A Design Method for Power Communication Systems Based on RIS Phased Array

Abstract

This invention discloses a design method for power communication systems based on a RIS phased array, aiming to solve the problems of insufficient signal coverage and limited transmission performance in non-line-of-sight scenarios in power communication. This method constructs additional reflection links by deploying a RIS phased array containing N×N passive reflection elements, establishing a hybrid channel model that includes a base station-user direct link and a RIS reflection link. With maximizing the system weighted sum rate as the optimization objective, an alternating optimization framework is used to jointly solve the base station active beamforming matrix and the RIS phase shift matrix. This invention can effectively improve the transmission rate and signal coverage reliability of power communication in non-line-of-sight scenarios, providing a new approach for high-reliability communication assurance in complex scenarios such as power emergency communication.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a design method for a power communication system based on RIS phased array assistance. Background Technology

[0002] With the increasing complexity of power systems and the continuous growth of communication demands, power communication places higher demands on system transmission rates, user capacity, and communication transmission stability. Beamforming technology, as a technique that can automatically adjust antenna array parameters and signal transmission direction according to environmental changes, can significantly improve communication transmission efficiency and quality. Furthermore, improved adaptive beamforming algorithms also possess a certain degree of anti-interference capability, making it highly valuable in power communication transmission. However, beamforming technology may not meet the needs of all complex communication scenarios, especially in emergency communication. Power emergency communication systems, as the core guarantee for power emergency repair and disaster relief, need to quickly rebuild highly reliable communication links in extreme scenarios such as base station failures and terrain obstruction.

[0003] To address the need for high-quality and stable communication links and provide reliable emergency communication support in the event of obstruction from obstacles, Reconfigurable Intelligent Surfaces (RIS) are widely used and deployed due to their unique characteristics of low cost, low power consumption, programmability, and ease of movement and deployment. RIS phased arrays typically consist of a large number of passive reflective elements that can intelligently adjust the phase of the incident signal and provide passive beam gain in certain scenarios. Therefore, under non-line-of-sight conditions, the application of RIS phased arrays can effectively optimize signal propagation paths, enhance signal strength in the target area, and reduce interference and energy loss.

[0004] By jointly optimizing RIS phased array technology and adaptive beamforming technology, the advantages of both can be fully utilized to achieve more efficient signal transmission and interference suppression, thereby improving the performance and robustness of the power dispatch emergency communication system. Summary of the Invention

[0005] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a power communication system design method based on RIS phased array assistance. By jointly optimizing the design of the base station beamforming signal and the beamforming at the RIS phased array, the signal-to-noise ratio and system weighted sum rate of the user terminal are improved, and the problem of insufficient communication signal coverage in non-line-of-sight scenarios is solved.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A design method for a power communication system based on RIS phased array assistance includes the following steps: Step 1: Deploy a transmitting base station equipped with M antennas, K single-antenna user terminals, and a RIS phased array containing N×N passive reflective elements. The RIS phased array is used to provide an additional controllable wireless reflection channel for communication between the base station and the user terminals; define the active beamforming matrix of the base station as follows: The phase shift matrix of the reflected beamforming of the RIS phased array is: Where M is the number of base station antennas, K is the total number of user terminals, N is the number of single-sided reflective elements of the RIS phased array, and N×N is the total number of passive reflective elements of the RIS phased array. Step 2: Establish a hybrid channel model that includes the direct link from the base station to the user terminal, the reflection link from the base station to the RIS phased array and then to the user terminal, and solve for the channel matrix and channel vector corresponding to each link; Step 3: Select the weighted sum rate of all users as the quantitative indicator of system transmission efficiency, and construct an optimized active beamforming matrix for the base station with the goal of maximizing the system weighted sum rate. With the phase shift matrix of the RIS phased array The joint optimization mathematical model; Step 4: Using an alternating optimization method, the joint optimization mathematical model is decomposed into two sub-optimization problems, and the optimal base station active beamforming matrix and RIS phased array phase shift matrix are obtained by alternating iterative solutions. Step 5: Based on the obtained optimal active beamforming matrix and optimal phase shift matrix, reconstruct the RIS-assisted wireless communication channel, and transmit the base station signal to the user terminal through the channel to achieve power communication signal coverage in non-line-of-sight scenarios.

