XL-RIS-assisted near-field multi-beam design method and system

Abstract

The invention relates to the technical field of radio communication, and discloses an XL-RIS-assisted near-field multi-beam design method and system, and the method comprises the steps: constructing an XL-RIS-assisted near-field communication multi-user receiving signal model; constructing a near-field multi-beam optimization model; the near-field multi-beam optimization model is solved, so that an XL-RIS beam reflection matrix can be obtained; and iteratively solving the near-field multi-beam optimization model to obtain a reflection matrix of a base station transmitting signal to the XL-RIS, and completing XL-RIS assisted system multi-beam optimization. Compared with the existing calculation, the beam optimization method provided by the invention does not need a complicated calculation problem, overcomes the problems of low convergence speed and high calculation complexity in large-scale array processing of the traditional method, and improves the optimization efficiency. The operation speed can be increased under the condition that the system performance is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of radio communication technology, and in particular to an XL-RIS-assisted near-field multi-beam design method and system. Background Art

[0002] With the development of 6G technology, XL-RIS technology has attracted attention due to its advantages in enhancing signal coverage and system capacity. In near-field communication, XL-RIS optimizes signal propagation and beam allocation through multi-beam design technology to meet the needs of different users. However, traditional multi-beam design algorithms have problems such as slow convergence speed and high computational complexity when processing large-scale arrays.

[0003] Currently, there are many studies on multi-beam design from the perspective of alternating optimization.

[0004] Prior art 1: Decai Shen, Linglong Dai, Xin Su, and Shiqiang Suo. Multi-beam design for near-field extremely large-scale ris-aided wireless communications. IEEE Transactions on Green Communications and Networking, 7(3):1542– 1553, 2023. Prior art 1 first proposed a multi-beam design method based on ultra-large-scale intelligent reflector (XL-RIS) assisted wireless communication. This method aims at the limitation that the existing near-field RIS system mainly focuses on single-beam design, and proposes a multi-beam design scheme to overcome the problem of beam gain loss in multi-user scenarios, thereby improving the transmission rate.

[0005] The method first uses a beam training step, in which all users independently determine the single beam direction and gain required by each user, without obtaining accurate channel state information (CSI). Then, the multi-beam design step uses these single beam directions and gains as targets, and generates multiple near-field beams pointing to the user's required area through an optimization algorithm. However, due to the constant modulus constraint of XL-RIS, the multi-beam design problem is essentially a constrained non-convex quadratic programming problem, which is difficult to solve directly. To solve this problem, a BCD-based MM algorithm is proposed, which decomposes the problem into a series of solvable sub-problems and gradually approaches the optimal solution through iterative optimization. In addition, in order to overcome the influence of the constant modulus constraint on the multi-beam optimization solution space, the phase factor of the multi-beam gain is introduced as an additional optimization variable, thereby enriching the optimization freedom. By optimizing the phase factor, the algorithm can better control the directivity and gain of the multi-beam, thereby improving the transmission rate.

[0006] In the existing technology, although the progress of massive antenna and XL-RIS technology has brought significant performance improvement to the multi-beam design method for near-field XL-RIS-assisted wireless communication, this technology still faces many challenges in practical application. Problems such as computational complexity, constant modulus constraints, beam training overhead, system parameter selection, and dynamic environment adaptability restrict its development. In order to overcome these difficulties and further tap the potential of multi-beam design, future research needs to focus on exploring efficient algorithms, low-complexity code books, dynamic channel estimation and beam tracking technologies, and optimizing system parameters. At the same time, it is necessary to study how to better utilize phase factors to overcome constant modulus constraints and further improve the performance of multi-beam design. Summary of the invention

[0007] The object of the present invention is to provide an XL-RIS-assisted near-field multi-beam design method and system to achieve at least one of the following objectives: 1) Solve the problem of channel modeling in near-field multi-beam design: For near-field wireless communication assisted by XL-RIS (ultra-large-scale intelligent reflector) in 6G communication, a near-field reflection channel model based on the Saleh-Valenzuela channel model and spherical wave array characteristics is proposed to more accurately reflect the channel characteristics;

[0008] 2) Solve the problems of slow convergence speed and high computational complexity of traditional algorithms: By introducing the multi-starting point Adam algorithm, the problems of slow convergence speed and high computational complexity of traditional algorithms in large-scale array processing are overcome, and the optimization efficiency is improved;

[0009] 3) Avoid local optimal solution: In order to avoid falling into the local optimal solution during the optimization process, a multi-starting point optimization strategy is adopted, starting the optimization from multiple initial points to increase the probability of finding the global optimal solution;

[0010] 4) Optimizing beamforming vectors to improve user rate: By using the multi-starting point Adam algorithm to optimize the beamforming vector, the strength of the user's received signal is further improved, thereby improving the system's transmission rate.

