Resource optimization method for wireless communication systems based on user-side reconfigurable smart surfaces

Abstract

This invention discloses a resource optimization method for a wireless communication system based on a user-side reconfigurable smart surface (RIS). The method includes: setting up a reconfigurable smart surface RIS array on the user side to construct a multi-user US-RIS wireless communication system; constructing a joint optimization objective function for the multi-user US-RIS wireless communication system based on the maximum transmit power constraints of each user and the phase shift constraints of the phase shift matrix of the user-side reconfigurable smart surface; and solving the joint optimization objective function to obtain the optimal user resource configuration of the multi-user US-RIS wireless communication system. The problem of solving the joint optimization objective function is decomposed into four sub-problems, and the optimal solutions to these sub-problems are obtained by using an alternating optimization method. This invention, by deploying a multi-layer RIS array on the user side, can flexibly adjust the phase and amplitude of the signal, thereby optimizing beamforming and improving the system's spectral efficiency and transmission rate. Simultaneously, a dynamic resource optimization algorithm is introduced to effectively coordinate interference and resource allocation among multiple users.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a resource optimization method for wireless communication systems based on user-side reconfigurable smart surfaces. Background Technology

[0002] With the development of wireless communication technology, reconfigurable intelligent surfaces (RIS) have become an important technology for improving the performance of communication systems. Currently, most reconfigurable intelligent surface (RIS) technologies are mainly applied at the base station side, optimizing the communication environment by controlling reflected signals. However, in traditional RIS designs, base station-side RIS face dimensionality limitations in multi-user scenarios, especially when using large-scale antenna arrays at the user side. Furthermore, current user-side RIS designs are typically only suitable for single-user scenarios and do not fully consider interference issues between multiple users or the complexity of resource allocation, which greatly limits the improvement of system performance. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a resource optimization method for wireless communication systems based on user-side reconfigurable smart surfaces. This method includes:

[0004] A reconfigurable smart surface RIS array is set up on the user side to build a multi-user US-RIS wireless communication system.

[0005] Based on the maximum transmit power constraints of each user and the phase shift constraints of the phase shift matrix of the user-side reconfigurable smart surface, a joint optimization objective function for a multi-user US-RIS wireless communication system is constructed.

[0006] Solving the joint optimization objective function yields the optimal user resource configuration for the multi-user US-RIS wireless communication system.

[0007] The joint optimization objective function based on the multi-user US-RIS wireless communication system is as follows:

[0008]

[0009] Constraints:

[0010] User transmit power constraints:

[0011]

[0012] Phase constraint of the phase shift matrix of the US-RIS array:

[0013]

[0014] In the formula, v represents the base station's receive vector for the user, and w u Let Θ represent the uplink beamforming vector of the u-th user. u,l P represents the phase shift matrix of the u-th user in the l-th layer of the US-RIS array. u SINR represents the transmit power of the u-th user. u This represents the signal-to-interference-to-noise ratio for the u-th user. P represents the transmit power of the u-th user. max θ represents the total transmit power of all users. u,l,m This represents the phase shift of the m-th diagonal element in the l-th layer US-RIS array for the u-th user, where U represents the total number of users. It means any.

[0015] The problem of solving the joint optimization objective function is decomposed into four sub-problems, specifically:

[0016] The first sub-problem: fixing the uplink beamforming vector w of the u-th user. u The phase shift matrix Θ of the u-th user in the l-th layer US-RIS array u,l and the transmit power P of the u-th user u Optimize the base station's receive vector v for users;

[0017] The second sub-problem: fixing the uplink beamforming vector w for the u-th user. u The transmit power P of the u-th user u Given the base station's received vector v for the user, optimize the phase shift matrix Θ of the u-th user in the l-th layer US-RIS array. u,l ;

[0018] The third sub-problem: fixing the phase shift matrix Θ of the u-th user in the l-th layer US-RIS array. u,l The transmit power P of the u-th user u Given the base station's receive vector v for a user, optimize the uplink beamforming vector w for the u-th user. u ;

[0019] The fourth sub-problem: fixing the uplink beamforming vector w of the u-th user. u The phase shift matrix Θ of the u-th user in the l-th layer US-RIS array u,l Given the base station's receive vector v for each user, optimize the transmit power P for the u-th user. u .

[0020] The optimal solutions to the four sub-problems are obtained by iteratively solving them using an alternating optimization method.