[0007] Preferably, in step 1, the active beamforming matrix satisfies ;in, Describes the set of complex matrices with M rows and K columns. For the beamforming weighting vector corresponding to the k-th user, ; The base station's transmitted signal is ,in The transmitted data symbols of the k-th user follow a cyclic symmetric complex Gaussian normal distribution with a mean of 0 and a variance of 1. External transmission signals from the base station The expression is: .

[0008] Preferably, in step 1, the phase shift matrix of the RIS phased array For a diagonal matrix, the expression is: ; in, This indicates a diagonalization operation, used to generate a diagonal matrix with the elements inside the parentheses as diagonal elements; The amplitude gain provided for the nth passive reflective element of the RIS phased array. The phase shift gain provided for the nth passive reflective element of the RIS phased array. ; is the base of the natural logarithm. The imaginary unit satisfies .

[0009] Preferably, in step 2, the channel vector from the base station to the user terminal is denoted as... The channel vector from the base station to the RIS phased array is denoted as... The channel vector from the RIS phased array to the user terminal is denoted as... ;in, Represents the set of M-dimensional complex column vectors. Describes the set of N rows and M columns of complex matrices. Represents a set of N-dimensional complex column vectors; The end-to-end superposition channel expression from the base station to the user is: .

[0010] Preferably, in the hybrid channel model of step 2, the channel from the base station to the RIS phased array and the channel from the RIS phased array to the user terminal both satisfy the line-of-sight transmission condition, and the channel vector follows a Rice distribution; the transmission from the base station to the user terminal is non-line-of-sight transmission, and the channel vector follows a Rayleigh distribution.

[0011] Preferably, in step 2, the channel matrix from the base station to the RIS phased array... Channel vector from RIS phased array to the k-th user terminal The corresponding Rice distribution channel model expressions are as follows: ; ; Among them, among them, and This indicates the transmission path loss of the corresponding channel. Rice factor, Indicates the array steering vector. and All are angle parameters. and This represents the non-line-of-sight components on the corresponding channel, all of which satisfy a complex Gaussian distribution; Channel vector from base station to the k-th user terminal The corresponding Rayleigh distribution expression is: ; in, This represents a cyclic symmetric complex Gaussian distribution, where the first parameter in parentheses is the distribution mean, and the second parameter is the distribution covariance matrix. The channel power corresponding to the path loss of the channel from the base station to the user terminal; It is an M-order identity matrix, that is, an M×M square matrix with 1s on the main diagonal and 0s on the rest. .

[0012] Preferably, in step 2, the signal transmitted by the base station is... In the environment, the noise received by user k is denoted as... At the same time, the base station will send multiple signals. The received signal model of the k-th user terminal. The expression is: ; in, Let K be the base station-to-user channel vector corresponding to the k-th user. Let be the RIS phased array-to-user channel vector corresponding to the k-th user. , and ; Let the additive white Gaussian noise at the k-th user terminal have a mean of 0 and a variance of . The cyclically symmetric complex Gaussian distribution, This represents the noise variance.

[0013] Preferably, based on the received signal model of the kth user terminal, the expression for the output signal-to-interference-plus-noise ratio (SIR) of the kth user terminal is obtained as follows: .

[0014] Preferably, in step 3, the expression for the system weighted sum rate WSR is: ; In the formula, This represents the weighting coefficient of the k-th user to the system transmission rate, used to characterize the communication priority of the k-th user, and .

[0015] Preferably, in step 3, when constructing the joint optimization mathematical model, solving for the parameters that maximize WSR includes: Given the channel vector To maximize the system weighted sum rate (WSR), the active beamforming parameters of the base station need to be optimized. RIS phased array reflective beamforming At this point, the optimization problem can be represented as P1: ; As can be seen from optimization problem P1, the maximum weighted sum rate WSR is determined by the base station transmission. Phase shift matrix of RIS phased array A joint decision.

[0016] Preferably, in step 4, an alternating optimization method is used to decompose the joint optimization mathematical model into two sub-optimization problems, specifically including: Active beamforming vector optimization: fixed reflection coefficient matrix The channel can be represented as: At this point, optimization problem P1 can be transformed into optimization problem P2: ; By using the weighted minimum mean square error algorithm, the optimization problem is transformed into a weighted minimum mean square error problem by introducing undetermined parameters, and the mean square error value is reduced by iterative calculation method to gradually approach the optimal solution. RIS phased array passive beamforming optimization: fixed active beamforming weighting vector Therefore, the optimization problem can be simplified, and the original system's maximum transmission rate optimization problem can be rewritten as: ; The optimal reflection matrix is ​​solved using the semidefinite programming SDP algorithm. The mean square error value is: ; in, Indicates the receiver factor. and error scaling factor; respectively for and Taking the derivative and setting it to 0, we obtain the two-parameter update rule: ; ; Will and After substituting, let MSE calculate about With the derivative being 0, we obtain the iterative expression for the active beamforming weighting vector: ; RIS phased array passive beamforming optimization: fixed active beamforming weighting vector Therefore, the optimization problem can be simplified, and the original system's maximum transmission rate optimization problem can be rewritten as P3: ; st ; The optimal reflection matrix is ​​solved using the semidefinite programming (SDP) algorithm.

