Wireless communication system resource optimization method based on user side reconfigurable intelligent surface

Abstract

The invention discloses a wireless communication system resource optimization method based on a user side reconfigurable intelligent surface, and the method comprises the steps: setting a reconfigurable intelligent surface RIS array at a user side, and constructing a wireless communication system based on a multi-user US-RIS; constructing a joint optimization objective function based on the multi-user US-RIS wireless communication system based on the maximum transmitting power constraint of each user and the phase shift constraint of the reconfigurable intelligent surface phase shift matrix of the user side; and solving the joint optimization objective function to obtain the optimal user resource configuration based on 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 solution of the four sub-problems is obtained by adopting an alternate optimization method. The multi-layer RIS array is deployed on the user side, the phase and amplitude of the signal can be flexibly adjusted, beam forming is further optimized, and the spectrum utilization rate and the transmission rate of the system are improved. And meanwhile, a dynamic resource optimization algorithm is introduced, so that interference and resource allocation among multiple users are effectively coordinated.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a method for optimizing wireless communication system resources based on a user-side reconfigurable intelligent surface. Background Art

[0002] With the development of wireless communication technology, reconfigurable intelligent surface (RIS) has become one of the important technologies to improve the performance of communication systems. At present, most reconfigurable intelligent surface (RIS) technologies are mainly used on the base station side to optimize the communication environment by controlling the reflected signal. However, in the traditional RIS design, the RIS on the base station side faces the problem of dimensionality limitation in multi-user scenarios, especially when a large-scale antenna array is used on the user side. This limitation is particularly prominent. Moreover, the current user-side RIS design is usually only applicable to single-user scenarios, and does not fully consider the interference problem between multiple users and the complexity of resource allocation, which greatly limits the improvement of system performance. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a wireless communication system resource optimization method based on a user-side reconfigurable smart surface, the method comprising:

[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 transmission power constraint of each user and the phase shift constraint of the user-side reconfigurable smart surface phase shift matrix, a joint optimization objective function based on the multi-user US-RIS wireless communication system is constructed.

[0006] Solve the joint optimization objective function to obtain the optimal user resource configuration based on 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 specifically:

[0008]

[0009] Constraints:

[0010] User transmit power constraints:

[0011]

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

[0013]

[0014] In the formula, v represents the receiving vector of the base station for the user, w u represents the uplink beamforming vector of the u-th user, Θ u,l represents the phase shift matrix of the u-th user in the US-RIS array at layer l, P u represents the transmission power of the u-th user, SINR u represents the signal-to-interference-noise ratio of the u-th user, represents the transmission power of the u-th user, P max represents the total transmit power of all users, θ u,l,m represents the phase shift of the mth diagonal element in the US-RIS array of the lth layer for the uth user, U represents the total number of users, Indicates any.

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

[0016] The first sub-problem: Fix the uplink beamforming vector w of the u-th user u , the phase shift matrix Θ of the u-th user in the US-RIS array at layer l u,l and the transmission power P of the uth user u , optimize the base station's receiving vector v for the user;

[0017] The second sub-problem: Fix the uplink beamforming vector w of the u-th user u , the transmission power P of the uth user u And the base station optimizes the phase shift matrix Θ of the u-th user in the US-RIS array at the lth layer for the user's receiving vector v u,l ;

[0018] The third sub-problem: Fix the phase shift matrix Θ of the u-th user in the US-RIS array at layer l u,l , the transmission power P of the uth user u and the base station's receiving vector v for the user, optimizes the uplink beamforming vector w for the uth user u ;

[0019] The fourth sub-problem: Fix the uplink beamforming vector w of the u-th user u , the phase shift matrix Θ of the u-th user in the US-RIS array at layer l u,l and the base station optimizes the transmission power P of the uth user for the user's receiving vector v u .

[0020] For the four sub-problems, an alternating optimization method is used to iteratively solve them to obtain their optimal solutions.

