Near-field large-scale reconfigurable intelligent surface topology optimization and beam design method

Abstract

Wireless communication assisted by a reconfigurable intelligent surface under near-field propagation faces the problem of array gain loss, and the increase of system capacity is limited. In order to further improve the communication system capacity of a large-scale intelligent reflecting surface (XL-RIS) in a near field and compensate for serious path loss, the invention provides a multi-user wireless communication system assisted by optimally deploying the large-scale intelligent reflecting surface, so that the near field range is further expanded, and the system capacity is improved. And the XL-RIS topological structure and the beam design are jointly optimized by taking the minimization of the beam gain error expected by the user as a target. In order to solve the non-convex problem, a self-adaptive tabu search algorithm, a Major-Minimization (MM) algorithm and the like are used for converting the sub-problems, and then alternate iterative optimization is carried out on the sub-problems. Particularly, on the premise that hardware cost is not increased, extra spatial freedom is obtained through selection of element positions, the user sum rate is effectively improved, and beam pointing is closer to the direction expected by the user. A simulation result verifies the effectiveness of the method, and a new solution thought is provided for the application of the XL-RIS in a near field communication system.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a near-field large-scale reconfigurable intelligent surface topology optimization and beam design method. Background Art

[0002] XL-array / surface has become a promising technology to achieve ultra-high spectral efficiency and ultra-spatial resolution in future sixth-generation (6G) communication systems. As wireless communications migrate to higher frequency bands, such as millimeter wave (mm Wave) or even terahertz (THz), the near-field range is expanded to tens or even hundreds of meters, making the near-field characteristics more significant. However, these technologies face problems such as severe free-space path loss and occlusion sensitivity, which are still difficult to completely solve using massive multiple input multiple output (m-MIMO) technology.

[0003] Reconfigurable Intelligent Surface (RIS) can actively and intelligently control electromagnetic waves in space through programming, thus forming an electromagnetic environment with controllable amplitude, phase, polarization and frequency. RIS has the characteristics of low power consumption and low cost. In RIS-assisted wireless communication systems, the deployment of large-scale reflection units can compensate for the serious "multiplicative fading" effect in cascade channels. Therefore, extremely large-scale RIS (XL-RIS) is considered to be one of the future development directions of RIS. At the same time, as the number of reflection units increases, the range of the near-field area will also expand, and scatterers and users are more likely to be located in the near-field area.

[0004] In the existing public technical content, the research mainly focuses on two aspects: codebook design and beam training. The system can select a beam pointing to the desired spatial angle from a pre-designed codebook through beam training. The existing beam training scheme is affected by the constant modulus constraint, which causes the effective direction of the beam to deviate from the area expected by the user, and there is still a large room for improvement between the user's achievable transmission rate and the actual performance. In addition, current work usually considers the classic XL-RIS, in which the XL-RIS elements are regularly arranged on a grid with a constant spacing, but the current work usually considers the classic XL-RIS, in which the XL-RIS elements are regularly arranged on a grid with a constant spacing. At this time, the performance is highly dependent on the number of XL-RIS elements. To improve the array gain of XL-RIS, the number of elements can only be continuously increased. Although RIS is low-cost, expanding the number of elements will increase the computational complexity and cause a certain waste of resources. Therefore, in multi-user XL-RIS-assisted wireless communications, it is necessary to further study new design schemes to overcome these challenges.

[0005] Therefore, in multi-user XL-RIS-assisted wireless communications, new design methods need to be further investigated to overcome these challenges. Summary of the invention

[0006] The purpose of the present invention is to provide a method for topology optimization and beam design of a large-scale reconfigurable intelligent surface based on near-field propagation, so as to further reduce the array gain loss of XL-RIS, make the designed beam closer to the user's desired direction, expand the range of near-field distance and improve the capacity of the communication system.

