Method and system for maximizing weighted sum rate of multi-user swipt system assisted by mm-ris

Abstract

This invention relates to a method and system for maximizing the weighted sum rate in a multi-user SWIPT system assisted by MM-RIS. The method is as follows: S1, the base station modulates and transmits a signal; S2, using the signal from S1 as input parameters, the weighted sum rate of the system users is calculated; S3, it is determined whether the result obtained in S2 has converged. If converged, proceed to S4; otherwise, jump to and proceed to S7; S4, using the result obtained in S3 as input parameters, beamforming matrix and energy matrix optimization methods are executed, and the output W is obtained. opt S5. Take the W obtained in S4 opt As input parameters, the multi-mode RIS phase shift matrix optimization method is executed, and the output Θ is obtained. opt S6. Take the W obtained from S4 opt S E Θ obtained from S5 opt After alternating optimization, use the current weighted sum rate as input parameters, then set t = t + 1, and jump to S3; S7, obtain the optimal value R. opt The weighted sum rate maximization method and system for the SWIPT system assisted by multimodal reconfigurable smart surfaces proposed in this invention can significantly improve the user's weighted sum rate.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of digital communication, and particularly relates to a multi-mode reconfigurable intelligent surface (MM-RIS) assisted multi-user wireless power transfer (SWIPT) system weighted sum-rate maximization method and system. BACKGROUND

[0002] The performance of a multi-user multiple-input multiple-output SWIPT system is easily affected by the external transmission environment in a channel and energy limited environment. The reconfigurable intelligent surface (RIS) can intelligently adjust the signal propagation environment to improve the communication performance. However, the existing RIS related research mainly focuses on the SWIPT system assisted by a single-function or double-function RIS. Although the double-function RIS has the functions of signal reflection and transmission or refraction, and can provide full spatial coverage, there is still a double fading problem, that is, the channel gain of the RIS assisted cascade channel is the product of the two links. SUMMARY

[0003] In view of the existing RIS half-space coverage and double-path attenuation problems, the application provides a multi-mode reconfigurable intelligent surface (MM-RIS) assisted SWIPT system weighted sum-rate (WSR) maximization method and system with reflection, refraction and amplification functions. The MM-RIS-SWIPT system combines the characteristics of passive RIS, double-function RIS and active RIS, can actively amplify the incident signal, convert the serious multiplicative attenuation into acceptable additive attenuation, and at the same time, divide the amplified signal into two parts to serve the users on both sides, thereby significantly improving the WSR of the SWIPT system user receiving end.

[0004] To achieve the above purpose, the application adopts the following technical solutions:

[0005] A multi-mode reconfigurable intelligent surface (MM-RIS) assisted multi-user SWIPT system weighted sum-rate maximization method is implemented according to the following steps:

[0006] S1: The base station performs digital modulation on the transmitted signal, transmits it to the multi-mode reconfigurable intelligent surface (MM-RIS), the user receiving end and the energy receiving end through the free space. The data type of the signal can be represented by a complex number; the digital modulation of this step can use the existing technology.

[0007] S2: At the destination user receiving end, the signal transmitted in step S1 is received and taken as an input parameter to calculate the weighted sum-rate ; wherein Σ is the summation symbol; Ri (n) denotes the rate of the ith user receiver at the nth iteration; denotes the weight factor of the ith user receiver, K I denotes the number of users, i, n are integers;

[0008] S3: Determine the weighted sum rate of the user end obtained in step S2 whether the following relationship is satisfied: ; if not, step S4 is executed, and if satisfied, step S7 is executed; wherein ε is a constant, preferably any real number between 0.01-0.5. For example, when the difference value of the weighted sum rate of the two consecutive iterations and the weighted sum rate of the latter, i.e. ε, is less than 0.5, it is considered that convergence has been reached, and iteration can be stopped; at this time, ε should be selected to be small enough to ensure accuracy, but not less than 0.01 to avoid unnecessary computational overhead.