[0017] Preferably, the iterative solution process in step 4 specifically includes the following steps: S401: Parameter Initialization: Set the initial value of the iteration count. t is the iteration count variable; set the convergence threshold. , The initial phase shift matrix of the RIS phased array is generated by using a preset positive real number and random phase. ; S402: Fix the phase shift matrix of the RIS phased array in the current iteration. Solve the active beamforming optimization subproblem to obtain the optimal active beamforming matrix for the current iteration. ; S403: Fix the active beamforming matrix for the current iteration. Solve the RIS phased array passive beamforming optimization subproblem to obtain the optimal phase shift matrix for the current iteration. ; S404: Convergence check: Calculate the system weighted sum rate for the current iteration. The system weighted sum rate with the previous iteration If satisfied If the algorithm converges, it stops iterating and outputs the optimal active beamforming matrix and the optimal phase shift matrix; if the convergence condition is not met, then let Then return to step S402 to continue the iteration.

[0018] Preferably, in step 1, the M antennas of the base station are a uniformly linearly distributed antenna array, and the array steering vector is constructed based on the antenna element spacing, signal incident angle, and carrier wavelength, and the system parameters satisfy... That is, the total number of RIS phased array reflector elements is much greater than the number of base station antennas, and the number of base station antennas is greater than the total number of user terminals.

[0019] Beneficial effects of this invention: The wireless communication system design method based on a smart reflector provided by this invention utilizes alternating iterative optimization of active and passive beamforming techniques. It jointly optimizes the active beamforming technology of the base station and the passive reflection beamforming of the smart reflector (RIS), aiming to maximize the system weighted sum rate by optimizing the weighting vector and reflection phase shift matrix to ensure the quality of the received signal when output power is limited. This significantly improves the communication quality of the wireless communication system under conditions of missing or poor line-of-sight paths, effectively enhancing the stability of emergency communication support.

[0020] This invention addresses the problem of insufficient communication signal coverage in non-line-of-sight scenarios by jointly optimizing active beamforming at the base station and passive beamforming using a RIS phase-shift matrix. First, a hybrid channel model incorporating direct links and RIS reflection links is established. Second, a two-level algorithm framework based on alternating optimization is designed to solve for the optimal beamforming parameters. Simulation experiments demonstrate that, under the same conditions, the proposed algorithm effectively improves the system weighted sum rate compared to beamforming algorithms without RIS phased arrays and algorithms without joint optimization. These findings provide a new approach to ensuring reliable signal coverage in power communication. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the wireless communication system based on RIS phased array assisted according to the present invention; Figure 2 This is a schematic diagram of the beamforming joint optimization design based on RIS phased array assisted by the present invention; Figure 3 The graph shows the relationship between the RIS phased array reflection unit and the system weighted sum rate under different optimization methods. Figure 4 The graph shows the relationship between base station transmit power and system weighted sum rate under different optimization methods. Detailed Implementation

[0022] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0023] Example 1: A design method for a power communication system based on RIS phased array assistance. By jointly optimizing the design of the base station beamforming signal and the beamforming at the RIS phased array, the signal-to-noise ratio and system weighted sum rate of the user terminal are improved, thus solving the problem of insufficient communication signal coverage in non-line-of-sight scenarios.

