Communication method and related equipment

Abstract

The invention discloses a communication method and related equipment, which are used for enabling the computing power of a communication node to be applied to artificial intelligence (AI) processing of a neural network, and also can improve the deployment flexibility of the neural network. In the method, the second data sent by the first communication device is obtained based on the first data, and a subsequent receiver (such as the second communication device) of the second data can process the second data to obtain the third data. Wherein the index of the first data in the first data set is used for determining fourth data in the second data set, and the fourth data is label data corresponding to the first data. Moreover, the second data and / or the third data are / is obtained based on a neural network, i.e., the neural network used for AI processing can comprise a neural network deployed in the first communication device and / or a neural network deployed in the second communication device.

Description

Technical Field

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

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

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

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

[0005] The present application provides a communication method and related equipment, which are used to enable the computing power of communication nodes to be applied to artificial intelligence (AI) processing of neural networks while also improving the flexibility of neural network deployment.

[0006] In a first aspect, the present application provides a communication method, which is performed by a first communication device, which may be a communication device (e.g., a terminal device or a network device), or the first communication device may be a partial component in a communication device (e.g., 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 the 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 resources carrying the second data; the index is determined based on the number of times the data in the first data set is processed.

[0007] 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 receiver of the second data (for example, the second communication device) can process the second data to obtain the third data. Among them, 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 receiver of the second data 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 can include a neural network deployed in the first communication device and / or a neural network deployed in the second communication device. Thereby, 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.

[0008] In addition, the index of the first data in the first data set is used to determine the label data (i.e., the fourth data) corresponding to the first data in the second data set, and the index satisfies at least one of the above items. In other words, after receiving the second data, the second communication device can determine the label 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, the air interface overhead can be reduced to improve communication efficiency.

[0009] 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. Among them, the first data set and the second data set can be included 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.

[0010] Optionally, the first data set may include N pieces of input data in one data set, and for this purpose, the first data set may also be replaced by other descriptions, such as N pieces of input data, N pieces of input data in a data set, etc. Correspondingly, the second data set may include M pieces of label data in the one data set, and for this purpose, the second data set may also be replaced by other descriptions, such as M pieces of label data, M pieces of label data in a data set, etc.

[0011] 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 one of 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. When one of the N pieces of input data corresponds to at least two of the M pieces of label data, the value of N may be less than or equal to the value of M.

[0012] In this application, the terms AI, neural network, AI neural network, machine learning, AI processing, and AI neural network processing can be used interchangeably.

[0013] In the present application, the data involved (such as first data, second data, third data, and fourth data, etc.) can be replaced by information, signals, etc.

[0014] Optionally, in the case where 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 transmission data obtained by the first communication device processing the first data, for this reason, the first neural network can be called a neural network deployed at the transmitting end, an encoding neural network, an AI encoding neural network, etc. Similarly, in the case where the third data is obtained by processing the second data based on the second neural network, since the third data is the data obtained by the second communication device processing the received second data based on the second neural network, for this reason, the second neural network can be called a neural network deployed at the receiving end, a decoding neural network, an AI decoding neural network, etc.

[0015] 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.

[0016] Based on the above technical solution, when the index of the first data in the first data set is determined based on the resources 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.

[0017] In a possible implementation manner 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, and the first information is used to indicate the index of the first data in the first data set.

[0018] 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 may determine the index based on the number of times the data in the second data set is processed. Accordingly, the first communication device may also send the first information so that the second communication device can 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.

[0019] It should be understood that the first communication device can perform multiple processing based on the data in the first data set to obtain and send the processing results (for example, one of the processing results is the second data obtained based on the first data). Correspondingly, among the multiple processing results, the first communication device can send corresponding indexes for some or all of the processing results (for example, send first information for the second data processing result). Thereafter, sending the corresponding index based on the part or all of the processing results can achieve alignment of the understanding of the index by the data sender and receiver, so as to avoid data processing errors caused by the misalignment of the understanding of the index by the data sender and receiver, thereby improving the robustness of the system.

[0020] In the present application, "alignment" may mean that when there are interactive messages / data / information between different communication devices, the two have a consistent understanding of the meaning, configuration method, index in the data set, etc. of the interactive messages / data / information.

[0021] 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.

[0022] 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.

[0023] 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 improve the flexibility of the solution implementation.

[0024] 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.

[0025] Based on the above technical solution, the first communication device can be used as the configured party of the first data set, and / or the first communication device can be used as the configured party of the second data set, so that the data sender and receiver can obtain the data set before exchanging data.

[0026] 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.

[0027] 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.

[0028] Based on the above technical solution, 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, so that the first communication device can 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.

[0029] 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.

[0030] 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 misaligned understanding of the index by the data sender and receiver, thereby improving the robustness of the system.

[0031] The second aspect of the present application provides a communication method, which is performed by a second communication device, which may be a communication device (such as a terminal device or a network device), or the second communication device may be a partial component in a 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 second aspect and its possible implementation, the communication method is described as being performed by the second communication device, wherein the second communication device may be a terminal device or a network device. In the 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 a first neural network, and / or the third data is obtained by processing the second data based on a 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.

[0032] 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. Among them, 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. 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.

[0033] In addition, the index of the first data in the first data set is used to determine the label data (i.e., the fourth data) corresponding to the first data in the second data set, and the index satisfies at least one of the above items. In other words, after receiving the second data, the second communication device can determine the label 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, the air interface overhead can be reduced to improve communication efficiency.

[0034] 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.

[0035] Based on the above technical solution, when the index of the first data in the first data set is determined based on the resources 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.

[0036] In a possible implementation manner 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.

[0037] 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 may determine the index based on the number of times the data in the second data set is processed. Accordingly, the second communication device may also receive the first information, so that the second communication device can 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.

[0038] It should be understood that the first communication device can perform multiple processing based on the data in the first data set to obtain and send the processing results (for example, one of the processing results is the second data obtained based on the first data). Correspondingly, among the multiple processing results, the first communication device can send corresponding indexes for some or all of the processing results (for example, send first information for the second data processing result). Thereafter, sending the corresponding index based on the part or all of the processing results can achieve alignment of the understanding of the index by the data sender and receiver, so as to avoid data processing errors caused by the misalignment of the understanding of the index by the data sender and receiver, thereby improving the robustness of the system.

[0039] 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.

[0040] In a possible implementation manner 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.

[0041] 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 improve the flexibility of the solution implementation.

[0042] 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.

[0043] Based on the above technical solution, the second communication device can serve as the configurator of the first data set, and / or the second communication device can serve as the configured party of the second data set, so that the data sender and receiver can acquire the data set before exchanging data.

[0044] 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.