[0021] The beneficial effects of this invention are as follows: Unlike traditional base station-side RIS, the US-RIS described in this invention is deployed on the user side, breaking the dimensionality limitation and facilitating the use of large-scale arrays on the user side. This not only improves communication performance but also provides more degrees of freedom for beamforming design in multi-user scenarios. In particular, the multi-layered structure of the US-RIS provides additional degrees of freedom for beamforming design, enabling more flexible control of signal phase and amplitude on the user side. Furthermore, unlike previous single-user scenarios, the design of this invention is applicable to multi-user mobile scenarios and specifically considers interference and resource allocation issues between users. This invention introduces a multi-user resource allocation mechanism, which enables the system to dynamically adapt to channel changes, ensuring robustness and high efficiency in complex environments. This method is applicable to multi-user mobile communication scenarios and can effectively cope with multipath propagation, interference, and changing channel environments, greatly expanding the application potential of RIS technology in multi-user scenarios. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the structure of a multi-user US-RIS wireless communication system in an embodiment of the present invention;

[0023] Figure 2 This is a flowchart of the algorithm for solving the joint optimization objective function based on a multi-user US-RIS wireless communication system in an embodiment of the present invention;

[0024] Figure 3 This is a simulation diagram showing the relationship between the user's maximum transmit power and the transmission rate in this invention;

[0025] Figure 4 This is a simulation diagram showing the relationship between the user's maximum transmit power and the signal-to-noise ratio in this invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] This invention proposes a resource optimization method for wireless communication systems based on user-side reconfigurable smart surfaces, referring to... Figure 1 As shown, the method includes:

[0028] S1: Set up a reconfigurable smart surface RIS array on the user side to build a multi-user US-RIS wireless communication system.

[0029] The multi-user US-RIS wireless communication system is as follows: There are U users (User, US) on the user side. Each user transmits uplink data through a user-side reconfigurable smart surface (US-RIS array). The base station is equipped with M receiving antennas. Each user has K transmitting antennas, and the US-RIS consists of L layers of RIS arrays, each layer containing N reflecting elements. Each user has an independent beamforming vector w and a US-RIS phase matrix Θ. The base station receives the signals from all users and processes them jointly. Each user's uplink transmission signal s... u ~CN(0,1) represents a normally distributed uplink symbol signal, after passing through the user's beamforming vector w u And through multiple reflections of the US-RIS array, it finally reaches the base station.

[0030] Assuming all channels from the user to the US-RIS array and from the US-RIS array to the base station are frequency selective, the channel matrix is ​​defined as follows:

[0031] f u,l f represents the channel matrix from the u-th user to the l-th layer US-RIS array. u,l ∈C N×K .

[0032] g u G represents the channel matrix from the last layer of the US-RIS array to the base station. u ∈C M×N .

[0033] The multi-user uplink transmission model can be represented as:

[0034]

[0035] Where y represents the sum of signals received by the base station from all users, k represents the loss factor of the electromagnetic wave as it passes through each layer of the RIS array, and θ u,l Θ represents the phase shift vector of the l-th layer RIS phase shift matrix for the u-th user. u,l Let Θ represent the phase matrix of the u-th user at the l-th layer of US-RIS. u,l =diag(θ) u,l ), f u,l w represents the channel matrix from the u-th user to the l-th layer US-RIS array. u S represents the beamforming vector independent for each user. u Let n represent the uplink transmission signal of each user, and n represent the noise received by the base station, where n ~ CN(0,σ). 2 I), σ 2 Represents noise power, I∈C M×1 It is a unit vector.

[0036] The base station processes all users using the joint receive vector v, and the final uplink received signal output by the base station is:

[0037]

[0038] Where z represents the final uplink received signal output by the base station, v represents the base station's receive vector for the user, and (.) H This represents the transpose of the expression, y represents the sum of signals received by the base station from all users, and g represents the transpose of the expression. u This represents the channel matrix from the last layer of the US-RIS array to the base station, k represents the loss factor of electromagnetic waves passing through each layer of the RIS array, and θ represents the channel matrix from the last layer of the US-RIS array to the base station. u,l Θ represents the phase shift vector of the l-th layer RIS phase shift matrix for the u-th user. u,l Let Θ represent the phase matrix of the u-th user at the l-th layer of US-RIS. u,l =diag(θ) u,l ), f u,l w represents the channel matrix from the u-th user to the l-th layer US-RIS array. u S represents the beamforming vector independent for each user. u Let n represent the uplink transmission signal of each user, and n represent the noise received by the base station, where n ~ CN(0,σ). 2 I), σ 2 Represents noise power, I∈C M×1 It is a unit vector.

[0039] Performance analysis of multilayer RIS array structure

[0040] This invention analyzes the performance of the structure from two key aspects: signal power distribution and control, and multi-user interference management.

[0041] a. Signal power distribution and control:

[0042] In a multi-layer US-RIS array structure, each user's signal propagates through multiple adjustable reflection layers, and the phase matrix w of each US-RIS layer... u It can be dynamically adjusted to control signal propagation and power distribution between layers. In multi-user scenarios, where different users share the same US-RIS array, effective control of signal power is crucial.