[0024] When communication coverage between a base station with M antennas and K users is poor due to obstruction, the wireless communication system design method based on RIS phased array assisted by the present invention is adopted, and the specific steps are as follows: Step 1: Deploy a base station equipped with M uniformly linearly distributed antennas. The beamforming matrix on the base station side is denoted as... The user end consists of K single-antenna users, and the network between the user end and the base station includes N... A RIS phased array with N passive reflective elements provides a controllable wireless channel, and the reflection coefficient matrix of the RIS phased array can be expressed as... ; Step 2: Establish mathematical models for each wireless channel, and solve for the received signal at user k based on the base station's transmitted signal s. ; Step 3: Select the weighted sum rate (WSR) of all users as the quantitative indicator of system transmission efficiency, and establish an optimization problem with maximizing WSR as the constraint. Step 4: Solve for the base station beamforming matrix and the RIS phase shift matrix using an alternating iterative method. Step 5: After solving for the base station beamforming matrix and RIS phase shift matrix, reconstruct the RIS-assisted wireless channel and transmit the base station signal to the user terminal through the newly created wireless channel to achieve end-to-end wireless signal coverage.

[0025] Furthermore, the weighting coefficients for base station transmit beamforming in step 1 are denoted as:

[0026] Among them, an upper limit is set for the transmission power. ,satisfy ; The RIS phased array reflection matrix in step 1 is denoted as... , parameters in the formula This represents the amplitude gain provided by the nth element in the RIS phased array to the incident signal. Indicates the phase change value; Parameters in the formula This represents the amplitude gain provided by the nth element in the RIS phased array to the incident signal. This represents the phase change value; the wireless communication system model encompasses components such as the base station, smart reflector, wireless propagation channel, and user terminal. The specific steps for constructing the system model are as follows: If an obstacle obstructs the path between the base station and the user terminal, rendering the original line-of-sight transmission channel unusable, it is necessary to deploy a RIS phased array system to provide a reflection link, ensuring that there are line-of-sight transmission paths between the base station and the RIS phased array, and between the RIS phased array and the user terminal.

[0027] The RIS-assisted downlink multi-user communication system model includes one base station (BS), one RIS phased array, and K single-antenna users. The specific parameters are as follows: The base station is equipped with M uniformly linearly distributed antennas, and the transmit beamforming weighting coefficient is... The base station's transmission power is subject to the maximum power. Limitations, satisfaction ; RIS phased array contains N N passive reflection elements, and the reflection coefficient matrix of the RIS phased array can be expressed as: , parameters in the formula This represents the amplitude gain provided by the nth element in the RIS phased array to the incident signal. Indicates the phase change value; The channel vector from the base station to the user terminal is In non-line-of-sight transmission, only a scattering path exists between the base station and the user terminal, and the channel vector... Follows Rayleigh distribution, Indicates channel power (related to path loss factor). The channel vector from the base station to the RIS phased array is The channel vector from the RIS phased array to the user terminal is: There are line-of-sight transmission paths between the base station and the RIS phased array, as well as between the RIS phased array and the user terminal. The channel vector follows a Ricean distribution, and the channel model expression is:

[0028]

[0029] in, and This indicates the transmission path loss of the corresponding channel. Rice factor, Indicates the array steering vector. and All are angle parameters. and This represents the non-line-of-sight components on the corresponding channel, all of which satisfy a complex Gaussian distribution; Let the signal transmitted by the base station be In the environment, the noise received by user k is denoted as... At the same time, the base station will send multiple signals, and the signal received by user k can be represented as:

[0030] The signal received by the user consists of three parts: the desired signal emitted by the corresponding antenna of the base station. Interference signals from other antennas of the base station and environmental noise Furthermore, the signal emitted by the base station passes through the scattering path and the RIS phased array reflection path, and is superimposed before reaching the user end.

[0031] To quantify the transmission performance of a communication system, the weighted sum rate (WSR) of all users on the user side is selected as a reference indicator for the system's spectral efficiency. Furthermore, the weighted sum rate of a multi-user system can be represented by the signal-to-interference-plus-noise ratio (SINR) output from the user end. The output signal-to-interference-plus-noise ratio of the receiver:

[0032] System weighted sum rate:

[0033] Indicates user The priority is set according to the importance and priority of different users, and the weight of each user's impact on the overall system speed is set differently.

[0034] The beamforming performance optimization design problem of a communication system based on intelligent reflector-assisted beamforming can be transformed into maximizing the weighted sum rate of the system by jointly optimizing the base station transmit power and the reflection matrix, while ensuring that the base station transmit power does not exceed the maximum limit.