[0021] Beneficial effects of the present invention: Unlike the traditional base station side RIS, the US-RIS described in the present invention is deployed on the user side, breaking the dimensionality limitation and facilitating the use of large-scale arrays on the user side, which not only improves the communication performance, but also provides more degrees of freedom for beamforming design in multi-user scenarios. In particular, the multi-layer structure of US-RIS provides additional degrees of freedom for beamforming design, which can more flexibly control the phase and amplitude of the signal on the user side. In addition, unlike the previous single-user scenario, the design of the present invention is suitable for multi-user mobile scenarios, and especially considers the interference problem and resource allocation problem between users. The present invention introduces a multi-user resource allocation mechanism. This design enables the system to dynamically adapt to channel changes and ensure robustness and high efficiency in complex environments. This method is suitable for multi-user mobile communication scenarios, and can effectively deal with multi-path propagation, interference and changing channel environments, greatly expanding the application potential of RIS technology in multi-user scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0024] Figure 3 It is a simulation diagram of the relationship between the maximum transmission power and the sum rate of the user in the present invention;

[0025] Figure 4 This is a simulation diagram of the relationship between the maximum transmission power of the user and the signal-to-noise ratio in the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] The embodiment of the present invention proposes a wireless communication system resource optimization method based on a user-side reconfigurable smart surface, 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 performs uplink transmission through the user-side reconfigurable smart surface (US-RIS array), and the base station is equipped with M receiving antennas. Each user has K transmitting antennas, and the US-RIS is composed of L layers of RIS arrays, each layer of US-RIS array contains N reflection units. Each user has an independent beamforming vector w and US-RIS phase matrix Θ. The base station receives the signals of all users and performs joint processing. The uplink transmission signal s of each user u ~CN(0,1) represents the normally distributed uplink symbol signal, which is passed through the user's beamforming vector w u , and multiple layers of reflections from the US-RIS array, and finally reaches the base station.

[0030] Assuming that 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:

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

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

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

[0034]

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

[0036] The base station processes all users through the joint receiving vector v, and the base station outputs the final uplink receiving signal as:

[0037]

[0038] Among them, z represents the final uplink received signal output by the base station, v represents the receiving vector of the base station for the user, (.) H represents the transpose, y represents the sum of the signals of all users received by the base station, g u represents the channel matrix from the last layer of US-RIS array to the base station, k represents the loss factor when the electromagnetic wave passes through each layer of RIS array, θ u,l represents the phase shift vector of the l-th layer RIS phase shift matrix of the u-th user, Θ u,l represents the phase matrix of the u-th user in the l-th layer of US-RIS, Θ u,l =diag(θ u,l ), f u,l represents the channel matrix from the uth user to the lth layer of US-RIS array, w u represents the independent beamforming vector for each user, S u represents the uplink transmission signal of each user, n represents the noise received by the base station, n~CN(0,σ 2 I), σ 2 represents the noise power, I∈C M×1 is a unit vector.

[0039] Performance Analysis of Multi-layer RIS Array Structure

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

[0041] a. Signal power distribution and control:

[0042] In the multi-layer US-RIS array structure, the signal of each user propagates through multiple adjustable reflection layers. The phase matrix w of each layer of US-RIS is u It can be adjusted dynamically to control the signal propagation and power distribution between layers. In multi-user scenarios, since 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 layer of US-RIS array, the user signal is effectively controlled and amplified between each layer of US-RIS array, so that the signal power that finally reaches the base station can be enhanced. The power control of each user is independent of other users, which ensures that when multiple users share resources under the same US-RIS array structure, the signals will not interfere with each other.

[0044] Inter-layer power distribution: Due to the multi-layer design of the US-RIS array, the power of the signal can be distributed and optimized between the layers of different US-RIS arrays. When the 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 the appropriate phase and be further amplified. In a multi-user scenario, in order to enhance the uplink transmission of each user, the system can optimize the power distribution for each user to maximize its signal power.