[0009] S4: Take the weighted sum rate WSR obtained in step S3 as an input parameter, execute the optimization method of the beam forming matrix W and the energy matrix S E , and output the optimal NxN-dimensional beam forming matrix W opt and the optimal NxN-dimensional energy matrix S E . Wherein N is a natural number, representing the number of base station antennas; the subscript represents the ith user; the superscript opt represents the optimal value of W;

[0010] S5: Take the optimal beam forming matrix W opt and the energy matrix S E obtained in step S4 as input parameters, execute the multi-mode RIS phase shift matrix optimization method, and output the optimal MxM-dimensional phase shift matrix . Wherein M is a natural number, representing the number of RIS units; the superscript opt represents the optimal value of ;

[0011] S6: According to W opt , S E obtained in step S4 and obtained in step S5, calculate the weighted sum rate of the current system user end ; let n=n+1, and jump to step S3;

[0012] S7: Take the WSR obtained in step S3 as the output of the final result, and obtain the optimal value of the WSR of the user.

[0013] Preferably, in step S4, the optimization method of the beam forming matrix W and the energy matrix S E adopts the following steps to achieve:

[0014] S4.1: The system includes a base station with a positive integer number of antennas, a multimode RIS with a total of positive integer M reflective and refractive elements, and K. I Single-antenna users, and K E A single-antenna energy receiver, and K I and K E It is a positive integer. Let i be the i-th user, then the signal-to-interference-plus-noise ratio (SINR) for the i-th user is... i Represented as:

[0015]

[0016] in, f is a 1xN column vector representing the equivalent channel coefficient matrix from the base station to the i-th user, with the superscript H indicating the conjugate transpose; b,i f is an Nx1 column vector representing the channel coefficient vector from the base station to the i-th user; r,i Let G be an Mx1 column vector representing the channel coefficient vector from the multi-mode RIS to the i-th user; G is an MxN matrix representing the channel coefficient matrix from the base station to the multi-mode RIS; w i It is an Nx1 dimensional vector representing the beamforming signal transmitted by the base station to the i-th user; Let be an M x M dimensional diagonal matrix, representing the phase shift matrix of the multimode RIS at the i-th user; where, Let β represent the amplitude and phase of the m-th cell at the i-th user, respectively. max ≥1 is the amplification factor; Let K be a constant, representing the variance of the Gaussian white noise at the user location; then K I The weighted sum of rates for each user is:

[0017]

[0018] Among them, R i Let be a real constant, representing the rate of the i-th user. Then the harvested power of the k-th energy receiver is:

[0019]

[0020] in, This represents the equivalent channel coefficient matrix from the base station to the k-th energy receiver; h b,k Let h be an Nx1 dimensional column vector representing the channel coefficient vector from the base station to the k-th energy receiver; r,k It is an Mx1 dimensional column vector, representing the channel coefficient vector from the multimode RIS to the k-th energy receiver; is a constant, representing the variance of the Gaussian white noise at the multimode RIS; For K E Energy collection efficiency of each energy receiver.

[0021] S4.2: Model the problem 1 of the system user end weighted sum rate as:

[0022]

[0023] wherein Tr(•) represents the trace of a matrix; is a constant, representing the output signal power limit of the multi-mode RIS; is a constant, representing the transmission power limit at the base station; is a constant, representing the minimum harvesting power threshold of the kth energy receiving end. Introduce an auxiliary variable , using the quadratic transformation method, the objective function of formula (4) is equivalent to:

[0024]

[0025] Fix the variable , calculate the partial derivative of the independent variable, so that is zero, and the optimal solution is obtained as:

[0026]

[0027] wherein , define the intermediate variable matrix:

[0028]

[0029] Then the transmit beamforming matrix and energy matrix optimization problem 2 is expressed as:

[0030]

[0031] S4.3: For W H D k W first-order Taylor expansion is obtained as:

[0032]

[0033] Then the optimization problem 2 is re-expressed as:

[0034]

[0035] S4.4: Calculate formula (16) by using the Lagrange relaxation method, which can be obtained by using the existing technology. Introduce three Lagrange multipliers, then the optimization problem 2 can be converted to:

[0036]

[0037] The subgradient method can use the prior art to obtain the first-order derivative of the function The update equation of is:

[0038]

[0039] S4.5: Let The first-order derivative of the function with respect to W is zero, formula (17) can be solved, and the optimal solution W is obtained opt is:

[0040]

[0041] Preferably, the optimization method of the MM-RIS reflection, refraction and amplification phase shift matrix The optimization method of the MM-RIS reflection, refraction and amplification phase shift matrix

[0042] S5.1: Define is the coefficient matrix of the reflection surface, is the coefficient matrix of the reflection surface. Fix the variable , define the intermediate variable matrix:

[0043]

[0044]

[0045] The optimization problem 3 of the MM-RIS reflection, refraction and amplification phase shift matrix is expressed as:

[0046]

[0047] S5.2: Use the successive lower bound maximization (SLBM) method (which can use the prior art) to express in the formula (30) as:

[0048]

[0049] Therefore, the optimization problem 3 can be re-expressed as:

[0050]

[0051] S5.3: Solve formula (32), that is, the optimal MF-RIS phase shift matrix .

[0052] The application also discloses an MM-RIS assisted multi-user SWIPT system weighted sum rate maximization system for executing the above method, which comprises the following modules:

[0053] ​Signal transmission module: After the base station performs digital modulation on the signal, it sends it to the multimode reconfigurable smart surface MM-RIS, the user receiver, and the energy receiver;

[0054] Weighted sum rate calculation module: The destination user receiver receives the signal transmitted by the base station and uses it as input parameters to calculate the weighted sum rate. ;in, K represents the weight factor of the i-th user receiver. I This represents the number of users, where i is an integer;

[0055] Judgment Module: Determines the weighted sum rate obtained from the weighted sum rate calculation module at the user end. Does it satisfy the following relationship: Where ε is a constant; if not satisfied, the beamforming matrix and energy matrix optimization modules will perform the operation; if satisfied, the user's optimal WSR value will be output.

[0056] Beamforming matrix and energy matrix optimization module: Using the weighted sum rate WSR obtained from the judgment module as input parameters, it executes the beamforming matrix W and energy matrix S optimization. E The optimization method outputs an optimal beamforming matrix W, whose elements are NxN dimensional vectors. opt and the NxN dimensional energy matrix S E Where N is a natural number representing the number of antennas at the base station; superscript opt This represents the optimal value of W;

[0057] Multimode RIS phase shift matrix optimization module: This module optimizes the beamforming matrix W obtained from the beamforming matrix and energy matrix optimization modules. opt and energy matrix S E As input parameters, the multi-mode RIS phase shift matrix optimization method is executed, and the optimal MxM-dimensional phase shift matrix is ​​output. Where M is a natural number representing the number of RIS units, and the superscript indicates the number of RIS units. opt express The optimal value;

[0058] Alternating Iterative Optimization Module: This module combines the W values ​​obtained from the beamforming matrix and energy matrix optimization modules. opt S E The phase shift matrix optimization module obtained from the multi-mode RIS Perform alternating iterative optimization to calculate the weighted sum rate of the current user terminal. Then let n = n + 1, which will be executed by the judgment module.

[0059] Some prior art related to this invention is briefly described below:

[0060] 1. Digital modulation methods such as binary phase shift keying (BPSK)

[0061] Digital modulation is an important method of modern communication, which has many advantages compared with analog modulation. Digital modulation has better anti-interference performance, stronger anti-channel loss, and better security; in digital transmission system, error control technology can be used to support complex signal conditions and processing technology, such as source coding, encryption technology and equalization. In digital modulation, the modulation signal can be represented as a time sequence of symbols or pulses, where each symbol can have m finite states, and each symbol can be represented by n bits. The main digital modulation methods at present are BPSK, QPSK, 8PSK, QAM, etc. See "G. Proakis, M. Salehi. Digital Communications, 5th Edition[J]. 2008" for details.

