set includes label data corresponding to the data in the first data set, and the fourth data is label data corresponding to the first data; the second communication device determines third data based on the second data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data.

[0077] Based on the above technical solution, the second data received by the second communication device is obtained based on the first data, and the second communication device can subsequently process the second data to obtain the third data. The second communication device can also receive a first index. Accordingly, after receiving the second data, the second communication device can process the third data based on the label data. Moreover, the second data and / or the third data are obtained based on a neural network, that is, the neural network used for AI processing may include a neural network deployed on the first communication device and / or a neural network deployed on the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to the AI ​​processing of the neural network while also improving the flexibility of the neural network deployment.

[0078] In addition, the second data received by the second communication device is the processing result of the first data, and the fourth data received by the second communication device is the label data corresponding to the first data. Thus, by sending the processing result of the first data and the label data corresponding to the first data, the second communication device can further process based on the label data, and can also make the solution applicable to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0079] Optionally, the first index is determined by the second index of the first data in the first data set.

[0080] In a possible implementation manner of the sixth aspect, the method further includes: the second communication device sending gradient information and / or a result of a loss function determined based on the third data and the fourth data.

[0081] Based on the above technical solution, the second communication device can process the second data to obtain third data, and the second communication device can also determine and transmit corresponding gradient information and / or loss function results based on the third data and the fourth data. This enables the first communication device to update or iterate the first neural network based on the gradient information and / or loss function results after receiving them.

[0082] In a seventh aspect, the present application provides a communication device, which is a first communication device, and includes a transceiver unit and a processing unit, wherein the processing unit is used to process the first data to obtain the second data; wherein the first data is the data in the first data set; the transceiver unit is used to send the second data, and the second data is used to determine the third data; wherein the index of the first data in the first data set is used to determine the fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network; the index satisfies at least one of the following: the index is determined based on the resource carrying the second data; the index is determined based on the number of times the data in the first data set is processed.

[0083] In the seventh aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects. For details, please refer to the first aspect and will not be repeated here.

[0084] In an eighth aspect of the present application, a communication device is provided, which is a second communication device, and includes a transceiver unit and a processing unit. The transceiver unit is used to receive second data, and the second data is obtained by processing first data, and the first data is data in the first data set; wherein the index of the first data in the first data set is used to determine fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is label data corresponding to the first data; the processing unit is used to determine third data based on the second data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network; the index satisfies at least one of the following: the index is determined based on the resource carrying the second data; the index is determined based on the number of times the data in the second data set is processed.

[0085] In the eighth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the second aspect and achieve corresponding technical effects. For details, please refer to the second aspect and will not be repeated here.

[0086] In a ninth aspect of the present application, a communication device is provided, which is a first communication device, comprising a transceiver unit and a processing unit, wherein the processing unit is used to process the first data to obtain the second data; the transceiver unit is used to send the second data and fourth data, wherein the second data is used to determine the third data, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network.

[0087] In the ninth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the third aspect and achieve corresponding technical effects. For details, please refer to the third aspect and will not be repeated here.

[0088] In the tenth aspect of the present application, a communication device is provided, which is a second communication device, and includes a transceiver unit and a processing unit. The transceiver unit is used to receive second data and fourth data, wherein the second data is obtained based on the first data, and the fourth data is label data corresponding to the first data; the processing unit is used to determine third data based on the second data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data.

[0089] In the tenth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the fourth aspect and achieve corresponding technical effects. For details, please refer to the fourth aspect and will not be repeated here.

[0090] In the eleventh aspect of the present application, a communication device is provided, which is a first communication device, and includes a transceiver unit and a processing unit, the processing unit is used to process the first data to obtain the second data; wherein the first data is the data in the first data set; the transceiver unit is used to send the second data and a first index, and the second data is used to determine the third data; wherein the first index is used to determine the fourth data in the second data set, the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the fourth data is obtained by processing the second data based on the neural network.

[0091] In the eleventh aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the fifth aspect and achieve corresponding technical effects. For details, please refer to the fifth aspect and will not be repeated here.

[0092] The twelfth aspect of the present application provides a communication device, which is a second communication device, and includes a transceiver unit and a processing unit. The transceiver unit is used to receive second data and a first index, and the second data is obtained based on the first data, and the first data is the data in the first data set; wherein the first index is used to determine fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; the processing unit is used to determine third data based on the second data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network.

[0093] In the twelfth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the sixth aspect and achieve corresponding technical effects. For details, please refer to the sixth aspect and will not be repeated here.

[0094] A thirteenth aspect of the present application provides a communication device, comprising at least one processor, wherein the at least one processor is coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the program or instructions so that the device implements the method described in any possible implementation method of any one of the first to sixth aspects.

[0095] In a fourteenth aspect, the present application provides a communication device comprising at least one logic circuit and an input / output interface; the logic circuit is used to execute the method described in any possible implementation method of any one of the first to sixth aspects.

[0096] A fifteenth aspect of the present application provides a communication system, which includes the above-mentioned first communication device and second communication device.

[0097] In the sixteenth aspect of the present application, a computer-readable storage medium is provided, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in any possible implementation of any aspect of the first to sixth aspects above.

[0098] In the seventeenth aspect of the present application, a computer program product (or computer program) is provided. When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of any one of the first to sixth aspects above.

[0099] In an eighteenth aspect, the present application provides a chip system comprising at least one processor for supporting a communication device to implement the method described in any possible implementation of any one of the first to sixth aspects.

[0100] In one possible design, the chip system may further include a memory for storing program instructions and data necessary for the first communication device. The chip system may be composed of a chip or may include a chip and other discrete components. Optionally, the chip system may further include an interface circuit that provides program instructions and / or data to the at least one processor.

[0101] Among them, the technical effects brought about by any design method in the seventh to eighteenth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to sixth aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figures 1a to 1c are schematic diagrams of a communication system provided by this application;

[0103] Figures 1d, 1e, and 2a to 2f are schematic diagrams of the AI ​​processing process involved in this application;

[0104] FIG3 is an interactive diagram of the communication method provided by this application;

[0105] Figures 4 and 5 are schematic diagrams of the AI ​​processing process provided by this application;

[0106] Figures 6 and 7 are interactive diagrams of the communication method provided by this application;

[0107] 8 to 12 are schematic diagrams of the communication device provided in this application. DETAILED DESCRIPTION

[0108] First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0109] (1) Terminal device: It can be a wireless terminal device that can receive network device scheduling and instruction information. The wireless terminal device can be a device that provides voice and / or data connectivity to the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.

[0110] Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN). Terminal devices can be mobile terminal devices, such as mobile phones (also known as "cellular" phones, mobile phones), computers, and data cards. For example, they can be portable, pocket-sized, handheld, computer-built-in, or vehicle-mounted mobile devices that exchange voice and / or data with the radio access network. Examples include personal communication service (PCS) phones, cordless phones, Session Initiation Protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablet computers, and computers with wireless transceiver capabilities. Wireless terminal equipment can also be called system, subscriber unit, subscriber station, mobile station, mobile station (MS), remote station, access point (AP), remote terminal equipment (remote terminal), access terminal equipment (access terminal), user terminal equipment (user terminal), user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

[0111] As an example and not a limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices may also be referred to as wearable smart devices or smart wearable devices, etc., which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, etc., as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0112] The terminal may also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle-to-everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc.

[0113] In addition, the terminal device may also be a terminal device in a communication system that has evolved after the fifth generation (5G) communication system (e.g., a sixth generation (6G) communication system) or a terminal device in a future public land mobile network (PLMN). For example, the 6G network can further expand the form and function of 5G communication terminals. 6G terminals include but are not limited to vehicles, cellular network terminals (with integrated satellite terminal functions), drones, and Internet of Things (IoT) devices.

[0114] In an embodiment of the present application, the terminal device may also obtain AI services provided by the network device. Optionally, the terminal device may also have AI processing capabilities.

[0115] (2) Network equipment: It can be a device in a wireless network. For example, the network equipment can be a RAN node (or device) that connects a terminal device to a wireless network, which can also be called a base station. Currently, some examples of RAN equipment include: base station, evolved NodeB (eNodeB), gNB (gNodeB) in a 5G communication system, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point AP, etc. In addition, in a network structure, the network equipment can include a centralized unit (CU) node, a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0116] Alternatively, a RAN node can be a macro base station, micro base station, indoor base station, relay node, donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. A RAN node can also be a server, wearable device, vehicle, or vehicle-mounted device. For example, the access network device in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

[0117] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, and different RAN nodes respectively implement part of the functions of the base station. For example, the RAN node can be a centralized unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). The CU and DU can be set separately, or they can be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0118] In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in an open access network (open RAN, O-RAN or ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. For the convenience of description, this application takes CU, CU-CP, CU-UP, DU and RU as examples for description. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0119] The communication between the access network device and the terminal device follows a certain protocol layer structure. The protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: a radio resource control (RRC) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, a media access control (MAC) layer, or a physical (PHY) layer. The user plane protocol layer may include at least one of the following: a service data adaptation protocol (SDAP) layer, a PDCP layer, an RLC layer, a MAC layer, or a physical layer.