[0035] The system output signal-to-interference-plus-noise ratio can be expressed as:

[0036]

[0037]

[0038] The above optimization problem is solved using an alternating cyclic optimization method: Parameter initialization: Set the number of iterations to Set convergence threshold The initial phase matrix is ​​set by random phase. ; Based on the initialized phase matrix Solve for the equivalent concatenated channel at this point: ; Alternating iteration: The reflection phase matrix of a fixed RIS phased array At that time, the received signal can be obtained:

[0039] By introducing the receiver factor and error scaling factor The mean square error value at user k at the receiving end is obtained: ; The optimal weighting vector is obtained by minimizing the mean square error (MSE). Iterative update rules: ; ; ; Obtaining active beamforming parameters Then, the solution is obtained using the semi-definite programming (SDP) method. : ;

[0040] After each iteration, check whether the result meets the convergence condition: ; like If the algorithm converges, the iteration stops; otherwise, let t = t + 1 and return to

[0039] to continue the iteration. For pre-set convergence parameters, those that satisfy the convergence conditions This is the optimal beamforming weighting vector.

[0041] Example 2: In this example, the system model is first determined, and the channel matrix between the receiver and transmitter is defined by mathematical formulas to accurately characterize the changes in the received signal at the user end during transmission. Then, with the optimization objectives of improving the output signal-to-interference-plus-noise ratio and the system weighted sum rate, and with the base station's transmit beamforming parameters and the reflection phase matrix of the RIS reflector as the optimization objects, a clear joint optimization problem of active and passive beamforming is constructed. Finally, by solving this joint optimization problem, the relevant parameters that fit the optimization objectives are determined.

[0042] 1. System Model: The invention is applied to wireless communication scenarios with reconfigurable smart reflectors (RIS). For example... Figure 1 As shown, the system model includes a base station equipped with M antennas, which transmits signals through the antennas; one RIS phased array for reflecting signals; and K single-antenna users.

[0043] RIS phased array equipped There are passive reflective elements, and the reflection coefficient matrix is ​​represented as follows: ;parameter This represents the amplitude gain provided by the nth element in the RIS phased array to the incident signal. This indicates a change in the provided phase; This represents the channel vector from the base station to the user. The channel vector from the base station to the RIS phased array is denoted as , and the channel vector from the RIS phased array to the user terminal is denoted as . The system model assumes that the line-of-sight transmission path is blocked, and the wireless channel only has a scattering transmission path. It follows a Rayleigh distribution. The channel is assisted by a RIS phased array. and Due to the superposition of line-of-sight transmission and scattering transmission, it can be assumed to follow a Ricean distribution. The channel model expression is:

[0044] (1) (2) in, This represents channel power (related to path loss factor). and This represents the path loss when the reference distance is 1. Rice factor, Indicates the array steering vector. and For angle parameters, and This represents the non-line-of-sight components on the corresponding channel, all of which satisfy a complex Gaussian distribution.

[0045] Let the signal transmitted by the base station be The environmental noise received by user k is uniformly represented as At the same time, the base station will send multiple signals, and the signal received by user k can be represented as: (3) in, This indicates that the base station transmits the desired signal from user k through two paths. Reaching the user side. This indicates interference signals originating from the base station; Then the signal-to-interference-plus-noise ratio (SINR) for user k is: (4) 2. Optimize the problem definition: Signal-to-interference-plus-noise ratio (SINR) is commonly used to evaluate the performance of communication links. A higher SINR indicates that the received signal is less affected by interference and noise, and the communication is more reliable and stable. As shown in equation (4), the output SINR is related to the base station's transmit beamforming vector. Passive beamforming matrix of RIS phased array reflector unit related.

[0046] Signal-to-interference-plus-noise ratio (SINR) addresses the communication needs of a single user. In real-world communication scenarios, there are distinctions in communication priorities and importance. Therefore, using the weighted sum rate (WSR) of all users as a reference indicator for system spectral efficiency is more in line with actual scenarios. The system weighted sum rate is expressed as: (5) Indicates user The priority of each user varies, and the importance and priority of each user differ, resulting in different weights of influence on the overall system's WSR.

[0047] In this embodiment of the invention, with the goal of maximizing the system weighted sum rate, joint optimization is performed using the base station transmit power, the phase shift matrix amplitude of the RIS phased array, and the phase parameters as constraints. The joint optimization problem can be expressed as: (6) (7) (8) The optimization problem (P1) is a non-convex optimization problem because the optimization variables... and The objective function and constraints are highly coupled. Therefore, this embodiment proposes an alternating optimization method to decompose the P1 optimization problem into two sub-problems that are solved separately. Specifically, first, the phase shift matrix variables are fixed. Optimize beamforming matrix Then give variables Optimize the phase shift matrix of the RIS phased array After each optimization, the convergence value under the current parameter value is calculated until the convergence value satisfies the pre-set parameter values.

