CN-121985345-A - Combined transmission optimization method and device combining coupling phase shift and hardware damage

CN121985345ACN 121985345 ACN121985345 ACN 121985345ACN-121985345-A

Abstract

The invention provides a joint transmission optimization method and device combining coupling phase shift and hardware damage, the method comprises the steps of obtaining communication data of a reflection intelligent super surface, wherein the communication data comprise first channel data between the reflection intelligent super surface and a base station and second channel data between the reflection intelligent super surface and a user, adopting a reinforcement learning algorithm, wherein the reinforcement learning algorithm comprises hardware damage constraint and coupling phase shift constraint, the reinforcement learning algorithm comprises a plurality of updating rounds, calculating a transmission matrix and a reflection matrix based on transmission phase shift, reflection phase shift, transmission amplitude and reflection amplitude in the current action in each updating round, calculating a reward function based on the transmission matrix, the reflection matrix, a hardware damage value and the current state, updating the state based on the reward function, and configuring actions in the last updating round in the reinforcement learning algorithm to reflect the intelligent super surface.

Inventors

GUO LI
LI YILIN
ZHU GUANGYU
LI DONGFENG
CHEN ZHITAO

Assignees

北京邮电大学

Dates

Publication Date: 20260505
Application Date: 20251215

Claims (10)

1. A joint transmission optimization method combining coupled phase shifting and hardware impairments, the method comprising the steps of: acquiring communication data of the reflecting intelligent super surface, wherein the communication data comprises first channel data between the reflecting intelligent super surface and a base station and second channel data between the reflecting intelligent super surface and a user; Adopting a reinforcement learning algorithm, wherein a state space of the reinforcement learning algorithm comprises a first channel matrix in the first channel data and a second channel matrix in the second channel data, an action space of the reinforcement learning algorithm comprises transmission phase shift, reflection phase shift, transmission amplitude and reflection amplitude in the communication data, and the reinforcement learning algorithm comprises coupling phase movement constraint; the reinforcement learning algorithm comprises a plurality of updating rounds, in each updating round, a transmission matrix and a reflection matrix are calculated based on the transmission phase shift, the reflection phase shift, the transmission amplitude and the reflection amplitude in the current action, a reward function is calculated based on the transmission matrix, the reflection matrix, the hardware damage value and the current state, and the state is updated based on the reward function; the action configuration in the last update round in the reinforcement learning algorithm is reflected to the intelligent super surface.
2. The joint transmission optimization method combining coupling phase shift and hardware impairment according to claim 1, wherein in the step of calculating a reward function based on a transmission matrix, a reflection matrix, a hardware impairment value, and a current state, a rate of a reflective intelligent super-surface for each user is calculated based on the transmission matrix, the reflection matrix, the hardware impairment value, a first channel matrix in the current state, and a second channel matrix, a total system rate is calculated based on the rate of each user, and the total system rate is taken as the reward function.
3. The joint transmission optimization method combining coupled phase shift and hardware impairment according to claim 2, wherein in the step of calculating the speed of the reflective smart hypersurface for each user based on the transmission matrix, the reflection matrix, the hardware impairment value, the first channel matrix and the second channel matrix in the current state, the signal-to-interference-and-noise ratio of the reflective smart hypersurface for each user is calculated based on the transmission matrix, the reflection matrix, the hardware impairment value, the first channel matrix and the second channel matrix in the current state, and the speed of the reflective smart hypersurface for each user is calculated based on the signal-to-interference-and-noise ratio.
4. The joint transmission optimization method combining coupled phase shift and hardware impairment according to claim 3, wherein in the step of calculating the signal-to-interference-and-noise ratio of the reflective intelligent super-surface for each user based on the transmission matrix, reflection matrix, hardware impairment value, first channel matrix and second channel matrix in the current state, the signal-to-interference-and-noise ratio of each user is calculated using the following formula: Wherein, the The channel vector representing the reflective intelligent super surface and the user k, and the transmission matrix is a transmission matrix or a reflection matrix; representing a transpose of the second channel matrix between the reflective intelligent subsurface and user k in the second channel data; Representing a first channel matrix; Representing transmission symbols transmitted by the base station to the user; representing a user Hardware damage value of (2); representing reflective smart subsurface and user in second channel data A transpose of the second channel matrix therebetween; Representing transmission symbols transmitted by the base station to the user; Representing reflective intelligent supersurfaces and users Is a channel vector of (a); Representing a preset noise parameter.
5. The joint transmission optimization method combining coupled phase shifting and hardware impairments of claim 4, wherein in the step of calculating the speed of the reflective intelligent subsurface for each user based on the signal-to-interference-and-noise ratio, the speed of each user is calculated using the following formula: Wherein, the Representing the signal-to-interference-and-noise ratio of user k, Representing the speed of the reflecting intelligent subsurface corresponding to user k.
6. The joint transmission optimization method combining coupling phase shift and hardware impairments according to claim 5, wherein in the step of calculating the total system rate based on the rate of each user, the sum of the speeds of all users corresponding to the reflective intelligent super-surface is calculated as the total system rate.
7. The joint transmission optimization method combining coupling phase shift and hardware impairments according to claim 1, wherein in the step of calculating the transmission matrix and the reflection matrix based on the transmission phase shift, the reflection phase shift, the transmission amplitude and the reflection amplitude in the current action, the transmission matrix is calculated using the following formula: Wherein, the A channel vector representing a reflecting user; N represents the dimension of the incoming signal reflecting the intelligent subsurface; a value representing a reflection amplitude for each dimension of the reflected user corresponding incoming signal; a value representing a reflected phase shift for each dimension of the reflected user corresponding incoming signal; A value representing a transmission amplitude for each dimension of the transmission corresponding incoming signal; Representing the value of the reflected phase shift for each dimension of the transmitted user corresponding to the incoming signal.
8. The joint transmission optimization method combining coupled phase shift and hardware impairments according to any one of claims 1-7, wherein the reinforcement learning algorithm employs a TD3 algorithm, and the coupled phase shift constraint includes: ; ; Wherein, the Values representing the transmitted phase shift and the reflected phase shift corresponding to the same dimension; values representing the transmitted and reflected amplitudes corresponding to the same dimension.
9. The joint transmission optimization method combining coupled phase shifting and hardware impairments of claim 1, wherein the first channel matrix is expressed as: The second channel matrix is expressed as: Wherein, the Representing a first matrix of channels and, Representing reflective intelligent supersurfaces and users A second channel matrix between the two, And Is a field response vector determined by the line of sight angle; Are all non-LoS components; Is a large scale fading coefficient; Is a Rician factor.
10. A joint transmission optimization device combining coupled phase shifting and hardware impairments, characterized in that the device comprises a computer arrangement comprising a processor and a memory, the memory having stored therein computer instructions for executing the computer instructions stored in the memory, the device realizing the steps of the method according to any of claims 1-9 when the computer instructions are executed by the processor.