[0120] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

[0121] Table 1

[0122] The network device may be any other device that provides wireless communication functionality to the terminal device. The embodiments of this application do not limit the specific technology and device form used by the network device. For ease of description, the embodiments of this application do not limit this.

[0123] The network equipment may also include core network equipment, which may include, for example, a mobility management entity (MME), a home subscriber server (HSS), a serving gateway (S-GW), a policy and charging rules function (PCRF), and a public data network gateway (PDN gateway, P-GW) in a fourth generation (4G) network; and network elements such as an access and mobility management function (AMF), a user plane function (UPF), or a session management function (SMF) in a 5G network. In addition, the core network equipment may also include other core network equipment in a 5G network and a next generation network of a 5G network.

[0124] In an embodiment of the present application, the above-mentioned network device may also have a network node with AI capabilities, which can provide AI services for terminals or other network devices. For example, it can be an AI node on the network side (access network or core network), a computing power node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.

[0125] In the embodiments of the present application, the apparatus for implementing the function of the network device may be the network device, or may be a device capable of supporting the network device in implementing the function, such as a chip system, which may be installed in the network device. In the technical solutions provided in the embodiments of the present application, the technical solutions provided in the embodiments of the present application are described by taking the network device as an example.

[0126] (3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used simultaneously. Configuration refers to the network device / server sending some parameter configuration information or parameter values ​​to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission based on these values ​​or information. Pre-configuration is similar to configuration, and can be parameter information or parameter values ​​pre-negotiated between the network device / server and the terminal device, or parameter information or parameter values ​​used by the base station / network device or terminal device as specified in the standard protocol, or parameter information or parameter values ​​pre-stored in the base station / server or terminal device. This application does not limit this.

[0127] Furthermore, these values ​​and parameters can be changed or updated.

[0128] (4) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. In addition, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects.

[0129] (5) “Sending” and “receiving” in the embodiments of the present application indicate the direction of signal transmission. For example, “sending information to XX” can be understood as the destination of the information being XX, which can include direct sending through the air interface, as well as indirect sending through the air interface by other units or modules. “Receiving information from YY” can be understood as the source of the information being YY, which can include direct receiving from YY through the air interface, as well as indirect receiving from YY through the air interface from other units or modules. “Sending” can also be understood as the “output” of the chip interface, and “receiving” can also be understood as the “input” of the chip interface.

[0130] In other words, sending and receiving can be performed between devices, for example, between a network device and a terminal device, or can be performed within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, wiring or interface.

[0131] It is understandable that information may be processed between the source and destination of information transmission, such as coding, modulation, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated.

[0132] (6) In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated may also be indirectly indicated by indicating other information, wherein the other information is associated with the information to be indicated; or only a part of the information to be indicated may be indicated, while the other part of the information to be indicated is known or agreed in advance. For example, the indication of specific information may be achieved by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication. It is understandable that for the sender of the indication information, the indication information can be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information can be used to determine the information to be indicated.

[0133] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments of this application, and the various methods / designs / implementations in each embodiment, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments and the various methods / designs / implementations in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various methods / designs / implementations in each embodiment can be combined to form new embodiments, methods, or implementations according to their inherent logical relationships. The following description of the implementation methods of this application does not constitute a limitation on the scope of protection of this application.

[0134] The present application can be applied to a long term evolution (LTE) system, a new radio (NR) system, or a communication system evolved after 5G (such as 6G, etc.). The communication system includes at least one network device and / or at least one terminal device.

[0135] Please refer to Figure 1a, which is a schematic diagram of a communication system in this application. Figure 1a exemplarily illustrates a network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. In the example shown in Figure 1a, terminal device 1 is a smart teacup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas pump, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

[0136] As shown in Figure 1a, the AI ​​configuration information sending entity can be a network device. The AI ​​configuration information receiving entity can be terminal devices 1-6. In this case, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 can send data to the network device, and the network device needs to receive data sent by terminal devices 1-6. At the same time, the network device can send configuration information to terminal devices 1-6.

[0137] For example, in Figure 1a, terminal devices 4 and 6 can also form a communication system. Terminal device 5 serves as a network device, i.e., the AI ​​configuration information sending entity; terminal devices 4 and 6 serve as terminal devices, i.e., the AI ​​configuration information receiving entities. For example, in a connected vehicle system, terminal device 5 sends AI configuration information to terminal devices 4 and 6, respectively, and receives data from them. Correspondingly, terminal devices 4 and 6 receive AI configuration information from terminal device 5 and send data to terminal device 5.

[0138] Taking the communication system shown in Figure 1a as an example, in addition to executing communication-related services, different devices (including between network devices, between network devices and terminal devices, and / or between terminal devices) may also execute AI-related services.

[0139] As shown in Figure 1b, taking the network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

[0140] As shown in Figure 1c, taking the terminal devices including a TV and a mobile phone as an example, communication-related services and AI-related services can also be performed between the TV and the mobile phone.

[0141] The technical solution provided in this application can be applied to a wireless communication system (e.g., the system shown in FIG. 1a , FIG. 1b , or FIG. 1c ). For example, an AI network element can be introduced into the communication system provided in this application to implement some or all AI-related operations. The AI ​​network element can also be referred to as an AI node, AI device, AI entity, AI module, AI model, or AI unit, etc. The AI ​​network element can be a network element built into the communication system. For example, the AI ​​network element can be an AI module built into: an access network device, a core network device, a cloud server, or a network management (OAM) to implement AI-related functions. The OAM can be a network management for a core network device and / or a network management for an access network device. Alternatively, the AI ​​network element can also be an independently set network element in the communication system. Optionally, the terminal or a chip built into the terminal can also include an AI entity to implement AI-related functions.

[0142] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0143] Artificial intelligence (AI) can imbue machines with human intelligence. For example, it can enable machines to simulate certain intelligent human behaviors using computer hardware and software. Machine learning methods can be used to achieve AI. In machine learning, a machine uses training data to learn (or train) a model. This model represents the mapping from input to output. The learned model can be used for inference (or prediction), meaning that the model can be used to predict the output corresponding to a given input. This output can also be called an inference result (or prediction result).

[0144] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0145] Taking supervised learning as an example, supervised learning can use machine learning algorithms to learn the mapping relationship from sample values ​​to sample labels based on collected sample values ​​and sample labels, and then express the learned mapping relationship using an AI model. The process of training a machine learning model is the process of learning this mapping relationship. During the training process, sample values ​​are input into the model to obtain the model's predicted value. The model parameters are optimized by calculating the error between the model's predicted value and the sample label (ideal value). After the mapping relationship is learned, the learned mapping can be used to predict new sample labels. The mapping relationship learned by supervised learning can include linear mappings or nonlinear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0146] In other words, the goal of supervised learning can be to learn the mapping between the input data in the training set and the output data (i.e., the labeled data) given a training set (consisting of multiple pairs of input data and labeled data), while also hoping that this mapping can be applied to data outside the training set. The training set is a collection of correct input and output pairs.

[0147] A neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, NNs can theoretically approximate any continuous function, enabling them to learn arbitrary mappings. Traditional communication systems require extensive expert knowledge to design communication modules. However, deep learning communication systems based on neural networks can automatically discover implicit patterns in massive data sets and establish mapping relationships between data, achieving performance superior to traditional modeling methods.

[0148] The idea of ​​a neural network is derived from the neuronal structure of the brain. For example, each neuron performs a weighted sum operation on its input values ​​and outputs the result through an activation function.

[0149] As shown in Figure 1d, it is a schematic diagram of the neuron structure. Assume that the input of the neuron is x=[x0,x1,…,x n ], and the weights corresponding to each input are w=[w,w1,…,w n ], where n is a positive integer, w i and x i It can be a decimal, an integer (such as 0, a positive integer or a negative integer, etc.), or a complex number. i As x i The weight of x iWeighted. The bias of the weighted sum of the input values ​​according to the weight is, for example, b. The activation function can take many forms. Assuming that the activation function of a neuron is: y = f(z) = max(0,z), then the output of the neuron is: For another example, if the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: b can be a decimal, an integer (eg, 0, a positive integer, or a negative integer), or a complex number, etc. The activation functions of different neurons in a neural network can be the same or different.