[0043] Signal Enhancement: By adjusting the phase configuration of each US-RIS array layer, user signals are effectively controlled and amplified across the US-RIS array layers, resulting in enhanced signal power reaching the base station. Each user's power control is independent of other users, ensuring that signals do not interfere with each other when multiple users share resources within the same US-RIS array structure.

[0044] Inter-layer power distribution: Due to the multi-layer design of the US-RIS array, signal power can be distributed and optimized between different layers of the US-RIS array. When a user approaches the first layer of the US-RIS array, the power distribution can be controlled by adjusting the reflection phase to ensure that the signal can enter the lower layer with an appropriate phase and be further amplified. In multi-user scenarios, to enhance the uplink transmission of each user, the system can optimize the power distribution for each user individually to maximize their signal power.

[0045] b. Multi-user interference management

[0046] In multi-user scenarios, interference management is a core issue affecting the performance of wireless communication systems. The multi-layered structure of the US-RIS array provides more degrees of freedom for interference control for each user, and can reduce interference between users through different phase configuration strategies.

[0047] Inter-layer interference isolation of US-RIS arrays: By optimizing the phase configuration of each layer of the US-RIS array, signals from different users can be separated on different layers, thus suppressing interference between users. The phase configuration of each layer of the US-RIS array can be dynamically adjusted based on the current Channel State Information (CSI) to ensure that the signal path of each user remains orthogonal or has low interference with the paths of other users. Especially in mobile scenarios, this dynamic adjustment mechanism can adaptively respond to changes in user location and channel state.

[0048] Multi-layer US-RIS array interference alignment: The multi-layer US-RIS structure allows interference from different users to be concentrated in certain specific channel directions, reducing interference to other user signals using interference alignment techniques. Through independent phase adjustment of each layer, the interference signal can be concentrated in a limited direction, while user signals are transmitted without interference in other directions.

[0049] c. Multilayer transmit beamformer design

[0050] The optimization objective of a multi-user US-RIS wireless communication system is to maximize the system's total data rate R. sum Under the conditions of satisfying transmit power constraints and phase constraints, the signal quality of each user is maximized and interference is minimized by dynamically adjusting the US-RIS configuration and optimizing the user beamformer.

[0051] The total data transmission rate R of the multi-user US-RIS wireless communication system sum Specifically:

[0052]

[0053] Among them, R sumSINR represents the total data transmission rate of a multi-user US-RIS wireless communication system. u This represents the signal-to-interference-to-noise ratio for the u-th user.

[0054] Signal-to-noise ratio (SINR) for each user u for:

[0055]

[0056] Where v represents the base station's receive vector for the user, (.) H This indicates the transpose of g. u Θ represents the channel matrix from the last layer US-RIS array to the base station for user u. u,l Let Θ represent the phase matrix of the u-th user in the l-th layer US-RIS array. u,l =diag(θ) u,l ), f u,l w represents the channel matrix from the u-th user to the l-th layer US-RIS array. u Let g represent the uplink beamforming vector of the u-th user. i Θ represents the channel matrix from the last layer US-RIS array to the base station for user i. i,l f represents the phase matrix of the i-th user at the l-th layer of US-RIS. i,l Let w represent the channel matrix from the i-th user to the i-th layer US-RIS array. i This represents the uplink beamforming vector for the i-th user. This indicates the received noise at the base station.

[0057] S2: Based on the maximum transmit power constraints of each user and the phase shift matrix Θ of the user-side reconfigurable smart surface (US-RIS array) u,l Based on the phase shift constraints, the joint optimization objective function of the multi-user US-RIS wireless communication system is constructed as follows:

[0058]

[0059] Constraints:

[0060] User transmit power constraints:

[0061]

[0062] Phase constraint of the phase shift matrix of the US-RIS array:

[0063]

[0064] In the formula, v represents the base station's receive vector for the user, and w uLet Θ represent the uplink beamforming vector of the u-th user. u,l P represents the phase shift matrix of the u-th user in the l-th layer of the US-RIS array. u SINR represents the transmit power of the u-th user. u This represents the signal-to-interference-to-noise ratio for the u-th user. P represents the transmit power of the u-th user. max θ represents the total transmit power of all users. u,l,m This represents the phase shift of the m-th diagonal element in the l-th layer US-RIS array for the u-th user, with an amplitude of 1, and U represents the total number of users.

[0065] In multi-user scenarios, the system needs to optimize the uplink transmission beamforming vector (UL-TBF), the phase shift matrix (TPS) of the US-RIS, and the base station's receive vector (RC) for each user separately. Since multiple users transmit signals simultaneously, interference between users needs to be considered, and the overall data rate of the system should be maximized by jointly optimizing these variables.

[0066] S3: Solve the joint optimization objective function to obtain the optimal user resource configuration of the multi-user US-RIS wireless communication system.

