Beam forming design method of intelligent reflector-assisted wireless communication system

Abstract

The invention discloses a beam forming design method for an intelligent reflector-assisted wireless communication system. The beam forming design method comprises the following steps: establishing an intelligent reflector-assisted wireless communication system model to obtain a transmitting beam forming vector and a phase shift matrix; based on the transmitted beam forming vector and the phase shift matrix, establishing an optimization objective function for maximizing the weighted sum rate of the user, and introducing a first auxiliary variable and a second auxiliary variable to carry out optimization solution on the optimization objective function; iteratively updating the optimization objective function by adopting a block coordinate descent method to obtain an initial optimal solution of the first auxiliary variable and an initial optimal solution of the second auxiliary variable; substituting the initial optimal solution into the optimization objective function to obtain a new optimization objective function; and based on the new optimization objective function, sequentially optimizing the transmitted beam forming vector and the phase shift matrix until the weighting and rate convergence of the user. The weighted sum rate of the UE is maximized by jointly optimizing the transmit beamforming vector of the BS and the phase shift matrix of the RIS.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and particularly relates to a beamforming design method for an intelligent reflecting surface assisted wireless communication system. Background Art

[0002] The statements in this part merely provide background technical information related to the present disclosure, and do not necessarily constitute prior art.

[0003] Intelligent reflecting surfaces have attracted much attention because they can flexibly regulate wireless signals and have the potential to improve the capacity and coverage of wireless networks. They are also widely regarded as a promising technology for the sixth-generation communication network due to their technical advantages such as low cost, low power consumption, and high reliability. Due to the severe path loss problem in RIS-assisted millimeter-wave communication systems, the signal coverage range is limited. Beamforming design can effectively improve this situation. The difficulty lies in the high coupling between the transmit beamforming of the base station (BS) and the phase shift matrix at the RIS. The problems to be solved are usually NP-hard non-convex optimization problems, with high complexity of optimization algorithms and difficulty in obtaining closed-form solutions.

[0004] In the prior art, the problem of maximizing the sum rate of data transmission in an RIS-assisted multi-user (MU) system has been proposed. The fractional programming (FP) method is used to jointly optimize the transmit beamforming and the reflection phase shift matrix to solve this problem. However, the beamforming of the above solutions has poor robustness, low sum rate of user equipment (UE), and high computational complexity. Therefore, how to improve the sum rate and reduce the computational complexity is one of the problems that need to be solved urgently at present. Summary of the Invention

[0005] To overcome the deficiencies of the above prior art, the present invention provides a beamforming design method for an intelligent reflecting surface assisted wireless communication system, and provides a low complexity-block coordinate descent (LC-BCD) method. By jointly optimizing the transmit beamforming vector of the BS and the phase shift matrix of the RIS, the weighted sum rate (WSR) of the UE is maximized.

[0006] To achieve the above object, one or more embodiments of the present invention provide the following technical solutions:

[0007] In a first aspect, the present invention provides a beamforming design method for an intelligent reflecting surface assisted wireless communication system, including:

[0008] Build a wireless communication system model assisted by an intelligent reflecting surface, and obtain the transmit beamforming vector of the base station and the phase shift matrix of the intelligent reflecting surface based on the system model;

[0009] Based on the transmit beamforming vector and the phase shift matrix, establish an optimization objective function that maximizes the weighted sum rate of the users; introduce a first auxiliary variable and a second auxiliary variable into the optimization objective function, and optimize and solve it based on the first auxiliary variable and the second auxiliary variable;

[0010] Use the block coordinate descent method to iteratively update the first auxiliary variable, the second auxiliary variable, the transmit beamforming vector, and the phase shift matrix in the optimization objective function to obtain the initial optimal solution of the first auxiliary variable and the initial optimal solution of the second auxiliary variable; substitute the initial optimal solution of the first auxiliary variable and the initial optimal solution of the second auxiliary variable into the optimization objective function to obtain a new optimization objective function;

[0011] Based on the new optimization objective function, optimize the transmit beamforming vector and the phase shift matrix in turn, and iterate and optimize until the weighted sum rate of the users converges, completing the joint optimization of the transmit beamforming vector and the phase shift matrix.