[0150] Furthermore, neural networks generally include multiple layers, each of which may include one or more neurons. Increasing the depth and / or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it comprises, and the number of neurons in each layer can be referred to as the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, a hidden layer, and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to an intermediate hidden layer. The hidden layer performs calculations on the received processing results to obtain a calculation result, which is then passed to the output layer or the next adjacent hidden layer, which ultimately obtains the output of the neural network. A neural network can include one hidden layer or multiple hidden layers connected in sequence, without limitation.

[0151] A neural network is, for example, a deep neural network (DNN). Depending on how the network is constructed, a DNN can include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

[0152] Figure 1e is a schematic diagram of a FNN network. A characteristic of FNN networks is that neurons in adjacent layers are fully connected. This characteristic typically requires a large amount of storage space and results in high computational complexity.

[0153] CNN is a neural network specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling along the time axis) and image data (discrete sampling along two dimensions) can both be considered grid-like data. CNNs do not utilize all input information at once for computation. Instead, they use a fixed-size window to intercept a portion of the information for convolution operations, significantly reducing the computational complexity of model parameters. Furthermore, depending on the type of information intercepted by the window (e.g., people and objects in an image represent different types of information), each window can use a different convolution kernel, enabling CNNs to better extract features from the input data.

[0154] RNNs are a type of DNN that utilizes feedback time series information. Their input consists of a new input value at the current moment and their own output value at the previous moment. RNNs are suitable for capturing temporally correlated sequence features and are particularly well-suited for applications such as speech recognition and channel coding.

[0155] During the machine learning model training process, a loss function can be defined. This function describes the gap or discrepancy between the model's output and the ideal target value. Loss functions can be expressed in various forms, and there are no restrictions on their specific form. The model training process can be viewed as adjusting some or all of the model's parameters to keep the loss function below a threshold or meet the target.

[0156] A model may also be referred to as an AI model, rule, or other name. An AI model can be considered a specific method for implementing an AI function. An AI model represents a mapping relationship or function between the input and output of a model. AI functions may include one or more of the following: data collection, model training (or model learning), model information release, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model verification, or inference result release, etc. AI functions may also be referred to as AI (related) operations, or AI-related functions.

[0157] The following is an exemplary description of the implementation process of the neural network with reference to the accompanying drawings.

[0158] 1. Fully connected neural network, also known as multilayer perceptron (MLP).

[0159] As shown in Figure 2a, an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (center). Each layer of the MLP contains several nodes, called neurons. Neurons in adjacent layers are connected to each other.

[0160] Optionally, considering neurons in two adjacent layers, the output h of a neuron in the next layer is the weighted sum of all neurons x connected to it in the previous layer and passes through an activation function, which can be expressed as: h=f(wx+b).

[0161] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0162] Alternatively, the output of the neural network can be recursively expressed as: y = f n (w n f n-1 (…)+b n ).

[0163] Where n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0164] In other words, a neural network can be understood as a mapping from an input data set to an output data set. Neural networks are typically initialized randomly, and the process of obtaining this mapping from random w and b using existing data is called neural network training.

[0165] Optionally, a specific training method is to use a loss function to evaluate the output results of the neural network.

[0166] As shown in Figure 2b, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the loss function reaches a minimum, which is the "better point (e.g., optimal point)" in Figure 2b. It is understood that the neural network parameters corresponding to the "better point (e.g., optimal point)" in Figure 2b can be used as the neural network parameters in the trained AI model information.

[0167] Alternatively, the gradient descent process can be expressed as:

[0168] Among them, θ is the parameter to be optimized (including w and b), L is the loss function, and η is the learning rate, which controls the step size of gradient descent. represents the derivative operation, represents the derivative of θ with respect to L.

[0169] Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

[0170] As shown in Figure 2c, the gradient of the previous layer parameters can be recursively calculated from the gradient of the next layer parameters, which can be expressed as:

[0171] Among them, w ij is the weight of node j connecting to node i, si is the weighted sum of the inputs to node i.

[0172] 2. Federated Learning (FL)

[0173] The concept of federated learning effectively solves the current difficulties faced by the development of artificial intelligence. On the premise of fully protecting user data privacy and security, it efficiently completes the model learning task by promoting the collaboration between various edge devices and central servers.

[0174] As shown in Figure 2d, the FL architecture is the most widely used training architecture in the current FL field. The FedAvg algorithm is the basic algorithm of FL. Its algorithm flow is roughly as follows:

[0175] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0176] (2) In the round t∈[1,T], client k∈[1,K] based on the local dataset For the received global model Perform E epochs of training to obtain local training results Report it to the central node.

[0177] (3) The central node aggregates and collects the local training results from all (or some) clients. Assume that the client set that uploads the local model in round t is The center will use the number of samples of the corresponding client as the weight to perform weighted averaging to obtain a new global model. The specific update rule is: The center then sends the latest version of the global model Broadcast to all client devices for a new round of training.

[0178] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0179] In addition to reporting local models You can also use the local gradient of training After reporting, the central node averages the local gradients and updates the global model according to the direction of the average gradient.

[0180] As you can see, in the FL framework, datasets exist on distributed nodes. Distributed nodes collect local datasets, perform local training, and report the local training results (models or gradients) to the central node. The central node itself does not have a dataset; it is only responsible for fusing the training results of distributed nodes to obtain a global model and send it to the distributed nodes.

[0181] 3. Decentralized learning: Different from federated learning, decentralized learning is another distributed learning architecture.

[0182] As shown in Figure 2e, consider a fully distributed system without a central node. The design goal f(x) of a decentralized learning system is generally the goal f of each node. i The mean of (x), that is Where n is the number of distributed nodes, x is the parameter to be optimized. In machine learning, x is the parameter of the machine learning (such as neural network) model. Each node uses local data and local target f i (x) Calculate local gradient Then it is sent to the neighboring nodes that can be communicated with. After any node receives the gradient information sent by its neighbor, it can update the parameter x of the local model according to the following formula:

[0183] in, represents the parameters of the local model after the k+1th (k is a natural number) update in the i-th node, Represents the parameters of the local model after the kth update in the i-th node (if k is 0, it means is the parameter of the local model of the i-th node that does not participate in the update), α k Represents the tuning coefficient, N i is the set of neighbor nodes of node i, |N i | represents the number of elements in the neighbor node set of node i, that is, the number of neighbor nodes of node i. Through information interaction between nodes, the decentralized learning system will eventually learn a unified model.

[0184] The technical solutions provided in this application can be applied to wireless communication systems (e.g., the systems shown in Figures 1a and 1b). In wireless communication systems, communication nodes generally have both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., performing signal transmission and reception processing) to enable communication between the network device and other communication nodes.

[0185] In communication networks, communication nodes may have excess computing power beyond supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue.

[0186] In one possible implementation, a communication node can act as a participating node in an AI learning system, applying its computing power to a specific component of the system. With the advent of the era of large models, deep learning models with massive parameters, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT), can accomplish increasingly complex tasks and achieve superior performance. However, for large models, even the inference process is limited by device capacity, so large models are typically stored on central cloud servers. Furthermore, each device in the network generates a massive amount of raw data daily, which requires multiple inference calls on the large model. Typically, a device (such as a communication node) sends data to a central server, which then performs inference using the data and returns the inference results to the device. This process consumes significant communication resources for data transmission and also risks the privacy of device data.

[0187] To better save communication costs and protect user data privacy, scholars have proposed distributed inference technology for deep neural networks. This approach distributes models to devices and uses the local computing power of the devices to infer the models, thereby reducing communication costs and ensuring data privacy.

[0188] For example, in the example shown in FIG2f , two communication nodes, Node 1 and Node 2, are used as an example to participate in the AI ​​learning system. Node 1 and Node 2 can both be communication nodes, such as terminal devices or network devices. The neural network used by the AI ​​learning system can include at least a sub-neural network deployed at Node 1 for AI encoding, and / or a sub-neural network deployed at Node 2 for AI decoding.

[0189] As an implementation example in Figure 2f, node 1 processes the encoded signal using the AI ​​encoding sub-neural network. This encoded signal undergoes quantization and physical layer processing to produce a wireless signal. Correspondingly, node 2 receives this signal via a wireless channel. Node 2 then processes the signal at the physical layer and dequantizes it, using it as input for AI decoding. This AI decoding process yields a decoded signal. Node 2 can also determine gradient data based on this decoded signal and the label data.