[0011] In order to achieve the above purpose, the technical solutions adopted are as follows: In a first aspect, a near-field multi-beam design method assisted by XL-RIS is provided, the method comprising: user 1-user K Construct XL-RIS-assisted near-field communication multi-user receiving signal model; Construct a near-field multi-beam optimization model; Solving the near-field multi-beam optimization model to obtain an XL-RIS beam reflection matrix; The near-field multi-beam optimization model is solved iteratively to obtain the reflection matrix of the base station transmitting signal to XL-RIS, and the XL-RIS-assisted system multi-beam optimization is completed.

[0012] Optionally, an XL-RIS-assisted near-field communication multi-user receiving signal model is constructed based on an XL-RIS-assisted near-field wireless communication system model; wherein the XL-RIS-assisted near-field wireless communication system model includes a base station, an XL-RIS, and a user device; wherein the base station, the XL-RIS, and the user device are communicatively connected with each other.

[0013] Optionally, constructing an XL-RIS-assisted near-field communication multi-user receiving signal model based on the XL-RIS-assisted near-field wireless communication system model, including: Set the location and number of antennas of the base station. Antennas, using uniform linear array; Set the position and number of reflector units of XL-RIS. XL-RIS uses OK The total number of reflection units is ; Set the location and number of user devices, system services A single-antenna user device; The XL-RIS-assisted near-field communication multi-user receiving signal model is determined to be expressed as: ; in, Indicates The signal received by the user equipment ∈ represents the preset code signal vector transmitted by the base station, represents a complex domain; ∈ represents the beamforming matrix of XL-RIS; Indicates The additive white Gaussian noise received by each user is represents a complex Gaussian distribution, Indicates the size of its variance; Indicates The reflection channel matrix of user equipment, , represents the channel matrix from the base station to XL-RIS, Indicates XL-RIS to The channel vector of each user equipment is represents the function used to construct a diagonal matrix. XL-RIS is designed To adjust the phase of the incident signal, represents the beamforming vector of XL-RIS.

[0014] Optionally, build a near-field multi-beam optimization model, including: The distance between the base station and the XL-RIS and the distance between the XL-RIS and the user equipment satisfy When , the reflection channel is modeled according to the spherical wavefront assumption to obtain the near-field reflection channel model; represents the Rayleigh distance, , is the array aperture, is the carrier wavelength; Under the spherical wavefront assumption, in the near-field reflection channel model, The reflection channel of a user equipment is expressed as: ; in, It is The reflection channel of each user equipment, From XL-RIS to The spatial coordinate vector of the user equipment, is the spatial coordinate vector from the base station to XL-RIS, is the gain or attenuation factor associated with the kth user device, , is the array steering vector, T is the matrix transpose; In the near-field reflection channel model, for the array steering vector under near-field conditions, and , expressed as: ; ; in, and Respectively represent the and Elements to The distance between the user equipment and the base station, is a natural constant, is pi, is an imaginary unit; Based on the near-field reflection channel model, the near-field multi-beam optimization model is determined to be expressed as: ; ; in, represents the actual beam gain for K target users, represents the set of real numbers, Represents the near-field steering matrix, each column of which is obtained by the near-field codebook The codeword corresponding to the user equipment obtained by beam training in , Indicates The reflection coefficient of each XL-RIS element, , represents the phase represented by the reflection coefficient of the nth XL-RIS element, , represents the objective function, Indicates constraints, represents the minimization of the objective function, Represents the transposed conjugate of a matrix.

[0015] Optionally, solving the near-field multi-beam optimization model to obtain an XL-RIS beam reflection matrix includes: Initialization parameters; wherein the parameters include beamforming vector, learning rate, first decay rate of momentum estimation β 1 and 2 decay rates β 2. First-order moment, second-order moment and constant value ϵ ; Calculate the gradient: For each iteration t , calculate the objective function relative to the current beamforming vector θt Gradient ; Update the first-order moment estimate by the following formula: ; in, represents the updated first-order moment estimate, represents the current first-order moment estimate; Update the second-order moment estimate by the following formula: ; in, represents the updated second-order moment estimate, represents the current second-order moment estimate; The bias-corrected first and second moment estimates are calculated using the following formula: ， ； in, represents the first order moment of the bias correction, represents the decay rate in the first-order moment estimate, represents the bias-corrected second-order moment estimate, represents the decay rate in the second-order moment estimate; The beamforming vector is updated by the following formula: ; in, represents the updated beamforming vector, represents the current beamforming vector, represents the learning rate, represents the bias-corrected first-order moment estimate, represents the bias-corrected second-order moment estimate; Check the convergence condition: if the convergence condition is met, stop the iteration; otherwise, continue to perform the iteration steps, which include calculating the gradient, updating the first-order moment estimate, calculating the first-order moment and the second-order moment estimate of the deviation correction, and updating the beamforming vector.