[0062] 2, Quadratic transformation method

[0063] Fractional programming problem refers to a nonlinear programming problem whose objective function is a fractional function, and its mathematical model is:

[0064]

[0065] Where A(x) and B(x) are continuous functions defined on , and B(x) is non-negative. Quadratic transformation is a common method to solve fractional programming problems, as described in the following theorem:

[0066]

[0067] Satisfy conditions C1: can be decoupled into new target form ; C2: when the variable x * maximizes A(x) / B(x), and only when x * and y * together can maximize g(x,y); . See "K. Shen and W. Yu, "Fractional programming for communication systems—Part I: Power control and beamforming," in IEEE Transactions on Signal Processing, vol. 66, no. 10, pp. 2616-2630, 15 May 15, 2018." for details.

[0068] 3, Lagrangian decomposition method

[0069] The Lagrangian decomposition method is a common method for dealing with non-convex mathematical optimization, the basic idea is to introduce the Lagrange multiplier for constraints, and then construct the Lagrangian function. Its mathematical model:

[0070]

[0071] Construct the Lagrangian function:

[0072]

[0073] Where, is the Lagrange multiplier. For x that violates the constraints of the original problem, there is a certain c i (x)>0, or a certain h j (x) ≠0, there is:

[0074]

[0075] Therefore, the optimal value of the original problem is:

[0076]

[0077] By the way of Lagrangian decomposition, some complex constraints are put into the objective function for processing, and then the problem is decomposed. See "S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, Aug. 2004." for details.

[0078] 4. Subgradient method

[0079] Subgradient method is an iterative method for solving convex optimization problems of convex functions. It can be used for non-differentiable objective functions. When the objective function is differentiable, for unconstrained problems, the subgradient method has the same search direction as the gradient descent method. In each iteration step of the subgradient method, a negative subgradient direction is selected as the search direction, and a certain rule is set for the search step size. The selection of subgradient can be the subgradient of the objective function, or the subdifferential of any function that violates the constraints. For non-smooth convex optimization problems, the subgradient method can guarantee global convergence. See K. Singh, S. Biswas, M.-L. Ku, and M. F. Flanagan, “Transceiver design and power control for full-duplex ultra-reliable low-latency communication systems,” IEEE Trans. Wireless Commun., vol. 21, no. 2, pp. 1392–1406, Feb. 2022.

[0080] 5. Successive lower bound maximization (SLBM) method

[0081] Successive lower bound maximization (SLBM) method is an optimization algorithm mainly used to solve optimization problems with nonlinear constraints. SLBM method adjusts parameters step by step to maximize the objective function while satisfying the constraints. The basic principle is to update the parameters iteratively to approach the optimal solution gradually. See S. Yan, S. Cai, W. Xia, J. Zhang and S. Xia, "A Reconfigurable Intelligent Surface Aided Dual-Function Radar and Communication System," 2022 2nd IEEE International Symposium on Joint Communications&Sensing (JC&S), Seefeld, Austria, 2022, pp. 1-6.

[0082] The present application has the following technical effects:

[0083] The application firstly fixes the reflection, refraction and amplification phase shift matrix of the MM-RIS, and solves the optimal transmit beamforming matrix and energy matrix by using the Lagrange decomposition method; secondly, fixes the beamforming matrix, and solves the reflection, refraction and amplification phase shift matrix of the MM-RIS by using the successive lower bound maximization method; finally, the optimal user weighted sum rate is obtained by alternating optimization, and the optimal improvement of the weighted sum rate of the multi-user MM-RIS-SWIPT system is realized. BRIEF DESCRIPTION OF DRAWINGS

[0084] Figure 1 A multi-mode RIS-assisted multi-user SWIPT system model diagram of the preferred embodiment of the application;

[0085] Figure 2 A multi-mode RIS-assisted multi-user SWIPT system user weighted sum rate maximization method flowchart of the preferred embodiment of the application;

[0086] Figure 3 A step flowchart for optimizing the transmit beamforming matrix and the energy matrix of the preferred embodiment of the application;

[0087] Figure 4 A step flowchart for optimizing the reflection, refraction phase shift matrix of the multi-mode RIS of the preferred embodiment of the application;

[0088] Figure 5 A graph of the convergence performance of the algorithm under different schemes;

[0089] Figure 6 A graph of the weighted sum rate performance with the increase of the number of RIS units under different schemes;

[0090] Figure 7 A multi-mode RIS-assisted multi-user SWIPT system user weighted sum rate maximization system block diagram of the preferred embodiment of the application. DETAILED DESCRIPTION

[0091] The application will be further described below in combination with specific embodiments.