[0045] In a possible implementation manner of the second aspect, the method further includes: the second communication device sends gradient information and / or a result of a loss function determined based on the third data and the fourth data.

[0046] Based on the above technical solution, the second communication device can process the second data to obtain the third data, and the second communication device 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, so that the first communication device can 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.

[0047] 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.

[0048] 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 misaligned understanding of the index by the data sender and receiver, thereby improving the robustness of the system.

[0049] 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 realize 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 executed by a 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.

[0050] 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 receiver of the second data (for example, 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. Correspondingly, after receiving the second data, the receiver of the second data and the fourth data can 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. 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.

[0051] 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 receiving party can further process based on the label data, and the scheme can also be applied to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0052] 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.

[0053] Based on the above technical solution, 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, so that the first communication device can 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.

[0054] The fourth aspect of the present application provides a communication method, which is performed by a second communication device, and the second communication device 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 the 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.

[0055] 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. Among them, the second communication device can also receive fourth data, and the fourth data is the label data corresponding to the first data. Correspondingly, after receiving the second data, the second communication device can 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. 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.

[0056] 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 while also enabling the solution to be applicable to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0057] 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.

[0058] Based on the above technical solution, the second communication device can process the second data to obtain the third data, and the second communication device 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, so that the first communication device can 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.

[0059] 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 a communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In 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 the 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.

[0060] 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 receiver of the second data (for example, 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 receiver of the second data and the fourth data can 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. 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.

[0061] 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 receiving party can further process based on the label data, and the scheme can also be applied to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0062] 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. Among them, the first data set and the second data set can be included 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.

[0063] In a possible implementation manner, the first index is determined by a second index of the first data in the first data set.

[0064] 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.

[0065] For example, the first index may 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 input data in the first data set is the j-th data in the second data set, i is the first index, j is the second index, and i is equal to j. That is, the label data of the first data of N input data is the first data in M ​​label data, the label data of the second data of N input data is the second data in M ​​label data, and so on, the label data of the N-th data of N input data is the M-th data in M ​​label data (N is equal to M).

[0066] For another example, the first index may be partially or completely different from the second index of the first data in the first data set. In other words, the label data of the i-th input data in the first data set is the j-th data in the second data set, i is the first index, j is the second index, and i and j may be partially or completely unequal. The mapping relationship between i and j may be preconfigured.

[0067] As an example that the mapping relationship between i and j can be preconfigured, 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 M label data, the label data of the second copy of N input data is the second copy of M label data, and the label data of the third copy of N input data is the first copy of M label data. In this example, the first index can be different from the second index part of the first data in the first data set.

[0068] As another example that 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, 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.

[0069] 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.

[0070] For example, when at least two of the N pieces of input data correspond to one of 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.

[0071] 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.

[0072] In a possible implementation manner of the fifth aspect, the method 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.

[0073] Based on the above technical solution, 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, so that the first communication device can 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.

[0074] In the 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 realize 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 the 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.

[0075] 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. Among them, 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. 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.

[0076] 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 at the same time, the scheme can be applicable to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0077] Optionally, the first index is determined by a second index of the first data in the first data set.

[0078] In a possible implementation manner of the sixth aspect, the method further includes: the second communication device sends gradient information and / or a result of a loss function determined based on the third data and the fourth data.

[0079] Based on the above technical solution, the second communication device can process the second data to obtain the third data, and the second communication device 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, so that the first communication device can 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.

[0080] 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 first data to obtain second data; wherein the first data is data in a first data set; the transceiver unit is used to send 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 by processing the first data based on a first neural network, and / or the third data is obtained by processing the second data based on a 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 first data set is processed.

[0081] 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.

[0082] 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, wherein 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 a first neural network, and / or the third data is obtained by processing the second data based on a 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.

[0083] 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.

[0084] A ninth aspect of 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 first data to obtain second data; the transceiver unit is used to send the second data and fourth data, wherein the second data is used to determine 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 a first neural network, and / or the third data is obtained by processing the second data based on a second neural network.

[0085] 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.

[0086] The tenth 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 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.

[0087] 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.

[0088] In an 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, wherein the processing unit is used to process first data to obtain second data; wherein the first data is data in a 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 third data; 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 label data corresponding to the first data; wherein the second data is obtained by processing the first data based on a first neural network, and / or the fourth data is obtained by processing the second data based on a neural network.

[0089] 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.

[0090] 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 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] A fifteenth aspect of the present application provides a communication system, which includes the above-mentioned first communication device and second communication device.

[0095] 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.

[0096] The seventeenth aspect of the present application provides a computer program product (or computer program). When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of any one of the first to sixth aspects above.

[0097] In an eighteenth aspect, the present application provides a chip system, which includes at least one processor for supporting a communication device to implement the method described in any possible implementation method of any one of the first to sixth aspects.

[0098] In a possible design, the chip system may also 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 devices. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data for the at least one processor.

[0099] 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 different design methods in the above-mentioned first to sixth aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1a to Figure 1c A schematic diagram of a communication system provided for this application;

[0101] Figure 1d , Figure 1e as well as Figure 2a to Figure 2f A schematic diagram of the AI ​​processing process involved in this application;

[0102] Figure 3 An interactive schematic diagram of the communication method provided by this application;

[0103] Figures 4 to 5 A schematic diagram of the AI ​​processing process provided for this application;

[0104] Figure 6 to Figure 7 An interactive schematic diagram of the communication method provided by this application;

[0105] Figures 8 to 12 A schematic diagram of a communication device provided in this application. DETAILED DESCRIPTION

[0106] First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0107] (1) Terminal device: It can be a wireless terminal device that can receive network device scheduling and instruction information. The wireless terminal device can be a device that provides voice and / or data connectivity to users, or a handheld device with wireless connection function, or other processing devices connected to a wireless modem.

[0108] The terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device can be a mobile terminal device, such as a mobile phone (or "cellular" phone, mobile phone), a computer and a data card, for example, a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device, which exchanges voice and / or data with the radio access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablet computers (Pads), computers with wireless transceiver functions, and other devices. The wireless terminal device can also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile station (MS), a remote station, an access point (AP), a remote terminal device (remote terminal), an access terminal device (access terminal), a user terminal device (user terminal), a user agent (user agent), a subscriber station (SS), a customer premises equipment (CPE), a terminal (terminal), a user equipment (UE), a mobile terminal (MT), etc.

[0109] As an example but not limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices may also be referred to as wearable smart devices or smart wearable devices, etc., which are a general term for the application of wearable technology to intelligently design and develop wearable devices for daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also powerful functions achieved through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include full-featured, large-size, and independent of smartphones to achieve complete or partial functions, such as smart watches or smart glasses, etc., as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various types of smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0110] The terminal may also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle to everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc.