[0016] Optionally, iteratively solving the near-field multi-beam optimization model to obtain a reflection matrix of a base station transmitting signal to XL-RIS, and completing XL-RIS-assisted system multi-beam optimization, including: Random initialization: Generate multiple initial beamforming vectors , where S represents the number of starting points, and the multiple initial beamforming vectors are randomly sampled according to Gaussian distribution or uniform distribution; Independent optimization: Each initial beamforming vector is independently optimized. The independent optimization includes calculating gradients, updating first-order moment estimates and second-order moment estimates, and updating beamforming vectors; Performance recording: recording performance parameters in each iteration, including channel gain and user rate; Local optimal selection: After each independent optimization is completed, the reflection matrix with the best performance is selected based on the performance parameters. As a local optimal solution; Global optimal determination: Select the reflection matrix with the best performance from all local optimal solutions As the global optimal solution; When the maximum number of iterations or the convergence threshold is reached, the currently determined global optimal solution is used as the reflection matrix of the base station transmitting the signal to XL-RIS, completing the XL-RIS-assisted system multi-beam optimization.

[0017] In a second aspect, an XL-RIS-assisted near-field multi-beam design system is provided, the system comprising: A first model building module is configured to build an XL-RIS-assisted near field communication multi-user reception signal model; A second model building module is configured to build a near-field multi-beam optimization model; A first solving module is configured to solve the near-field multi-beam optimization model to obtain an XL-RIS beam reflection matrix; The second solving module is configured to iteratively solve the near-field multi-beam optimization model to obtain a reflection matrix of the base station transmitting signal to the XL-RIS, and complete the XL-RIS-assisted system multi-beam optimization.

[0018] The beneficial effects of the present invention are: 1. Significantly improved optimization efficiency: By introducing the multi-starting point Adam algorithm, the convergence speed of the algorithm is significantly accelerated. Compared with the traditional MM algorithm, the calculation time is reduced and the optimization efficiency is improved.

[0019] 2. Enhanced the probability of obtaining the global optimal solution: The multi-starting point optimization strategy is adopted to avoid the algorithm from falling into the local optimum, increase the possibility of finding the global optimal solution, and ensure the reliability of the optimization results.

[0020] 3. Improved the transmission rate of the system: By optimizing the beamforming vector, the user's received signal strength is enhanced, which significantly improves the system's transmission rate and communication quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a near-field multi-user scenario according to the prior art is shown.

[0022] Figure 2 A flow chart of an XL-RIS-assisted near-field multi-beam design method according to an embodiment of the present invention is shown.

[0023] Figure 3 A minimum rate simulation diagram under different numbers of users according to an embodiment of the present invention is shown.

[0024] Figure 4 A minimum rate simulation diagram under different SNRs according to an embodiment of the present invention is shown.

[0025] Figure 5 A structural diagram of an XL-RIS-assisted near-field multi-beam design system according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0026] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0027] The specific implementation of the present invention is further described in detail below in conjunction with the drawings and examples.

[0028] Figure 1 Schematic diagram of a near-field multi-user scenario provided by an embodiment of the present invention. Figure 1 As shown, the near-field multi-user scenario includes a base station BS, an ultra-large-scale intelligent reflection surface XL-RIS, and multiple user equipments User 1 to User K that are communicatively connected to each other.

[0029] Based on Figure 1 The near-field multi-user scenario model shown. Figure 2 A flowchart of an XL-RIS-assisted near-field multi-beam design method provided in an embodiment of the present invention. An XL-RIS-assisted near-field multi-beam design method is provided in an embodiment of the present invention. Figure 2 As shown, the method includes steps S1 to S4, which are described in detail as follows: S1, construct an XL-RIS-assisted near-field communication multi-user receiving signal model.