[0092] Figure 1 A multi-mode RIS-assisted multi-user SWIPT system model diagram of the preferred embodiment of the application. The system contains a base station with an antenna number of a positive integer N, a MM-RIS with a reflection and refraction unit number of a positive integer M, a single-antenna user, and a single-antenna energy receiving end, and is a positive integer.

[0093] Figure 2This is a flowchart of a weighted sum rate maximization method for an MM-RIS-assisted multi-user SWIPT system according to an embodiment of the present invention. This embodiment is completed through the following steps:

[0094] Step 1: The base station digitally modulates the transmitted signal, passes it through free space, and sends it to MM-RIS, the user receiver, and the power receiver;

[0095] Step 2: The destination user receiver receives the transmitted signal from step S1 as input parameters and calculates its weighted sum rate. In this step, the iteration number t=1 and the convergence judgment constant ε=0.01 are initialized, and the beamforming vector W is input. (1) MF-RIS phase shift matrix Energy Matrix ;

[0096] Step 3: Judgment Whether it is true or not, among which, This refers to the weighted sum rate value obtained from the nth iteration. If it is true, proceed to step seven; otherwise, execute step four. This can characterize the convergence performance of the method;

[0097] Step 4: Using the weighted sum rate WSR obtained in step S3 as input parameters, obtain the optimal beamforming matrix W in NxN dimensions using the beamforming matrix and energy matrix optimization method. (t) and the NxN dimensional energy matrix S E ;

[0098] Step 5: Calculate the optimal beamforming matrix W from Step 4. (t) and energy matrix S E Using these parameters as input, the multi-mode RIS phase shift matrix optimization method is executed to obtain the optimal MxM-dimensional phase shift matrix. ;

[0099] Step Six: According to W (t) S E and The alternating optimization results are used to calculate the weighted sum rate of the current system's user terminals. And let t = t + 1, then proceed to step three;

[0100] Step 7: Output the maximum weighted sum rate R of the system users. opt .

[0101] Figure 3 The flowchart for optimizing the transmit beamforming matrix and energy matrix in this embodiment of the invention is mainly completed through the following steps:

[0102] Step 1: Initialize the number of iterations The convergence criterion constant ε = 0.01 is used as the input initial signal value;

[0103] Step 2: Determine whether f1(m)≤ε holds true, where f1(m) represents the target value of equation (17) obtained by the m-th iteration. If it holds true, the optimal beam matrix is ​​obtained through equation (21), and the process jumps to step 7; otherwise, proceed to step 3.

[0104] Step 3: Determine if the weighted sum rate converges. If it converges, proceed to Step 6; otherwise, proceed to Step 4.

[0105] Step 4: Solve for the weighted sum rate R (m) And update the current beamforming matrix W (m) ;

[0106] Step 5: Update the Lagrange variables And m = m + 1;

[0107] Step Six: Calculate the target values ​​of equations (A1) and (A2), and denot them as follows: and f1(m), and m = m + 1, then proceed to step two;

[0108] Step 7: The final output is the optimal transmit beamforming vector W. opt .

[0109] Figure 4 The flowchart for optimizing the MM-RIS reflection, refraction, and amplification phase shift matrix in this embodiment of the invention is mainly completed through the following steps:

[0110] Step 1: Initialize the number of iterations n=0, and the initial weighted sum rate. And the convergence judgment constant ε=0.01, input beamforming matrix W;

[0111] Step 2: Determine if f2(n) ≤ ε, where f2(n) represents the target value of equation (32) obtained in the nth iteration. If it is true, the optimal phase shift matrix is ​​obtained by the successive lower bound maximization method, and the process jumps to step 7; otherwise, proceed to step 3.