[0007] The above technical objectives of the present invention are achieved through the following technical solutions:

[0008] A near-field large-scale reconfigurable smart surface topology optimization and beam design method is applied to a wireless communication system, characterized in that the wireless communication system comprises the following steps:

[0009] Step 1: Establish an optimized XL-RIS-assisted multi-user wireless communication system, give the signal model of XL-RIS-assisted wireless communication, and give the channel model in the near-field environment. The receiving user is located in the near-field area and is affected by the spherical beam reflected from the optimized large-scale reconfigurable smart surface.

[0010] Step 2: Establish an objective function to represent the problem of minimizing the user's expected beam gain error. Consider the actual performance of k XL-RIS beams pointing to the user. Subject to the constraints of the optimally deployed XL-RIS topology and beam weights, decompose the problem into irregular reconfigurable smart surface topology structure and beamforming vector quantum problems. Use the Majorization-Minimization algorithm and adaptive tabu search algorithm to convert the non-convex subproblem into a convex problem.

[0011] Step 3: Design a joint optimization scheme to jointly optimize the irregular large-scale reconfigurable smart surface topology and beamforming vectors to minimize the user's expected beam gain error, thereby ensuring that the beam is more accurately aligned with the user's expected direction, thereby significantly improving the transmission rate in multi-user scenarios.

[0012] Further preferably, Step 1 is specifically expressed as:

[0013] Step 1.1: An optimally deployed XL-RIS assisted multi-user wireless communication system includes a base station, an optimally deployed large-scale reconfigurable smart surface, and K communication users. The spherical beam reflected by the large-scale reconfigurable smart surface affects the users. Due to the propagation characteristics of the large-scale reconfigurable smart surface, all users are located in the near field area.

[0014] Step 1.2: Definition For the user set, represents the XL-RIS topology matrix after optimized deployment, where Define z n ∈{1,0} to determine whether the XL-RIS element is deployed on the nth grid point, i.e., z n = 1 means that the XL-RIS element selects the nth grid point, z n = 0 means that the nth grid point is not selected, and the received signal of the kth user is expressed as

[0015]

[0016] Where: is a precoded transmit signal at a base station; denote the channel from XL-RIS to the kth user and the channel from the base station to XL-RIS respectively; represents the beamforming matrix of XL-RIS; represents the additive white Gaussian noise received by the kth user. The XL-RIS composed of a large number of reflective elements can adjust the phase of the incident signal by designing Θ;

[0017]

[0018] Where: θ represents the XL-RIS beamforming vector; represents the reflection coefficient of the nth XL-RIS unit in the optimal deployment, and the received signal model is

[0019]

[0020] Where: The reflection channel of the kth user is defined as

[0021] Step 1.3: Based on the system model, the transmission and rate of user k in the XL-RIS assisted communication system after optimized deployment is:

[0022]

[0023] Step 1.4: Since the deployment positions of the base station and XL-RIS are fixed, it is assumed that the precoding at the base station has been designed. In this system model, the base station is regarded as a single-antenna transmitter. Referring to the far-field propagation model, the effective reflection channel under near-field propagation It can be written as;

[0024]

[0025] Where: is the effective gain; r re,k is the spatial coordinate vector from XL-RIS to the kth user; r in is the spatial coordinate vector from the base station to XL-RIS; the system directly determines the XL-RIS beamforming vector through the beam training process. At each small time scale, the channel remains unchanged. The first process is performed independently by all users to obtain the desired beam direction of each user with the desired beam gain. According to these beam parameters, the beamforming vector of XL-RIS is designed and deployed in the design process. Finally, the beamforming vector is used for data transmission. In order to obtain the desired XL-RIS beamforming, the beam training process is modeled as

[0026]

[0027] Where: c i is the XL-RIS beamforming codeword selected from codebook C.