[0067] As some preferred embodiments, solving the joint optimization objective function of the multi-user US-RIS wireless communication system is decomposed into four sub-problems, specifically:

[0068] The first sub-problem: fixing the uplink beamforming vector w for each user u The phase shift matrix Θ for each user in the l-th layer of US-RIS u,l and the transmit power P of each user u Optimize the base station's receive vector v for each user u。

[0069] The first sub-problem is to optimize the base station's optimal receive vector (RC).

[0070] In multi-user scenarios, the base station receives signals from multiple users. To maximize the signal-to-noise ratio (SINR) for each user, this invention requires optimizing the base station's receive vector v. u First, with the beamforming vector w fixed for each user... u and the US-RIS phase shift matrix Θ u,l In the case of optimizing the base station receive vector v u .

[0071] To simplify the problem, this invention assumes that the channels between users are independent and that interference between users is significant for each user. Therefore, for each user, this invention can optimize its received vector v by solving an eigenvalue problem. u For the u-th user, the signal-to-noise ratio (SINR) expression is:

[0072]

[0073] To optimize the base station's receive vector v, this invention solves the maximum eigenvalue problem to obtain the optimal receive vector for each user:

[0074]

[0075] In the formula, v opt This represents the optimal reception vector for the base station to the user. H This indicates the transpose of g. u This represents the channel matrix from the user's last layer US-RIS array to the base station, Θ u,l Let Θ represent the phase matrix of the u-th user at the l-th layer of US-RIS. u,l =diag(θ) u,l ), f u,l w represents the channel matrix from the u-th user to the l-th layer US-RIS array. u Let g represent the uplink beamforming vector of the u-th user. i Θ represents the channel matrix from the last layer US-RIS array to the base station for user i. i,l f represents the phase matrix of the i-th user at the l-th layer of US-RIS. i,l Let w represent the channel matrix from the i-th user to the i-th layer US-RIS array. i This represents the uplink beamforming vector for the i-th user. This represents the received noise at the base station.

[0076] The second sub-problem: fixing the uplink beamforming vector w for each user u Transmit power P for each user u Given the base station's received vector v for the user, optimize the phase shift matrix Θ of the u-th user in the l-th layer of the US-RIS. u,l .

[0077] The second subproblem is to optimize the phase shift matrix Θ of the US-RIS array. u,l .

[0078] In multi-user scenarios, each user has an independent US-RIS phase shift matrix Θ. u,lTo maximize system performance, the phase configuration of each US-RIS layer needs to be optimized to effectively enhance the signal of each user while reducing interference between other users.

[0079]

[0080] For the sake of simplicity in expression, a new function ξ is defined. (u,p,q) Let represent the channel matrix for user u from layer p to layer q:

[0081]

[0082] For ease of calculation, the denominator is usually treated as a fixed value to maximize the numerator. Therefore, the phase shift vector of the phase shift matrix of the u-th user in the l-th layer US-RIS array is:

[0083]

[0084] In the formula, Let v represent the phase shift vector of the phase shift matrix of the u-th user at the l-th layer of US-RIS, and v represent the vector of the base station relative to the user. H This indicates the transpose of g. u Let ξ represent the channel matrix from the last layer US-RIS array to the base station for the u-th user, where j represents a unit imaginary number. (u,l-1,1) Let ξ represent the channel matrix from layer 1 to layer (l-1) of the US-RIS array for the u-th user signal. (u,L,l+1) This represents the channel matrix from layer (l+1) of the US-RIS array to layer L, representing the transmission of the u-th user signal. It means any.

[0085] This expression ensures that the phase of each layer is consistent with the phase on the channel propagation path, so that the signals can be effectively superimposed when they reach the base station, maximizing SINR.

[0086] Θ u,l =diag(θ) u,l )

[0087] In the formula, Θ u,l Let θ represent the phase shift matrix of the u-th user in the l-th layer of US-RIS. u,l This represents the phase shift vector of the US-RIS array at layer l for the u-th user.

[0088] The third sub-problem: Fixing the phase shift matrix Θ of the u-th user in the l-th layer of US-RIS u,l Transmit power P for each user u Given the base station's receive vector v for a user, optimize the uplink beamforming vector w for the u-th user. u .

[0089] The third sub-problem is to optimize the uplink beamforming vector w for each user. u .

[0090] To maximize the signal-to-noise ratio (SINR) for each user, the phase matrix Θ of the fixed US-RIS needs to be... u,l and base station receive vector v u In this case, optimize the user beamforming vector.