[0112] Step 3: Determine if the weighted sum rate converges. If it converges, proceed to Step 6; otherwise, proceed to Step 4.

[0113] Step 4: Solve for the weighted sum rate R (n) And update the current phase shift matrix. ;

[0114] Step 5: Update intermediate variables And n = n + 1;

[0115] Step six: calculate the target value of formula (A4) and (A5), respectively, as , and , n = n + 1, then step two is executed;

[0116] Step seven: finally obtain the optimal MM-RIS phase shift matrix .

[0117] Figure 5 The figure is the convergence performance of the algorithm under different RIS-aided multi-user SWIPT systems. Among them, the number of users is set to 4, the base station transmit power is set to 30dBm, the number of RIS elements is 30, and the number of base station antennas is 4. As can be seen from the figure, the WSR under different RIS assistance increases with the increase of the number of iterations, and finally tends to be stable. The WSR of the multi-mode-RIS assistance scheme proposed in the application is always better than the other three schemes, and converges about 21-26 times, and the convergence is about 22bps / Hz, which verifies the effectiveness and convergence of the method of the application.

[0118] Figure 6 The figure is the weighted sum rate performance figure when the number of RIS elements increases under different schemes. As can be seen, the WSR under different schemes increases with the increase of the number of RIS elements, and the WSR of the multi-mode-RIS system scheme proposed in the application is always better than the benchmark. This is because by jointly designing its reflection, transmission and amplification characteristics, the multi-mode RIS can provide active signal amplification for both users, thereby significantly improving the weighted sum rate of all users. These results show that introducing multi-mode RIS in the SWIPT system is a technology that can improve system performance and has a wide application prospect.

[0119] As shown in Figure 7 , the embodiment discloses a multi-user SWIPT system weighted sum rate maximization system assisted by MM-RIS, which is used to execute the above method, and includes the following modules:

[0120] The signal sending module: the base station digitally modulates the transmitted signal, and sends the multi-mode reconfigurable intelligent surface MM-RIS, the user receiving end and the energy receiving end;

[0121] The weighted sum rate calculation module: the user receiving end receives the signal sent by the base station as an input parameter, and calculates the weighted sum rate ; wherein R i (n) represents the weighted sum rate value obtained by the nth calculation, and n is a constant;

[0122] The judgment module: judges whether the user end weighted sum rate

[0123] satisfies the following relationship: ; wherein, ε is a constant; if not satisfied, execute by the beamforming matrix and energy matrix optimization module; if satisfied, output the WSR optimal value of the user;

[0124] The beamforming matrix and energy matrix optimization module: taking the weighted sum rate WSR obtained by the judgment module as an input parameter, executing the beamforming matrix W and energy matrix S E Optimization method, output N x N dimension optimal beamforming matrix W opt And N x N dimension optimal energy matrix S E ; wherein, N is a natural number, representing the number of base station antennas; superscript opt represents the optimal value of W;

[0125] The multi-mode RIS phase shift matrix optimization module: taking the optimal beamforming matrix W opt Energy matrix S E obtained by the beamforming matrix and energy matrix optimization module as an input parameter, executing the multi-mode RIS phase shift matrix optimization method, and outputting M x M dimension optimal phase shift matrix ; wherein, M is a natural number, representing the number of RIS units; superscript opt represents the optimal value of ;

[0126] The alternating iteration optimization module: taking W opt , S E obtained by the beamforming matrix and energy matrix optimization module and obtained by the multi-mode RIS phase shift matrix optimization module are alternately optimized to calculate the weighted sum rate of the current user end; and let n = n + 1, execute by the judgment module.

[0127] The other contents of the embodiment can refer to the above-mentioned method embodiment.

[0128] Although the embodiments of the present application have been clearly described. For those skilled in the art, various changes, modifications, replacements and variations can be made to these embodiments without departing from the principles and spirits of the method of the present application. The scope of the present application is defined by the appended claims and their equivalents, which still belong to the scope of the method described in the present application and are still considered to be within the protection scope of the present application.