[0012] For a further technical solution, use a channel model to build a wireless communication system model assisted by an intelligent reflecting surface. The base station sends a pilot signal that reaches the user through the intelligent reflecting surface. The base station BS is configured with M antennas, the intelligent reflecting surface RIS is provided with N reflecting units, and there are K single-antenna users UE in the system model.

[0013] For a further technical solution, calculate the signal-to-interference-plus-noise ratio (SINR) at the user based on the system model. Specifically, the channel between the base station and each user includes a BS-UE link and a BS-RIS-UE cascaded link. The channel h from the base station to the k-th user k can be expressed as:

[0014]

[0015] where, (·) H denotes the conjugate transpose, and respectively represent the baseband equivalent channels from the base station to user k, from the intelligent reflecting surface to user k, and from the base station to the intelligent reflecting surface;

[0016] Let The signal y received by the k-th user k can be expressed as:

[0017]

[0018] where, θ H denotes the conjugate transpose of the phase shift vector, Hr,k Denote the intelligent reflecting surface cascaded channel matrix, Denote the transmit beamforming vector transmitted by the base station to the k-th user in the downlink, s k Denote the transmission symbol sent to the k-th user, Denote the additive white Gaussian noise received at user k;

[0019] The signal-to-interference-plus-noise ratio γ at the k-th user k Can be expressed as:

[0020]

[0021] Where, Denote the noise power of the additive white Gaussian noise.

[0022] For a further technical solution, the optimization objective function is expressed as:

[0023]

[0024] Where, Is the transmit beamforming matrix of the base station; θ is the phase shift vector of the RIS, Ξ k Is the weighting coefficient of the k-th user, P T Is the maximum transmit power of the BS.

[0025] For a further technical solution, in the optimization objective function, the Lagrangian dual transform is used to introduce the first auxiliary variable α = [α 1 , α 2 , ···, α K T , and the quadratic transform in fractional programming is used to introduce the second auxiliary variable β = [β 1 , β 2 , ···, β K T , and the optimization objective function after introducing the auxiliary variables is obtained, which is expressed as:

[0026]

[0027] Where, α k Denote the first auxiliary variable corresponding to user k, Denote the real part operation, (·) * Denote the conjugate, β k Denote the second auxiliary variable corresponding to user k.

[0028] For a further technical solution, the initial optimal solution of the first auxiliary variable and the initial optimal solution of the second auxiliary variable are obtained and expressed as:

[0029] ​​

[0030] Among them, represents the initial optimal solution of the first auxiliary variable corresponding to user k, represents the initial optimal solution of the complex-valued auxiliary variable corresponding to user k.

[0031] For a further technical solution, the optimization of the transmit beamforming vector is specifically as follows: for the base station transmit beamforming matrix W, based on the first-order Taylor expansion of the current iteration point W (t) to optimize the transmit beamforming vector w k , construct a locally convex approximation function to update W (t+1) .

[0032] For a further technical solution, the second-order Taylor expansion is used to optimize the phase shift matrix.

[0033] In a second aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps in the beamforming design method of the intelligent reflecting surface-assisted wireless communication system as described in the first aspect.

[0034] In a third aspect, the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the beamforming design method of the intelligent reflecting surface-assisted wireless communication system as described in the first aspect.

[0035] The above one or more technical solutions have the following beneficial effects:

[0036] For the optimization goal of maximizing the WSR, the present invention uses the Lagrangian dual transformation to introduce the first auxiliary variable α, uses the FP method to introduce the second auxiliary variable β, and obtains the initial optimal solutions of α and β and . After substituting them into the objective function, the first-order Taylor expansion is used to optimize the transmit beamforming vector W, and the second-order Taylor expansion is used to optimize the RIS phase shift vector θ. Then, the optimization variables α, β, W, and θ are iteratively updated to find the optimal solution, thereby achieving the maximization of the WSR.