[0030] In this embodiment, the XL-RIS-assisted near-field communication multi-user received signal model can respond to the input multi-user received signal data suitable for the XL-RIS-assisted near-field communication scenario to obtain the beamforming vector of XL-RIS. The beamforming vector of XL-RIS can be used as the input of the near-field multi-beam optimization model in the subsequent step S2, and based on the solution steps of steps S3 and S4, the optimal reflection matrix of the base station transmit signal to XL-RIS is obtained, thereby realizing system multi-beam optimization.

[0031] In some embodiments, an XL-RIS-assisted near-field communication multi-user receiving signal model is constructed based on an XL-RIS-assisted near-field wireless communication system model; wherein the XL-RIS-assisted near-field wireless communication system model includes a base station, XL-RIS, and a user equipment.

[0032] In this embodiment, the implementation steps of constructing the XL-RIS-assisted near-field communication multi-user receiving signal model based on the XL-RIS-assisted near-field wireless communication system model are specifically to initialize the system based on the XL-RIS-assisted near-field wireless communication system model, and then determine the expression of the XL-RIS-assisted near-field communication multi-user receiving signal model based on the initialized system model.

[0033] Specifically, system initialization includes base station initialization, XL-RIS initialization, and user equipment initialization. Base station initialization includes: setting the location and number of antennas of the base station, and equipping the base station with The initialization of the Ultra-Large Scale Intelligent Reflector (XL-RIS) includes: setting the position and number of reflector units of XL-RIS. OK The total number of reflective units is UEs initialization includes: setting the location and number of single-antenna UEs, system service A single-antenna user device.

[0034] The expression of the XL-RIS-assisted near-field communication multi-user receiving signal model is that for the k-th user device, the received signal The model is: ; in, Indicates The signal received by the user equipment ∈ represents the preset code signal vector transmitted by the base station, represents a complex domain; ∈ represents the beamforming matrix of XL-RIS; Indicates The additive white Gaussian noise received by each user is represents a complex Gaussian distribution, Indicates the size of its variance; Indicates The reflection channel matrix of user equipment, , represents the channel matrix from the base station to XL-RIS, Indicates XL-RIS to The channel vector of each user equipment is represents the function used to construct a diagonal matrix. XL-RIS is designed To adjust the phase of the incident signal, represents the beamforming vector of XL-RIS.

[0035] S2, build a near-field multi-beam optimization model.

[0036] In this embodiment, the near-field multi-beam optimization model is obtained by modeling the near-field multi-beam optimization problem. Figure 1 In the scenario shown, a far-field channel model and a near-field channel model are determined.

[0037] First, for the far-field channel model, the channel matrix from the base station to XL-RIS : Channel gain from base station to XL-RIS It can be expressed as the sum of multiple multipath components, specifically: ; in, It is The complex gain of the paths, represents the receiving far-field steering vector of RIS, and Indicates the azimuth and elevation angles of the signal received by the user on the path. represents the far-field steering vector of the base station, represents the departure angle on the path at the base station side, the channel vector from XL-RIS to the user equipment : Under far-field conditions, the channel from RIS to user equipment It is expressed as the sum of multiple multipath components, specifically: ; in, It is The complex gain of the paths, represents the steering vector on the user side, represents the azimuth, Indicates the pitch angle. Set the initial reflection matrix , where each element Represents the phase offset of the nth reflection unit, and the initial value is a random phase.

[0038] When using a DFT codebook with S codewords for beam training, the beam gains on all S discrete spatial angle grids can be measured and obtained. Based on these measurement results, an expected beam gain vector is constructed for all users. , and then define the multi-beam optimization problem: ; ; Then we focus on the near-field channel model. According to the characteristics of electromagnetic wave propagation, when the distance between the base station and XL-RIS and the distance between XL-RIS and the user equipment satisfy When the reflection channel is , it should be modeled as a near-field channel based on the spherical wavefront assumption, where the Rayleigh distance Defined as: ,in is the array aperture, is the carrier wavelength.

[0039] Under the spherical wavefront assumption, The reflection channel of a user equipment can be expressed as: ; in, is the spatial coordinate vector from XL-RIS to the kth user equipment, is the spatial coordinate vector from the base station to XL-RIS. For the array steering vector under near-field conditions and , which is expressed as: ; ; in, and They represent the first ) element to the kth user equipment and to the base station, is the wavelength of the signal.

