MM-RIS assisted multi-user SWIPT system weighting and rate maximization method and system

Abstract

The invention relates to an MM-RIS assisted multi-user SWIPT system weighting and rate maximization method and system. The method comprises the following steps: S1, a base station modulates and sends a signal; s2, taking the signal in the S1 as an input parameter, calculating the weighted sum rate # imgabs0 # S3 of a system user, judging whether the # imgabs1 # obtained in the S2 is converged or not, and if the # imgabs1 # obtained in the S2 is converged, executing S4; otherwise, skipping to and executing S7; s4, taking the # imgabs2 # obtained in the S3 as an input parameter, executing a beam forming matrix and energy matrix optimization method, and outputting Wopt; s5, taking the Wopt obtained in the step S4 as an input parameter, executing a multimode RIS phase shift matrix optimization method, and outputting theta opt; s6, Wopt and SE obtained in S4 and theta opt obtained in S5 are alternately optimized and then serve as input parameters, the current weighted sum rate # imgabs3 # is calculated, then t is made to be equal to t + 1, and the step S3 is executed; and S7, the optimal value Ropt of the # imgabs4 # is obtained. According to the multi-mode reconfigurable intelligent surface assisted SWIPT system weighting and rate maximization method and system provided by the invention, the weighting and rate of a user can be remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital communication, and in particular relates to a weighted sum rate maximization method and system for a multi-user wireless power-carrying communication (SWIPT) system assisted by a multi-mode reconfigurable intelligent surface (MM-RIS). Background Art

[0002] The performance of multi-user multi-input multi-output SWIPT systems is easily affected by the external transmission environment in channel and energy-constrained environments. Reconfigurable Intelligence Surface (RIS) can intelligently adjust the signal propagation environment to improve communication performance. However, existing RIS-related research mainly focuses on SWIPT systems assisted by single-function or dual-function RIS. Although the dual-function RIS has the dual functions of signal reflection and transmission or refraction and can provide full spatial coverage, it still has the problem of double fading, that is, the channel gain of the RIS-assisted cascade channel is the product of the two links. Summary of the invention

[0003] In view of the existing RIS half-space coverage and two-way attenuation problems, the present invention proposes 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 proposed in the present invention combines the characteristics of passive RIS, dual-function RIS and active RIS, can actively amplify the incident signal, convert severe multiplicative attenuation into acceptable additive attenuation, and divide the amplified signal into two parts to serve users on both sides, significantly improving the WSR of the SWIPT system user receiving end.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A weighted sum rate maximization method for a multi-user SWIPT system assisted by a multi-mode reconfigurable smart surface (MM-RIS) is implemented in the following steps:

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

[0007] S2: At the destination user receiving end, receive the signal sent in step S1 and use it as an input parameter to calculate its weighted sum rate Among them, ∑ is the summation symbol; Ri (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;

[0008] S3: Determine the user terminal weighted sum rate obtained in step S2 Does it satisfy the following relationship: If not, execute step S4; if satisfied, jump to and execute step S7; wherein ε is a constant, preferably any real number between 0.01-0.5. For example, when the ratio of the weighted sum rate difference of two consecutive iterations to the weighted sum rate of the latter iteration, i.e., ε, is less than 0.5, it is considered that convergence has been achieved and the 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 input parameters and perform beamforming matrix W and energy matrix S E The optimization method outputs the N×N dimensional optimal beamforming matrix W 0pt and the N×N optimal energy matrix S E Where N is a natural number, indicating the number of base station antennas; the subscript i indicates the i-th user; the superscript 0pt represents the optimal value of W;

[0010] S5: The optimal beamforming matrix W obtained in step S4 is opt and the energy matrix S E As input parameters, the multi-mode RIS phase shift matrix optimization method is executed, and the M×M dimensional optimal phase shift matrix Θ is output. opt . Where M is a natural number, indicating the number of RIS units; opt represents the optimal value of Θ;

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

[0012] S7: The WSR obtained in step S3 is output as the final result to obtain the optimal WSR value of the user.