[0111] In addition, the terminal device may also be a terminal device in a communication system that evolves after the fifth generation (5th generation, 5G) communication system (e.g., a sixth generation (6th generation, 6G) communication system, etc.) or a terminal device in a public land mobile network (PLMN) that evolves in the future, etc. Exemplarily, the 6G network can further expand the form and function of the 5G communication terminal, and the 6G terminal includes but is not limited to a car, a cellular network terminal (with integrated satellite terminal function), a drone, and an Internet of Things (IoT) device.

[0112] 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.

[0113] (2) Network equipment: It can be equipment in a wireless network, for example, the network equipment can be a RAN node (or device) that connects a terminal device to a wireless network, which can also be called a base station. At present, some examples of RAN equipment are: base station, evolved NodeB (eNodeB), gNB (gNodeB) in a 5G communication system, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point AP, etc. In addition, in a network structure, the network equipment may include a centralized unit (CU) node, a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0114] Optionally, the RAN node may also be a macro base station, a micro base station or an indoor station, a relay node or a donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. The RAN node may also be a server, a wearable device, a vehicle or an onboard device, etc. For example, the access network device in the vehicle to everything (V2X) technology may be a road side unit (RSU).

[0115] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, and different RAN nodes respectively implement part of the functions of the base station. For example, the RAN node can be a centralized unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU). The CU and DU can be set separately, or can also be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU) or a remote radio head (RRH).

[0116] In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in an open access network (open RAN, O-RAN or ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. For the convenience of description, CU, CU-CP, CU-UP, DU and RU are described as examples in this application. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0117] 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.

[0118] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, refer to Table 1 below.

[0119] Table 1

[0120] ORAN network elements 3GPP protocol layer functions O-CU-CP RRC+PCDP-Control Plane (PDCP-C) O-CU-UP SDAP+PCDP-User Plane (PDCP-U) O-DU RLC+MAC+PHY-high O-RU PHY-low

[0121] The network device may be any other device that provides wireless communication functions for the terminal device. The embodiments of the present application do not limit the specific technology and specific device form used by the network device. For the convenience of description, the embodiments of the present application do not limit.

[0122] The network equipment may also include core network equipment, such as mobility management entity (MME), home subscriber server (HSS), serving gateway (S-GW), policy and charging rules function (PCRF), public data network gateway (PDN gateway, P-GW) in the fourth generation (4G) network; access and mobility management function (AMF), user plane function (UPF) or session management function (SMF) and other network elements in the 5G network. In addition, the core network equipment may also include other core network equipment in the 5G network and the next generation network of the 5G network.

[0123] In an embodiment of the present application, the above-mentioned network device may also have a network node with AI capabilities, which can provide AI services for terminals or other network devices. For example, it may be an AI node on the network side (access network or core network), a computing node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.

[0124] In the embodiment of the present application, the device for realizing the function of the network device may be a network device, or may be a device capable of supporting the network device to realize the function, such as a chip system, which may be installed in the network device. In the technical solution provided in the embodiment of the present application, the technical solution provided in the embodiment of the present application is described by taking the device for realizing the function of the network device as an example that the network device is used as the device.

[0125] (3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used at the same time. Configuration refers to the network device / server sending some parameter configuration information or parameter values ​​to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission based on these values ​​or information. Pre-configuration is similar to configuration, and can be parameter information or parameter values ​​pre-negotiated between the network device / server and the terminal device, or parameter information or parameter values ​​used by the base station / network device or terminal device specified by the standard protocol, or parameter information or parameter values ​​pre-stored in the base station / server or terminal device. This application does not limit this.

[0126] Furthermore, these values ​​and parameters can be changed or updated.

[0127] (4) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the objects associated with each other are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. And, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects.

[0128] (5) "Send" and "receive" in the embodiments of the present application indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information is XX, which can include direct sending through the air interface, and also include indirect sending through the air interface by other units or modules. "Receive information from YY" can be understood as the source of the information is YY, which can include direct receiving from YY through the air interface, and also include indirect receiving from YY through the air interface from other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

[0129] 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.

[0130] 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.

[0131] (6) In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated may also be indirectly indicated by indicating other information, wherein the other information is associated with the information to be indicated; or only a part of the information to be indicated may be indicated, while the other part of the information to be indicated is known or agreed in advance. For example, the indication of specific information may be realized by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication. It is understandable that, for the sender of the indication information, the indication information may be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information may be used to determine the information to be indicated.

[0132] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments in this application, and the various methods / designs / implementations in each embodiment, if there is no special description and logical conflict, the terms and / or descriptions between different embodiments and the various methods / designs / implementations in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various methods / designs / implementations in each embodiment can be combined to form new embodiments, methods, or implementations according to their inherent logical relationships. The implementation methods of this application described below do not constitute a limitation on the scope of protection of this application.

[0133] 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.

[0134] See also Figure 1a , which is a schematic diagram of a communication system in this application. Figure 1a In the example, a network device and six terminal devices are shown, and the six terminal devices are terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. Figure 1a In the example shown, terminal device 1 is a smart tea cup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas station, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

[0135] like Figure 1a As shown, the AI ​​configuration information sending entity may be a network device. The AI ​​configuration information receiving entity may be terminal devices 1-6. At this time, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 may send data to the network device, and the network device needs to receive data sent by terminal devices 1-6. At the same time, the network device may send configuration information to terminal devices 1-6.

[0136] For example, in Figure 1a In the communication system, terminal device 4-terminal device 6 can also form a communication system. Among them, terminal device 5 acts as a network device, that is, an AI configuration information sending entity; terminal device 4 and terminal device 6 act as terminal devices, that is, AI configuration information receiving entities. For example, in the Internet of Vehicles system, terminal device 5 sends AI configuration information to terminal device 4 and terminal device 6 respectively, and receives data sent by terminal device 4 and terminal device 6; correspondingly, terminal device 4 and terminal device 6 receive AI configuration information sent by terminal device 5, and send data to terminal device 5.

[0137] by Figure 1a Taking the communication system shown as an example, in addition to executing communication-related services, different devices (including between network devices and network devices, between network devices and terminal devices, and / or between terminal devices and terminal devices) may also execute AI-related services.

[0138] like Figure 1b As shown, taking the network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and communication-related services and AI-related services can also be performed between different terminal devices.

[0139] like Figure 1c As shown, taking the terminal devices including a TV and a mobile phone as an example, communication-related services and AI-related services can also be performed between the TV and the mobile phone.