[0040] Since the angle and distance information of the channel are more complex, the near-field codebook grid is much larger than the far-field DFT codebook grid, resulting in a significant increase in computational complexity. To this end, we focus on the limited beam gain only for the target user position and propose a more streamlined near-field multi-beam design. The specific optimization problem is defined as: ; ; in, represents the actual beam gain for K target users, represents the set of real numbers, Represents the near-field steering matrix, each column of which is obtained by the near-field codebook The codeword corresponding to the user equipment obtained by beam training in , Indicates The reflection coefficient of each XL-RIS element, , represents the phase represented by the reflection coefficient of the nth XL-RIS element, , represents the objective function, Indicates constraints, It represents the minimization of the objective function Represents the transposed conjugate of a matrix.

[0041] The above specific optimization problem is used as a near-field multi-beam optimization model.

[0042] S3, solve the near-field multi-beam optimization model to obtain the XL-RIS beam reflection matrix.

[0043] In some embodiments, step S3 specifically includes: Step S31, initialization parameters: Set the initial beamforming vector , typically a zero vector or a small random value.

[0044] Set the initial learning rate , which controls the magnitude of parameter updates. Usually set to 0.01.

[0045] Set the decay rate of the momentum estimate β 1 and β 2. Usually β 1=0.9 and β 2=0.999.

[0046] Initialize the first order moment m 0 and the second moment v 0 is the zero vector.

[0047] Set a sufficiently small value ϵ to ensure numerical stability, usually ϵ=10−8.

[0048] Step S32, calculate the gradient: For each iteration t , calculate the objective function (e.g., beamforming error ) relative to the current beamforming vector θ t Gradient .

[0049] Step S33, update the first-order moment estimate: Update the first-order moment estimate using the current gradient: ; in, represents the updated first-order moment estimate, Represents the current first moment estimate.

[0050] Step S34, update the second-order moment estimate: Update the second-order moment estimate using the current gradient: ; in, represents the updated second-order moment estimate, Represents the current second-order moment estimate.

[0051] Step S35, calculate the first-order moment and second-order moment estimate of the deviation correction: Compute bias-corrected estimates of the first and second moments to obtain more accurate momentum and variance: ， ； in, represents the first order moment of the bias correction, represents the decay rate in the first-order moment estimate, represents the bias-corrected second-order moment estimate, represents the decay rate in the second-order moment estimate.

[0052] Step S36, updating the beamforming vector: Update the beamforming vector using the bias-corrected moment estimate: ; in, represents the updated beamforming vector, represents the current beamforming vector, represents the learning rate, represents the bias-corrected first-order moment estimate, Represents the bias-corrected second-order moment estimate.

[0053] Step S37, check convergence conditions: If the convergence condition is met (for example, the modulus of the gradient is small enough or the preset maximum number of iterations is reached), the iteration is stopped; otherwise, the process returns to step S32 to continue the iteration.

[0054] S4, iteratively solving the near-field multi-beam optimization model, obtaining a reflection matrix of the base station transmitting signal to XL-RIS, and completing the XL-RIS-assisted system multi-beam optimization.

[0055] In some embodiments, step S4 enhances the global search capability of the algorithm by introducing multiple random initial points into the optimization algorithm, effectively avoids local optimal solutions, and increases the probability of finding a global optimal solution. The steps include: S41, Random Initialization: Generate multiple initial beamforming vectors , where S represents the number of starting points. These initial vectors are randomly sampled according to Gaussian or uniform distribution to ensure that different regions of the solution space are covered.

[0056] S42, independent optimization: For each initial point , and apply the Adam algorithm to optimize independently. Each optimization process follows the standard steps of the Adam algorithm, including computing gradients, updating first-order moment and second-order moment estimates, and updating the beamforming vector.

[0057] S43, Performance Recording: Channel gain and user rate are recorded in each iteration to monitor the optimization process and provide a basis for the final result selection.

[0058] S44, local optimal selection: After each independent optimization is completed, select the reflection matrix with the best performance as a local optimal solution.

[0059] S45, global optimal determination: select the reflection matrix with the best performance from all local optimal solutions As the global optimal solution, .

[0060] S46, Algorithm termination condition: Set the algorithm termination condition, such as reaching the maximum number of iterations or the convergence threshold.

[0061] The multi-starting point Adam optimization algorithm of the present invention realizes efficient global optimization in the multi-beam design problem assisted by an ultra-large scale intelligent reflector (XL-RIS).

[0062] Figure 3 This is a simulation diagram of the minimum rate under different numbers of users provided by the embodiment of the present invention. Figure 4 It is the minimum rate simulation diagram under different SNR of the present invention. Figure 3 and Figure 4 It can be seen that the XL-RIS-assisted near-field multi-beam design method proposed in the embodiment of the present invention performs better than the traditional optimization algorithm based on the MM algorithm, and the time complexity is greatly reduced. Therefore, according to the relevant results, the method of the present invention can effectively overcome the problems of slow convergence speed and high computational complexity of traditional algorithms in large-scale array processing, and improve the efficiency of optimization.