[0013] Preferably, in step S4, the beamforming matrix W and the energy matrix S E The optimization method is implemented in the following steps:

[0014] S4.1: The system comprises a base station with a positive integer number of antennas N, a multimode RIS with a positive integer number of reflection and refraction units M, and K I single-antenna users, and K E single antenna energy receiving end, and K I and K E is a positive integer. Let i be the i-th user, then the signal to interference and noise ratio SINR corresponding to the i-th user is i It is expressed as:

[0015]

[0016] in, is a 1×N-dimensional column vector, representing the equivalent channel coefficient matrix from the base station to the i-th user, and the superscript H represents the conjugate transpose; f b,i is an N×1-dimensional column vector, representing the channel coefficient vector from the base station to the i-th user; f r,i is an M×1-dimensional column vector, representing the channel coefficient vector from the multimode RIS to the i-th user; G is an M×N-dimensional matrix, representing the channel coefficient matrix from the base station to the multimode RIS; w i is an N×1-dimensional vector, representing the beamforming signal transmitted by the base station to the i-th user; is an M×M dimensional diagonal matrix, representing the phase shift matrix of the multimode RIS at the i-th user; where and Respectively represent the amplitude and phase of the mth unit at the ith user, β max ≥1 is the amplification factor; is a constant, representing the variance of Gaussian white noise at the user; then K I The weighted sum rate of each user is:

[0017]

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

[0019]

[0020] in, represents the equivalent channel coefficient matrix from the base station to the kth energy receiving end; h b,k is an N×1-dimensional column vector, representing the channel coefficient vector from the base station to the kth energy receiving end; h r,k is an M×1-dimensional column vector, representing the channel coefficient vector from the multimode RIS to the k-th energy receiving end; is a constant, representing the variance of Gaussian white noise at the multimode RIS; η k KE The energy collection efficiency of the energy receiving end.

[0021] S4.2: Model Problem 1 with the system user-side weighted sum rate R as:

[0022]

[0023] Where Tr(·) represents the trace of the matrix; is a constant, representing the output signal power limit of the multimode RIS; is a constant representing the transmit power limit at the base station; Q k,min is a constant, representing the minimum harvesting power threshold of the kth energy receiving end. Introducing auxiliary variables Using the secondary transformation method, the objective function of formula (4) is equivalent to:

[0024]

[0025] Fixed variable {{w i},S E ,Θ i}, calculate the partial derivative of the independent variable so that Zero, the optimal solution for:

[0026]

[0027] in, make Fixed variables {ρ, v, Θ i}, define the intermediate variable matrix:

[0028]

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

[0030]

[0031] S4.3: Yes Middle W H D k The first-order Taylor expansion of W yields:

[0032]

[0033] Then the optimization problem 2 is reformulated as:

[0034]

[0035] S4.4: Use the Lagrangian decomposition method to calculate formula (16). The Lagrangian decomposition method can use existing technology.

[0036] By introducing three Lagrange multipliers λ, μ, and γ, the optimization problem 2 can be transformed into:

[0037]

[0038] The sub-gradient method is used. The sub-gradient method can adopt the existing technology and use the first-order derivative of the L(W,λ,μ,γ) function to obtain the update equations of λ,μ,γ:

[0039]

[0040] S4.5: Let the first-order derivative of the function L(W,λ,μ,γ) with respect to W be zero, and we can solve equation (17) and obtain the optimal solution W opt for:

[0041]

[0042] Preferably, the optimization method of the MM-RIS reflection, refraction and amplification phase shift matrix θ in step S5 is implemented by the following steps:

[0043] S5.1: Definitions is the coefficient matrix of the reflection surface, is the coefficient matrix of the refractive surface. i},S E}, define the intermediate variable matrix:

[0044]

[0045]

[0046]

[0047]

[0048] R m =diag(0 1×(m-1) ,1,0 1×(KIM-(KI-1)m) ), (26)

[0049]

[0050]

[0051]

[0052] Then the optimization problem 3 of the MM-RIS reflection, refraction and amplification phase shift matrix Θ is expressed as:

[0053]

[0054] S5.2: Using the successive lower bound maximization (SLBM) method (which can be based on existing technology), u in the C2 formula shown in equation (30) is H Q k u is represented by:

[0055] u H Q k u≥u H (t)Q k u+u H Q k u(t)-u H (t)Q k u(t), (31)

[0056] Therefore, the optimization problem 3 can be restated as:

[0057]

[0058] S5.3: Solve equation (32) to obtain the optimal MF-RIS phase shift matrix Θ opt .

