US-20260128777-A1 - COMMUNICATION METHOD AND RELATED DEVICE

US20260128777A1US 20260128777 A1US20260128777 A1US 20260128777A1US-20260128777-A1

Abstract

A communication method and a related device, to improve processing efficiency and reduce a processing delay in a wireless policy optimization process in a manner of determining channel characteristic information by using a neural network, to improve communication efficiency. In the method, a first device determines first information, where the first information indicates channel characteristic information between the first device and a second device, and the first information is obtained by a first neural network by processing channel information of the first device. The first device sends the first information.

Inventors

Jian Wang
Gongzheng Zhang
Rong Li

Assignees

HUAWEI TECHNOLOGIES CO., LTD.

Dates

Publication Date: 20260507
Application Date: 20251230

Claims (20)

1 . A method, comprising: determining first information, wherein the first information indicates channel characteristic information between a first device and a second device, the channel characteristic information comprises at least one of a channel eigenvalue or a channel eigenvector, and the first information is obtained by a first neural network by processing channel information of the first device; and sending the first information to the second device.
2 . The method according to claim 1 , wherein the first neural network comprises a first module, a second module, and a third module, wherein the first module is used to perform randomized range finder (RRF) processing on the channel information of the first device to obtain an RRF result; the second module is used to perform low rank approximation (LRA) processing on the channel information of the first device and the RRF result to obtain an LRA result; and the third module is used to perform singular value decomposition (SVD) processing on the LRA result to obtain the first information.
3 . The method according to claim 2 , wherein a matrix dimension of a matrix representation of the channel information of the first device is m×n, a matrix dimension of a matrix representation of the LRA result is l×n, m is a quantity of antenna ports of the second device, n is a quantity of antenna ports of the first device, and l is an oversampling parameter; and a value of l is less than that of m.
4 . The method according to claim 2 , wherein the first module comprises a first submodule, a second submodule, and a third submodule, wherein the first submodule is used to perform data preprocessing on the channel information of the first device based on a preprocessing parameter, to obtain a preprocessing result; the second submodule is used to perform randomized neural network processing on the preprocessing result based on an oversampling parameter, to obtain a randomized neural network processing result; and the third submodule is used to perform orthogonal triangular (QR) decomposition processing on the randomized neural network processing result to obtain the RRF result.
5 . The method according to claim 4 , wherein the second submodule comprises a first sub-neural network and T second sub-neural networks, wherein T is an integer greater than or equal to 0; and the first sub-neural network is used to perform a basic randomization operation of the randomized neural network processing, and each of the T second sub-neural networks is used to perform an enhanced randomization operation of the randomized neural network processing.
6 . The method according to claim 1 , wherein that the first information is obtained by the first neural network by processing the channel information of the first device comprises: the first information is obtained by the first neural network by processing the channel information of the first device based on a first parameter, wherein the first parameter comprises at least one of an oversampling parameter or a preprocessing parameter.
7 . The method according to claim 4 , further comprising: receiving indication information indicating at least one of the oversampling parameter or the preprocessing parameter.
8 . The method according to claim 4 , further comprising: sending indication information indicating at least one of the oversampling parameter or the preprocessing parameter.
9 . A method, comprising: determining K pieces of first information, wherein the K pieces of first information respectively indicate channel characteristic information between K first devices and a second device, the channel characteristic information comprises at least one of a channel eigenvalue or a channel eigenvector, each piece of the K pieces of first information is obtained by a first neural network by processing channel information of a corresponding one of the K first devices, and K is an integer greater than or equal to 1; and communicating with at least one of the K first devices based on the K pieces of first information.
10 . The method according to claim 9 , wherein the first neural network comprises a first module, a second module, and a third module, wherein the first module is used to perform randomized range finder (RRF) processing on the channel information of the first device to obtain an RRF result; the second module is used to perform low rank approximation (LRA) processing on the channel information of the first device and the RRF result to obtain an LRA result; and the third module is used to perform singular value decomposition (SVD) processing on the LRA result to obtain the first information.
11 . The method according to claim 10 , wherein a matrix dimension of a matrix representation of the channel information of the first device is m×n, a matrix dimension of a matrix representation of the LRA result is l×n, m is a quantity of antenna ports of the second device, n is a quantity of antenna ports of the first device, and l is an oversampling parameter; and a value of l is less than that of m.
12 . The method according to claim 10 , wherein the first module comprises a first submodule, a second submodule, and a third submodule, wherein the first submodule is used to perform data preprocessing on the channel information of the first device based on a preprocessing parameter, to obtain a preprocessing result; the second submodule is used to perform randomized neural network processing on the preprocessing result based on an oversampling parameter, to obtain a randomized neural network processing result; and the third submodule is used to perform orthogonal triangular (QR) decomposition processing on the randomized neural network processing result to obtain the RRF result.
13 . The method according to claim 12 , wherein the second submodule comprises a first sub-neural network and T second sub-neural networks, wherein T is an integer greater than or equal to 0; and the first sub-neural network is used to perform a basic randomization operation of the randomized neural network processing, and each of the T second sub-neural networks is used to perform an enhanced randomization operation of the randomized neural network processing.
14 . The method according to claim 9 , wherein that the first information is obtained by the first neural network by processing the channel information of the first device comprises: the first information is obtained by the first neural network by processing the channel information of the first device based on a first parameter, wherein the first parameter comprises at least one of an oversampling parameter or a preprocessing parameter.