[0045] b. Multi-user interference management

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

[0047] Interference isolation between US-RIS array layers: By optimizing the phase configuration of each layer of US-RIS array, the signals of different users can be separated on different layers, so that the interference between users is suppressed. The phase configuration of each layer of US-RIS array can be dynamically adjusted according to the current channel state information (CSI) to ensure that the signal path of each user remains orthogonal or 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 status.

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

[0049] c. Multi-layer transmit beamformer design

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

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

[0052]

[0053] Among them, R sumRepresents the total data transmission rate based on the multi-user US-RIS wireless communication system, SINR u represents the signal-to-interference-noise ratio of the u-th user.

[0054] SINR of each user u for:

[0055]

[0056] Where v represents the receiving vector of the base station for the user, (.) H Indicates transposition, g u represents the channel matrix of user u from the last layer of US-RIS array to the base station, Θ u,l represents the phase matrix of the u-th user in the US-RIS array at layer l, Θ u,l =diag(θ u,l ), f u,l represents the channel matrix from the uth user to the lth layer of US-RIS array, w u represents the uplink beamforming vector of the u-th user, g i represents the channel matrix of user i from the last layer of US-RIS array to the base station, Θ i,l represents the phase matrix of the i-th user in the l-th layer of US-RIS, f i,l represents the channel matrix from the ith user to the ith US-RIS array, w i represents the uplink beamforming vector of the i-th user, Represents the receive noise at the base station.

[0057] S2: Based on the maximum transmit power constraint of each user and the phase shift matrix Θ of the user-side reconfigurable smart surface (US-RIS array) u,l The phase shift constraint is used to construct the joint optimization objective function based on the multi-user US-RIS wireless communication system, which is specifically:

[0058]

[0059] Constraints:

[0060] User transmit power constraints:

[0061]

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

[0063]

[0064] In the formula, v represents the receiving vector of the base station for the user, w urepresents the uplink beamforming vector of the u-th user, Θ u,l represents the phase shift matrix of the u-th user in the US-RIS array at layer l, P u represents the transmission power of the u-th user, SINR u represents the signal-to-interference-noise ratio of the u-th user, represents the transmission power of the u-th user, P max represents the total transmit power of all users, θ u,l,m represents the phase shift of the mth diagonal element in the US-RIS array of the lth layer of the uth user, whose amplitude is 1, and U represents the total number of users.

[0065] In a multi-user scenario, the system needs to optimize the uplink transmission beamforming vector (UL-TBF), the phase shift matrix (TPS) of US-RIS, and the receiving vector (RC) of the base station for each user. Since multiple users transmit signals at the same time, it is necessary to consider the interference between users and maximize the total data rate of the system by jointly optimizing these variables.

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

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

[0068] The first sub-problem: fix the uplink beamforming vector w for each user u , the phase shift matrix Θ of each user in the lth layer of US-RIS u,l and each user's transmission power P u , optimize the base station's receiving vector v for each user u。

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

[0070] In a multi-user scenario, the base station receives signals from multiple users. In order to maximize the signal-to-noise ratio (SINR) of each user, the present invention needs to optimize the base station's receiving vector v u First, fix the beamforming vector w for each user u and the US-RIS phase shift matrix Θ u,l In the case of optimizing the base station receiving vector v u .

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

[0072]

[0073] In order to optimize the receiving vector v of the base station, the present invention solves the maximum eigenvalue problem to obtain the optimal receiving vector of the base station for each user:

[0074]

[0075] In the formula, v opt represents the best receiving vector of the base station for the user, (.) H Indicates transposition, g u represents the channel matrix from the last layer of US-RIS array to the base station, Θ u,l represents the phase matrix of the u-th user in the l-th layer of US-RIS, Θ u,l =diag(θ u,l ), f u,l represents the channel matrix from the uth user to the lth layer of US-RIS array, w u represents the uplink beamforming vector of the u-th user, g i represents the channel matrix of user i from the last layer of US-RIS array to the base station, Θ i,l represents the phase matrix of the i-th user in the l-th layer of US-RIS, f i,l represents the channel matrix from the ith user to the ith US-RIS array, w i represents the uplink beamforming vector of the i-th user, represents the receiving noise at the base station.