[0190] After that, after node 2 obtains gradient data based on the sub-neural network processing of AI decoding, the gradient data is quantized and processed at the physical layer to obtain a wireless signal; correspondingly, after node 1 receives the wireless signal through transmission through the wireless channel, node 1 obtains gradient data after physical layer processing and dequantization processing. Subsequently, node 1 can optimize the sub-neural network for AI encoding deployed in node 1 based on the gradient data (such as training / updating / iteration, etc.).

[0191] Optionally, after node 2 obtains the gradient data, node 2 can also optimize the neural network (such as training / updating / iteration, etc.) of the sub-neural network for AI encoding deployed in node 2 based on the gradient data.

[0192] It should be noted that the node 2 can also calculate the result of the loss function based on the decoding result and the label data, and the result of the loss function can also be used to optimize the neural network. The above implementation is only explained by taking the example of node 2 determining the gradient data.

[0193] In addition, the optimization process of the neural network may need to execute the above-mentioned AI encoding and AI decoding processes multiple times. During the multiple executions, node 1 can perform AI encoding processing on multiple input data and then send it, and node 2 can process the AI ​​decoding results through multiple label data to obtain gradient data. In this case, the multiple input data and the multiple label data can be data in the same training data set (refer to the implementation process of supervised learning in the previous article). However, for different nodes, in a certain AI encoding and AI decoding process, how to align the indexes of the input data used for the AI ​​encoding and the label data used after AI decoding in the same training data set is a technical problem that needs to be solved urgently.

[0194] To address the above issues, this application provides a communication method and related equipment for enabling the computing power of communication nodes to be applied to artificial intelligence (AI) processing of neural networks while also improving the flexibility of neural network deployment. This will be described in detail below with reference to the accompanying drawings.

[0195] Please refer to FIG3 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0196] It should be noted that, in Figure 3, the method is illustrated by taking the first communication device and the second communication device as the execution subjects of the interaction diagram as an example, but the present application does not limit the execution subjects of the interaction diagram. For example, in Figure 3 and Figure 6 below, the execution subject of the method can be replaced by a chip, a chip system, a processor, a logic module or software in the communication device. The first communication device can be a terminal device and the second communication device can be a network device, or the first communication device can be a network device and the second communication device can be a terminal device, or the first communication device and the second communication device are both terminal devices (for example, the method can be applied to the communication process of different terminal devices in a side link communication scenario).

[0197] S301. A first communication device performs a first process on first data to obtain second data, wherein the first data is data in the first data set.

[0198] S302. The first communication device sends second data, and correspondingly, the second communication device receives the second data.

[0199] S303. The second communication device determines third data based on the second data.

[0200] It should be understood that in the above technical solution, the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data. Optionally, the first data set and the second data set can be contained in one data set. In this one data set, N input data and M label data can be included, and N and M are both positive integers; and the first data set can include the N input data, and the second data set can include the M label data. In other words, the first data set can be called an input data set, a neural network input data set, etc., and the second data set can be called a label data set, a neural network label data set, etc. In addition, the AI ​​neural network can be processed based on the same data set to achieve iteration, update, etc. of the AI ​​neural network.

[0201] Optionally, when each of the N pieces of input data corresponds to different label data in the M pieces of label data, the value of N is equal to the value of M. When at least two of the N pieces of input data correspond to the same piece of label data in the M pieces of label data, the value of N may be greater than or equal to the value of M. When one of the N pieces of input data corresponds to at least two pieces of label data in the M pieces of label data, the value of N may be less than or equal to the value of M.

[0202] Optionally, when the second data is obtained by processing the first data in the first data set based on the first neural network, since the second data is the send data obtained by the first communication device processing the first data, the first neural network can be referred to as a neural network deployed at the sending end, an encoding neural network, an AI encoding neural network, etc. Similarly, when the third data is obtained by processing the second data based on the second neural network, since the third data is data obtained by the second communication device processing the received second data based on the second neural network, the second neural network can be referred to as a neural network deployed at the receiving end, a decoding neural network, an AI decoding neural network, etc.

[0203] In this application, terms such as AI, neural network, AI neural network, machine learning, AI processing, and AI neural network processing can be used interchangeably.

[0204] In this application, the data involved (such as first data, second data, third data, and fourth data, etc.) can be replaced by information, signals, etc.

[0205] Furthermore, in the above technical solution, the index of the first data in the first data set is used to determine fourth data in the second data set, where the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data. In other words, after the second communication device determines the third data based on the second data in step S303, the second communication device can determine corresponding gradient information and / or the result of the loss function based on the label data corresponding to the first data (i.e., the fourth data) and the third data.

[0206] Optionally, in the method shown in Figure 3, after step S303, the method further includes: the second communication device sends the gradient information and / or the result of the loss function determined based on the third data and the fourth data to the first communication device. Specifically, the receiver of the second data (e.g., the second communication device) can process the second data to obtain the third data, and the receiver can also determine and send the corresponding gradient information and / or the result of the loss function based on the third data and the fourth data. This enables the first communication device to update or iterate the first neural network based on the gradient information and / or the result of the loss function after receiving the gradient information and / or the result of the loss function.

[0207] In addition, the index of the first data in the first data set satisfies at least one of the following manner A and manner B.

[0208] Mode A: The index is determined based on the resource carrying the second data.

[0209] Specifically, when the index of the first data in the first data set satisfies Method A, the value of the index is determined by at least one of the time domain resource index of the resource, the frequency domain resource index of the resource, and the resource block size of the resource. Thus, when the index of the first data in the first data set is determined based on the resource carrying the second data, the index can be determined by at least one of the above items, thereby increasing the flexibility of the solution implementation.

[0210] Among them, method A can be understood as using the synchronized information of the data sender and receiver to generate the same index value on both sides, thereby achieving alignment of the understanding of the index. For example, assuming the training batch size is N batch , the number of data set samples is N dataset , a round of training can be considered as using N batch Sample data is collected until all samples in the data set are used up. The following provides some implementation examples by taking the resource carrying the second data as a time domain resource index as an example.

[0211] In an implementation example, the system frame number n in the time domain resource index of the data sender and receiver is used. f To generate index values. For example, the index values ​​of the data in the dataset satisfy:

[0212] (n f +n o )×N batch %N dataset ,((n f +n o )×N batch +1)%N dataset ,…,((n f +n o +1)×N batch -1)%N dataset ;

[0213] Among them, % represents the remainder operation, n f Indicates the system frame number, n o is the offset value (used to traverse the dataset).

[0214] In another implementation example, the index value of the data in the data set can also be the same as the system frame number n in the time domain resource index. f and subframe number n sf For example, the index values ​​of the data in the dataset satisfy:

[0215] (n f ×N sf_f +n sf +n o )×N batch %N dataset ,((nf ×N sf_f +n sf +n o )×N batch +1)%N dataset ,…,((n f ×N sf_f +n sf +n o +1)×N batch -1)%N dataset ;

[0216] Among them, % represents the remainder operation, n f Indicates the system frame number, N sf_f Indicates the number of subframes contained in each frame, n sf Indicates the subframe number, n o is the offset value (used to traverse the dataset).

[0217] In another implementation example, the index value of the data in the data set can also be the same as the system frame number n in the time domain resource index. f and time slot number n slot For example, the index values ​​of the data in the dataset satisfy:

[0218] (n f ×N slot_f +n slot +n o )×N batch %N dataset ,((n f ×N slot_f +n slot +n o )×N batch +1)%N dataset ,…,((n f ×N slot_f +n slot +n o +1)×N batch -1)%N dataset .

[0219] Among them, % represents the remainder operation, n f Indicates the system frame number, N slot_f Indicates the number of time slots contained in each frame, n sf Indicates the subframe number, n o is the offset value (used to traverse the dataset).

[0220] It should be understood that the above implementation diagram only takes the resource carrying the second data as the time domain resource index as an example. In the above implementation example, the time domain resource index can be replaced by other data related to the information synchronized between the sender and receiver, such as the number of physical resources in the frequency domain carrying the second data, including but not limited to the number of resource blocks, the number of subcarriers, etc.

[0221] Optionally, method A can be understood as a real-time data alignment method, where real-time can be understood as a relatively fixed time interval between the process of the first communication device performing the first processing in step S301 and the process of the second communication device performing the second processing in step S303; and / or, the time interval between the process of the first communication device sending the processing result of the first processing (i.e., the second data) and the process of the second communication device sending the gradient data corresponding to the second processing (and / or the result of the loss function) is relatively fixed.

[0222] Figure 4 illustrates an implementation example of Method A (i.e., real-time data alignment). In this example, the first of every six frames (e.g., frames numbered 1, 7, or 13) is used to transmit the second data sent by the first communication device, and the fourth of every six frames (e.g., frames numbered 4, 10, or 16) is used to transmit the gradient data (and / or loss function results) sent by the second communication device. In other words, the time interval between the time-domain resources carrying the second data and the time-domain resources carrying the gradient data (and / or loss function results) can be preconfigured.