[0048] 3. Solving optimization problems using the alternating iterative method: 1) Active beamforming weighted vector optimization: Phase reflection coefficient matrix of fixed RIS phased array At this point, the spatial channel can be uniformly represented as: (9) At this point, the optimization problem P1 can be transformed into: P2: (10) (11) As shown in (10), the optimization function for maximizing the weighted sum rate (WSR) is non-convex and cannot be solved directly. Therefore, we consider using the Weighted Minimum Mean-Square Error (WMMSE) algorithm. By introducing undetermined parameters, the optimization problem is transformed into a weighted minimum mean-square error problem. The mean-square error is then reduced through iterative calculation to gradually approach the optimal solution. The mean-square error can be expressed as: (12) Indicates the receiver factor. and error scaling factor; Equation (12) respectively for and Taking the derivative and setting it to 0, we obtain the two-parameter update rule: ; ; Will and After substituting into equation (12), let MSE calculate the value of MSE. With the derivative being 0, the active beamforming weighting vector is obtained as:

[0049] 2) Weighted vector optimization of passive RIS phased array reflection elements: Fixed active beamforming weighting vector At this point, the signal emitted by the base station is only the signal required by the user, so the optimization problem can be simplified. The original maximum transmission rate optimization problem can be rewritten as P3: P3:

[0050]

[0051] At this point, the optimization problem only contains variables. And corresponding to cascaded channels for different receivers They are mutually independent. And the constraints... and It will not affect the magnitude of the phase matrix. Furthermore, since the system weighted rate function is a convex function, the system reaches its maximum transmission rate when the internal function, i.e., the user-received signal-to-interference-plus-noise ratio (SINR), reaches its maximum value. Clearly, to ensure the optimization function reaches its maximum value, the joint optimization problem of active beamforming at the base station and passive beamforming of the RIS phased array can be expressed as:

[0052]

[0053] The optimal reflection matrix is ​​solved using the semi-definite programming (SDP) algorithm.

[0054] To further verify the effectiveness of this invention, simulations were performed using Matlab or other platforms to verify the system's communication performance. Specifically, as follows... Figure 2 As shown, the base station antenna is located at (0m, 0m), and the number of antennas is... The RIS phased array is located at (120m, 50m), and the number of reflective elements is... The user is located at (150m, 0m), and there is a building obstructing the signal between the user and the base station antenna, resulting in no line-of-sight transmission. Furthermore, the base station's transmit power is... =-5~25dBm, Rice factor =10.

[0055] In the experiment, the following reference methods were compared: Method 1: Phased array without RIS: Under the same channel conditions and obstacle obstruction communication model, the impact of active beamforming coefficients on the overall transmission rate is studied when there is no line-of-sight transmission channel.

[0056] Method 2: Based on the base station to user channel Pre-calculation of the beamforming vector for maximum transmission ratio Then, the effect of the phase shift matrix of the RIS phased array on the overall transmission rate is solved separately.

[0057] Figure 3 This paper presents the change in system weighted sum rate as the number of RIS units increases when the base station transmit power is 15 dBm. The increase in the number of RIS reflection units indicates that increasing the number of reflection units in the RIS phased array can increase the received power of the wireless device within a given time period, thus improving the system's signal transmission efficiency. Furthermore, the alternating optimization algorithm studied in this paper achieves a higher weighted sum rate for the same number of RIS units compared to the MRT algorithm with a fixed base station beam direction, demonstrating that the method in this embodiment can fully consider the combined effects of active and passive beamforming, achieving greater system signal transmission efficiency.

[0058] Figure 4 The fixed number of RIS phased array elements is 8 At 8 o'clock, the relationship between the system weighted sum rate and the base station transmit power is shown. When the base station transmit power increases, the transmission rate of all algorithms increases, indicating that the base station power affects the system's transmission rate. Since the base station transmit power has a maximum limit, the algorithm proposed in this embodiment has a higher weighted sum rate under the same base station transmit power, fully verifying the superiority of this method.

[0059] Depend on Figure 4 As can be seen, the beamforming optimization method based on RIS phased array assisted provided by the present invention, through alternating iterative optimization of active and passive beamforming technology, can effectively improve the system's transmission rate and provide stable communication guarantee when sudden situations occur between the base station and the user end, causing line-of-sight communication to be affected.