[0028] Further preferably, Step 2 is specifically expressed as:

[0029] Step 2.1: Considering the actual performance of k XL-RIS beams pointing to the user, subject to the constraints of the optimally deployed XL-RIS topology and beam weights, the p1 objective function represents minimizing the user's expected beam gain error:

[0030]

[0031] C3:1 T z=N

[0032] Where: XL-RIS beamforming vector θ and topology structure Z are variables to be optimized; is the codeword θ obtained during the beam training process k The K-column steering vector matrix composed of; is the expected beam gain vector, C1 represents the unit mode constraint between beams; C2 and C3 represent the constraints of the optimal deployment of the XL-RIS topology structure, where the N diagonal elements of the topology matrix Z are assigned 1, and the remaining N S -N diagonal elements are assigned 0, and the objective function P1 is rewritten as

[0033]

[0034] C3:1 T z=N

[0035] Step 2.2: Use the adaptive tabu search method to search for the optimal XL-RIS topology. Specifically, for a given topology, P2 can be simplified as:

[0036]

[0037] C2:z=z0

[0038] Step 2.3: After obtaining a fixed suboptimal topology Z, the Minorize-Maximization (MM) algorithm is used to solve the problem. Optimize and decompose P3 into a series of approximate sub-problems, solve them iteratively in turn, and approach the optimal solution of the original objective function. For the optimization problem x∈X, min x f(x), the MM algorithm constructs a strict proxy function {q(x|x t )}(t=1,2,…), each proxy function is used as the objective function of the sub-problem, denoted by q(x|x t ) is the target of the t+1th iteration, where x t+1 =argmin x q(x|x t ), taking the t+1th iteration as an example, q(x|x t ) satisfies the following four properties:

[0039]

[0040] The first feature in equation 1 ensures that each surrogate function is an upper bound of the original objective function. The second and third features in equations 2 and 3 ensure that the first-order gradients between the original objective function and the surrogate function are equal at the unique intersection point. Equation 4 ensures that the upper bound is strictly decreasing from the tth to the t+1th iteration, so that f(x t+1 )=q(x t+1 |x t )≤q(x t |x t )=f(x t ) holds, and then by solving the corresponding sequence of subproblems {q(x|x t )}, (t=1,2,…), min q(x|x t ) decreases monotonically with respect to t and eventually converges to the optimal value of the original problem;

[0041] Step 2.4: Lemma 1: The proxy function q(θ|θ) that satisfies the above four characteristics at the same time t ) can be defined as (11), for any given θ t and any available θ, where λ max Indicates ΞΞ H The biggest feature

[0042]

[0043] Step 2.5: XL-RIS beamforming θ solves a series of {q(θ|θ t )}, (t=1,2,…) The minimization problem is solved. For t+1 iterations, substitute M into λ max I N×N , the optimization is expressed as

[0044]

[0045] Step 2.6: In order to enrich the degree of freedom of the optimization target, the beam phase gain optimization variable is introduced to solve the phase factor of the expected beam gain. As an optimizable variable, The optimization problem P3 is rewritten as

[0046]

[0047] C2:z=z0

[0048]

[0049] For a given θ t and z0, the phase factor Can be optimized to

[0050]

[0051] Further preferably, Step 3 is specifically expressed as:

[0052] Step 3.1: Randomly generate a topological matrix Z1, generate the neighborhood of Z1, obtain the sum rate of the above neighbors through beamforming, select the optimal neighbor and obtain the topological matrix Z2 through the adaptive tabu search algorithm, perform the next iteration according to Z2, and when the maximum number of iterations is reached or the termination condition is met, the suboptimal solution of P1 is obtained, which is expressed as: Z opt and θ opt ;

[0053] Step 3.2: The feasible selection positions of XL-RIS components have additional spatial freedom, which not only expands the near-field area under the condition of limited number of components, but also improves the carrying capacity of users in this area and the overall capacity of the system.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] By comparing the conventional XL-RIS beam design deployed in the existing far-field and near-field propagation channels, the present invention not only achieves a significant improvement in system capacity, but also ensures that the beam is more accurately aligned with the user's desired direction, thereby significantly improving the transmission rate in multi-user scenarios. Without increasing the number of components, the potential for improving the performance of wireless communication systems by optimizing the structure of XL-RIS deployment provides a new strategic direction for the design of near-field communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the structure of a wireless communication system for optimizing the deployment of XL-RIS to assist multiple users in one embodiment;