[0063] On the other hand, the embodiment of the present invention also provides a near-field multi-beam design system assisted by XL-RIS, see Figure 5 The system comprises: A first model building module 501 is configured to build an XL-RIS-assisted near field communication multi-user reception signal model; A second model building module 502 is configured to build a near-field multi-beam optimization model; A first solving module 503 is configured to solve the near-field multi-beam optimization model to obtain an XL-RIS beam reflection matrix; The second solving module 504 is configured to iteratively solve the near-field multi-beam optimization model to obtain a reflection matrix of the base station transmitting signal to the XL-RIS, and complete the XL-RIS-assisted system multi-beam optimization.

[0064] In some embodiments, the first model building module is further configured to build an XL-RIS-assisted near-field communication multi-user received signal model based on the XL-RIS-assisted near-field wireless communication system model; wherein the XL-RIS-assisted near-field wireless communication system model includes a base station, an XL-RIS, and a user equipment; wherein the base station, the XL-RIS, and the user equipment are communicatively connected with each other.

[0065] In some embodiments, the first model building module is further configured to: Set the location and number of antennas of the base station. Antennas, using uniform linear array; Set the position and number of reflector units of XL-RIS. XL-RIS uses OK The total number of reflection units is ; Set the location and number of user devices, system services A single-antenna user device; The XL-RIS-assisted near-field communication multi-user receiving signal model is determined to be expressed as: ; in, Indicates The signal received by the user equipment ∈ represents the preset code signal vector transmitted by the base station, represents a complex domain; ∈ represents the beamforming matrix of XL-RIS; Indicates The additive white Gaussian noise received by each user is represents a complex Gaussian distribution, Indicates the size of its variance; Indicates The reflection channel matrix of user equipment, , represents the channel matrix from the base station to XL-RIS, Indicates XL-RIS to The channel vector of each user equipment is represents the function used to construct a diagonal matrix. XL-RIS is designed To adjust the phase of the incident signal, represents the beamforming vector of XL-RIS.

[0066] In some embodiments, the second model building module is further configured to: The distance between the base station and the XL-RIS and the distance between the XL-RIS and the user equipment satisfy When , the reflection channel is modeled according to the spherical wavefront assumption to obtain the near-field reflection channel model; represents the Rayleigh distance, , is the array aperture, is the carrier wavelength; Under the spherical wavefront assumption, in the near-field reflection channel model, The reflection channel of a user equipment is expressed as: ; in, It is The reflection channel of each user equipment, From XL-RIS to The spatial coordinate vector of the user equipment, is the spatial coordinate vector from the base station to XL-RIS, is the gain or attenuation factor associated with the kth user device, , is the array steering vector, T is the matrix transpose; In the near-field reflection channel model, for the array steering vector under near-field conditions, and , expressed as: ; ; in, and Respectively represent the and Elements to The distance between the user equipment and the base station, is a natural constant, is pi, is an imaginary unit; Based on the near-field reflection channel model, the near-field multi-beam optimization model is determined to be expressed as: ; ; in, represents the actual beam gain for K target users, represents the set of real numbers, Represents the near-field steering matrix, each column of which is obtained by the near-field codebook The codeword corresponding to the user equipment obtained by beam training in , Indicates The reflection coefficient of each XL-RIS element, , represents the phase represented by the reflection coefficient of the nth XL-RIS element, , represents the objective function, Indicates constraints, It represents the minimization of the objective function It should be noted that the XL-RIS-assisted near-field multi-beam design system and the previously described method belong to the same technical concept, have the same technical principles and beneficial effects, and will not be described in detail here.

[0067] The above implementation modes are only used to illustrate the present invention, but not to limit the present invention. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention. The patent protection scope of the present invention should be defined by the claims.

Claims

1. An XL-RIS-assisted near-field multi-beam design method, characterized in that: The method comprises: Construct XL-RIS-assisted near-field communication multi-user receiving signal model; Construct a near-field multi-beam optimization model; Solving the near-field multi-beam optimization model to obtain an XL-RIS beam reflection matrix; The near-field multi-beam optimization model is solved iteratively to obtain the reflection matrix of the base station transmitting signal to XL-RIS, and the XL-RIS-assisted system multi-beam optimization is completed.