[0059] The present invention also discloses a MM-RIS-assisted multi-user SWIPT system weighted sum rate maximization system, which is used to execute the above method and comprises the following modules:

[0060] Signal sending module: After the base station performs digital modulation on the signal, it sends it to the multi-mode reconfigurable intelligent surface MM-RIS, the user receiving end and the energy receiving end;

[0061] Weighted sum rate calculation module: The destination user receiving end receives the signal sent by the base station and uses it as an input parameter to calculate the weighted sum rate in, represents the weight factor of the i-th user receiving end, K I Represents the number of users, i is an integer;

[0062] Judgment module: judges the user-side weighted sum rate obtained by the weighted sum rate calculation module Does it satisfy the following relationship: Where ε is a constant; if it is not satisfied, it is performed by the beamforming matrix and energy matrix optimization module; if it is satisfied, the optimal WSR value of the user is output;

[0063] Beamforming matrix and energy matrix optimization module: Take the weighted sum rate WSR obtained by the judgment module as input parameters and perform beamforming matrix W and energy matrix S E The optimization method outputs the optimal beamforming matrix W, which is an N×N dimensional vector. opt and the N×N dimensional energy matrix S E; Where N is a natural number, indicating the number of antennas in the base station; superscript opt represents the optimal value of W;

[0064] Multimode RIS phase shift matrix optimization module: The optimal beamforming matrix W obtained by the beamforming matrix and energy matrix optimization module opt and the energy matrix S E As input parameters, the multi-mode RIS phase shift matrix optimization method is executed, and the M×M dimensional optimal phase shift matrix Θ is output. opt ; Where M is a natural number, indicating the number of RIS units, and the superscript opt represents the optimal value of Θ;

[0065] Alternating iterative optimization module: The W obtained by the beamforming matrix and energy matrix optimization module is opt , S E and the Θ obtained by the multi-mode RIS phase shift matrix optimization module opt Perform alternating iterative optimization to calculate the weighted sum rate of the current user end Then let n=n+1, and the judgment module executes.

[0066] Some prior arts related to the present invention are briefly as follows:

[0067] 1. Digital modulation methods such as binary phase shift keying (BPSK)

[0068] Digital modulation is an important method of modern communication, and it has many advantages over analog modulation. Digital modulation has better anti-interference performance, stronger resistance to channel loss, and better security; error control technology can be used in digital transmission systems to support complex signal conditions and processing technologies, such as source coding, encryption technology, and equalization. In digital modulation, the modulated 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 currently include BPSK, QPSK, 8PSK, QAM, etc. For details, see "G. Proakis, M. Salehi. Digital Communications, 5th Edition [J]. 2008".

[0069] 2. Secondary transformation method

[0070] Fractional programming problem refers to a nonlinear programming problem whose objective function is a fractional function. Its mathematical model is:

[0071]

[0072] Where A(x) and B(x) are continuous functions defined on x∈χ, and B(x) is non-negative. Quadratic transformation is a common method for solving fractional programming problems, as described in the following theorem:

[0073]

[0074] Satisfying condition C1: can be decoupled into a new target form g(x,y)=f(A(x))q1(y)+h(B(x))q2(y); C2: when variable x * Maximizing A(x) / B(x) occurs when and only when x * and * Together we can maximize g(x,y); C3:y * = argmax y g(x,y), g(x,y * )=A(x) / B(x); C4: For details, see "K.Shen and W.Yu, "Fractionalprogramming for communication systems—Part I: Power control and beamforming," in IEEE Transactions on Signal Processing, vol.66, no.10, pp.2616-2630, 15May15, 2018."

[0075] 3. Lagrangian decomposition method

[0076] Lagrange decomposition method is a common method for dealing with non-convex mathematical optimization. The basic idea is to introduce Lagrange multipliers for constraints and then construct Lagrange functions. Its mathematical model is:

[0077]

[0078] Construct the Lagrangian function:

[0079]

[0080] Among them, α i 30, b j ∈R is a Lagrange multiplier. For x that violates the constraints of the original problem, there exists some c i (x)>0, or some h j (x) 10, we have:

[0081]

[0082] Therefore, the optimal value of the original problem is:

[0083]

[0084] Some complex constraints are placed on the objective function through Lagrangian decomposition, and then the problem is decomposed. For details, see "S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, UK: Cambridge Univ. Press, Aug. 2004."