[0057] Figure 2 A schematic diagram of a flow chart of an optimization scheme for optimizing and deploying an XL-RIS transmission scheme in an embodiment;

[0058] Figure 3 The XL-RIS topology is optimized and deployed in the present invention. Due to the large number of components, the present invention selects a local situation of the topology to illustrate, where the blue squares represent the selected positions of the XL-RIS components;

[0059] Figure 4 The relationship between users and rates and the number of grid points for optimal deployment of XL-RIS;

[0060] Figure 5 For performance comparison of sum rate and number of users. DETAILED DESCRIPTION

[0061] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0062] Embodiment, a near-field large-scale reconfigurable smart surface topology optimization and beam design method, comprising the following steps:

[0063] Step 1: Establish an optimized XL-RIS-assisted multi-user wireless communication system, give the signal model of XL-RIS-assisted wireless communication, and give the channel model in the near-field environment. The receiving user is located in the near-field area and is affected by the spherical beam reflected from the optimized large-scale reconfigurable smart surface.

[0064] The method for establishing a wireless communication system model for optimizing the deployment of XL-RIS-assisted multi-users is characterized in that Step 1 is specifically described as follows:

[0065] Step 1.1: An optimally deployed XL-RIS assisted multi-user wireless communication system includes a base station, an optimally deployed large-scale reconfigurable smart surface, and K communication users. The spherical beam reflected by the large-scale reconfigurable smart surface affects the users. Due to the propagation characteristics of the large-scale reconfigurable smart surface, all users are located in the near field area.

[0066] Step 1.2: Definition For the user set, represents the XL-RIS topology matrix after optimized deployment, where Define z n ∈{1,0} to determine whether the XL-RIS element is deployed on the nth grid point, i.e., z n = 1 means that the XL-RIS element selects the nth grid point, z n = 0 means that the nth grid point is not selected. The received signal of the kth user is expressed as

[0067]

[0068] Where: is a precoded transmit signal at a base station; denote the channel from XL-RIS to the kth user and the channel from the base station to XL-RIS respectively; represents the beamforming matrix of XL-RIS; represents the additive white Gaussian noise received by the kth user. The XL-RIS composed of a large number of reflective elements can adjust the phase of the incident signal by designing Θ;

[0069]

[0070] Where: θ represents the XL-RIS beamforming vector; represents the reflection coefficient of the nth XL-RIS unit in the optimal deployment. According to formula (2), the received signal model is

[0071]

[0072] Where: The reflection channel of the kth user is defined as

[0073] Step 1.3: Based on the system model, the transmission and rate of user k in the XL-RIS assisted communication system after optimized deployment is:

[0074]

[0075] Step 1.4: Since the deployment locations of the base station and XL-RIS are fixed, it is assumed that the precoding at the base station has been designed. In this system model, the base station is regarded as a single-antenna transmitter. Referring to the far-field propagation model, the effective reflection channel under near-field propagation It can be written as;

[0076]

[0077] Where: is the effective gain; r re,k is the spatial coordinate vector from XL-RIS to the kth user; r in is the spatial coordinate vector from the base station to XL-RIS; the system directly determines the XL-RIS beamforming vector through the beam training process, and the channel remains unchanged at each small time scale. The first process is performed independently by all users to obtain the desired beam direction of each user with the desired beam gain. Based on these beam parameters, the beamforming vector of the optimized deployment of XL-RIS is designed in the design process, and finally the beamforming vector is used for data transmission. In order to obtain the desired XL-RIS beamforming, the beam training process is modeled as

[0078]

[0079] Where: c i is the XL-RIS beamforming codeword selected from codebook C.