2. The method according to claim 1, characterized in that An XL-RIS-assisted near-field communication multi-user receiving signal model is constructed based on an XL-RIS-assisted near-field wireless communication system model; wherein the XL-RIS-assisted near-field wireless communication system model includes a base station, an XL-RIS, and a user device; wherein the base station, the XL-RIS, and the user device are communicatively connected with each other.

3. The method according to claim 2, characterized in that Based on the XL-RIS assisted near field wireless communication system model, an XL-RIS assisted near field communication multi-user receiving signal model is constructed, including: Set the location and number of antennas of the base station. Antennas, using uniform linear array; Set the position and number of reflector units of XL-RIS. XL-RIS uses OK The uniform planar array structure of the column has a total number of reflection units. ; Set the location and number of user devices, system services A single-antenna user device; The XL-RIS-assisted near-field communication multi-user receiving signal model is determined to be expressed as: ； in, Indicates The signal received by the user equipment ∈ represents the preset code signal vector transmitted by the base station, represents a complex domain; ∈ represents the beamforming matrix of XL-RIS; Indicates The additive white Gaussian noise received by each user is represents a complex Gaussian distribution, Indicates the variance of the complex Gaussian distribution; Indicates The reflection channel matrix of each user equipment is , represents the channel matrix from the base station to XL-RIS, Indicates XL-RIS to The channel vector of each user equipment is represents the function used to construct a diagonal matrix. XL-RIS is designed To adjust the phase of the incident signal, represents the beamforming vector of XL-RIS.

4. The method according to claim 3, characterized in that Build a near-field multi-beam optimization model, including: The distance between the base station and the XL-RIS and the distance between the XL-RIS and the user equipment satisfy When , the reflection channel is modeled according to the spherical wavefront assumption to obtain the near-field reflection channel model; represents the Rayleigh distance, , is the array aperture, is the carrier wavelength; Under the spherical wavefront assumption, in the near-field reflection channel model, The reflection channel of a user equipment is expressed as: ； in, It is The reflection channel of each user equipment, From XL-RIS to The spatial coordinate vector of the user equipment, is the spatial coordinate vector from the base station to XL-RIS, is the gain or attenuation factor associated with the kth user device, , is the array steering vector, T is the matrix transpose; In the near-field reflection channel model, for the array steering vector under near-field conditions, and , expressed as: ； ； in, and Respectively represent the and Elements to The distance between the user equipment and the base station, is a natural constant, is pi, represents an imaginary unit; Based on the near-field reflection channel model, the near-field multi-beam optimization model is determined to be expressed as: ； ； in, represents the actual beam gain for K target users, represents the set of real numbers, Represents the near-field steering matrix, each column of which is obtained by the near-field codebook The codeword corresponding to the user equipment obtained by beam training in , Indicates The reflection coefficient of each XL-RIS element, , represents the phase represented by the reflection coefficient of the nth XL-RIS element, , represents the objective function, Indicates constraints, represents the minimization of the objective function, Represents the transposed conjugate of a matrix.

5. The method according to claim 4, characterized in that The near-field multi-beam optimization model is solved to obtain the XL-RIS beam reflection matrix, including: Initialization parameters; wherein the parameters include beamforming vector, learning rate, first decay rate of momentum estimation β 1 and 2 decay rates β 2. First-order moment, second-order moment and constant value ϵ ; Calculate the gradient: For each iteration t , calculate the objective function relative to the current beamforming vector θt Gradient ; Update the first-order moment estimate by the following formula: ； in, represents the updated first-order moment estimate, represents the current first-order moment estimate; Update the second-order moment estimate by the following formula: ； in, represents the updated second-order moment estimate, represents the current second-order moment estimate; The bias-corrected first and second moment estimates are calculated using the following formula: ， ； in, represents the first order moment of the bias correction, represents the decay rate in the first-order moment estimate, represents the bias-corrected second-order moment estimate, represents the decay rate in the second-order moment estimate; The beamforming vector is updated by the following formula: ； in, represents the updated beamforming vector, represents the current beamforming vector, represents the learning rate, represents the bias-corrected first-order moment estimate, represents the bias-corrected second-order moment estimate; ϵ Represents a constant value; Check the convergence condition: if the convergence condition is met, stop the iteration; otherwise, continue to perform the iteration steps, which include calculating the gradient, updating the first-order moment estimate, calculating the first-order moment and the second-order moment estimate of the deviation correction, and updating the beamforming vector.