[0091] The optimization objective is to maximize the SINR for each user while satisfying transmit power constraints:

[0092]

[0093] When optimizing user beamforming vectors, it is typically assumed that the denominator (interference plus noise term) is constant. Thus, the optimization problem simplifies to:

[0094]

[0095] make The problem then simplifies to:

[0096]

[0097] According to the Cauchy-Schwarz inequality, w must be made u and The direction is consistent, and the amplitude is... That is, the optimal beamforming vector for the u-th user is:

[0098]

[0099] In the formula, Let v represent the optimal beamforming vector for the u-th user, and v represent the base station's receive vector for the user. H This represents the transpose of the expression, y represents the sum of signals received by the base station from all users, and g represents the transpose of the expression. u This represents the channel matrix from the last layer of the US-RIS array to the base station, k represents the loss factor of electromagnetic waves passing through each layer of the RIS array, and θ represents the channel matrix from the last layer of the US-RIS array to the base station. u,l Θ represents the phase shift vector of the l-th layer RIS phase shift matrix for the u-th user. u,l Let Θ represent the phase matrix of the u-th user in the l-th layer US-RIS array. u,l =diag(θ) u,l ), f u,l This represents the channel matrix from the u-th user to the l-th layer US-RIS array.

[0100] The fourth sub-problem: Fixing the phase shift matrix Θ of the u-th user in the l-th layer of US-RIS.u,l The base station receives the vector v for the u-th user. u and the uplink beamforming vector w of the u-th user u Optimize the transmit power P of the u-th user u .

[0101] The fourth sub-problem is to optimize the transmit power for each user.

[0102] To make the system's total data rate R sum To maximize this, we also need to optimize the transmit power P for each user. u Optimize while meeting total power constraints.

[0103] The objective function for optimizing transmit power for each user is:

[0104]

[0105] Construct the Lagrangian function with Lagrange multipliers λ:

[0106]

[0107] Where λ≥0 are Lagrange multipliers, P max This represents the total transmit power of all users.

[0108] Find P for the function above. u The partial derivative of makes Simplifying, we get:

[0109]

[0110] v represents the base station's receive vector for the user, (.) H This indicates the transpose of g. u Θ represents the channel matrix from the last layer US-RIS array to the base station for the u-th user. u,l Let Θ represent the phase matrix of the u-th user in the l-th layer US-RIS array. u,l =diag(θ) u,l ), f u,l w represents the channel matrix from the u-th user to the l-th layer US-RIS array. u Let g represent the uplink beamforming vector of the u-th user. i Θ represents the channel matrix from the last layer US-RIS array to the base station for the i-th user. i,l f represents the phase matrix of the i-th user in the l-th layer US-RIS array. i,l Let w represent the channel matrix from the i-th user to the i-th layer US-RIS array. i Let ||v|| represent the uplink beamforming vector of the i-th user. 2σ 2 P represents the received noise power at the base station. i This represents the transmit power of any i-th user other than the u-th user.

[0111] To ensure the non-negativity of power, the optimal power allocation formula is:

[0112]

[0113] In the formula, Let λ represent the optimal transmit power for each user, and λ represent the Lagrange multipliers, determined by the total power constraint. The bisection method is used to solve for λ, and then power allocation is performed.

[0114] In order to dynamically adjust the corresponding optimal solution according to real-time channel changes and ensure good wireless communication in changing communication environments, the channel state can be updated at regular intervals.

[0115] Channel state f between user and US-RIS u,l The channel between the user and the US-RIS can be obtained through channel sounding or pilot signals. Each user sends a specific pilot signal to the US-RIS, and the US-RIS estimates the channel from the user to each layer of the US-RIS by measuring these signals. The channel state estimation process can be implemented using the classical least squares (LS) method. The US-RIS can then feed back the estimated channel information between the user and the US-RIS to the base station and the user through the base station for further beamforming optimization and phase control. Feedback can be performed in time slots through a preset communication protocol.

[0116] Channel status g between US-RIS and base station u The channel between the US-RIS and the base station can be estimated by the base station based on the signal reflected from the US-RIS. After receiving the signal reflected from the US-RIS, the base station estimates the channel state from each layer of US-RIS to the base station. This process also relies on pilot signals to obtain real-time channel information. The base station then feeds back the estimated channel state between the US-RIS and the base station to both the US-RIS and the user terminal to help with adjustments in US-RIS phased array optimization and user terminal beamforming.

[0117] Figure 2 The flowchart shows the algorithm for solving the joint optimization objective function based on a multi-user US-RIS wireless communication system in this embodiment of the invention.

[0118] In some preferred embodiments, reference is made to Figure 2As shown, the optimal solutions to the four sub-problems are obtained iteratively using an alternating optimization method. The specific process includes:

[0119] System settings: Channel matrix f from user to US-RIS array and from US-RIS array to base station u,1 f u,2 , ..., f u,l and g u Total transmit power P of all users max The number of users U and the real-time feedback mechanism for changes in system channels.

[0120] Initialization: Initialize the base station's receive vector v and the user's beamforming vector w. u US-RIS phase shift matrix Θ u,l and the user's transmit power p u Specifically, the base station's receive vector v is initialized as the identity matrix, Θ u,l The phase shift angle is randomly generated within [0, 2π), w u Initialize as a unit vector, and P u Then the total power P is evenly distributed max For each user.