[0080] Step 2: Establish an objective function to represent the problem of minimizing the user's expected beam gain error. Considering the actual performance of k XL-RIS beams pointing to the user, subject to the constraints of the optimized deployed XL-RIS topology and beam weights, the problem is decomposed into irregular reconfigurable smart surface topology structure and beamforming vector quantum problems. The non-convex subproblem is converted into a convex problem through the Majorization-Minimization (MM) algorithm and the adaptive taboo search algorithm.

[0081] The objective function represents the problem of minimizing the user's expected beam gain error, which is characterized by Step 2, specifically expressed as:

[0082] Step 2.1: Considering the actual performance of k XL-RIS beams pointing to the user, subject to the constraints of the optimally deployed XL-RIS topology and beam weights, the p1 objective function represents minimizing the user's expected beam gain error:

[0083]

[0084] Where: XL-RIS beamforming vector θ and topology structure Z are variables to be optimized; is the codeword θ obtained during the beam training process k The K-column steering vector matrix composed of; is the desired beam gain vector. C1 represents the unit mode constraint between beams; C2 and C3 represent the constraints of the optimal deployment of the XL-RIS topology structure, where the N diagonal elements of the topology matrix Z are assigned a value of 1, and the remaining N S -N diagonal elements are assigned 0. The objective function P1 is rewritten as

[0085]

[0086] Step 2.2: Use the adaptive tabu search method to search for the optimal XL-RIS topology. Specifically, for a given topology, P2 can be simplified as:

[0087]

[0088] Step 2.3: After obtaining a fixed suboptimal topology Z, the Minorize-Maximization (MM) algorithm is used to solve the problem. Optimize. Decompose P3 into a series of approximate sub-problems, solve them iteratively in turn, and approach the optimal solution of the original objective function. For the optimization problem x∈X, min x f(x), the MM algorithm constructs a strict proxy function {q(x|x t )}(t=1,2,…), each proxy function is used as the objective function of the sub-problem. Let q(x|x t ) is the target of the t+1th iteration, where x t+1 =argmin x q(x|x t ). Taking the t+1th iteration as an example, q(x|x t ) satisfies the following four properties:

[0089]

[0090] The first feature in (10a) ensures that each proxy function is an upper bound of the original objective function. The second and third features in (10b) and (10c) ensure that the first-order gradients between the original objective function and the proxy function are equal at the unique intersection point. (10d) ensures that the upper bound is strictly decreasing from the tth to the t+1th iteration.

[0091] Let f(x t+1 )=q(x t+1 |x t )≤q(x t |x t )=f(x t ) holds, and then by solving the corresponding sequence of subproblems {q(x|x t )}, (t=1,2,…), min q(x|x t ) decreases monotonically with respect to t and eventually converges to the optimal value of the original problem.

[0092] The optimization problem is reformulated as

[0093]

[0094] Lemma 1: The proxy function q(θ|θ t ) can be defined as (12), for any given θ t and any available θ, where λ max Indicates ΞΞ H The biggest feature

[0095]

[0096] XL-RIS beamforming θ is solved by iterative optimization to solve a series of {q(θ|θ t )}, (t=1,2,…) The minimization problem is solved. For the t+1 iteration, substitute M into λ max I N×N , the optimization is expressed as

[0097]

[0098] In order to enrich the degree of freedom of the optimization objective, the beam phase gain optimization variable is introduced to solve the phase factor of the expected beam gain. As an optimizable variable, The optimization problem P3 is rewritten as

[0099]

[0100] For a given θ t and z0, the phase factor Can be optimized to

[0101]

[0102] Step 3: Design a joint optimization scheme to jointly optimize the irregular large-scale reconfigurable smart surface topology and beamforming vectors to minimize the user's expected beam gain error, thereby ensuring that the beam is more accurately aligned with the user's expected direction, thereby significantly improving the transmission rate in multi-user scenarios.