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

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

[0078] In a multi-user scenario, each user has an independent US-RIS phase shift matrix Θ u,lIn order to maximize the system performance, it is necessary to optimize the phase configuration of each layer of US-RIS so that the signal of each user is effectively enhanced while reducing the interference between other users.

[0079]

[0080] In order to simplify the expression, a new function ξ is defined (u,p,q) , represents the channel matrix of user u from the pth layer to the qth layer:

[0081]

[0082] Generally, for the sake of simplicity of calculation, the denominator is considered as a fixed value to maximize the numerator. Therefore, the phase shift vector of the phase shift matrix of the US-RIS array at the lth layer for the uth user is:

[0083]

[0084] In the formula, represents the phase shift vector of the phase shift matrix of the uth user in the US-RIS layer l, v represents the vector of the base station for the user, (.) H Indicates transposition, g u represents the channel matrix of the u-th user from the last layer of US-RIS array to the base station, j represents a unit imaginary number, ξ (u,l-1,1) represents the channel matrix of the u-th user signal transmitted from the 1st layer to the l-1th layer of the US-RIS array, ξ (u,L,l+1) represents the channel matrix of the u-th user signal transmitted from the l+1th layer to the Lth layer of the US-RIS array, Indicates any.

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

[0086] Θ u,l =diag(θ u,l )

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

[0088] The third sub-problem: Fix the phase shift matrix Θ of the u-th user in the l-th layer of US-RIS u,l , each user's transmission power P u and the base station's receiving vector v for the user, optimizes the uplink beamforming vector w for the uth user u .

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

[0090] In order to maximize the signal-to-noise ratio (SINR) of each user, it is necessary to fix the phase matrix Θ of US-RIS u,l and the base station receiving vector v u In the case of , optimize the user beamforming vector.

[0091] The optimization goal is to maximize the SINR of each user while satisfying the transmit power constraint:

[0092]

[0093] When optimizing the user beamforming vector, it is usually assumed that the denominator (interference plus noise term) is a constant. In this way, the optimization problem is simplified to:

[0094]

[0095] make The problem is simplified to:

[0096]

[0097] According to the Cauchy-Schwarz inequality, we need to make w 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, represents the optimal beamforming vector of the uth user, v represents the receiving vector of the base station for the user, (.) H represents the transpose, y represents the sum of the signals of all users received by the base station, g u represents the channel matrix from the last layer of US-RIS array to the base station, k represents the loss factor when the electromagnetic wave passes through each layer of RIS array, θ u,l represents the phase shift vector of the l-th layer RIS phase shift matrix of the u-th user, Θ u,l represents the phase matrix of the u-th user in the US-RIS array at layer l, Θ u,l =diag(θ u,l ), f u,l represents the channel matrix from the u-th user to the l-th layer US-RIS array.

[0100] The fourth sub-problem: Fix the phase shift matrix Θ of the u-th user in the l-th layer of US-RISu,l , the base station's receiving vector v for the uth user u and the uplink beamforming vector w of the u-th user u , optimize the transmission power P of the uth user u .

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

[0102] In order to make the total data rate of the system R sum To maximize, we also need to set the transmission power P of each user u Optimize while satisfying the total power constraint.

[0103] The transmission power optimization objective function for each user is:

[0104]

[0105] Construct the Lagrangian function with the Lagrangian multiplier λ:

[0106]

[0107] where λ ≥ 0 is the Lagrange multiplier, P max Represents the total transmit power of all users.