[0121] Iterative Process: With other variables fixed, the first to fourth subproblems are optimized sequentially. Each variable is updated iteratively, and the user's signal-to-interference-plus-noise ratio (SINR) is calculated and convergence is determined until the SINR meets the accuracy threshold ε or the maximum number of iterations is reached. The entire optimization process continues until the change in the joint objective function is less than a preset threshold, achieving global optimization of system resources.

[0122] Specifically:

[0123] Optimal receive vector v opt : Fix the uplink beamforming vector w of the u-th user u The phase shift matrix Θ of the u-th user in the l-th layer US-RIS array u,l and the transmit power P of the u-th user u Optimize the base station's receive vector v for users.

[0124] Optimal US-RIS phase shift matrix The uplink beamforming vector w of the u-th user is fixed. u The transmit power P of the u-th user u Given the base station's received vector v for the user, optimize the phase shift matrix Θ of the u-th user in the l-th layer US-RIS array. u,l .

[0125] Optimal beamforming vector The phase shift matrix Θ of the u-th user in the l-th layer US-RIS array is fixed. u,l The transmit power P of the u-th user u Given the base station's receive vector v for a user, optimize the uplink beamforming vector w for the u-th user. u .

[0126] Optimal power allocation The uplink beamforming vector w of the u-th user is fixed. u The phase shift matrix Θ of the u-th user in the l-th layer US-RIS array u,l Given the base station's receive vector v for each user, optimize the transmit power P for the u-th user. u .

[0127] Calculate SINR and determine if it converges: If SINR does not converge, repeat the above steps until convergence.

[0128] If the SINR result reaches its maximum value and remains unchanged, or if the maximum number of iterations is reached, then the iteration loop will exit.

[0129] Output: The optimal receive vector v of the base station opt Optimal US-RIS array phase shift matrix User's optimal beamforming vector and the optimal transmit power allocation for users

[0130] Simulation verification:

[0131] Simulation environment: Simulation verification using the MATLAB R2023b platform.

[0132] A wireless communication network consisting of a base station (BS), a US-RIS array, and user terminals (Users) was simulated using the MATLAB R2023b platform. The distance between the User and the RIS is 0.10m, the distance between each layer of the RIS is 0.02m, and the distance between the RIS and the base station (BS) is 20m. A two-layer uniform planar array (UPA) configuration of RIS elements is used, with each RIS layer containing 12×16 antenna elements. The element size is... The system is densely packed, the frequency of the transmitted signal is set to f = 10 GHz, and the system noise power is σ. 2 =1×10 -6 The transmission loss of W and RIS is set to 0.8, and the total transmit power P of all users is... max The range is from 1W to 5W.

[0133] To compare the effectiveness of the present invention, a comparison is made between the method of the present invention and the power allocation method of a traditional multilayer UC-RIS in terms of maximum speed, wherein the relationship between maximum speed and power is as follows: Figure 3 As shown, the optimized multilayer UC-RIS scheme achieves significantly higher rates than conventional methods under various power conditions. Especially at higher transmit powers (e.g., close to 5W), the maximum rate of the method in this invention is increased by approximately 45% compared to conventional methods.

[0134] Figure 3 This is a simulation diagram showing the relationship between the user's maximum transmit power and the transmission rate in this invention. Figure 3 In the middle, the horizontal axis P max The vertical axis represents the total transmit power of all users, the vertical axis represents the total rate of users, Optimization-Multi-layer UC-RIS represents the multi-layer user-side RIS-assisted resource optimization method of the present invention, and Multi-layer UC-RIS represents the traditional unoptimized method.

[0135] Figure 4 This is a simulation diagram showing the relationship between the user's maximum transmit power and the signal-to-noise ratio in this invention. Figure 4 This paper demonstrates the signal-to-noise ratio (SNR) changes of the optimized multilayer UC-RIS and the conventional multilayer UC-RIS as transmit power increases. The method of this invention improves the SNR by approximately 1 dB compared to the conventional approach at the same transmit power. Figure 3 The paper further demonstrates the effectiveness of the optimization method in improving the signal-to-noise ratio.

[0136] From simulation Figure 3 , 4 As can be seen, this invention demonstrates excellent performance in both overall system transmission rate and signal-to-noise ratio. Compared to traditional methods, this invention maintains high performance while increasing the transmission power of each user, fully demonstrating its practicality and superiority.