[0223] It can be understood that in Figure 4, in addition to exchanging the second data and gradient data (and / or the result of the loss function), the first communication device and the second communication device can also exchange other data, such as other communication signals shown in Figure 4, such as system information, reference signals, channel information obtained by measuring based on reference signals, etc.

[0224] Mode B: The index is determined based on the number of times the data in the first data set is processed.

[0225] When the index of the first data in the first data set satisfies mode B, the method further includes: the first communication device sending first information, where the first information is used to indicate the index of the first data in the first data set.

[0226] Specifically, in approach B, since the second communication device may not be able to perceive the number of times data in the first data set has been processed, the second communication device may determine the index based on the number of times data in the second data set has been processed. Accordingly, the first communication device may further transmit first information, enabling the second communication device to determine the index of the first data in the first data set based on the first information, and subsequently determine the fourth data in the second data set based on the index.

[0227] It should be understood that the first communication device can perform multiple processing operations based on the data in the first data set to obtain and send processing results (for example, one of the processing results is the second data obtained based on the first data). Accordingly, among the multiple processing results, the first communication device can send corresponding indexes for some or all of the processing results (for example, sending first information for the second data processing result). Thereafter, sending corresponding indexes based on some or all of the processing results can achieve alignment of the understanding of the indexes by the data sender and receiver, thereby avoiding data processing errors caused by the misalignment of the understanding of the indexes by the data sender and receiver, thereby improving the robustness of the system.

[0228] Optionally, the first communication device can send the index corresponding to the partial processing result for the partial processing result, without sending the index corresponding to the entire processing result for the entire processing result, which can save overhead. In a possible implementation of method B, the first information is one of the multiple information transmitted based on the first cycle; the method also includes: the first communication device receives or sends configuration information, and the configuration information is used to configure the first cycle. Specifically, the first information can be one of the periodic information transmitted based on the first cycle. Prior to this, the first communication device can receive or send configuration information for configuring the first cycle, so that the first communication device can serve as both the configurator of the first cycle and the configured party of the first cycle, which can align the understanding of the first cycle between the data sender and receiver, and also improve the flexibility of the solution implementation.

[0229] For example, the first communication device may set a sample index counter, which is used to accumulate the number of times the first processing is performed on the data in the first data set, and determine the index of the first data in the first data set based on the accumulated value in step S301; accordingly, the second communication device may set a sample index counter, which is used to accumulate the number of times the second processing is performed on the data in the second data set, and determine the index of the first data used to generate the second data in the first data set based on the accumulated value after receiving the second data in step S302 (or determine the index of the fourth data used after step S303 in the second data set).

[0230] In addition, the first communication device can set a timer of a first period. When the timer expires, the first communication device can also send the first information in step S302 when sending the second data, so that the first communication device and the second communication device can synchronize the indexes applicable to both through the first information to prevent the two from losing step due to the misalignment of the sample index counters.

[0231] Exemplarily, the configuration information may be carried in an RRC message. For example, the configuration information may implement the configuration of the first period through a data synchronization period (DataSyncPeriod) information element in the RRC message.

[0232] Optionally, method B can be understood as a data synchronization method in a non-real-time system. Non-real-time herein can be understood as meaning that the time interval between the first communication device performing the first process in step S301 and the second communication device performing the second process in step S303 is not relatively fixed, and / or the time interval between the first communication device transmitting the processing result (i.e., the second data) of the first process and the second communication device transmitting the gradient data (and / or the result of the loss function) corresponding to the second process is not relatively fixed.

[0233] Figure 5 is an implementation example of method B (i.e., non-real-time data alignment method). In this example, multiple AI tasks can be executed between the first communication device and the second communication device, and the execution cycles of different AI tasks or the triggering of data transmission and reception of different AI tasks may be different. For example, the scale of input data of different AI tasks may be different. For another example, the scale of gradient data (and / or loss function results) of different AI tasks may be different.

[0234] In the example shown in Figure 5, the data involved in one AI task may include the second data transmitted with frame number 1 and the gradient data (and / or the result of the loss function) transmitted with frame number 4, that is, the two are separated by 2 frames (that is, frames with frame numbers 2 and 3); the data involved in another AI task may include the second data transmitted with frame number 5 and the gradient data (and / or the result of the loss function) transmitted with frame number 10, that is, the two are separated by 4 frames (that is, frames with frame numbers 6, 7, 8 and 9); the data involved in another AI task may include the second data transmitted with frame number 17 and the gradient data (and / or the result of the loss function) transmitted with frame number 18, that is, the two are separated by 0 frames (that is, the two are two adjacent frames).

[0235] In method C, the index is determined based on the resource carrying the second data and the number of times the data in the first data set is processed.

[0236] When the index of the first data in the first data set satisfies method A and method B, the index can be determined based on the resource carrying the second data (for example, at least one of the time domain resource index, frequency domain resource index, and resource block size of the resource) and the number of times the data in the first data set is processed.

[0237] For example, the value of the index may be the result of a mathematical operation (e.g., the sum, difference, or product of the two values) between the value of the time-domain resource index of the resource and the value of the number of processing times. For another example, the value of the index may be the result of a mathematical operation (e.g., the sum, difference, or product of the two values) between the value of the resource block size of the resource and the value of the number of processing times.

[0238] In one possible implementation, the method shown in FIG3 further includes: the first communication device receiving or sending indication information indicating that the index satisfies the at least one item (i.e., the indication information is used to indicate mode A and / or mode B, or the indication information is used to indicate mode A, mode B, or mode C). Specifically, the first communication device may further receive or send indication information indicating that the index satisfies the at least one item, so that the data sender and receiver can align their understanding of the index of the data in the data set based on the indication information, thereby avoiding data processing errors caused by misaligned understanding of the index by the data sender and receiver, thereby improving the robustness of the system.

[0239] In one possible implementation, the method shown in FIG3 further includes: the first communication device receiving indication information indicating the first data set; and / or the first communication device sending indication information indicating the second data set. Specifically, the first communication device can serve as the configured party of the first data set, and / or the first communication device can serve as the configured party of the second data set, so that both the data sender and the data receiver can obtain the data sets before exchanging data.

[0240] Optionally, the first data set may be preconfigured for the first communication device, and / or the second data set may be preconfigured for the second communication device. In this way, overhead can be reduced.

[0241] Based on the technical solution shown in Figure 3, the second data sent by the first communication device in step S301 is obtained based on the first data, and the second communication device can subsequently process the second data in step S302 to obtain third data. The index of the first data in the first data set is used to determine the fourth data in the second data set, and the fourth data is the label data corresponding to the first data. In other words, after the recipient of the second data receives the second data in step S302 and determines the third data in step S303, the second communication device can determine the label data in the second data set and process the third data based on the label data. In addition, the second data and / or the third data are obtained based on a neural network, that is, the neural network used for AI processing may include a neural network deployed in the first communication device and / or a neural network deployed in the second communication device. Therefore, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to the AI ​​processing of the neural network while also improving the flexibility of the neural network deployment.

[0242] In addition, the index of the first data in the first data set is used to determine the tag data (i.e., fourth data) corresponding to the first data in the second data set, and the index satisfies at least one of the above conditions. In other words, after receiving the second data, the second communication device can determine the tag data in the second data set based on the resource carrying the data or the number of times the data in the data set is processed. In this way, air interface overhead can be reduced to improve communication efficiency.

[0243] Please refer to FIG6 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0244] S601. A first communication device performs a first process on first data to obtain second data, wherein the first data is data in the first data set.

[0245] S602. The first communication device sends the second data and the fourth data, and correspondingly, the second communication device receives the second data and the fourth data.

[0246] S603. The second communication device determines third data based on the second data.

[0247] In one possible implementation, the method shown in FIG6 further includes: the first communication device receives gradient information and / or a result of a loss function determined based on the third data and the fourth data. Specifically, the receiver of the second data (e.g., the second communication device) can process the second data to obtain the third data, and the receiver can also determine and send the corresponding gradient information and / or the result of the loss function based on the third data and the fourth data. This enables the first communication device to update or iterate the first neural network based on the gradient information and / or the result of the loss function after receiving the gradient information and / or the result of the loss function.

[0248] It should be noted that, in the technical solution shown in FIG6 , the implementation process of the first communication device and the second communication device (such as the first data to the fourth data, the first processing and the second data, etc.) can refer to FIG3 and related implementation methods above.