15 . The method according to claim 12 , further comprising: receiving indication information indicating at least one of the oversampling parameter or the preprocessing parameter.
16 . The method according to claim 12 , further comprising: sending indication information indicating at least one of the oversampling parameter or the preprocessing parameter.
17 . A communication apparatus, comprising at least one processor, and one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to perform operations comprising: determining first information, wherein the first information indicates channel characteristic information between a first device and a second device, the channel characteristic information comprises at least one of a channel eigenvalue or a channel eigenvector, and the first information is obtained by a first neural network by processing channel information of the first device; and sending the first information to the second device.
18 . The communication apparatus according to claim 17 , wherein the first neural network comprises a first module, a second module, and a third module, wherein the first module is used to perform randomized range finder (RRF) processing on the channel information of the first device to obtain an RRF result; the second module is used to perform low rank approximation (LRA) processing on the channel information of the first device and the RRF result to obtain an LRA result; and the third module is used to perform singular value decomposition (SVD) processing on the LRA result to obtain the first information.
19 . The communication apparatus according to claim 18 , wherein a matrix dimension of a matrix representation of the channel information of the first device is m×n, a matrix dimension of a matrix representation of the LRA result is l×n, m is a quantity of antenna ports of the second device, n is a quantity of antenna ports of the first device, and l is an oversampling parameter; and a value of l is less than that of m.
20 . The communication apparatus according to claim 18 , wherein the first module comprises a first submodule, a second submodule, and a third submodule, wherein the first submodule is used to perform data preprocessing on the channel information of the first device based on a preprocessing parameter, to obtain a preprocessing result; the second submodule is used to perform randomized neural network processing on the preprocessing result based on an oversampling parameter, to obtain a randomized neural network processing result; and the third submodule is used to perform orthogonal triangular (QR) decomposition processing on the randomized neural network processing result to obtain the RRF result.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation n of International Application No. PCT/CN2023/104750, filed on Jun. 30, 2023, the disclosure of which is hereby incorporated by reference in its entirety. TECHNICAL FIELD The embodiments relate to the field of communication, for example, to a communication method and a related device. BACKGROUND Wireless communication may be transmission communication performed between a plurality of communication nodes without propagation through a conductor or a cable. A network device and a terminal device may be used as different communication nodes to perform communication in a wireless communication manner. In a communication system, wireless policy optimization (for example, resource allocation, channel estimation, and signal detection) is an important issue in wireless communication. However, how to improve processing efficiency and reduce a processing delay in an implementation process of wireless policy optimization is an urgent problem to be resolved. SUMMARY The embodiments provide a communication method and a related device, to improve processing efficiency and reduce a processing delay in a wireless policy optimization process in a manner of determining channel characteristic information by using a neural network, to improve communication efficiency. A first aspect of embodiments provides a communication method. The method is performed by a first device. Alternatively, the method is performed by a part of components (for example, a processor, a chip, or a chip system) in a first device. Alternatively, the method may be implemented by a logical module or software that can implement all or a part of functions of a first device. In the first aspect and a possible embodiment of the first aspect, an example in which the method is performed by the first device is used for description. The first device may be a terminal device or a network device. In the method, the first device determines first information, where the first information indicates channel characteristic information between the first device and a second device, and the first information is obtained by a first neural network by processing channel information of the first device. The first device sends the first information. According to the foregoing solution, the first information sent by the first device indicates the channel characteristic information between the first device and the second device, and the first information is obtained by the first neural network by processing the channel information of the first device. In other words, the first device may obtain, through neural network processing, the channel characteristic information for wireless policy optimization. Subsequently, after the first device sends the first information indicating the channel characteristic information, a receiver of the first information can implement wireless policy optimization based on the first information. Therefore, determining the channel characteristic information by using a neural network can improve processing efficiency and reduce a processing delay in a wireless policy optimization process, to improve communication efficiency. Optionally, the channel characteristic information indicated by the first information may include a channel eigenvalue and/or a channel eigenvector. Alternatively, the channel characteristic information indicated by the first information may include other information indicating a channel characteristic. The channel eigenvalue and/or the channel eigenvector may alternatively be other names, for example, a channel characteristic parameter. It should be understood that the channel information of the first device may include at least one of channel information of a channel between the first device and the second device and channel information of a channel between the second device and the first device. The receiver of the first information may be the second device. The second device may be a terminal device or a network device. The channel has a plurality of possible forms. For example, when the first device is a terminal device and the second device is a network device, the channel between the first device and the second device may be an uplink channel, and the channel between the second device and the first device may be a downlink channel. For another example, when the first device is a network device and the second device is a terminal device, the channel between the first device and the second device may be a downlink channel, and the channel between the second device and the first device may be an uplink channel. For another example, when the first device is a terminal device and the second device is a terminal device, the channel between the first device and the second device may be a sidelink communication channel. For another example, when the first device is a network device and the second device is a network device, the channel between the first device a