Description

Combined transmission optimization method and device combining coupling phase shift and hardware damage Technical Field The invention relates to the technical field of intelligent wireless communication, in particular to a joint transmission optimization method and device combining coupling phase shift and hardware damage. Background In the field of intelligent wireless communication, with the continuous improvement of the demands for global coverage and dynamic resource allocation, a transmissive and reflective intelligent super surface (Simultaneously TRANSMITTING AND REFLECTING Reconfigurable Intelligent Surface, STAR-RIS) is developed as a novel air interface control technology, and is gradually developed as one of the key enabling means for supporting future intelligent communication systems. The STAR-RIS has the core value of breaking through the functional limitation that the traditional intelligent super surface can only act on a reflection space, and can realize the cooperative regulation and control of transmission and reflection behaviors on the same physical structure on the incident electromagnetic wave, thereby providing theoretical basis and technical possibility for seamless coverage and differentiated area service in a three-dimensional space. After conceptual presentation, the related studies were first developed around the underlying modeling of STAR-RIS, aimed at abstracting its physical operating mechanisms and building a simplified mathematical model for analysis. Initial studies are generally based on the idealized assumption that each subsurface unit is considered to be capable of independently, lossless and continuously modulating the amplitude and phase of reflected and transmitted signals, thereby providing near-perfect degrees of freedom for modulation. Under this modeling assumption, the academic community focuses on discussing the potential gain of STAR-RIS in typical application scenarios (e.g., non-orthogonal multiple access NOMA, integrated sensing and communication ISAC, unmanned aerial vehicle assisted communication UAV, etc.), and evaluating its theoretical performance boundaries by constructing a joint active and passive beamforming algorithm. Research shows that STAR-RIS has significant advantages in terms of improving system capacity, extending coverage and enhancing user access flexibility. However, these work multi-focuses on the performance upper bound analysis under ideal conditions, and the built optimization problem of the work multi-focuses on the fact that the high-dimensional variable and the strong coupling structure are involved, and the work multi-focuses on the non-convex optimization category of NP difficulty, and the inherent constraint of the actual physical system is not fully considered. Disclosure of Invention In view of the foregoing, embodiments of the present invention provide a joint transmission optimization method that combines coupled phase shifting with hardware impairments to obviate or mitigate one or more of the disadvantages of the related art. One aspect of the present invention provides a joint transmission optimization method combining coupled phase shifting and hardware impairments, the method comprising the steps of: acquiring communication data of the reflecting intelligent super surface, wherein the communication data comprises first channel data between the reflecting intelligent super surface and a base station and second channel data between the reflecting intelligent super surface and a user; Adopting a reinforcement learning algorithm, wherein a state space of the reinforcement learning algorithm comprises a first channel matrix in the first channel data and a second channel matrix in the second channel data, an action space of the reinforcement learning algorithm comprises transmission phase shift, reflection phase shift, transmission amplitude and reflection amplitude in the communication data, and the reinforcement learning algorithm comprises coupling phase movement constraint; the reinforcement learning algorithm comprises a plurality of updating rounds, in each updating round, a transmission matrix and a reflection matrix are calculated based on the transmission phase shift, the reflection phase shift, the transmission amplitude and the reflection amplitude in the current action, a reward function is calculated based on the transmission matrix, the reflection matrix, the hardware damage value and the current state, and the state is updated based on the reward function; the action configuration in the last update round in the reinforcement learning algorithm is reflected to the intelligent super surface. By adopting the scheme, the scheme adds the hardware damage constraint and the coupling phase shift constraint in the application of the reinforcement learning algorithm, adds the hardware damage value in the calculation of the reward function, and simultaneously constrains the transmission phase shift and the reflection