[0060] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A design method for a power communication system based on RIS phased array assistance, characterized in that: Includes the following steps: Step 1: Deploy a transmitting base station equipped with M antennas, K single-antenna user terminals, and a RIS phased array containing N×N passive reflective elements. The RIS phased array is used to provide an additional controllable wireless reflection channel for communication between the base station and the user terminals; define the active beamforming matrix of the base station as follows: The phase shift matrix of the reflected beamforming of the RIS phased array is: Where M is the number of base station antennas, K is the total number of user terminals, N is the number of single-sided reflective elements of the RIS phased array, and N×N is the total number of passive reflective elements of the RIS phased array. Step 2: Establish a hybrid channel model that includes the direct link from the base station to the user terminal, the reflection link from the base station to the RIS phased array and then to the user terminal, and solve for the channel matrix and channel vector corresponding to each link; Step 3: Select the weighted sum rate of all users as the quantitative indicator of system transmission efficiency, and construct an optimized active beamforming matrix for the base station with the goal of maximizing the system weighted sum rate. With the phase shift matrix of the RIS phased array The joint optimization mathematical model; Step 4: Using an alternating optimization method, the joint optimization mathematical model is decomposed into two sub-optimization problems, and the optimal base station active beamforming matrix and RIS phased array phase shift matrix are obtained by alternating iterative solutions. Step 5: Based on the obtained optimal active beamforming matrix and optimal phase shift matrix, reconstruct the RIS-assisted wireless communication channel, and transmit the base station signal to the user terminal through the channel to achieve power communication signal coverage in non-line-of-sight scenarios.

2. The design method for a power communication system based on RIS phased array assistance according to claim 1, characterized in that, In step 1, the active beamforming matrix satisfies ;in, Describes the set of complex matrices with M rows and K columns. For the beamforming weighting vector corresponding to the k-th user, ; The base station's transmitted signal is ,in The transmitted data symbols of the k-th user follow a cyclic symmetric complex Gaussian normal distribution with a mean of 0 and a variance of 1. External transmission signals from the base station The expression is: 。 3. The design method for a power communication system based on RIS phased array assistance according to claim 1, characterized in that, In step 1, the phase shift matrix of the RIS phased array For a diagonal matrix, the expression is: ； in, This indicates a diagonalization operation, used to generate a diagonal matrix with the elements inside the parentheses as diagonal elements; The amplitude gain provided for the nth passive reflective element of the RIS phased array. The phase shift gain provided for the nth passive reflective element of the RIS phased array. ; is the base of the natural logarithm. The imaginary unit satisfies .

4. The design method for a power communication system based on RIS phased array assistance according to claim 3, characterized in that, In step 2, the channel vector from the base station to the user terminal is denoted as... The channel vector from the base station to the RIS phased array is denoted as... The channel vector from the RIS phased array to the user terminal is denoted as... ;in, Represents the set of M-dimensional complex column vectors. Describes the set of N rows and M columns of complex matrices. Represents a set of N-dimensional complex column vectors; The end-to-end superposition channel expression from the base station to the user is: 。 5. The design method for a power communication system based on RIS phased array assistance according to claim 4, characterized in that, In the hybrid channel model of step 2, the channel from the base station to the RIS phased array and the channel from the RIS phased array to the user terminal both satisfy the line-of-sight transmission condition, and the channel vector follows a Rice distribution; the transmission from the base station to the user terminal is non-line-of-sight transmission, and the channel vector follows a Rayleigh distribution.

6. The design method for a power communication system based on RIS phased array assistance according to claim 5, characterized in that, In step 2, the channel matrix from the base station to the RIS phased array... Channel vector from RIS phased array to the k-th user terminal The corresponding Rice distribution channel model expressions are as follows: ； ； Among them, among them, and This indicates the transmission path loss of the corresponding channel. Rice factor, Indicates the array steering vector. and All are angle parameters. and This represents the non-line-of-sight components on the corresponding channel, all of which satisfy a complex Gaussian distribution; Channel vector from base station to the k-th user terminal The corresponding Rayleigh distribution expression is: ； in, This represents a cyclic symmetric complex Gaussian distribution, where the first parameter in parentheses is the distribution mean, and the second parameter is the distribution covariance matrix. The channel power corresponding to the path loss of the channel from the base station to the user terminal; It is an M-order identity matrix, that is, an M×M square matrix with 1s on the main diagonal and 0s on the rest. .