[0103] The joint optimization scheme jointly optimizes the irregular large-scale reconfigurable smart surface topology and the beamforming vector to minimize the user's expected beam gain error, which is characterized by Step 3, which is specifically expressed as:

[0104] Step 3.1: Randomly generate a topological matrix Z1, generate the neighborhood of Z1, obtain the sum rate of the above neighbors through beamforming, select the optimal neighbor and obtain the topological matrix Z2 through the adaptive tabu search algorithm, and perform the next iteration based on Z2. When the maximum number of iterations is reached or the termination condition is met, the suboptimal solution of P1 is obtained, which is expressed as: Z opt and θ opt .

[0105] Step 3.2: The feasible selection positions of XL-RIS components have additional spatial freedom, which not only expands the near-field area under the condition of limited number of components, but also improves the carrying capacity of users in this area and the overall capacity of the system.

[0106] like Figure 1 The figure shows an optimized deployment of XL-RIS assisted multi-user wireless communication system. The receiving user is located in the near field area and is affected by the spherical beam reflected from the XL-RIS. The N XL-RIS elements of the optimized deployment of large-scale reconfigurable smart surface are irregularly distributed on Ns grid points (Ns>N) on the amplified surface. Based on this, the signal model of XL-RIS assisted wireless communication is established, and the channel model in the near field environment is given. For the non-convex optimization problem established in Step 2, as shown in Figure 2 As shown, the objective function is established to represent the problem of minimizing the user's expected beam gain error. Only the actual performance of k XL-RIS beams pointing to the user is considered. Subject to the constraints of the optimized deployed XL-RIS topology and beam weights, the problem is decomposed into two sub-problems: irregular reconfigurable smart surface topology structure and beamforming vector. Through the Majorization-Minimization (MM) algorithm and the adaptive taboo search algorithm, the non-convex sub-problem is converted into a convex problem for alternating optimization and solving, thereby ensuring that the beam is more accurately aligned with the user's expected direction, thereby significantly improving the transmission rate in multi-user scenarios. The specific steps are as follows:

[0107] First, the topological matrix Z1 is randomly generated, and the neighborhood of Z1 is generated based on the cross entropy algorithm of neighbor extraction. Then, the sum rate of the above neighbors is obtained through beamforming, and the optimal neighbor is selected and the topological matrix Z2 is obtained through the ATS algorithm. The next iteration is performed according to Z2. Finally, when the maximum number of iterations is reached or the termination condition is met, the suboptimal solution of P1 is obtained, which is expressed as: Z opt and θ opt .

[0108] For fixed beamforming vectors, an optimized XL-RIS topology ATS algorithm is proposed. This algorithm iteratively searches for possible topologies through an adaptive mobility criterion to obtain a suboptimal XL-RIS topology, such as Figure 3 shown.

[0109] After obtaining a fixed suboptimal topology Z, the Minorize-Maximization (MM) algorithm is used to solve problem P2. Optimize. Decompose P2 into a series of approximate sub-problems, solve them iteratively in sequence, and approach the optimal solution of the original objective function.

[0110] In this implementation case, a three-dimensional scene with a topological structure in a Cartesian coordinate system is considered. It is assumed that the XL-RIS is optimally deployed in the yOz plane, with its center at the origin (0,0,0), and the spacing between XL-RIS units is d R =λ / 2, in this scenario, 512 XL-RIS elements are deployed, and the grid is deployed in multiples of the number of elements. The near-field channel and the far-field channel are generated by codebooks respectively. The other parameters are set to the carrier frequency of 10GHz. When the grid size is 64*16, the XL-RIS array aperture reaches 0.9674m, and the near-field scene coverage distance is 62.39m; when the grid size is 128*8, the XL-RIS array aperture reaches 1.9237m, and the near-field propagation coverage distance is expanded to 246.72m. According to these parameter settings, MATLAB is used for simulation.

[0111] like Figure 4 As shown, by increasing the surface size to change the irregular proportion of the optimally deployed XL-RIS, the user and rate performance can be effectively improved. As the number of grid points of the optimally deployed XL-RIS increases, the user and rate performance of the optimally deployed XL-RIS can be effectively improved.