[0249] Based on the technical solution shown in Figure 6, the second data sent by the first communication device in step S602 is obtained based on the first data, and the second communication device can subsequently process the second data in step S603 to obtain third data. Among them, the first communication device can also send fourth data in step S602, and the fourth data is the label data corresponding to the first data. Accordingly, after the second communication device receives the fourth data in step S602, it can process the third data based on the fourth data (that is, the label data corresponding to the first data). In addition, the second data and / or the third data are obtained based on a neural network, that is, the neural network used for AI processing may include a neural network deployed in the first communication device and / or a neural network deployed in the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to the AI ​​processing of the neural network while also improving the flexibility of the neural network deployment.

[0250] In addition, the second data sent by the first communication device is the processing result of the first data, and the fourth data sent by the first communication device is the label data corresponding to the first data. Thus, by sending the processing result of the first data and the label data corresponding to the first data, the recipient can further process based on the label data, and the solution can also be applied to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0251] It should be understood that in the implementation process shown in Figure 3 above, the first communication device can obtain the first data set through configuration or pre-configuration, and the second communication device can obtain the second data set through configuration or pre-configuration, and both the first communication device and the second communication device need to align the indexes by the number of times the resources carrying the second data or the data in the data set are processed. In the technical solution shown in Figure 6, the difference is that the first and second data sets can be deployed on the first communication device, while the second communication device does not need to deploy the data set, and the index of the data set is also maintained only on the first communication device side. The advantage is that there is no need to maintain synchronized index values ​​on both sides, which can reduce the overhead and implementation complexity of the second communication device.

[0252] Optionally, in the technical solution shown in FIG. 6 , since the fourth data transmitted in step S602 is tag data, the technical solution shown in FIG. 6 may be applicable to a scenario where the data size of the tag data is relatively small.

[0253] Please refer to FIG7 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0254] S701. A first communication device performs a first process on first data to obtain second data, wherein the first data is data in the first data set.

[0255] S702. The first communication device sends second data and a first index, and correspondingly, the second communication device receives the second data and the first index.

[0256] S703. The second communication device determines third data based on the second data.

[0257] It should be understood that in the above technical solution, the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data. The first data set and the second data set can be contained in one data set. In this one data set, N input data and M label data can be included, where N and M are both positive integers; and the first data set can include the N input data, and the second data set can include the M label data. In other words, the first data set can be called an input data set, a neural network input data set, etc., and the second data set can be called a label data set, a neural network label data set, etc. In addition, the AI ​​neural network can be processed based on the same data set to achieve iteration, update, etc. of the AI ​​neural network.

[0258] In a possible implementation, the first index is determined by the second index of the first data in the first data set.

[0259] In an implementation example, when each of N pieces of input data corresponds to different label data in M ​​pieces of label data, the value of N is equal to the value of M.

[0260] For example, the first index can be the same as the second index of the first data in the first data set. In other words, the label data of the i-th piece of input data in the first data set is the j-th piece of data in the second data set, i is the first index, j is the second index, and i and j are equal. That is, the label data of the first piece of N pieces of input data is the first piece of data in the M pieces of labeled data, the label data of the second piece of N pieces of input data is the second piece of data in the M pieces of labeled data, and so on. The label data of the N-th piece of N pieces of input data is the M-th piece of data in the M pieces of labeled data (N and M are equal).

[0261] For another example, the first index may be partially or completely different from the second index of the first data in the first dataset. In other words, the label data of the i-th piece of input data in the first dataset is the j-th piece of data in the second dataset, i is the first index, j is the second index, and i and j may be partially or completely different. The mapping relationship between i and j can be preconfigured.

[0262] As an example of a preconfigured mapping relationship between i and j, i can be traversed from 1 to N, and j can be traversed from N (N equals M) to 1. For example, taking the values ​​of N and M as 3, the label data of the first copy of N input data is the third copy of the M label data, the label data of the second copy of N input data is the second copy of the M label data, and the label data of the third copy of N input data is the first copy of the M label data. In this example, the first index can be different from the second index portion of the first data in the first data set.

[0263] As another example in which the mapping relationship between i and j can be preconfigured, the mapping relationship between i and j can be configured or preconfigured to align the data sender and receiver. For example, still taking the value of N and M as 3 as an example, the label data of the first copy of N input data is the third copy of the M label data, the label data of the second copy of N input data is the first copy of the M label data, and the label data of the third copy of N input data is the second copy of the M label data. In this example, the first index can be completely different from the second index of the first data in the first data set.

[0264] In another implementation example, the mapping relationship between the first index and the second index of the first data in the first data set is preconfigured.

[0265] For example, when at least two of the N pieces of input data correspond to the same label data in the M pieces of label data, the value of N may be greater than or equal to the value of M. Accordingly, in this case, the first index may be less than or equal to the second index of the first data in the first data set.

[0266] For another example, when one of the N pieces of input data corresponds to at least two pieces of label data in the M pieces of label data, the value of N may be less than or equal to the value of M. Accordingly, in this case, the first index may be greater than or equal to the second index of the first data in the first data set.

[0267] In one possible implementation, the method shown in FIG7 further includes: the first communication device receives gradient information and / or a result of a loss function determined based on the third data and the fourth data. Specifically, the receiver of the second data (e.g., the second communication device) can process the second data to obtain the third data, and the receiver can also determine and send the corresponding gradient information and / or the result of the loss function based on the third data and the fourth data. This enables the first communication device to update or iterate the first neural network based on the gradient information and / or the result of the loss function after receiving the gradient information and / or the result of the loss function.

[0268] Based on the technical solution shown in Figure 7, the second data sent by the first communication device in step S702 is obtained based on the first data, and the second communication device can subsequently process the second data to obtain third data in step S703. The first communication device can also send a first index. Accordingly, after receiving the second data, the recipient of the second data and the fourth data can process the third data based on the label data. Moreover, the second data and / or the third data are obtained based on a neural network, that is, the neural network used for AI processing may include a neural network deployed in the first communication device and / or a neural network deployed in the second communication device. Thus, when the communication device in the communication system acts as an AI participating node, the computing power of the communication device can be applied to the AI ​​processing of the neural network while also improving the flexibility of the neural network deployment.

[0269] In addition, the second data sent by the first communication device is the processing result of the first data, and the fourth data sent by the first communication device is the label data corresponding to the first data. Thus, by sending the processing result of the first data and the label data corresponding to the first data, the recipient can further process based on the label data, and the solution can also be applied to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0270] It should be understood that in the implementation process shown in Figure 3, the first communication device can obtain the first data set through configuration or pre-configuration, and the second communication device can obtain the second data set through configuration or pre-configuration. In addition, both the first and second communication devices need to align the indexes using the resources carrying the second data or the number of times the data in the data sets are processed. The difference in the technical solution shown in Figure 7 is that the first and second communication devices do not need to set a sample index counter for synchronization, which can reduce implementation complexity.

[0271] Optionally, in the technical solution shown in FIG7 , since the data transmitted in step S702 includes the second index, the technical solution shown in FIG7 may be applicable to scenarios with a small data set size (or a small number of indexes in the data set).

[0272] Referring to Figure 8 , an embodiment of the present application provides a communication device 800. This communication device 800 can implement the functions of the second communication device or the first communication device in the above-described method embodiment, thereby also achieving the beneficial effects of the above-described method embodiment. In this embodiment of the present application, the communication device 800 can be the first communication device (or second communication device), or it can be an integrated circuit or component, such as a chip, within the first communication device (or second communication device).

[0273] It should be noted that the transceiver unit 802 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.

[0274] In one possible implementation, when the device 800 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 800 includes a processing unit 801 and a transceiver unit 802; the processing unit 801 is used to process the first data to obtain second data; wherein the first data is the data in the first data set; the transceiver unit 802 is used to send the second data, and the second data is used to determine the third data; wherein the index of the first data in the first data set is used to determine the fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data; the index satisfies at least one of the following: the index is determined based on the resource carrying the second data; the index is determined based on the number of times the data in the first data set is processed.

[0275] In one possible implementation, when the device 800 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 800 includes a processing unit 801 and a transceiver unit 802; the transceiver unit 802 is used to receive second data, which is obtained by processing the first data, and the first data is the data in the first data set; wherein the index of the first data in the first data set is used to determine fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; the processing unit 801 is used to determine third data based on the second data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network; the index satisfies at least one of the following: the index is determined based on the resources carrying the second data; the index is determined based on the number of times the data in the second data set is processed.

[0276] In one possible implementation, when the device 800 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 800 includes a processing unit 801 and a transceiver unit 802; the processing unit 801 is used to process the first data to obtain second data; the transceiver unit 802 is used to send the second data and fourth data, wherein the second data is used to determine the third data, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network.