[0140] The technical solution provided by this application can be applied to wireless communication systems (such as Figure 1a , Figure 1b or Figure 1cThe system shown in the figure), for example, the communication system provided in the present application can introduce an AI network element to implement some or all AI-related operations. The AI ​​network element may also be referred to as an AI node, an AI device, an AI entity, an AI module, an AI model, or an AI unit, etc. The AI ​​network element may be a network element built into a communication system. For example, the AI ​​network element may be an AI module built into: an access network device, a core network device, a cloud server, or a network management (operation, administration and maintenance, OAM) to implement AI-related functions. The OAM may be a network management device for a core network device and / or a network management device for an access network device. Alternatively, the AI ​​network element may also be an independently set network element in the communication system. Optionally, the terminal or the chip built into the terminal may also include an AI entity to implement AI-related functions.

[0141] The following is a brief introduction to artificial intelligence (AI) that may be involved in this application.

[0142] Artificial intelligence (AI) can give machines human intelligence, for example, it can allow machines to use computer hardware and software to simulate certain intelligent behaviors of humans. In order to realize artificial intelligence, machine learning methods can be used. In machine learning methods, the machine uses training data to learn (or train) to obtain a model. The model represents the mapping from input to output. The learned model can be used for reasoning (or prediction), that is, the model can be used to predict the output corresponding to a given input. Among them, the output can also be called an inference result (or prediction result).

[0143] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0144] 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 the collected sample values ​​and sample labels, and use AI models to express the learned mapping relationship. The process of training a machine learning model is the process of learning this mapping relationship. During the training process, the sample value is input into the model to obtain the model's predicted value, and 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 mapping or nonlinear mapping. According to the type of label, the learning task can be divided into classification task and regression task.

[0145] In other words, the goal of supervised learning can be to learn the mapping relationship between the input data and the output data (i.e., the labeled data) in a given training set (containing multiple pairs of input data and labeled data), and at the same time, hope that the mapping relationship can also be applied to data outside the training set. The training set is a collection of correct input and output pairs.

[0146] Neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, neural network can theoretically approximate any continuous function, so that neural network has the ability to learn any mapping. Traditional communication systems require rich expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover implicit pattern structures from large data sets, establish mapping relationships between data, and obtain performance that is superior to traditional modeling methods.

[0147] The idea of ​​neural networks comes from the neuron structure of brain tissue. For example, each neuron performs a weighted sum operation on its input values ​​and outputs the operation result through an activation function.

[0148] like Figure 1d As shown in Figure 1, it is a schematic diagram of the neuron structure. Assume that the input of the neuron is x = [x0, x1, ..., x n ], and the weights corresponding to each input are w=[w,w1,…,w n ], where n is a positive integer, w i and x i It can be a decimal, an integer (such as 0, a positive integer or a negative integer, etc.), or a complex number. i As x i The weight of x i Weighted. The bias of weighted summation of input values ​​according to the weight is, for example, b. The activation function can take many forms. Assuming that the activation function of a neuron is: y = f(z) = max(0,z), the output of the neuron is: For another example, the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: b can be a decimal, an integer (eg, 0, a positive integer or a negative integer), or a complex number, etc. The activation functions of different neurons in a neural network can be the same or different.

[0149] In addition, a neural network generally includes multiple layers, each of which may include one or more neurons. By increasing the depth and / or width of a neural network, the expressive power of the neural network can be improved, providing a more powerful information extraction and abstract modeling capability for complex systems. Among them, the depth of a neural network may refer to the number of layers included in the neural network, and the number of neurons included in each layer may be referred to as the width of the layer. In one implementation, the neural network includes an input layer and an output layer. The input layer of the neural network processes the received input information through neurons, passes the processing results to the output layer, and the output layer obtains the output result of the neural network. In another implementation, the neural network includes an input layer, a hidden layer, and an output layer. The input layer of the neural network processes the received input information through neurons, passes the processing results to the middle hidden layer, the hidden layer calculates the received processing results, obtains the calculation results, and the hidden layer passes the calculation results to the output layer or the next adjacent hidden layer, and finally the output layer obtains the output result of the neural network. Among them, a neural network may include one hidden layer, or include multiple hidden layers connected in sequence, without limitation.

[0150] The neural network is, for example, a deep neural network (DNN). Depending on how the network is constructed, DNN may include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

[0151] Figure 1e This is a schematic diagram of an FNN network. The characteristic of the FNN network is that the neurons in adjacent layers are fully connected to each other. This characteristic makes FNN usually require a large amount of storage space and leads to high computational complexity.

[0152] CNN is a neural network that is specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling on the time axis) and image data (discrete sampling on two dimensions) can be considered to be data with a grid-like structure. CNN does not use all the input information for calculations at once, but uses a fixed-size window to intercept part of the information for convolution operations, which greatly reduces the amount of calculation of model parameters. In addition, depending on the type of information intercepted by the window (for example, people and objects in a picture are different types of information), each window can use different convolution kernel operations, which enables CNN to better extract the features of the input data.

[0153] RNN is a type of DNN network that uses feedback time series information. Its input includes the new input value at the current moment and its own output value at the previous moment. RNN is suitable for obtaining sequence features that are correlated in time, and is particularly suitable for applications such as speech recognition and channel coding.

[0154] In the above machine learning model training process, a loss function can be defined. The loss function describes the gap or difference between the output value of the model and the ideal target value. The loss function can be expressed in many forms, and there is no restriction on the specific form of the loss function. The model training process can be regarded as the following process: by adjusting some or all parameters of the model, the value of the loss function is less than the threshold value or meets the target requirements.

[0155] Models can also be referred to as AI models, rules or other names. AI models can be considered as specific methods for implementing AI functions. AI models characterize the mapping relationship or function between the input and output of a model. AI functions may include one or more of the following: data collection, model training (or model learning), model information publishing, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model verification, or reasoning result publishing, etc. AI functions can also be referred to as AI (related) operations, or AI-related functions.

[0156] The implementation process of the neural network will be described exemplarily below with reference to the accompanying drawings.

[0157] 1. Fully connected neural network, also called multilayer perceptron (MLP).

[0158] like Figure 2a As shown in the figure, an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (middle). Each layer of the MLP contains several nodes, called neurons. The neurons in two adjacent layers are connected to each other.

[0159] Optionally, considering the neurons of two adjacent layers, the output h of the neurons in the next layer is the weighted sum of all the neurons x in the previous layer connected to it and passes through the activation function, which can be expressed as:

[0160] 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:

[0163] y=f n (w n f n-1 (…)+bn ).

[0164] Among them, n is the index of the neural network layer, 1<=n<=N, where N is the total number of neural network layers.

[0165] In other words, a neural network can be understood as a mapping relationship from an input data set to an output data set. Usually, neural networks are randomly initialized, and the process of obtaining this mapping relationship from random w and b using existing data is called neural network training.