[0112] from Figure 5 As shown in the figure, the system and rate of the optimized XL-RIS beam design in the near-field environment are better than the conventional method. Whether in the near-field or far-field environment, the proposed optimized deployment scheme still has good performance and can effectively improve the performance of the entire user system.

[0113] This specific embodiment is merely an explanation of the present invention and is not a limitation of the present invention. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed. However, as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. A near-field large-scale reconfigurable smart surface topology optimization and beam design method, applied to wireless communication systems, characterized in that: The wireless communication system comprises the following steps: Step 1: Establish an optimized XL-RIS-assisted multi-user wireless communication system, give the signal model of XL-RIS-assisted wireless communication, and give the channel model in the near-field environment. The receiving user is located in the near-field area and is affected by the spherical beam reflected from the optimized large-scale reconfigurable smart surface. Step 2: Establish an objective function to represent the problem of minimizing the user's expected beam gain error. Consider the actual performance of k XL-RIS beams pointing to the user. Subject to the constraints of the optimally deployed XL-RIS topology and beam weights, decompose the problem into irregular reconfigurable smart surface topology structure and beamforming vector quantum problems. Use the Majorization-Minimization algorithm and adaptive tabu search algorithm to convert the non-convex subproblem into a convex problem. Step 3: Design a joint optimization scheme to jointly optimize the irregular large-scale reconfigurable smart surface topology and beamforming vectors to minimize the user's expected beam gain error, thereby ensuring that the beam is more accurately aligned with the user's expected direction, thereby significantly improving the transmission rate in multi-user scenarios.

2. According to claim 1, a near-field large-scale reconfigurable smart surface topology optimization and beam design method is characterized in that: Step 1, specifically stated as follows: Step 1.1: An optimally deployed XL-RIS assisted multi-user wireless communication system includes a base station, an optimally deployed large-scale reconfigurable smart surface, and K communication users. The spherical beam reflected by the large-scale reconfigurable smart surface affects the users. Due to the propagation characteristics of the large-scale reconfigurable smart surface, all users are located in the near field area. Step 1.2: Definition For the user set, represents the XL-RIS topology matrix after optimized deployment, where Define z n ∈{1,0} to determine whether the XL-RIS element is deployed on the nth grid point, i.e., z n = 1 means that the XL-RIS element selects the nth grid point, z n =0 means that the nth grid point is not selected, and the received signal of the kth user is expressed as: Where: is a precoded transmit signal at a base station; denote the channel from XL-RIS to the kth user and the channel from the base station to XL-RIS respectively; represents the beamforming matrix of XL-RIS; represents the additive white Gaussian noise received by the kth user. The XL-RIS composed of a large number of reflective elements can adjust the phase of the incident signal by designing Θ; Where: θ represents the XL-RIS beamforming vector; represents the reflection coefficient of the nth XL-RIS unit in the optimal deployment, and the received signal model is Where: The reflection channel of the kth user is defined as Step 1.3: Based on the system model, the transmission and rate of user k in the XL-RIS assisted communication system after optimized deployment is: Step 1.4: Since the deployment positions of the base station and XL-RIS are fixed, it is assumed that the precoding at the base station has been designed. In this system model, the base station is regarded as a single-antenna transmitter. Referring to the far-field propagation model, the effective reflection channel under near-field propagation It can be written as; Where: is the effective gain; r re,k is the spatial coordinate vector from XL-RIS to the kth user; r in is the spatial coordinate vector from the base station to XL-RIS; the system directly determines the XL-RIS beamforming vector through the beam training process. At each small time scale, the channel remains unchanged. The first process is performed independently by all users to obtain the desired beam direction of each user with the desired beam gain. According to these beam parameters, the beamforming vector of XL-RIS is designed and deployed in the design process. Finally, the beamforming vector is used for data transmission. In order to obtain the desired XL-RIS beamforming, the beam training process is modeled as Where: c i is the XL-RIS beamforming codeword selected from codebook C.