[0108] Find P for the above function u The partial derivative of Simplifying it gives:

[0109]

[0110] v represents the receiving vector of the base station for the user, (.) H Indicates transposition, g u represents the channel matrix of the u-th user from the last layer of US-RIS array to the base station, Θ u,l represents the phase matrix of the u-th user in the US-RIS array at layer l, Θ u,l =diag(θ u,l ), f u,l represents the channel matrix from the uth user to the lth layer of US-RIS array, w u represents the uplink beamforming vector of the u-th user, g i represents the channel matrix of the i-th user from the last layer of US-RIS array to the base station, Θ i,l represents the phase matrix of the i-th user in the US-RIS array at layer l, f i,l represents the channel matrix from the ith user to the ith US-RIS array, w i represents the uplink beamforming vector of the i-th user, ||v|| 2σ 2 represents the received noise power at the base station, P i represents the transmission power of any other i-th user except the u-th user.

[0111] Because the non-negativity of power must be guaranteed, the optimal power allocation formula is:

[0112]

[0113] In the formula, represents the optimal transmission power of each user, λ represents the Lagrange multiplier, and the total power constraint The binary method is used to solve λ and then perform power allocation.

[0114] In order to dynamically adjust the corresponding optimal solution as the real-time channel changes and ensure good wireless communication in a changing communication environment, the channel status can be updated at intervals.

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

[0116] The channel status g between US-RIS and the base station u :The channel between US-RIS and the base station can be estimated by the base station through the signal reflected from US-RIS. After receiving the signal reflected from 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 US-RIS and the base station to US-RIS and the user end to help make adjustments in US-RIS phased matrix optimization and user end beamforming.

[0117] Figure 2 The present invention is a flowchart of an 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.

[0118] In some preferred embodiments, reference Figure 2As shown, the four sub-problems are iteratively solved by using an alternating optimization method to obtain their optimal solutions, and 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 , the total transmission power of all users P max , number of users U, real-time feedback mechanism of system channel changes.

[0120] Initialization: Initialize the base station's receiving vector v and the user's beamforming vector w u 、US-RIS phase shift matrix Θ u,l And the user's transmission power p u Specifically, the base station's receiving vector v is initialized as the unit matrix, Θ u,l The phase shift angle is randomly generated in [0, 2π), w u Initialized as a unit vector, and P u The total power P is evenly distributed max To each user.

[0121] Iterative process: With the remaining variables fixed, optimize the first to fourth sub-problems in turn, update each variable iteratively, calculate the user's signal to interference and noise ratio SINR and determine whether it converges, until SINR meets the accuracy threshold ε or reaches the maximum number of iterations. The entire optimization process continues until the change in the joint objective function is less than the preset threshold, achieving global optimization of system resources.

[0122] Specific:

[0123] The optimal receiving vector v opt : Fixed uplink beamforming vector w for the uth user u , the phase shift matrix Θ of the u-th user in the US-RIS array at layer l u,l and the transmission power P of the uth user u , optimize the base station's receiving vector v for the user.

[0124] Optimal US-RIS phase shift matrix Fixed uplink beamforming vector w for the uth user u , the transmission power P of the uth user u And the base station optimizes the phase shift matrix Θ of the u-th user in the US-RIS array at the lth layer for the user's receiving vector v u,l .

[0125] Optimal beamforming vector The phase shift matrix Θ of the fixed u-th user in the US-RIS array at layer l is u,l , the transmission power P of the uth user u and the base station's receiving vector v for the user, optimizes the uplink beamforming vector w for the uth user u .

[0126] Optimal power allocation Fixed uplink beamforming vector w for the uth user u , the phase shift matrix Θ of the u-th user in the US-RIS array at layer l u,l and the base station optimizes the transmission power P of the uth user for the user's receiving vector v u .

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

[0128] If the SINR result reaches the maximum value and does not change or reaches the maximum number of iterations, the iteration loop is exited.