7. The design method for a power communication system based on RIS phased array assistance according to claim 6, characterized in that, In step 2, let the signal transmitted by the base station be... In the environment, the noise received by user k is denoted as... At the same time, the base station will send multiple signals. The received signal model of the k-th user terminal. The expression is: ； in, Let K be the base station-to-user channel vector corresponding to the k-th user. Let be the RIS phased array-to-user channel vector corresponding to the k-th user. , and ; Let the additive white Gaussian noise at the k-th user terminal have a mean of 0 and a variance of . The cyclically symmetric complex Gaussian distribution, This represents the noise variance.

8. The design method for a power communication system based on RIS phased array assistance according to claim 7, characterized in that, Based on the received signal model of the kth user terminal, the expression for the output signal-to-interference-plus-noise ratio of the kth user terminal is obtained as follows: 。 9. The design method for a power communication system based on RIS phased array assistance according to claim 8, characterized in that, In step 3, the expression for the system weighted sum rate WSR is: ； In the formula, This represents the weighting coefficient of the k-th user to the system transmission rate, used to characterize the communication priority of the k-th user, and .

10. The design method for a power communication system based on RIS phased array assistance according to claim 9, characterized in that, In step 3, when constructing the joint optimization mathematical model, the parameters for maximizing WSR are solved, including: Given the channel vector To maximize the system weighted sum rate (WSR), the active beamforming parameters of the base station need to be optimized. RIS phased array reflective beamforming At this point, the optimization problem can be represented as P1: ； As can be seen from optimization problem P1, the maximum weighted sum rate WSR is determined by the base station transmission. Phase shift matrix of RIS phased array A joint decision.

11. The design method for a power communication system based on RIS phased array assistance according to claim 10, characterized in that, In step 4, an alternating optimization method is used to decompose the joint optimization mathematical model into two sub-optimization problems, specifically including: Active beamforming vector optimization: fixed reflection coefficient matrix The channel can be represented as: At this point, optimization problem P1 can be transformed into optimization problem P2: ； By using the weighted minimum mean square error algorithm, the optimization problem is transformed into a weighted minimum mean square error problem by introducing undetermined parameters, and the mean square error value is reduced by iterative calculation method to gradually approach the optimal solution. RIS phased array passive beamforming optimization: fixed active beamforming weighting vector Therefore, the optimization problem can be simplified, and the original system's maximum transmission rate optimization problem can be rewritten as: ； The optimal reflection matrix is ​​solved using the semidefinite programming SDP algorithm. The mean square error value is: ; in, Indicates the receiver factor. and error scaling factor; respectively for and Taking the derivative and setting it to 0, we obtain the two-parameter update rule: ； ； Will and After substituting, let MSE calculate about With the derivative being 0, we obtain the iterative expression for the active beamforming weighting vector: ； RIS phased array passive beamforming optimization: fixed active beamforming weighting vector Therefore, the optimization problem can be simplified, and the original system's maximum transmission rate optimization problem can be rewritten as P3: ； s.t. ; The optimal reflection matrix is ​​solved using the semidefinite programming (SDP) algorithm.

12. The design method for a power communication system based on RIS phased array assistance according to claim 1, characterized in that, The iterative solution process in step 4 specifically includes the following steps: S401: Parameter Initialization: Set the initial value of the iteration count. t is the iteration count variable; set the convergence threshold. , The initial phase shift matrix of the RIS phased array is generated by using a preset positive real number and random phase. ; S402: Fix the phase shift matrix of the RIS phased array in the current iteration. Solve the active beamforming optimization subproblem to obtain the optimal active beamforming matrix for the current iteration. ; S403: Fix the active beamforming matrix for the current iteration. Solve the RIS phased array passive beamforming optimization subproblem to obtain the optimal phase shift matrix for the current iteration. ; S404: Convergence check: Calculate the system weighted sum rate for the current iteration. The system weighted sum rate with the previous iteration If satisfied If the algorithm converges, it stops iterating and outputs the optimal active beamforming matrix and the optimal phase shift matrix; if the convergence condition is not met, then let Then return to step S402 to continue the iteration.

13. The design method for a power communication system based on RIS phased array assistance according to claim 1, characterized in that, In step 1, the M antennas of the base station are a uniformly linearly distributed antenna array. The array steering vector is constructed based on the antenna element spacing, signal incident angle, and carrier wavelength, and the system parameters satisfy... That is, the total number of RIS phased array reflector elements is much greater than the number of base station antennas, and the number of base station antennas is greater than the total number of user terminals.

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