[0277] In one possible implementation, when the device 800 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 800 includes a processing unit 801 and a transceiver unit 802; the transceiver unit 802 is used to receive second data and fourth data, wherein the second data is obtained based on the first data, and the fourth data is label data corresponding to the first data; the processing unit 801 is used to determine third data based on the second data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data.

[0278] In one possible implementation, when the device 800 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 800 includes a processing unit 801 and a transceiver unit 802; the processing unit 801 is used to process the first data to obtain second data; wherein the first data is the data in the first data set; the transceiver unit 802 is used to send the second data and a first index, and the second data is used to determine the third data; wherein the first index is used to determine the fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the fourth data is obtained by processing the second data based on the neural network.

[0279] In one possible implementation, when the device 800 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 800 includes a processing unit 801 and a transceiver unit 802; the transceiver unit 802 is used to receive second data and a first index, the second data is obtained based on the first data, and the first data is the data in the first data set; wherein the first index is used to determine fourth data in the second data set, the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; the processing unit 801 is used to determine third data based on the second data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data.

[0280] It should be noted that, for details on the information execution process of the units of the above-mentioned communication device 800, please refer to the description in the method embodiment shown above in this application, and no further details will be given here.

[0281] Please refer to Figure 9, which is another schematic structural diagram of a communication device 900 provided in this application. The communication device 900 includes a logic circuit 901 and an input / output interface 902. The communication device 900 may be a chip or an integrated circuit.

[0282] The transceiver unit 802 shown in FIG8 may be a communication interface, which may be the input / output interface 902 in FIG9 , which may include an input interface and an output interface. Alternatively, the communication interface may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0283] Optionally, the logic circuit 901 is used to process the first data to obtain second data; wherein the first data is the data in the first data set; the input and output interface 902 is used to send the second data, and the second data is used to determine the third data; wherein the index of the first data in the first data set is used to determine the fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data; the index satisfies at least one of the following: the index is determined based on the resources carrying the second data; the index is determined based on the number of times the data in the first data set is processed.

[0284] Optionally, the input-output interface 902 is used to receive second data, which is obtained by processing the first data, and the first data is the data in the first data set; wherein the index of the first data in the first data set is used to determine fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; the logic circuit 901 is used to determine third data based on the second data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network; the index satisfies at least one of the following: the index is determined based on the resources carrying the second data; the index is determined based on the number of times the data in the second data set is processed.

[0285] Optionally, the logic circuit 901 is used to process the first data to obtain second data; the input-output interface 902 is used to send the second data and fourth data, wherein the second data is used to determine the third data, and the fourth data is label data corresponding to the first data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network.

[0286] Optionally, the input-output interface 902 is used to receive second data and fourth data, wherein the second data is obtained based on the first data, and the fourth data is label data corresponding to the first data; the logic circuit 901 is used to determine third data based on the second data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data.

[0287] Optionally, the logic circuit 901 is used to process the first data to obtain second data; wherein the first data is the data in the first data set; the input and output interface 902 is used to send the second data and a first index, and the second data is used to determine the third data; wherein the first index is used to determine the fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the fourth data is obtained by processing the second data based on the neural network.

[0288] Optionally, the input-output interface 902 is used to receive second data and a first index, the second data is obtained based on the first data, and the first data is the data in the first data set; wherein the first index is used to determine fourth data in the second data set, the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; the logic circuit 901 is used to determine third data based on the second data; wherein the second data is obtained based on the first neural network processing the first data, and / or the third data is obtained based on the second neural network processing the second data.

[0289] The logic circuit 901 and the input / output interface 902 may also execute other steps executed by the first communication device or the second communication device in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

[0290] In a possible implementation, the processing unit 801 shown in FIG. 8 may be the logic circuit 901 in FIG. 9 .

[0291] Optionally, the logic circuit 901 may be a processing device, and the functions of the processing device may be partially or entirely implemented by software. The functions of the processing device may be partially or entirely implemented by software.

[0292] Optionally, the processing device may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform corresponding processing and / or steps in any one of the method embodiments.

[0293] Alternatively, the processing device may include only a processor. A memory for storing the computer program is located outside the processing device, and the processor is connected to the memory via circuits / wires to read and execute the computer program stored in the memory. The memory and processor may be integrated or physically separate.

[0294] Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processor units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic devices (PLDs), or other integrated chips, or any combination of the above chips or processors.

[0295] Please refer to Figure 10, which shows the communication device 1000 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 1000 can specifically be a communication device serving as a terminal device in the above-mentioned embodiments. The example shown in Figure 10 is that the terminal device is implemented through the terminal device (or a component in the terminal device).

[0296] Herein, a possible logical structure diagram of the communication device 1000 is shown. The communication device 1000 may include but is not limited to at least one processor 1001 and a communication port 1002 .

[0297] The transceiver unit 802 shown in FIG8 may be a communication interface, which may be the communication port 1002 in FIG10 , which may include an input interface and an output interface. Alternatively, the communication port 1002 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0298] Further optionally, the device may also include at least one of a memory 1003 and a bus 1004. In an embodiment of the present application, the at least one processor 1001 is used to control and process the actions of the communication device 1000.

[0299] In addition, the processor 1001 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0300] It should be noted that the communication device 1000 shown in Figure 10 can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment and achieve the corresponding technical effects of the terminal device. The specific implementation methods of the communication device shown in Figure 10 can refer to the description in the aforementioned method embodiment and will not be repeated here.

[0301] Please refer to Figure 11, which is a structural diagram of the communication device 1100 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 1100 can specifically be a communication device as a network device in the above-mentioned embodiments. The example shown in Figure 11 is that the network device is implemented through the network device (or a component in the network device), wherein the structure of the communication device can refer to the structure shown in Figure 11.

[0302] The communication device 1100 includes at least one processor 1111 and at least one network interface 1114. Further optionally, the communication device also includes at least one memory 1112, at least one transceiver 1113 and one or more antennas 1115. The processor 1111, the memory 1112, the transceiver 1113 and the network interface 1114 are connected, for example, via a bus. In an embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. The antenna 1115 is connected to the transceiver 1113. The network interface 1114 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1114 may include a network interface between the communication device and the core network device, such as an S1 interface, and the network interface may include a network interface between the communication device and other communication devices (such as other network devices or core network devices), such as an X2 or Xn interface.

[0303] The transceiver unit 802 shown in FIG8 may be a communication interface, which may be the network interface 1114 in FIG11 , which may include an input interface and an output interface. Alternatively, the network interface 1114 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0304] Processor 1111 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process software program data, for example, to support the communication device in performing the actions described in the embodiments. The communication device may include a baseband processor and a central processing unit. The baseband processor is primarily used to process communication protocols and communication data, while the central processing unit is primarily used to control the entire terminal device, execute software programs, and process software program data. Processor 1111 in Figure 11 may integrate the functions of both a baseband processor and a central processing unit. Those skilled in the art will appreciate that the baseband processor and the central processing unit may also be independent processors interconnected via a bus or other technology. Those skilled in the art will appreciate that a terminal device may include multiple baseband processors to accommodate different network standards, multiple central processing units to enhance its processing capabilities, and various components of the terminal device may be connected via various buses. The baseband processor may also be referred to as a baseband processing circuit or a baseband processing chip. The central processing unit may also be referred to as a central processing circuit or a central processing chip. The functionality for processing communication protocols and communication data may be built into the processor or stored in memory as a software program, which is executed by the processor to implement the baseband processing functionality.

[0305] The memory is primarily used to store software programs and data. Memory 1112 can exist independently and be connected to processor 1111. Alternatively, memory 1112 can be integrated with processor 1111, for example, within a single chip. Memory 1112 can store program code for executing the technical solutions of the embodiments of the present application, and execution is controlled by processor 1111. The various computer program codes executed can also be considered drivers for processor 1111.

[0306] Figure 11 shows only one memory and one processor. In an actual terminal device, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device. The memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the present embodiment.

[0307] The transceiver 1113 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal. The transceiver 1113 can be connected to the antenna 1115. The transceiver 1113 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1115 can receive radio frequency signals. The receiver Rx of the transceiver 1113 is used to receive the radio frequency signal from the antenna, convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or digital intermediate frequency signal to the processor 1111 so that the processor 1111 can further process the digital baseband signal or digital intermediate frequency signal, such as demodulation and decoding. In addition, the transmitter Tx in the transceiver 1113 is also used to receive a modulated digital baseband signal or digital intermediate frequency signal from the processor 1111, convert the modulated digital baseband signal or digital intermediate frequency signal into a radio frequency signal, and transmit the radio frequency signal through one or more antennas 1115. Specifically, the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal. The order of the down-mixing and analog-to-digital conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal. The order of the up-mixing and digital-to-analog conversion processes is adjustable. The digital baseband signal and the digital intermediate frequency signal may be collectively referred to as digital signals.