[0137] This invention aims to solve the problems of resource allocation and signal transmission efficiency in multi-user communication. By deploying a multi-layer RIS (Risk-Resistant Array) on the user side, the phase and amplitude of the signal can be adjusted more flexibly, thereby optimizing beamforming and improving the system's spectral utilization and transmission rate. Simultaneously, a dynamic resource optimization algorithm is introduced to effectively coordinate interference and resource allocation among multiple users, ensuring efficient system operation in complex multipath propagation environments. Simulation results show that, in channel environments with interference and noise, the maximum data rate of this invention is increased by approximately 45% compared to traditional methods, and the signal-to-noise ratio is improved by approximately 1 dB, maintaining high performance and fully demonstrating the practicality and superiority of the optimization scheme.

[0138] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include ROM, RAM, disk, or optical disk, etc.

[0139] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A resource optimization method for a wireless communication system based on a user-side reconfigurable smart surface, characterized in that, The method includes: A reconfigurable smart surface RIS array is configured on the user side to construct a multi-user US-RIS-based wireless communication system. This system includes U users, the reconfigurable smart surface RIS array, and a base station. The reconfigurable smart surface RIS array comprises an L-layer US-RIS array with N reflection elements. Each user transmits uplink data through the reconfigurable smart surface, and the data reaches the base station after multiple reflections. The base station is equipped with M receiving antennas, and each user has K transmitting antennas. The independent beamforming vector for each user is represented by w. u The communication system is modeled as follows: f u,l Indicates the range from the u-th user to the 1st user. The channel matrix of the layered US-RIS array, g u This represents the channel matrix from the last layer of the US-RIS array to the base station; The multi-user uplink transmission model can be represented as: ， Where y represents the sum of signals received by the base station from all users, and k represents the loss factor of the electromagnetic wave as it passes through each layer of the RIS array. This represents the phase shift vector of the US-RIS phase shift matrix at level l for the u-th user. This indicates that the u-th user is in the US-RIS... The phase matrix of the layer, f u,l Indicates the range from the u-th user to the 1st user. The channel matrix of the layered US-RIS array, S u This represents the uplink transmission signal for each user, where n represents the noise received by the base station. , Indicates noise power. It is a unit vector; The base station uses the joint receive vector After processing all users, the base station outputs the final uplink received signal as follows: ， ； Where z represents the final uplink received signal output by the base station, v represents the base station's receive vector for the user, and (.) H This indicates the transpose, and k represents the loss factor when the electromagnetic wave passes through each layer of the US-RIS array. The total data transmission rate of the multi-user US-RIS wireless communication system Specifically: ， ； in, SINR represents the total data transmission rate of a multi-user US-RIS wireless communication system. u w represents the signal-to-interference-to-noise ratio for the u-th user. i Let g represent the uplink beamforming vector of the i-th user. i Let f represent the channel matrix from the last layer of the US-RIS array of the i-th user to the base station. i,l This represents the channel matrix from the i-th user to the l-th layer US-RIS array. This indicates the received noise at the base station; Based on the maximum transmit power constraints of each user and the phase shift constraints of the user-side reconfigurable smart surface phase shift matrix, a joint optimization objective function for a multi-user US-RIS wireless communication system is constructed, which is as follows: ； Constraints: User transmit power constraints: ； Phase constraint of the phase shift matrix of the US-RIS array: ； In the formula, v represents the base station's receive vector for the user, and w u This represents the uplink beamforming vector for the u-th user. This indicates that the u-th user is in the th... The phase shift matrix of the layered US-RIS array, P u SINR represents the transmit power of the u-th user. u This represents the signal-to-interference-to-noise ratio for the u-th user. P represents the transmit power of the u-th user. max This represents the total transmit power of all users. This represents the phase shift of the m-th diagonal element in the l-th layer US-RIS array for the u-th user, where U represents the total number of users. Indicates any; The problem of solving the joint optimization objective function is decomposed into four sub-problems, which are then alternately optimized to obtain the optimal user resource configuration for the multi-user US-RIS wireless communication system. The four sub-problems are as follows: The first sub-problem: fixing the uplink beamforming vector w of the u-th user. u The phase shift matrix of the u-th user in the l-th layer US-RIS array and the transmit power P of the u-th user u Optimize the base station's receive vector v for users; The second sub-problem: fixing the uplink beamforming vector w for the u-th user. u The transmit power P of the u-th user u Given the base station's received vector v for the user, optimize the phase shift matrix of the u-th user in the l-th layer US-RIS array. ; The third sub-problem: fixing the phase shift matrix of the u-th user in the l-th layer US-RIS array. The transmit power P of the u-th user u Given the base station's receive vector v for a user, optimize the uplink beamforming vector w for the u-th user. u ; The fourth sub-problem: fixing the uplink beamforming vector w of the u-th user. u The phase shift matrix of the u-th user in the l-th layer US-RIS array Given the base station's receive vector v for each user, optimize the transmit power P for the u-th user. u .