[0129] Output: The optimal receiving vector v of the base station opt , optimal US-RIS array phase shift matrix The optimal beamforming vector for the user And the optimal transmission power allocation for users

[0130] Simulation verification:

[0131] Simulation environment: MATLAB R2023b platform simulation verification

[0132] A wireless communication network including a base station (BS), a US-RIS array and a user terminal (User) is simulated on the MATLAB R2023b simulation platform. The distance between the User and the RIS is 0.10m, the distance between each layer of the RIS is 0.02m, the distance between the RIS and the base station BS is 20m, and a two-layer uniform planar array (UPA) RIS element configuration is used. Each layer of RIS contains 12×16 antenna elements, where the unit size is Tightly packed, the frequency of the transmitted signal is set to f = 10 GHZ, and the system noise power is σ 2 =1×10 -6 W, the transmission loss of RIS is set to 0.8, and the total transmission power of all users is P max The range is 1W to 5W.

[0133] In order to compare the effectiveness of the effect of the present invention, the present invention and the traditional multi-layer UC-RIS power allocation method are compared in terms of maximum rate, where the relationship between maximum rate and power is as follows: Figure 3 As shown, the rate of the optimized multi-layer UC-RIS solution under various power conditions is significantly higher than that of the traditional method. In particular, when the transmission power is high (eg, close to 5W), the maximum rate of the method of the present invention is increased by about 45% compared with the traditional solution.

[0134] Figure 3 This is a simulation diagram of the relationship between the maximum transmission power and the sum rate of the user in the present invention. Figure 3 In the middle, the horizontal axis P max represents the total transmission power of all users, the vertical axis Rate represents the total rate of the users, Optimization-Multi-layer UC-RIS represents the resource optimization method assisted by the multi-layer user-side RIS of the present invention, and Multi-layer UC-RIS represents the traditional method without resource optimization.

[0135] Figure 4 This is a simulation diagram of the relationship between the maximum transmission power of the user and the signal-to-noise ratio in the present invention. Figure 4 The change of signal-to-noise ratio between the multi-layer UC-RIS optimized by the present invention and the traditional multi-layer UC-RIS when the transmission power increases is demonstrated. Under the same transmission power, the signal-to-noise ratio of the present invention method is improved by about 1dB compared with the traditional solution. Figure 3 The improvement effect of the optimization method on the signal-to-noise ratio is further demonstrated intuitively in the figure.

[0136] From Simulation Figure 3 , 4 It can be seen that the present invention performs well in both the overall transmission rate and signal-to-noise ratio of the system. Compared with the traditional method, the present invention still maintains a high performance when increasing the transmission power of each user, which fully demonstrates the practicality and superiority of the present invention.

[0137] The present invention aims to solve the problems of resource allocation and signal transmission efficiency in multi-user communications. By deploying multiple layers of RIS on the user side, the phase and amplitude of the signal can be adjusted more flexibly, thereby optimizing beamforming and improving the spectrum utilization and transmission rate of the system. At the same time, a dynamic resource optimization algorithm is introduced to effectively coordinate the interference and resource allocation between multiple users to ensure the efficient operation of the system in a complex multi-path propagation environment. After simulation practice, in the face of interference and noisy channel environments, the maximum rate of the present invention is increased by about 45% compared with the traditional method, and the signal-to-noise ratio is increased by about 1dB compared with the traditional method, maintaining a high performance, which fully reflects the practicality and superiority of the optimization scheme.

[0138] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which can include: ROM, RAM, disk or CD, etc.

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

Claims

1. A wireless communication system resource optimization method based on a user-side reconfigurable smart surface, characterized in that: The method includes: A reconfigurable smart surface RIS array is set up on the user side to build a multi-user US-RIS wireless communication system; Based on the maximum transmission power constraint of each user and the phase shift constraint of the user-side reconfigurable smart surface phase shift matrix, a joint optimization objective function based on the multi-user US-RIS wireless communication system is constructed. Solve the joint optimization objective function to obtain the optimal user resource configuration based on the multi-user US-RIS wireless communication system.