[0308] The transceiver 1113 may also be referred to as a transceiver unit, a transceiver, a transceiver device, etc. Optionally, a device in the transceiver unit that implements a receiving function may be referred to as a receiving unit, and a device in the transceiver unit that implements a transmitting function may be referred to as a transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the transmitting unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0309] It should be noted that the communication device 1100 shown in Figure 11 can be specifically used to implement the steps implemented by the network device in the aforementioned method embodiment, and to achieve the corresponding technical effects of the network device. The specific implementation methods of the communication device 1100 shown in Figure 11 can refer to the description in the aforementioned method embodiment, and will not be repeated here one by one.

[0310] Please refer to FIG12 , which is a schematic structural diagram of the communication device involved in the above-mentioned embodiment provided in an embodiment of the present application.

[0311] It can be understood that the communication device 120 includes, for example, modules, units, elements, circuits, or interfaces, which are appropriately configured together to implement the technical solutions provided in this application. The communication device 120 can be the terminal device or network device described above, or a component (such as a chip) in these devices, used to implement the method described in the following method embodiment. The communication device 120 includes one or more processors 121. The processor 121 can be a general-purpose processor or a dedicated processor. For example, it can be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a RAN node, terminal, or chip, etc.), execute software programs, and process data of software programs.

[0312] Optionally, in one design, the processor 121 may include a program 123 (sometimes also referred to as code or instructions), which may be executed on the processor 121 to cause the communication device 120 to perform the methods described in the following embodiments. In yet another possible design, the communication device 120 includes circuitry (not shown in FIG12 ).

[0313] Optionally, the communication device 120 may include one or more memories 122 on which a program 124 (sometimes also referred to as code or instructions) is stored. The program 124 can be run on the processor 121, so that the communication device 120 executes the method described in the above method embodiment.

[0314] Optionally, the processor 121 and / or the memory 122 may include AI modules 127 and 128, which are used to implement AI-related functions. The AI ​​module can be implemented through software, hardware, or a combination of software and hardware. For example, the AI ​​module may include a wireless intelligent control (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0315] Optionally, data may be stored in the processor 121 and / or the memory 122. The processor and the memory may be provided separately or integrated together.

[0316] Optionally, the communication device 120 may further include a transceiver 125 and / or an antenna 126. The processor 121 may also be sometimes referred to as a processing unit, and controls the communication device (e.g., a RAN node or terminal). The transceiver 125 may also be sometimes referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, and is configured to implement the transceiver functions of the communication device through the antenna 126.

[0317] The transceiver unit 802 shown in FIG8 may be a communication interface, which may be the transceiver 125 in FIG12 . The transceiver 125 may include an input interface and an output interface. Alternatively, the transceiver 125 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0318] An embodiment of the present application further provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in the possible implementation methods of the first communication device or the second communication device in the aforementioned embodiment.

[0319] An embodiment of the present application also provides a computer program product (or computer program). When the computer program product is executed by the processor, the processor executes the method that may be implemented by the above-mentioned first communication device or second communication device.

[0320] An embodiment of the present application also provides a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation methods of the above-mentioned communication device. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data to the at least one processor. In one possible design, the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device. The chip system can be composed of chips, or it can include chips and other discrete devices, wherein the communication device can specifically be the first communication device or the second communication device in the aforementioned method embodiment.

[0321] An embodiment of the present application further provides a communication system, wherein the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

[0322] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0323] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0324] In addition, the functional units in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the contributing part or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Claims

1. A communication method, characterized in that: include: Processing the first data to obtain second data; wherein the first data is the data in the first data set; Sending the second data, where the second data is used to determine the third data; wherein the index of the first data in the first data set is used to determine the fourth data in the second data set, the second data set includes label data corresponding to the data in the first data set, and the fourth data is the label data corresponding to the first data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network; The index satisfies at least one of the following: The index is determined based on a resource carrying the second data; The index is determined based on the number of times data in the first data set is processed.

2. The method according to claim 1, characterized in that The index is determined based on a resource carrying the second data, including: The value of the index is determined by at least one of a time domain resource index of the resource, a frequency domain resource index of the resource, and a resource block size of the resource.

3. The method according to claim 1 or 2, characterized in that: The index is determined based on the number of times data in the first data set is processed, and the method further includes: Sending first information, where the first information is used to indicate an index of the first data in the first data set.

4. The method according to claim 3, characterized in that The first information is one of a plurality of information transmitted based on a first period; the method further includes: Receive or send configuration information, where the configuration information is used to configure the first period.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: receiving indication information indicating the first data set; and / or, Send indication information indicating the second data set.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Receive gradient information and / or a result of a loss function determined based on the third data and the fourth data.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Receive or send indication information indicating that the index satisfies the at least one item.

8. A communication method, characterized in that: include: receiving second data, where the second data is obtained by processing the first data, and the first data is data in the first data set; wherein an index of the first data in the first data set is used to determine fourth data in the second data set, and the second data set includes label data corresponding to the data in the first data set, and the fourth data is label data corresponding to the first data; Determining third data based on the second data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network; The index satisfies at least one of the following: The index is determined based on a resource carrying the second data; The index is determined based on a number of times data in the second data set is processed.

9. The method according to claim 8, characterized in that The index is determined based on a resource carrying the second data, including: The value of the index is determined by at least one of a time domain resource index of the resource, a frequency domain resource index of the resource, and a resource block size of the resource.

10. The method according to claim 8 or 9, characterized in that: The index is determined based on the number of times data in the second data set is processed, and the method further includes: Sending first information, where the first information is used to indicate an index of the first data in the first data set.

11. The method according to claim 10, characterized in that The first information is one of a plurality of information transmitted based on a first period; the method further includes: Receive or send configuration information, where the configuration information is used to configure the first period.

12. The method according to any one of claims 8 to 11, characterized in that The method further comprises: sending indication information indicating the first data set; and / or, Indication information indicating the second data set is received.

13. The method according to any one of claims 8 to 12, characterized in that: The method further comprises: Sending gradient information and / or a result of a loss function determined based on the third data and the fourth data.

14. The method according to any one of claims 8 to 13, characterized in that The method further comprises: Receive or send indication information indicating that the index satisfies the at least one item.

15. A communication method, characterized in that: include: Processing the first data to obtain second data; The second data and fourth data are sent, wherein the second data is used to determine the third data, and the fourth data is label data corresponding to the first data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network.

16. The method according to claim 15, characterized in that The method further comprises: Receive gradient information and / or a result of a loss function determined based on the third data and the fourth data.

17. A communication method, characterized in that: include: receiving second data and fourth data, wherein the second data is obtained based on the first data, and the fourth data is label data corresponding to the first data; Determine third data based on the second data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network.

18. The method according to claim 17, characterized in that The method further comprises: Sending gradient information and / or a result of a loss function determined based on the third data and the fourth data.

19. A communication method, characterized in that: include: Processing the first data to obtain second data; wherein the first data is the data in the first data set; Send the second data and the first index, the second data is used to determine the third data; wherein the first index is used to determine the fourth data in the second data set, the second data set includes the label data corresponding to the data in the first data set, and the fourth data is label data corresponding to the first data; The second data is obtained by processing the first data based on the first neural network, and / or the fourth data is obtained by processing the second data based on the neural network.

20. The method according to claim 19, characterized in that The method further comprises: Receive gradient information and / or a result of a loss function determined based on the third data and the fourth data.

21. A communication method, characterized in that: include: Receive second data and a first index, wherein the second data is obtained based on the first data, and the first data is data in the first data set; wherein the first index is used to determine fourth data in the second data set, the second data set includes label data corresponding to the data in the first data set, and the fourth data is label data corresponding to the first data; Determine third data based on the second data; wherein the second data is obtained by processing the first data based on the first neural network, and / or the third data is obtained by processing the second data based on the second neural network.

22. The method according to claim 21, characterized in that The method further comprises: Sending gradient information and / or a result of a loss function determined based on the third data and the fourth data.

23. A communication device, characterized in that: Comprising means for performing the method as claimed in any one of claims 1 to 22.

24. A communication device, characterized in that: The method comprises at least one processor coupled to a memory; the at least one processor is configured to execute the method according to any one of claims 1 to 22.

25. The communication device according to claim 24, characterized in that The communication device is a chip or a chip system.

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

27. A computer program product, characterized in that The method comprises instructions which, when executed on a computer, cause the computer to perform the method according to any one of claims 1 to 22.

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