[0166] Optionally, a specific method of training is to use a loss function to evaluate the output results of the neural network.

[0167] like Figure 2b As shown, the error can be back-propagated, and the neural network parameters (including w and b) can be iteratively optimized by the gradient descent method until the loss function reaches the minimum value, that is, Figure 2b The term "better point (e.g., optimal point)" is used in the context of Figure 2b The neural network parameters corresponding to the “better point (e.g., optimal point)” in the training can be used as the neural network parameters in the trained AI model information.

[0168] Alternatively, the gradient descent process can be expressed as:

[0169]

[0170] Among them, θ is the parameter to be optimized (including w and b), L is the loss function, η is the learning rate, which controls the step size of gradient descent. represents the derivative operation, It means taking the derivative of θ with respect to L.

[0171] Optionally, the back-propagation process utilizes the chain rule for partial derivatives.

[0172] like Figure 2c As shown, the gradient of the previous layer parameters can be recursively calculated by the gradient of the next layer parameters, which can be expressed as:

[0173]

[0174] Among them, w ij is the weight of node j connecting node i, s i is the weighted sum of the inputs to node i.

[0175] 2. Federated Learning (FL)

[0176] The concept of federated learning effectively solves the current difficulties faced by the development of artificial intelligence. On the premise of fully protecting user data privacy and security, it efficiently completes the model learning tasks by promoting the collaboration of various edge devices and central servers.

[0177] like Figure 2d As shown in the figure, the FL architecture is the most widely used training architecture in the current FL field. The FedAvg algorithm is the basic algorithm of FL. Its algorithm flow is as follows:

[0178] (1) The center initializes the model to be trained And broadcast it to all client devices.

[0179] (2) In round t∈[1,T], client k∈[1,K] based on the local dataset For the received global model Perform E epochs of training to obtain local training results Report it to the central node.

[0180] (3) The central node aggregates and collects local training results from all (or some) clients. Assume that the client set that uploads the local model in round t is The center will use the number of samples of the corresponding client as the weight to perform weighted averaging to obtain a new global model. The specific update rule is: The center then sends the latest version of the global model Broadcast to all client devices for a new round of training.

[0181] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0182] In addition to reporting local models You can also use the local gradient of the training After reporting, the central node averages the local gradients and updates the global model according to the direction of the average gradient.

[0183] As you can see, in the FL framework, the data set exists in the distributed nodes, that is, the distributed nodes collect local data sets, perform local training, and report the local results (models or gradients) obtained from the training to the central node. The central node itself does not have a data set, and is only responsible for fusing the training results of the distributed nodes to obtain the global model and send it to the distributed nodes.

[0184] 3. Decentralized learning: Different from federated learning, there is another distributed learning architecture - decentralized learning.

[0185] like Figure 2eAs shown in Figure 2, consider a fully distributed system without a central node. The design goal f(x) of a decentralized learning system is generally the goal f of each node. i The mean of (x), that is Where n is the number of distributed nodes, x is the parameter to be optimized. In machine learning, x is the parameter of the machine learning (such as neural network) model. Each node uses local data and local target f i (x) Calculate local gradient Then it is sent to the neighboring nodes that can be communicated with. After any node receives the gradient information sent by its neighbor, it can update the parameter x of the local model according to the following formula:

[0186]

[0187] 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.

[0188] The technical solution provided by this application can be applied to wireless communication systems (such as Figure 1a or Figure 1b In the wireless communication system, communication nodes generally have signal transceiving capabilities and computing capabilities. Taking network devices with computing capabilities as an example, the computing capabilities of network devices mainly provide computing support for signal transceiving capabilities (for example, sending and receiving signals) to achieve communication tasks between network devices and other communication nodes.

[0189] In a communication network, the computing power of communication nodes may have surplus computing power in addition to providing computing power support for the above communication tasks. Therefore, how to utilize this computing power is a technical problem that needs to be solved urgently.

[0190] In one possible implementation, the communication node can be used as a participating node of the AI ​​learning system, and the computing power of the communication node is applied to a certain link of the AI ​​learning system. With the advent of the era of large models, deep learning models with massive parameters, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformer (GPT), can complete more and more complex tasks and achieve better performance. However, for large models, even the reasoning process of the model will be limited by the device capacity, so generally large models are stored on cloud central servers. At the same time, each device in the network generates a huge amount of raw data every day, which requires multiple calls to the large model for reasoning. Generally speaking, the device (such as a communication node) can send data to the central server, the central server uses the data for reasoning, and then the central server returns the reasoning result to the device. This process will consume a lot of communication resources for data transmission, and the privacy of device data will also be at risk.

[0191] In order to better save communication overhead and protect the privacy of user data, scholars have proposed distributed reasoning technology for deep neural networks. The approach is to distribute the model to devices and use the local computing power of the devices to infer the model, thereby reducing communication overhead and obtaining data privacy protection.

[0192] For example, in Figure 2f In the example shown, it is taken that two communication nodes, Node 1 and Node 2, participate in the AI ​​learning system. Wherein, both Node 1 and Node 2 can be communication nodes, such as terminal devices or network devices. Wherein, 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.

[0193] As Figure 2f An implementation example is shown in which node 1 processes the encoding result based on the sub-neural network for AI encoding, and the encoding result is quantized and processed at the physical layer to obtain a wireless signal; correspondingly, after node 2 receives the wireless signal through the transmission of the wireless channel, node 2 processes the wireless signal at the physical layer and dequantizes the signal, and obtains the decoding result after AI decoding. In addition, node 2 can also determine the gradient data based on the decoding result and the label data.

[0194] Afterwards, 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 neural network (such as training / updating / iteration, etc.) of the sub-neural network for AI encoding deployed in node 1 based on the gradient data.

[0195] Optionally, after node 2 obtains the gradient data, node 2 can also optimize the sub-neural network for AI encoding deployed in node 2 based on the gradient data (e.g., training / updating / iteration, etc.).

[0196] 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 for optimizing the neural network. The above implementation is only explained by taking the node 2 determining the gradient data as an example.

[0197] 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.

[0198] In order to solve the above problems, the present application provides a communication method and related equipment, which are used to enable the computing power of communication nodes to be applied to artificial intelligence (AI) processing of neural networks while also improving the flexibility of neural network deployment. The following will be described in detail with reference to the accompanying drawings.

[0199] See also Figure 3 , is a schematic diagram of an implementation of the communication method provided in this application, and the method includes the following steps.