6. The method according to claim 5, characterized in that Iteratively solve the near-field multi-beam optimization model to obtain the reflection matrix of the base station transmitting signal to XL-RIS, and complete the XL-RIS-assisted system multi-beam optimization, including: Random initialization: Generate multiple initial beamforming vectors , where S represents the number of starting points, and the multiple initial beamforming vectors are randomly sampled according to Gaussian distribution or uniform distribution; Independent optimization: performing independent optimization on each initial beamforming vector, wherein the independent optimization includes calculating a gradient, updating a first-order moment estimate and a second-order moment estimate, and updating a beamforming vector; Performance recording: recording performance parameters in each iteration, including channel gain and user rate; Local optimal selection: After each independent optimization is completed, the reflection matrix with the best performance is selected based on the performance parameters. As a local optimal solution; Global optimality determination: Select the reflection matrix with the best performance from all local optimal solutions As the global optimal solution; When the maximum number of iterations or the convergence threshold is reached, the currently determined global optimal solution is used as the reflection matrix of the base station transmitting the signal to XL-RIS, completing the XL-RIS-assisted system multi-beam optimization.

7. An XL-RIS-assisted near-field multi-beam design system, characterized in that: The system comprises: A first model building module is configured to build an XL-RIS-assisted near field communication multi-user reception signal model; A second model building module is configured to build a near-field multi-beam optimization model; A first solving module is configured to solve the near-field multi-beam optimization model to obtain an XL-RIS beam reflection matrix; The second solving module is configured to iteratively solve the near-field multi-beam optimization model to obtain a reflection matrix of the base station transmitting signal to the XL-RIS, and complete the XL-RIS-assisted system multi-beam optimization.

8. The system according to claim 7, characterized in that The first model building module is further configured to build an XL-RIS-assisted near-field communication multi-user receiving signal model based on the XL-RIS-assisted near-field wireless communication system model; wherein the XL-RIS-assisted near-field wireless communication system model includes a base station, an XL-RIS, and a user equipment; wherein the base station, the XL-RIS and the user equipment are communicatively connected with each other.

9. The system according to claim 8, characterized in that The first model building module is further configured to: Set the location and number of antennas of the base station. Antennas, using uniform linear array; Set the position and number of reflector units of XL-RIS. XL-RIS uses OK The uniform planar array structure of the column has a total number of reflection units. ; Set the location and number of user devices, system services A single-antenna user device; The XL-RIS-assisted near-field communication multi-user receiving signal model is determined to be expressed as: ； in, Indicates The signal received by the user equipment ∈ represents the preset code signal vector transmitted by the base station, represents a complex domain; ∈ represents the beamforming matrix of XL-RIS; Indicates The additive white Gaussian noise received by each user is represents a complex Gaussian distribution, Indicates the variance of complex Gaussian distribution; Indicates The reflection channel matrix of each user equipment is , represents the channel matrix from the base station to XL-RIS, Indicates XL-RIS to The channel vector of each user equipment is represents the function used to construct a diagonal matrix. XL-RIS is designed To adjust the phase of the incident signal, represents the beamforming vector of XL-RIS.

10. The system according to claim 9, characterized in that The second model building module is further configured to: The distance between the base station and the XL-RIS and the distance between the XL-RIS and the user equipment satisfy When , the reflection channel is modeled according to the spherical wavefront assumption to obtain the near-field reflection channel model; represents the Rayleigh distance, , is the array aperture, is the carrier wavelength; Under the spherical wavefront assumption, in the near-field reflection channel model, The reflection channel of a user equipment is expressed as: ； in, It is The reflection channel of each user equipment, From XL-RIS to The spatial coordinate vector of the user equipment, is the spatial coordinate vector from the base station to XL-RIS, is the gain or attenuation factor associated with the kth user device, , is the array steering vector, T is the matrix transpose; In the near-field reflection channel model, for the array steering vector under near-field conditions, and , expressed as: ； ； in, and Respectively represent the and Elements to The distance between the user equipment and the base station, is a natural constant, is pi, is an imaginary unit, Based on the near-field reflection channel model, the near-field multi-beam optimization model is determined to be expressed as: ； ； in, represents the actual beam gain for K target users, represents the set of real numbers, Represents the near-field steering matrix, each column of which is obtained by the near-field codebook The codeword corresponding to the user equipment obtained by beam training in , Indicates The reflection coefficient of each XL-RIS element, , represents the reflection coefficient of the nth XL-RIS element, , represents the objective function, Indicates constraints, It represents the minimization of the objective function Represents the transposed conjugate of a matrix.

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