3. The near-field large-scale reconfigurable smart surface topology optimization and beam design method according to claim 1, characterized in that: Step 2, specifically stated as follows: Step 2.1: Considering the actual performance of k XL-RIS beams pointing to the user, subject to the constraints of the optimally deployed XL-RIS topology and beam weights, the p1 objective function represents minimizing the user's expected beam gain error: P1: s·t C1: C2: C3:1 T from=N Where: XL-RIS beamforming vector θ and topology structure Z are variables to be optimized; is the codeword θ obtained during the beam training process k The K-column steering vector matrix composed of; is the expected beam gain vector, C1 represents the unit mode constraint between beams; C2 and C3 represent the constraints of the optimal deployment of the XL-RIS topology structure, where the N diagonal elements of the topology matrix Z are assigned 1, and the remaining N S -N diagonal elements are assigned 0, and the objective function P1 is rewritten as P2: s·t C1: C2: C3:1 T from=N Step 2.2: Use the adaptive tabu search method to search for the optimal XL-RIS topology. Specifically, for a given topology, P2 can be simplified as: P3: s·t C1: C2:z=z0 Step 2.3: After obtaining a fixed suboptimal topology Z, the Minorize-Maximization (MM) algorithm is used to solve the problem. Optimize and decompose P3 into a series of approximate sub-problems, solve them iteratively in turn, and approach the optimal solution of the original objective function. For the optimization problem x∈X, min x f(x), the MM algorithm constructs a strict proxy function {q(x|x t )}(t=1,2,…), each proxy function is used as the objective function of the sub-problem, denoted by q(x|x t ) is the target of the t+1th iteration, where x t+1 =argmin x q(x|x t ), taking the t+1th iteration as an example, q(x|x t ) satisfies the following four properties: The first feature in equation 1 ensures that each surrogate function is an upper bound of the original objective function. The second and third features in equations 2 and 3 ensure that the first-order gradients between the original objective function and the surrogate function are equal at the unique intersection point. Equation 4 ensures that the upper bound is strictly decreasing from the tth to the t+1th iteration, so that f(x t+1 )=q(x t+1 |x t )≤q(x t |x t )=f(x t ) holds, and then by solving the corresponding sequence of subproblems {q(x|x t )}, (t=1,2,…), min q(x|x t ) decreases monotonically with respect to t and eventually converges to the optimal value of the original problem; Step 2.4: Lemma 1: The proxy function q(θ|θ) that satisfies the above four characteristics at the same time t ) can be defined as (11), for any given θ t and any available θ, where λ max Indicates ΞΞ H The biggest feature Step 2.5: XL-RIS beamforming θ solves a series of {q(θ|θ t )}, (t=1,2,…) The minimization problem is solved. For t+1 iterations, substitute M into λ max I N×N , the optimization is expressed as Step 2.6: In order to enrich the degree of freedom of the optimization target, the beam phase gain optimization variable is introduced to solve the phase factor of the expected beam gain. As an optimizable variable, The optimization problem P3 is rewritten as P3: s·t C1: C2:z=z0 C3: For a given θ t and z0, the phase factor Can be optimized to 4. The near-field large-scale reconfigurable smart surface topology optimization and beam design method according to claim 1, characterized in that: Step 3, specifically stated as follows: Step 3.1: Randomly generate a topological matrix Z1, generate the neighborhood of Z1, obtain the sum rate of the above neighbors through beamforming, select the optimal neighbor and obtain the topological matrix Z2 through the adaptive tabu search algorithm, perform the next iteration according to Z2, and when the maximum number of iterations is reached or the termination condition is met, the suboptimal solution of P1 is obtained, which is expressed as: Z opt and θ opt ; Step 3.2: The feasible selection positions of XL-RIS components have additional spatial freedom, which not only expands the near-field area under the condition of limited number of components, but also improves the carrying capacity of users in this area and the overall capacity of the system.

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