[0200] It should be noted that in Figure 3 In the example, the first communication device and the second communication device are used as the execution subjects of the interaction indication to illustrate the method, but the present application does not limit the execution subjects of the interaction indication. Figure 3 and later Figure 6In the method, the execution subject can be replaced by a chip, a chip system, a processor, a logic module or software in a communication device. The first communication device can be a 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 both the first communication device and the second communication device are terminal devices (for example, the method can be applied to the communication process of different terminal devices in a sidelink communication scenario).

[0201] 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.

[0202] S302. The first communication device sends second data, and correspondingly, the second communication device receives the second data.

[0203] S303. The second communication device determines third data based on the second data.

[0204] 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 included 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.

[0205] 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 one of 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. When one of the N pieces of input data corresponds to at least two of the M pieces of label data, the value of N may be less than or equal to the value of M.

[0206] Optionally, in the case where 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 transmission data obtained by the first communication device processing the first data, for this reason, the first neural network can be called a neural network deployed at the transmitting end, an encoding neural network, an AI encoding neural network, etc. Similarly, in the case where the third data is obtained by processing the second data based on the second neural network, since the third data is the data obtained by the second communication device processing the received second data based on the second neural network, for this reason, the second neural network can be called a neural network deployed at the receiving end, a decoding neural network, an AI decoding neural network, etc.

[0207] In this application, the terms AI, neural network, AI neural network, machine learning, AI processing, and AI neural network processing can be used interchangeably.

[0208] In the present application, the data involved (such as first data, second data, third data, and fourth data, etc.) can be replaced by information, signals, etc.

[0209] In addition, in the above technical solution, 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 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. 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 the 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.

[0210] Optionally, in Figure 3 In the method shown, 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. After receiving the gradient information and / or the result of the loss function, the first communication device can update or iterate the first neural network based on the gradient information and / or the result of the loss function.

[0211] 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.

[0212] Mode A: The index is determined based on the resource carrying the second data.

[0213] Specifically, when the index of the first data in the first data set satisfies mode 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 specifically determined by at least one of the above items to improve the flexibility of the solution implementation.

[0214] Method A can be understood as using the synchronized information between 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 that the batch size of the training 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 of 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.

[0215] 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:

[0216] (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 ;

[0217] Among them, % represents the remainder operation, n f Indicates the system frame number, n o is the offset value (used to traverse the data set).

[0218] 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:

[0219] (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 ;

[0220] 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 data set).

[0221] 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:

[0222] (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 .

[0223] 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 data set).

[0224] 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 information related to the synchronized information between the sender and the receiver of the data, 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.

[0225] 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.

[0226] Figure 4 This is an implementation example of mode A (i.e., real-time data alignment mode). In this example, the first frame in every six frames (e.g., frames with frame numbers 1 / 7 / 13) is used to transmit the second data sent by the first communication device, and the fourth frame in every six frames (e.g., frames with frame numbers 4 / 10 / 16) is used to transmit the gradient data (and / or the result of the loss function) sent by the second communication device. In other words, the time interval between the time domain resource carrying the second data and the time domain resource carrying the gradient data (and / or the result of the loss function) can be preconfigured.

[0227] It is understandable that in Figure 4 In addition to exchanging the second data and the gradient data (and / or the result of the loss function), the first communication device and the second communication device may also exchange other data, such as Figure 4 Other communication signals shown include, for example, system information, reference signals, channel information measured based on reference signals, etc.

[0228] Mode B: The index is determined based on the number of times the data in the first data set is processed.

[0229] When the index of the first data in the first data set satisfies mode B, the method further includes: the first communication device sends first information, where the first information is used to indicate the index of the first data in the first data set.

[0230] Specifically, in mode B, since the second communication device may not be able to perceive the number of times data in the first data set is processed, the second communication device may determine the index based on the number of times data in the second data set is processed. Accordingly, the first communication device may also send the first information so that the second communication device can 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.

[0231] It should be understood that the first communication device can perform multiple processing based on the data in the first data set to obtain and send the processing results (for example, one of the processing results is the second data obtained based on the first data). Correspondingly, among the multiple processing results, the first communication device can send corresponding indexes for some or all of the processing results (for example, send first information for the second data processing result). Thereafter, sending the corresponding index based on the part or all of the processing results can achieve alignment of the understanding of the index by the data sender and receiver, so as to avoid data processing errors caused by the misalignment of the understanding of the index by the data sender and receiver, thereby improving the robustness of the system.

[0232] Optionally, the first communication device may send an index corresponding to a partial processing result for a partial processing result, without sending an index corresponding to the entire processing result for all processing results, which can save overhead. In a possible implementation of method B, the first information is one of a plurality of information transmitted based on a 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 may be one of the periodic information transmitted based on the first cycle. Prior to this, the first communication device may receive or send configuration information for configuring the first cycle, so that the first communication device can serve as both a configurator and a configured party of the first cycle, which can align the understanding of the first cycle between the data sender and receiver, and can also improve the flexibility of the solution implementation.

[0233] 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).

[0234] 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 sample index counters.

[0235] 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) element in the RRC message.

[0236] Optionally, mode B can be understood as a data synchronization mode in a non-real-time system. The non-real-time here can be understood as the 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 is not relatively fixed, and / or, the time interval between the process of the first communication device sending the processing result (i.e., the second data) of the first processing and the process of the second communication device sending the gradient data (and / or the result of the loss function) corresponding to the second processing is not relatively fixed.

[0237] Figure 5 This 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.

[0238] exist Figure 5 In the example shown, 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, i.e., the two are separated by 2 frames (i.e., 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, i.e., the two are separated by 4 frames (i.e., 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, i.e., the two are separated by 0 frames (i.e., the two are two adjacent frames).

[0239] In mode 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.

[0240] 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.

[0241] For example, the value of the index may be a mathematical operation result of the value of the time domain resource index of the resource and the value of the number of processing times (e.g., the sum of the two values, the difference of the two values, the product of the two values, etc.). For another example, the value of the index may be a mathematical operation result of the value of the resource block size of the resource and the value of the number of processing times (e.g., the sum of the two values, the difference of the two values, the product of the two values, etc.).

[0242] In one possible implementation, Figure 3 The method shown also includes: the first communication device receives or sends indication information indicating that the index satisfies the at least one item (that is, the indication information is used to indicate mode A and / or mode B, or the indication information is used to indicate mode A or mode B or mode C). Specifically, the first communication device may also 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, 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.

[0243] In one possible implementation, Figure 3 The method also includes: the first communication device receives indication information indicating the first data set; and / or, the first communication device sends indication information indicating the second data set. Specifically, the first communication device can be used as a configured party of the first data set, and / or, the first communication device can be used as a configured party of the second data set, so that both the data sender and the data receiver can obtain the data set before exchanging data.

[0244] 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.