[0085] 4. Subgradient method

[0086] The 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 of the subgradient method, a negative subgradient direction is selected as the search direction, and the search step size is set according to certain rules. The subgradient can be the subgradient of the objective function or the subdifferential of any function that violates the constraint. For non-smooth convex optimization problems, the subgradient method can guarantee global convergence. For details, see "K. Singh, S. Biswas, M.-L. Ku, and MFFlanagan, "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.".

[0087] 5. Successive Lower Bound Maximization (SLBM) method

[0088] The successive lower bound maximization method is an optimization algorithm mainly used to solve optimization problems with nonlinear constraints. The SLBM method gradually adjusts the parameters so that the objective function reaches the maximum value while satisfying the constraints. Its basic principle is to gradually approach the optimal solution by iteratively updating the parameters. For details, 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.".

[0089] The present invention has the following technical effects:

[0090] The present invention first fixes the MM-RIS reflection, refraction and amplification phase shift matrix, and uses the Lagrangian decomposition method to solve the optimal transmission beamforming matrix and energy matrix; secondly, the beamforming matrix is ​​fixed, and the MM-RIS reflection, refraction and amplification phase shift matrix is ​​solved by the successive lower bound maximization method; finally, alternating optimization is performed to obtain the optimal user weighted sum rate, thereby achieving the optimal improvement of the weighted sum rate of the multi-user MM-RIS-SWIPT system. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A model diagram of a multi-mode RIS-assisted multi-user SWIPT system according to a preferred embodiment of the present invention;

[0092] Figure 2 A flow chart of a method for maximizing user weighting and rate in a multi-mode RIS-assisted multi-user SWIPT system according to a preferred embodiment of the present invention;

[0093] Figure 3 A flow chart of the steps of optimizing the transmit beamforming matrix and the energy matrix for the preferred embodiment of the present invention;

[0094] Figure 4 A flow chart showing the steps of optimizing the multimode RIS reflection and refraction phase shift matrix according to a preferred embodiment of the present invention;

[0095] Figure 5 It is the algorithm convergence performance diagram under different schemes;

[0096] Figure 6 It is the weighted sum rate performance diagram under different schemes as the number of RIS units increases;

[0097] Figure 7 This is a block diagram of a user weighting and rate maximization system for a multi-mode RIS-assisted multi-user SWIPT system according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0098] The present invention will be further described below in conjunction with specific embodiments.

[0099] Figure 1 The multi-mode RIS-assisted multi-user SWIPT system model diagram of the preferred embodiment of the present invention. The system includes a base station with a positive integer N of antennas, an MM-RIS with a positive integer M of reflection and refraction units, K I single-antenna users, and K E Single antenna energy receiver, K I and K E is a positive integer.

[0100] Figure 2 This is a flow chart of a method for maximizing the weighted sum rate of a MM-RIS-assisted multi-user SWIPT system according to an embodiment of the present invention. This embodiment is accomplished by the following steps:

[0101] Step 1: The base station digitally modulates the transmitted signal and sends it to the MM-RIS, the user receiving end and the energy receiving end via free space;

[0102] Step 2: The destination user receiving end receives the signal sent in step S1 and uses it as an input parameter to calculate its weighted sum rate. In this step, the number of iterations 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

[0103] Step 3: Judgement Is it established, among which, Refers to the weighted sum rate value calculated at the tth iteration. If it is true, jump to step 7, otherwise execute step 4; here The convergence performance of the method can be characterized;

[0104] Step 4: Take the weighted sum rate WSR obtained in step S3 as the input parameter, and obtain the optimal beamforming matrix W of the N×N dimensional vector according to the beamforming matrix and energy matrix optimization method. (t) and the N×N dimensional energy matrix S E ;

[0105] Step 5: Substitute the optimal beamforming matrix W from step 4 (t) and the energy matrix S EAs input parameters, the multi-mode RIS phase shift matrix optimization method is executed to obtain the M×M dimensional optimal phase shift matrix

[0106] Step 6: According to W (t) , S E and The alternating optimization result calculates the weighted sum rate of the current system user end Let t = t + 1, and then execute step 3;

[0107] Step 7: Output the maximum weighted sum rate R of system users opt .