2. The method for optimizing wireless communication system resources based on user-side reconfigurable smart surface according to claim 1, characterized in that: The total data transmission rate R based on the multi-user US-RIS wireless communication system sum Specifically: Among them, R sum Represents the total data transmission rate based on the multi-user US-RIS wireless communication system, SINR u represents the signal interference noise ratio of the u-th user, v represents the receiving vector of the base station for the user, (.) H Indicates transposition, g u represents the channel matrix from the last layer of US-RIS array of the u-th user to the base station, θ u,l,m represents the mth phase shift angle in the phase shift vector of the lth layer RIS phase shift matrix of the uth user, Θ u,l represents the phase matrix of the u-th user in the US-RIS array at layer l, Θ u,l =diag(θ u,l ), Θ i,l represents the phase shift control matrix of the US-RIS array of the i-th user; f u,l represents the channel matrix from the uth user to the lth layer of US-RIS array, w u represents the uplink beamforming vector of the u-th user, w i represents the uplink beamforming vector of the ith user, g i represents the channel matrix from the last layer of US-RIS array of the i-th user to the base station, f i,l represents the channel matrix from the ith user to the lth layer of US-RIS array, Represents the receive noise at the base station.

3. The method for optimizing wireless communication system resources based on user-side reconfigurable smart surface according to claim 1 or 2, characterized in that: The joint optimization objective function based on the multi-user US-RIS wireless communication system is specifically: Constraints: User transmit power constraints: Phase constraints of the US-RIS array phase shift matrix: In the formula, v represents the receiving vector of the base station for the user, w u represents the uplink beamforming vector of the u-th user, Θ u,l represents the phase shift matrix of the u-th user in the US-RIS array at layer l, P u represents the transmission power of the u-th user, SINR u represents the signal-to-interference-noise ratio of the u-th user, represents the transmission power of the u-th user, P max represents the total transmit power of all users, θ u,l,m represents the phase shift of the mth diagonal element in the US-RIS array of the lth layer for the uth user, U represents the total number of users, Indicates any.

4. The method for optimizing wireless communication system resources based on user-side reconfigurable smart surface according to claim 3, characterized in that: The problem of solving the joint optimization objective function is decomposed into four sub-problems, specifically: The first sub-problem: Fix the uplink beamforming vector w of the u-th user u , the phase shift matrix Θ of the u-th user in the US-RIS array at layer l u,l and the transmission power P of the uth user u , optimize the base station's receiving vector v for the user; The second sub-problem: Fix the uplink beamforming vector w of the u-th user u , the transmission power P of the uth user u And the base station optimizes the phase shift matrix Θ of the u-th user in the US-RIS array at the lth layer for the user's receiving vector v u,l ; The third sub-problem: Fix the phase shift matrix Θ of the u-th user in the US-RIS array at layer l u,l , the transmission power P of the uth user u and the base station's receiving vector v for the user, optimizes the uplink beamforming vector w for the uth user u ; The fourth sub-problem: Fix the uplink beamforming vector w of the u-th user u , the phase shift matrix Θ of the u-th user in the US-RIS array at layer l u,l and the base station optimizes the transmission power P of the uth user for the user's receiving vector v u .

5. The method for optimizing wireless communication system resources based on user-side reconfigurable smart surface according to claim 4, characterized in that: For the four sub-problems, an alternating optimization method is used to iteratively solve them to obtain their optimal solutions. The specific process includes: Initialize the base station's receiving vector v as the unit matrix and the phase shift matrix Θ of the US-RIS array u,l is a random value in the range [0, 2π), the user's uplink beamforming vector w u is the unit matrix and the user's transmission power P u P is evenly distributed max / U; Optimize the first to fourth sub-problems in turn, and update each variable through iteration, which is specifically: The uplink beamforming vector w of a fixed user u 、The phase shift matrix Θ of the user in the US-RIS array at the lth layer u,l and the user's transmission power P u , optimize the base station's receiving vector v for the user; The uplink beamforming vector w of a fixed user u , user's transmission power P u and the base station receives the user's receiving vector v, optimizes the user's phase shift matrix Θ in the US-RIS array at the lth layer u,l ; The phase shift matrix Θ of the fixed user in the US-RIS array at layer l is u,l , user's transmission power P u and the base station's receiving vector v for the user, optimizing the user's uplink beamforming vector w u ; The uplink beamforming vector w of a fixed user u 、The phase shift matrix Θ of the user in the US-RIS array at the lth layer u,l and the base station's receiving vector v for the user, optimizing the user's transmit power P u ; The SINR of the user is calculated and whether it converges is determined until the SINR meets the accuracy threshold ε or the maximum number of iterations is reached and the iteration is stopped.