[0245] based on Figure 3In the technical solution shown, 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 the third data. Among them, 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 receiver 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 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.

[0246] In addition, the index of the first data in the first data set is used to determine the label data (i.e., the fourth data) corresponding to the first data in the second data set, and the index satisfies at least one of the above items. In other words, after receiving the second data, the second communication device can determine the label 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, the air interface overhead can be reduced to improve communication efficiency.

[0247] See also Figure 6 , is a schematic diagram of an implementation of the communication method provided in this application, and the method includes the following steps.

[0248] S601. The 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.

[0249] 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.

[0250] S603. The second communication device determines third data based on the second data.

[0251] In one possible implementation, Figure 6The method shown also includes: the first communication device receives the gradient information and / or the result of the 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. After receiving the gradient information and / or the result of the loss function, the first communication device can update or iterate the first neural network based on the gradient information and / or the result of the loss function.

[0252] It should be noted that Figure 6 In the technical solution shown, 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 the above Figure 3 and related implementation methods.

[0253] based on Figure 6 In the technical solution shown, 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 the 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. Correspondingly, 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.

[0254] 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 receiving party can further process based on the label data, and the scheme can also be applied to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0255] It should be understood that in the aforementioned Figure 3In the implementation process shown, the first communication device can obtain the first data set by configuration or pre-configuration, and the second communication device can obtain the second data set by 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. Figure 6 In the technical solution shown, the difference is that the first data set and the second data set 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 only maintained 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.

[0256] Optionally, in Figure 6 In the technical solution shown, since the fourth data transmitted in step S602 is tag data, Figure 6 The technical solution shown can be applicable to scenarios where the data scale of label data is small.

[0257] See also Figure 7 , is a schematic diagram of an implementation of the communication method provided in this application, and the method includes the following steps.

[0258] S701. The 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.

[0259] S702. The first communication device sends the second data and the first index, and correspondingly, the second communication device receives the second data and the first index.

[0260] S703. The second communication device determines third data based on the second data.

[0261] 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. Among them, the first data set and the second data set can be included 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.

[0262] In a possible implementation manner, the first index is determined by a second index of the first data in the first data set.

[0263] 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.

[0264] For example, the first index may 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 input data in the first data set is the j-th data in the second data set, i is the first index, j is the second index, and i is equal to j. That is, the label data of the first data of N input data is the first data in M ​​label data, the label data of the second data of N input data is the second data in M ​​label data, and so on, the label data of the N-th data of N input data is the M-th data in M ​​label data (N is equal to M).

[0265] For another example, the first index may be partially or completely different from the second index of the first data in the first data set. In other words, the label data of the i-th input data in the first data set is the j-th data in the second data set, i is the first index, j is the second index, and i and j may be partially or completely unequal. The mapping relationship between i and j may be preconfigured.

[0266] As an example that the mapping relationship between i and j can be preconfigured, 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 M label data, the label data of the second copy of N input data is the second copy of M label data, and the label data of the third copy of N input data is the first copy of M label data. In this example, the first index can be different from the second index part of the first data in the first data set.

[0267] As another example that 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, 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.

[0268] 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.

[0269] For example, when at least two of the N pieces of input data correspond to one of 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.

[0270] 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.

[0271] In one possible implementation, Figure 7 The method shown also includes: the first communication device receives the gradient information and / or the result of the 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. After receiving the gradient information and / or the result of the loss function, the first communication device can update or iterate the first neural network based on the gradient information and / or the result of the loss function.

[0272] based on Figure 7 In the technical solution shown, the second data sent by the first communication device in step S702 is obtained based on the first data, and subsequently the second communication device can process the second data to obtain the third data in step S703. 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 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.

[0273] 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 receiving party can further process based on the label data, and the scheme can also be applied to scenarios with a small amount of label data, so as to minimize the transmission overhead of the label data.

[0274] It should be understood that in the aforementioned Figure 3 In the implementation process shown, the first communication device can obtain the first data set by configuration or pre-configuration, and the second communication device can obtain the second data set by 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. Figure 7 In the technical solution shown, the difference is that the first communication device and the second communication device may not need to set a sample index counter for synchronization, which can reduce the implementation complexity.

[0275] Optionally, in Figure 7 In the technical solution shown, since the data transmitted in step S702 includes the second index, Figure 7 The technical solution shown can be applicable to scenarios where the data set size is small (or the number of indexes in the data set is small).

[0276] See also Figure 8 , the embodiment of the present application provides a communication device 800, which can implement the functions of the second communication device or the first communication device in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment. In the embodiment of the present application, the communication device 800 can be the first communication device (or the second communication device), or it can be an integrated circuit or component inside the first communication device (or the second communication device), such as a chip.

[0277] 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.

[0278] In a 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 the 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 resources carrying the second data; the index is determined based on the number of times the data in the first data set is processed.

[0279] In a 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.

[0280] 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 the 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.

[0281] 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.

[0282] In a 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 the 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, 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 fourth data is obtained based on the neural network processing the second data.

[0283] 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.

[0284] It should be noted that the information execution process and other contents of the units of the above-mentioned communication device 800 can be specifically referred to the description in the method embodiment shown in the above-mentioned application, and will not be repeated here.

[0285] See also Fig. 9, is another schematic structural diagram of a communication device 900 provided in the present application, wherein 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.

[0286] in, Figure 8 The transceiver unit 802 shown may be a communication interface, which may be Fig. 9 The input / output interface 902 in the communication interface may include an input interface and an output interface. Alternatively, the communication interface may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0287] Optionally, the logic circuit 901 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 input-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.

[0288] Optionally, the input-output interface 902 is used to receive 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 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.

[0289] Optionally, the logic circuit 901 is used to process the first data to obtain the 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.

[0290] 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.

[0291] Optionally, the logic circuit 901 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 input-output interface 902 is used to send the second data and the 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.

[0292] 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.

[0293] 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.

[0294] In one possible implementation, Figure 8 The processing unit 801 shown can be Fig. 9The logic circuit 901 in.

[0295] Optionally, the logic circuit 901 may be a processing device, and the functions of the processing device may be partially or completely implemented by software. The functions of the processing device may be partially or completely implemented by software.

[0296] 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.

[0297] Alternatively, the processing device may include only a processor. A memory for storing a computer program is located outside the processing device, and the processor is connected to the memory via a circuit / wire to read and execute the computer program stored in the memory. The memory and the processor may be integrated together, or may be physically independent of each other.

[0298] Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGA), application specific integrated circuits (ASIC), system on chip (SoC), central processor unit (CPU), network processor (NP), digital signal processor (DSP), microcontroller unit (MCU), programmable logic device (PLD) or other integrated chips, or any combination of the above chips or processors.