[0108] Figure 3 The flowchart of the steps of optimizing the transmit beamforming matrix and the energy matrix according to the embodiment of the present invention is mainly completed by the following steps:

[0109] Step 1: Initialize the number of iterations m = 1, λ (1) =1,μ (1) =1,γ (1) =1, R (1) =0 and convergence judgment constant ε=0.01, input the initial signal value;

[0110] Step 2: Determine whether f1(m)≤ε holds, where f1(m) represents the target value of formula (17) calculated at the mth iteration. If it holds, the optimal beam matrix is ​​obtained by formula (21), and the process goes to step 7; otherwise, the process goes to step 3.

[0111] Step 3: Determine whether the weighted sum rate converges. If so, execute step 6; otherwise, execute step 4.

[0112] Step 4: Solve the weighted sum rate R (m) And update the current beamforming matrix W (m) ;

[0113] Step 5: Update the Lagrangian variable λ (m) =λ (1) , μ (m) =μ (1) and γ (m) =γ (1) and m=m+1;

[0114] Step 6: Calculate the target values ​​of equations (A1) and (A2), respectively and f1(m), and m=m+1, then execute step 2;

[0115] Step 7: The final output is the optimal transmit beamforming vector W opt .

[0116] Figure 4 The flowchart of the steps of optimizing the MM-RIS reflection, refraction and amplification phase shift matrix according to the embodiment of the present invention is mainly completed by the following steps:

[0117] Step 1: Initialize the number of iterations n = 0, the initial weighted sum rate and convergence judgment constant ε = 0.01, input beamforming matrix W;

[0118] Step 2: Determine f2(n)≤ε, where f2(n) represents the target value of equation (32) obtained by the nth iteration. If it is true, the optimal phase shift matrix is ​​obtained by the successive lower bound maximization method, and jump to step 7; otherwise, execute step 3;

[0119] Step 3: Determine whether the weighted sum rate converges. If so, execute step 6; otherwise, execute step 4.

[0120] Step 4: Solve the weighted sum rate R (n) And update the current phase shift matrix

[0121] Step 5: Update intermediate variables and n=n+1;

[0122] Step 6: Calculate the target values ​​of equations (A4) and (A5), respectively and f2(n), and n=n+1, then execute step 2;

[0123] Step 7: Finally get the optimal MM-RIS phase shift matrix Θ opt .

[0124] Figure 5 The figure is a convergence performance diagram of the proposed algorithm under different RIS-assisted multi-user SWIPT systems. Among them, the number of users is set to 4, the base station transmission power is set to 30dBm, the number of RIS elements is 30, and the number of base station antennas is 4. It can be seen from the figure that the WSR under different RIS assistance increases with the increase of the number of iterations and eventually tends to be stable. The WSR of the multi-mode-RIS-assisted scheme proposed in the present invention is always better than the other three schemes, converging about 21-26 times, and about 22bps / Hz when converging, which verifies the effectiveness and convergence of the method of the present invention.

[0125] Figure 6The weighted sum rate performance diagrams for different schemes as the number of RIS units increases are shown. It can be seen that the WSR for different schemes increases with the number of RIS units, and the WSR for the multimode-RIS system scheme proposed in the present invention is always better than the benchmark. This is because by jointly designing its reflection, transmission and amplification characteristics, the multimode RIS can provide active signal amplification for both users, thereby significantly improving the weighted sum rate for all users. These results show that the introduction of multimode RIS in SWIPT systems is a technology with broad application prospects that can improve system performance.

[0126] like Figure 7 As shown, this embodiment discloses a MM-RIS-assisted multi-user SWIPT system weighted sum rate maximization system, which is used to execute the above method, and includes the following modules:

[0127] Signal transmission module: The base station digitally modulates the transmitted signal and sends it to the multi-mode reconfigurable intelligent surface MM-RIS, the user receiving end and the energy receiving end;

[0128] Weighted sum rate calculation module: The destination user receiving end receives the signal sent by the base station and uses it as an input parameter to calculate the weighted sum rate Among them, R i (n) represents the weighted sum rate value obtained by the nth calculation, where n is a constant;

[0129] Judgment module: judges the user-side weighted sum rate obtained by the weighted sum rate calculation module Does it satisfy the following relationship: Where ε is a constant; if it is not satisfied, it is performed by the beamforming matrix and energy matrix optimization module; if it is satisfied, the optimal WSR value of the user is output;