Claims

1. A method of weighted sum rate maximization for a multi-user SWIPT system assisted by a MM-RIS, the method comprising: The specific steps are as follows: S1: After the base station performs digital modulation on the signal, the signal is sent to the multi-mode reconfigurable intelligent surface MM-RIS, the user receiving end and the energy receiving end; S2: the destination user receiving end receives the signal sent by the base station in step S1, and calculates the weighted sum rate as an input parameter ; wherein, ∑ is a summation symbol; R i (n) represents the rate of the i-th user receiving end at the n-th iteration; represents the weight factor of the i-th user receiving end, K I represents the number of users, i and n are integers; S3: judging the user-end weighted sum rate obtained in step S2 whether the following relation is satisfied: ; wherein ε is a constant; if not, step S4 is executed; if yes, step S7 is executed. S4: taking the weighted sum rate WSR obtained in step S3 as an input parameter, performing optimization of a beamforming matrix W and an energy matrix S E , outputting an NxN-dimensional optimal beamforming matrix W opt and an NxN-dimensional optimal energy matrix S E ; wherein N is a natural number, representing the number of base station antennas; the superscript opt represents the optimal value of W. S5: obtaining an optimal beamforming matrix W opt and an energy matrix S E As an input parameter, the multi-mode RIS phase shift matrix optimization method is executed, and an MxM-dimensional optimal phase shift matrix is output ; wherein M is a natural number, representing the number of RIS units; the superscript opt represents the optimal value of S6: W obtained from step S4 opt , S E and step S5 , calculate the weighted sum rate of the current system user end ; let n = n + 1, jump to step S3; S7: The WSR obtained in step S3 is taken as the output of the final result, and the optimal value of the WSR of the user is obtained.

2. The MM-RIS assisted multi-user SWIPT system weighted sum rate maximization method according to claim 1, characterized in that, In step S3, when the weighted sum rate change of two consecutive iterations is less than a set value, the iteration is stopped, and step S7 is executed.

3. The MM-RIS assisted multi-user SWIPT system weighted sum rate maximization method of claim 1, wherein, In step S4, the beamforming matrix W and the energy matrix S E are optimized using the following steps: S4.1: The SWIPT system comprises a base station with a positive integer number of antennas, a multi-mode RIS with a positive integer M of total number of reflecting and refracting units, K I single-antenna users, and K E single-antenna energy receiving ends, and K I and K E are positive integers; the signal-to-interference-and-noise ratio (SINR) i of the i-th user is represented as: (1) where, is an NxN dimensional column vector representing the equivalent channel coefficient matrix from the base station to the i-th user, and the superscript H denotes the conjugate transpose; f b,i is an Nx1 dimensional column vector representing the channel coefficient vector from the base station to the i-th user; f r,i is an Mx1 dimensional column vector representing the channel coefficient vector from the multi-mode RIS to the i-th user; G is an MxN dimensional matrix representing the channel coefficient matrix from the base station to the multi-mode RIS; w i is an Nx1 dimensional vector representing the beamformed signal transmitted by the base station to the i-th user; is an MxM dimensional diagonal matrix representing the phase shift matrix of the multi-mode RIS at the i-th user; where, and, respectively, represent the amplitude and phase of the m-th element at the i-th user, is an amplification factor; is a constant representing the variance of the Gaussian white noise at the user; then K I the weighted sum rate of K users is: (2) where R i is a real constant, and represents the rate of the ith user; the harvested power of the kth energy receiver is: (3) wherein, denotes the equivalent channel coefficient matrix from the base station to the kth energy receiver; h b,k is an Nx1 dimensional column vector denoting the channel coefficient vector from the base station to the kth energy receiver; h r,k is an Mx1 dimensional column vector denoting the channel coefficient vector from the multi-mode RIS to the kth energy receiver; is a constant denoting the variance of the Gaussian white noise at the multi-mode RIS; is the energy harvesting efficiency of the K E energy receivers; S4.2: Model problem 1 of the system user end weighted sum rate R as: (4) where Tr(•) denotes the trace of a matrix; is a constant, representing the output signal power limit of the multi-mode RIS; is a constant, representing the transmit power limit at the base station; is a constant, representing the minimum harvested power threshold of the th energy receiver; introduce auxiliary variable Using the quadratic transformation method, the objective function of formula (4) is equivalent to: (5) Fixed variable , compute the partial derivative of the argument, so that the optimal solution is obtained is: (6) (7) wherein , fix the variable , define the intermediate variable matrix: (8) (9) (10) (11) (12) (13) Then the transmit beamforming matrix and energy matrix optimization problem 2 is expressed as: (14) S4.3: To A first order Taylor expansion gives: (15) Then the optimization problem 2 is re-expressed as: (16) S4.4: The optimization problem 2 is transformed into the following problem by using the Lagrange decomposition method to calculate equation (16) and introducing three Lagrange multipliers. (17) The update equation of the function is obtained by using the second gradient method and the first derivative of the function The update equation of the function is obtained by using the second gradient method and the first derivative of the function The update equation of the function is obtained by using the second (18) (19) (20) S4.5: Let The first derivative of the function with respect to W is zero, solve equation (17) and get the optimal solution W opt is: (21)。 4. The MM-RIS assisted multi-user SWIPT system weighted sum rate maximization method according to claim 3, characterized in that, In step S5, the multi-mode RIS phase shift matrix The optimization method is implemented using the following steps: S5.1: Definitions is the coefficient matrix for the reflecting surface, is the coefficient matrix for the refracting surface; fixed variable and define the intermediate variable matrix: (22) (23) (24) (25) (26) Then the multi-mode RIS reflection, refraction and magnification phase shift matrix The optimization problem 3 is formulated as: S5.2: Using the successive lower bound maximization method, u in the formula of C2 in formula (30) is maximized H Q k u is expressed as: The optimization problem 3 is re-expressed as: S5.3: Solving equation (32) to obtain the optimal multi-mode RIS phase shift matrix .