6. The method for optimizing wireless communication system resources based on user-side reconfigurable smart surface according to claim 4, characterized in that: Solve the first sub-problem and obtain the optimal receiving vector of the base station for each user: In the formula, v opt represents the optimal receiving vector of the base station for the user, k represents the loss of electromagnetic waves when passing through each layer, (.) H Indicates transposition, g u represents the channel matrix of the u-th user from the last layer of US-RIS array to the base station, L represents the number of layers of US-RIS array, and f u,l represents the channel matrix from the u-th user to the l-th layer US-RIS array.

7. The method for optimizing wireless communication system resources based on user-side reconfigurable smart surface according to claim 4, characterized in that: Solve the second sub-problem and obtain the optimal phase shift matrix Θ for each user in the lth layer of US-RIS u,l Specifically: In the formula, represents the optimal phase shift vector of the phase shift matrix of the US-RIS array of the lth layer for the uth user, j represents a unit imaginary number, diag(.) represents a diagonal matrix, and f u,l represents the channel matrix from the uth user to the lth layer of US-RIS array, ξ (u,l-1,1) represents the channel matrix for the u-th user signal to be transmitted from the 1st layer of the US-RIS array to the l-1th layer, (.) H represents the transpose, ξ (u,L,l+1) represents the channel matrix of the u-th user signal transmitted from the l+1th layer to the Lth layer of the US-RIS array, g u represents the channel matrix of the u-th user from the last layer of US-RIS array to the base station, represents any value, θ u,l,m represents the phase shift of the mth diagonal element in the US-RIS array of the lth layer of the uth user, and its amplitude is 1.

8. The method for optimizing wireless communication system resources based on user-side reconfigurable smart surface according to claim 4, characterized in that: Solving the third sub-problem, we get the optimal beamforming vector for each user: In the formula, represents the optimal beamforming vector for the u-th user, (.) H represents the transpose, gu represents the channel matrix from the last layer of US-RIS array to the base station, k represents the loss factor of the electromagnetic wave when passing through each layer of RIS array, Θ u,l represents the phase matrix of the u-th user in the US-RIS array at layer l, Θ u,l =diag(θ u,l ), θ u,l represents the phase shift vector of the US-RIS array phase shift matrix of the u-th user, f u,l represents the channel matrix from the u-th user to the l-th layer of the US-RIS array, and L represents the number of layers of the US-RIS array.

9. The method for optimizing wireless communication system resources based on user-side reconfigurable smart surface according to claim 4, characterized in that: Solve the fourth sub-problem and obtain the optimal transmission power of each user, which is: In the formula, represents the optimal transmission power of each user, λ represents the Lagrange multiplier introduced by the Lagrange multiplier method, (.) H Indicates transposition, g u represents the channel matrix from the last layer of US-RIS array of the u-th user to the base station, θ u,l,m represents the phase shift of the mth diagonal element in the US-RIS array of the uth user in the lth layer, with an amplitude of 1, Θ u,l represents the phase matrix of the u-th user in the US-RIS array at layer l, Θ u,l =diag(θ u,l ), θ u,l represents the phase shift vector of the US-RIS array phase shift matrix of the u-th user, Θ i,l represents the phase shift control of the US-RIS array of the i-th user; f u,l represents the channel matrix from the uth user to the lth layer of US-RIS array, w i represents the uplink beamforming vector of the i-th user, P i represents the transmission power of any other i-th user except the u-th user, gi represents the channel matrix from the last layer of US-RIS array of the i-th user to the base station, and f i,l represents the channel matrix from the ith user to the ith US-RIS array, Represents the receive noise at the base station.

Images