[0130] Beamforming matrix and energy matrix optimization module: Take the weighted sum rate WSR obtained by the judgment module as input parameters and perform beamforming matrix W and energy matrix S E Optimization method, output N×N dimensional optimal beamforming matrix W opt and the N×N optimal energy matrix S E ; Where N is a natural number, indicating the number of base station antennas; superscript opt represents the optimal value of W;

[0131] Multimode RIS phase shift matrix optimization module: The optimal beamforming matrix W obtained by the beamforming matrix and energy matrix optimization module is opt Energy Matrix S E As input parameters, the multi-mode RIS phase shift matrix optimization method is executed, and the M×M dimensional optimal phase shift matrix Θ is output. opt ; Where M is a natural number, indicating the number of RIS units; superscriptopt represents the optimal value of Θ;

[0132] Alternating iterative optimization module: The W obtained by the beamforming matrix and energy matrix optimization module is opt , S E and the multi-mode RIS phase shift matrix optimization module Θ opt Perform alternating optimization to calculate the weighted sum rate of the current user end Then let n=n+1, and the judgment module executes.

[0133] For other contents of this embodiment, please refer to the above method embodiment.

[0134] Although the embodiments of the present invention have been clearly described, it is clear that those skilled in the art may make various changes, modifications, substitutions and variations to these embodiments without departing from the principle and spirit of the method of the present invention. The scope of the present invention is defined by the appended claims and their equivalents, and still falls within the scope of the method of the present invention and is still considered to be the scope of protection of the present invention.

Claims

1. A weighted sum rate maximization method for a multi-user SWIPT system assisted by MM-RIS, characterized in that: The specific steps are as follows: S1: The base station performs digital modulation on the signal and sends it to the multi-mode reconfigurable smart 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 uses it as an input parameter to calculate its weighted sum rate Among them, ∑ is the 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: Determine the user terminal weighted sum rate obtained in step S2 Does it satisfy the following relationship: Wherein, ε is a constant; if not satisfied, execute step S4; if satisfied, jump and execute step S7; S4: Take the weighted sum rate WSR obtained in step S3 as input parameters and perform beamforming matrix W and energy matrix S E The optimization method outputs the N×N dimensional optimal beamforming matrix W opt and the N×N optimal energy matrix S E ; Where N is a natural number, indicating the number of base station antennas; superscript opt represents the optimal value of W; S5: The optimal beamforming matrix W obtained in step S4 is opt and the energy matrix S E As input parameters, the multi-mode RIS phase shift matrix optimization method is executed, and the M×M dimensional optimal phase shift matrix Θ is output. opt ; Where M is a natural number, indicating the number of RIS units; superscript opt represents the optimal value of Θ; S6: According to the W obtained in step S4 opt , S E and θ obtained in step S5 opt , calculate the weighted sum rate of the current system user end Then let n=n+1 and jump to step S3; S7: The WSR obtained in step S3 is output as the final result to obtain the optimal WSR value of the user.

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 the set value, the iteration is stopped, and the process jumps to and executes step S7.