5. A system for weighted sum rate maximization in a multi-user SWIPT system assisted by a MM-RIS for performing the method according to any one of claims 1 to 4, characterized in that Comprise the following modules: Signal sending module: after the base station performs digital modulation on the signal, the signal is sent to the multi-mode reconfigurable intelligent surface MM-RIS, the user receiving end and the energy receiving end; Weighted sum rate calculation module: the destination user receiving end receives the signal sent by the base station and takes it as an input parameter to calculate the weighted sum rate ; wherein, Σ is a summation symbol; R i (n) represents the rate of the i-th user receiving end at the n-th iteration; represents the weight factor of the i-th user receiving end, K I represents the number of users, i and n are integers; judgment module: judging the user-side weighted sum rate obtained by the weighted sum rate calculation module whether the following relationship is satisfied: ; wherein, ε is a constant; if not satisfied, performing by the beamforming matrix and energy matrix optimization module; if satisfied, outputting the WSR optimal value of the user; Beamforming matrix and energy matrix optimization module: taking the weight sum rate WSR obtained by the judgment module as an input parameter, performing an optimization method of a beamforming matrix W and an energy matrix S E , outputting an N x N dimension optimal beamforming matrix W opt and an N x N dimension optimal energy matrix S E ; wherein N is a natural number, representing the number of antennas of the base station; superscript opt represents the optimal value of W. Multi-mode RIS phase shift matrix optimization module: the optimal beamforming matrix W obtained by the beamforming matrix and energy matrix optimization module opt and energy matrix S E As an input parameter, the multi-mode RIS phase shift matrix optimization method is executed, and the optimal phase shift matrix of MxM dimension is output ; wherein M is a natural number, representing the number of RIS units, and the superscript opt represents The optimal value of Alternating iterative optimization module: the W obtained by the beamforming matrix and energy matrix optimization module opt , S E and the multi-mode RIS phase shift matrix optimization module Alternating iterative optimization is performed to calculate the weight and rate of the current user end ; let n = n + 1, and execute by the judgment module.

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