2. The resource optimization method for a wireless communication system based on a user-side reconfigurable smart surface according to claim 1, characterized in that, For the four sub-problems, an iterative solution using an alternating optimization method is employed to obtain their optimal solutions. The specific process includes: The initialization base station's receive vector v is the identity matrix and the phase shift matrix of the US-RIS array. For the range in Random values ​​between, user's uplink beamforming vector w u For the identity matrix and the user's transmit power P u For uniformly distributed P max / U; The first to fourth subproblems are optimized sequentially by iteratively updating each variable, specifically as follows: Uplink beamforming vector w for fixed users u The phase shift matrix of the user in the l-th layer US-RIS array and the user's transmit power P u Optimize the base station's receive vector v for users; Uplink beamforming vector w for fixed users u The user's transmit power P u Given the base station's received vector v for the user, optimize the phase shift matrix of the user in the l-th layer US-RIS array. ; The phase shift matrix of the fixed user in the l-th layer US-RIS array The user's transmit power P u Given the base station's receive vector v for the user, optimize the user's uplink beamforming vector w. u ; Uplink beamforming vector w for fixed users u The phase shift matrix of the user in the l-th layer US-RIS array Given the base station's receive vector v for the user, optimize the user's transmit power P. u ; Calculate the user's signal-to-interference-to-noise ratio (SINR) and determine if it converges until the SINR meets the accuracy threshold. Alternatively, it can stop iterating when the maximum number of iterations is reached.

3. The resource optimization method for a wireless communication system based on a user-side reconfigurable smart surface according to claim 1, characterized in that, Solving the first subproblem yields the optimal reception vector for each user at the base station, specifically: ， In the formula, Let represent the optimal reception vector of the base station for the user, and k represent the loss of the electromagnetic wave as it passes through each layer. H This indicates the transpose of g. u Let f represent the channel matrix from the last layer of the US-RIS array to the base station for the u-th user, where L represents the layer number of the US-RIS array, and f is the channel matrix from the last layer of the US-RIS array to the base station. u,l This represents the channel matrix from the u-th user to the l-th layer US-RIS array.

4. The resource optimization method for a wireless communication system based on a user-side reconfigurable smart surface according to claim 1, characterized in that, Solve the second subproblem to obtain the number of users in US-RIS. Optimal phase shift matrix of the layer Specifically: ， ； In the formula, This represents the optimal phase shift vector of the l-th layer US-RIS array phase shift matrix for the u-th user, where j represents the unit imaginary number, diag(.) represents the diagonal matrix, and f u,l This represents the channel matrix from the u-th user to the l-th layer US-RIS array. Let represent the channel matrix for the u-th user signal transmitted from layer 1 to layer (l-1) of the US-RIS array. H This indicates the transpose. G represents the channel matrix for transmitting the u-th user signal from layer l+1 to layer L of the US-RIS array. u This represents the channel matrix from the last layer of the US-RIS array to the base station for the u-th user. Indicates any, This represents the phase shift of the m-th diagonal element in the l-th layer US-RIS array for the u-th user, with an amplitude of 1.

5. The resource optimization method for a wireless communication system based on a user-side reconfigurable smart surface according to claim 1, characterized in that, Solving the third subproblem yields the optimal beamforming vector for each user: ， In the formula, Let represent the optimal beamforming vector for the u-th user, (.). H This indicates the transpose of g. u This represents the channel matrix from the last layer of the US-RIS array to the base station, where k represents the loss factor of electromagnetic waves passing through each layer of the RIS array. This represents the phase matrix of the u-th user in the l-th layer of the US-RIS array. , f represents the phase shift vector of the l-th layer US-RIS array phase shift matrix for the u-th user. u,l Let L represent the channel matrix from the u-th user to the l-th layer US-RIS array, where L represents the number of layers in the US-RIS array.

6. The resource optimization method for a wireless communication system based on a user-side reconfigurable smart surface according to claim 1, characterized in that, Solving the fourth subproblem yields the optimal transmit power for each user, specifically: ， In the formula, This represents the optimal transmit power for each user. Let represent the Lagrange multipliers introduced through the Lagrange multiplier method, (.). H This indicates the transpose of g. u This represents the channel matrix from the last layer of the US-RIS array of the u-th user to the base station. This represents the phase shift of the m-th diagonal element in the l-th layer US-RIS array for the u-th user, with an amplitude of 1. This represents the phase matrix of the u-th user in the l-th layer of the US-RIS array. , This represents the phase shift vector of the l-th layer US-RIS array phase shift matrix for the u-th user. This represents the phase shift control of the l-th layer US-RIS array for the i-th user; f u,l Indicates the range from the u-th user to the 1st user. The channel matrix of the layered US-RIS array, w i Let P represent the uplink beamforming vector of the i-th user. i Let g represent the transmit power of any i-th user other than the u-th user. i Let f represent the channel matrix from the last layer of the US-RIS array of the i-th user to the base station. i,l This represents the channel matrix from the i-th user to the i-th layer of the US-RIS array. This indicates the received noise at the base station.

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