[0299] See also Fig.10 , is a communication device 1000 involved in the above embodiment provided in an embodiment of the present application, and the communication device 1000 may specifically be a communication device as a terminal device in the above embodiment, Fig.10 The example shown is implemented by a terminal device (or a component in the terminal device).

[0300] 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 .

[0301] in, Figure 8 The transceiver unit 802 shown may be a communication interface, which may be Fig.10 The communication port 1002 in the embodiment may include an input interface and an output interface. Alternatively, the communication port 1002 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0302] 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 .

[0303] 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 various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and the like. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0304] It should be noted that Fig.10 The communication device 1000 shown can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment, and achieve the corresponding technical effects of the terminal device. Fig.10 The specific implementation methods of the communication device shown can all refer to the description in the aforementioned method embodiment, and will not be described in detail here.

[0305] See also Fig.11 , is a schematic diagram of the structure of the communication device 1100 involved in the above embodiment provided in the embodiment of the present application. The communication device 1100 may specifically be the communication device as the network device in the above embodiment. Fig.11 The example shown is that the network device is implemented by the network device (or a component in the network device), wherein the structure of the communication device can refer to Fig.11 The structure shown.

[0306] 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, through a bus. In an embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. The antenna 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.

[0307] in, Figure 8 The transceiver unit 802 shown may be a communication interface, which may be Fig.11 The network interface 1114 in the embodiment may include an input interface and an output interface. Alternatively, the network interface 1114 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0308] The processor 1111 is mainly used to process the communication protocol and communication data, and to control the entire communication device, execute the software program, and process the data of the software program, for example, to support the communication device to perform the actions described in the embodiment. The communication device may include a baseband processor and a central processing unit. The baseband processor is mainly used to process the communication protocol and communication data, and the central processing unit is mainly used to control the entire terminal device, execute the software program, and process the data of the software program. Fig.11 The processor 1111 in the figure can integrate the functions of the baseband processor and the central processor. It can be understood by those skilled in the art that the baseband processor and the central processor can also be independent processors, which are interconnected through technologies such as buses. It can be understood by those skilled in the art that the terminal device can include multiple baseband processors to adapt to different network formats, and the terminal device can include multiple central processors to enhance its processing capabilities. The various components of the terminal device can be connected through various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The central processor can also be described as a central processing circuit or a central processing chip. The function of processing the communication protocol and communication data can be built into the processor, or it can be stored in the memory in the form of a software program, and the processor executes the software program to realize the baseband processing function.

[0309] The memory is mainly used to store software programs and data. The memory 1112 can exist independently and be connected to the processor 1111. Optionally, the memory 1112 can be integrated with the processor 1111, for example, integrated into a chip. Among them, the memory 1112 can store program codes for executing the technical solutions of the embodiments of the present application, and the execution is controlled by the processor 1111. The various types of computer program codes executed can also be regarded as drivers of the processor 1111.

[0310] Fig.11 Only one memory and one processor are shown. In an actual terminal device, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device, etc. The memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the embodiments of the present application.

[0311] The transceiver 1113 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal, and 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, and the receiver Rx of the transceiver 1113 is used to receive the radio frequency signal from the antenna, and convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or the digital intermediate frequency signal to the processor 1111, so that the processor 1111 further processes the digital baseband signal or the digital intermediate frequency signal, such as demodulation and decoding. In addition, the transmitter Tx in the transceiver 1113 is also used to receive a modulated digital baseband signal or a digital intermediate frequency signal from the processor 1111, and convert the modulated digital baseband signal or the digital intermediate frequency signal into a radio frequency signal, and send the radio frequency signal through one or more antennas 1115. Specifically, the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion processing on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal, and the order of the down-mixing and analog-to-digital conversion processing is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion processing on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal, and the order of the up-mixing and digital-to-analog conversion processing is adjustable. The digital baseband signal and the digital intermediate frequency signal can be collectively referred to as a digital signal.

[0312] 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 for implementing a receiving function may be regarded as a receiving unit, and a device in the transceiver unit for implementing a sending function may be regarded as a sending unit, that is, the transceiver unit includes a receiving unit and a sending unit, the receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the sending unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0313] It should be noted that Fig.11 The communication device 1100 shown can be specifically used to implement the steps implemented by the network device in the aforementioned method embodiment, and achieve the corresponding technical effects of the network device. Fig.11 The specific implementation methods of the communication device 1100 shown can all refer to the description in the aforementioned method embodiment, and will not be repeated here.

[0314] See also Fig.12 , which is a structural diagram of the communication device involved in the above-mentioned embodiments provided in the embodiments of the present application.

[0315] It can be understood that the communication device 120 includes, for example, modules, units, elements, circuits, or interfaces, etc., which are appropriately configured together to perform the technical solutions provided in this application. The communication device 120 may be the terminal device or network device described above, or a component (such as a chip) in these devices, to implement the method described in the following method embodiment. The communication device 120 includes one or more processors 121. The processor 121 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control the communication device (such as a RAN node, a terminal, or a chip, etc.), execute software programs, and process data of software programs.

[0316] Optionally, in one design, the processor 121 may include a program 123 (sometimes also referred to as code or instruction), and the program 123 may be executed on the processor 121 so that the communication device 120 performs the method described in the following embodiments. In another possible design, the communication device 120 includes a circuit ( Fig.12 not shown).

[0317] 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.

[0318] Optionally, the processor 121 and / or the memory 122 may include an AI module 127, 128, and the AI ​​module is used to implement AI-related functions. The AI ​​module may be implemented by software, hardware, or a combination of software and hardware. For example, the AI ​​module may include a radio intelligence control (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0319] Optionally, data may also be stored in the processor 121 and / or the memory 122. The processor and the memory may be provided separately or integrated together.

[0320] 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, which controls the communication device (e.g., a RAN node or a terminal). The transceiver 125 may also be sometimes referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, etc., which is used to implement the transceiver function of the communication device through the antenna 126.

[0321] in, Figure 8 The transceiver unit 802 shown may be a communication interface, which may be Fig.12 The transceiver 125 in the embodiment may include an input interface and an output interface. Alternatively, the transceiver 125 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0322] 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.

[0323] 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.

[0324] An embodiment of the present application also provides a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation methods of the above-mentioned communication device. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data for the at least one processor. In one possible design, the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device. The chip system can be composed of chips, and may also include chips and other discrete devices, wherein the communication device can specifically be the first communication device or the second communication device in the aforementioned method embodiment.

[0325] An embodiment of the present application also provides a communication system, and the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

[0326] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0327] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0328] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

Claims

1. A communication method, characterized in that: include: 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; Sending the second data and the first index, wherein 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; 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.

Images