3. The MM-RIS-assisted multi-user SWIPT system weighted sum rate maximization method according to claim 1, characterized in that: In step S4, the beamforming matrix W and the energy matrix S E The optimization method is implemented in the following steps: S4.1: The SWIPT system includes a base station with a positive integer number of antennas N, a multimode RIS with a positive integer number of reflection and refraction units M, and K I single antenna users, and K E single antenna energy receiver, and K I and K E is a positive integer; SINR corresponding to the i-th user i It is expressed as: in, is a 1×N-dimensional column vector, representing the equivalent channel coefficient matrix from the base station to the i-th user, and the superscript H represents the conjugate transpose; f b,i is an N×1-dimensional column vector, representing the channel coefficient vector from the base station to the i-th user; f r,i is an M×1-dimensional column vector, representing the channel coefficient vector from the multimode RIS to the i-th user; G is an M×N-dimensional matrix, representing the channel coefficient matrix from the base station to the multimode RIS; w i is an N×1-dimensional vector, representing the beamforming signal transmitted by the base station to the i-th user; is an M×M dimensional diagonal matrix, representing the phase shift matrix of the multimode RIS at the i-th user; where and Respectively represent the amplitude and phase of the mth unit at the ith user, β max ≥1 is the amplification factor; is a constant, representing the variance of Gaussian white noise at the user; then K I The weighted sum rate of each user is: Among them, R i is a real constant, representing the rate of the i-th user; then the harvested power of the k-th energy receiving end is: in, represents the equivalent channel coefficient matrix from the base station to the kth energy receiving end; h b,k is an N×1-dimensional column vector, representing the channel coefficient vector from the base station to the kth energy receiving end; h r,k is an M×1-dimensional column vector, representing the channel coefficient vector from the multimode RIS to the k-th energy receiving end; is a constant, representing the variance of Gaussian white noise at the multimode RIS; η k K E Energy collection efficiency of each energy receiving end; S4.2: Model Problem 1 with the system user-side weighted sum rate R as: Where Tr(·) represents the trace of the matrix; is a constant, representing the output signal power limit of the multimode RIS; is a constant representing the transmit power limit at the base station; Q k,min is a constant, indicating the minimum harvesting power threshold of the kth energy receiving end; an auxiliary variable is introduced Using the secondary transformation method, the objective function of formula (4) is equivalent to: Fixed variable {{w i },S E ,Θ i }, calculate the partial derivative of the independent variable so that Zero, the optimal solution for: in, make Fixed variables {ρ, v, Θ i }, define the intermediate variable matrix: Then the transmit beamforming matrix and energy matrix optimization problem 2 can be expressed as: S4.3: Yes Middle W H D k The first-order Taylor expansion of W yields: Then the optimization problem 2 is reformulated as: S4.4: Using the Lagrangian decomposition method to calculate equation (16), introducing three Lagrangian multipliers λ, μ, and γ, the optimization problem 2 is transformed into: Using the subgradient method and the first-order derivative of the L(W,λ,μ,γ) function, we get the update equations for λ,μ,γ as follows: S4.5: Let the first-order derivative of the function L(W,λ,μ,γ) with respect to W be zero, solve equation (17), and obtain the optimal solution W opt for:

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 θ optimization method is implemented by the following steps: S5.1: Definitions is the coefficient matrix of the reflection surface, is the coefficient matrix of the refractive surface; fixed variables {ρ,v,{w i },S E }, and define the intermediate variable matrix: Then the optimization problem 3 of the multimode RIS reflection, refraction and amplification phase shift matrix Θ is expressed as: S5.2: Using the successive lower bound maximization method, we can transform u in the C2 formula shown in equation (30) into H Q k u is represented by: u H Q k u≥u H (t)Q k u+u H Q k u(t)-u H (t)Q k u(t), (31) The optimization problem 3 is reformulated as: S5.3: Solve equation (32) to obtain the optimal multimode RIS phase shift matrix Θ opt .

5. A MM-RIS-assisted multi-user SWIPT system weighted sum rate maximization system, for executing the method according to any one of claims 1 to 4, characterized in that: Includes the following modules: Signal sending module: After the base station performs digital modulation on the signal, it sends it 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 uses it as an input parameter to calculate the weighted sum rate Among them, ∑ is the 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: judges the user-side weighted sum rate obtained by the weighted sum rate calculation module Does it satisfy the following relationship: Where ε is a constant; if it is not satisfied, it is performed by the beamforming matrix and energy matrix optimization module; if it is satisfied, the optimal WSR value of the user is output; Beamforming matrix and energy matrix optimization module: Take the weighted sum rate WSR obtained by the judgment module as input parameters and perform beamforming matrix W and energy matrix S E The optimization method outputs the N×N dimensional optimal beamforming matrix W opt and the N×N optimal energy matrix S E ; Where N is a natural number, indicating the number of antennas in the base station; superscript opt represents the optimal value of W; Multimode RIS phase shift matrix optimization module: The optimal beamforming matrix W obtained by the beamforming matrix and energy matrix optimization module opt and the energy matrix S E As input parameters, the multi-mode RIS phase shift matrix optimization method is executed, and the M×M dimensional optimal phase shift matrix Θ is output. opt ; Where M is a natural number, indicating the number of RIS units, and the superscript opt represents the optimal value of Θ; Alternating iterative optimization module: The W obtained by the beamforming matrix and energy matrix optimization module is opt , S E and the Θ obtained by the multi-mode RIS phase shift matrix optimization module opt Perform alternating iterative optimization to calculate the weighted sum rate of the current user end Then let n=n+1, and execute it by the judgment module.

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