[0037] The method of the present invention can reduce the computational complexity while saving computational resources and improving the beamforming accuracy, and is applicable to the wireless communication system assisted by the RIS technology in 6G. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings forming a part of this specification are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0039] Figure 1 is the flowchart of the beamforming design method in the embodiment of the present invention;

[0040] Figure 2 is the schematic diagram of the intelligent reflecting surface assisted wireless communication system in the embodiment of the present invention;

[0041] Figure 3 is the curve of the WSR versus the running round n in the embodiment of the present invention loop in the embodiment of the present invention;

[0042] Figure 4 is the curve of the WSR versus the maximum transmit power P in the embodiment of the present invention T in the embodiment of the present invention;

[0043] Figure 5 is the curve of the WSR versus the number of reflecting elements N when the number of antennas M = 16 in the embodiment of the present invention;

[0044] Figure 6 is the curve of the WSR versus the number of reflecting elements N when the number of antennas M = 64 in the embodiment of the present invention;

[0045] Figure 7 is the curve of the running time T versus n in the embodiment of the present invention loop in the embodiment of the present invention. Detailed implementation manners

[0046] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0047] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0048] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0049] Embodiment 1

[0050] As Figure 1 shown, this embodiment discloses a beamforming design method for an intelligent reflecting surface assisted wireless communication system, and the method includes the following steps:

[0051] S1: Establish a wireless communication system model assisted by an intelligent reflecting surface, and obtain the transmit beamforming vector of the base station and the phase shift matrix of the intelligent reflecting surface based on the system model;

[0052] This invention only focuses on the research of beamforming methods, so it is assumed that perfect CSI can be obtained for channel estimation.

[0053] In this embodiment, as Figure 2 shown, first, use the Saleh-Valenzuela channel model to establish a downlink millimeter-wave multiple-input single-output (MIMO) system model assisted by a reconfigurable intelligent surface (RIS). Assume beamforming design is carried out under the condition of perfect CSI. The base station BS sends a pilot signal through the RIS to reach the user UE. The base station BS is configured with M antennas, the RIS is provided with N reflection units, and there are a total of K single-antenna users in the system.

[0054] The transmit signal x of the base station BS is expressed as:

[0055]

[0056] where K represents the number of single-antenna users, represents the transmit beamforming vector transmitted by the base station to the k-th user in the downlink, and s k represents the transmission symbol sent to the k-th user.

[0057] The phase shift matrix Θ of the intelligent reflecting surface RIS is a diagonal matrix, expressed as:

[0058] Θ = diag(θ 1 , θ 2 , …, θ N ) (2)

[0059] where represents the phase shift of the n-th RIS unit, j represents the imaginary unit, represents the phase shift angle of the n-th RIS reflection unit, satisfying θ = [θ 1 , θ 2 , …, θ N H represents the phase shift vector; β n represents the amplitude of the n-th RIS unit, satisfying β n ∈ [0, 1]. In this embodiment, β n is set to 1 to maximize the signal reflection of the RIS.

[0060] The channel between the BS and each UE consists of two parts, namely the BS-UE link and the BS-RIS-UE cascaded link. Therefore, the channel h from the BS to the k-th user k can be expressed as:

[0061]

[0062] where (·) H denotes the conjugate transpose, and respectively represent the baseband equivalent channels from the base station to user k, from the intelligent reflecting surface to user k, and from the base station to the intelligent reflecting surface.

[0063] Let the signal y received by the k-th user k can be expressed as:

[0064]

[0065] where θ H denotes the conjugate transpose of the phase shift vector, H r,k represents the RIS cascaded channel matrix, denotes the additive white Gaussian noise (AWGN) received at user k.

[0066] The signal-to-interference-plus-noise ratio (SINR) γ at the k-th user k can be expressed as:

[0067]

[0068] where denotes the noise power of the additive white Gaussian noise.

[0069] S2: Based on the transmit beamforming vector and the phase shift matrix, establish an optimization objective function that maximizes the weighted sum rate of the users; introduce a first auxiliary variable and a second auxiliary variable into the optimization objective function, and optimize and solve it based on the first auxiliary variable and the second auxiliary variable; by introducing the first auxiliary variable, the original problem is transformed into a convex optimization problem, making the logarithmic function processing more convenient, and by introducing the second auxiliary variable, the fractional optimization problem is decoupled to simplify the calculation.

[0070] In this embodiment, an optimization objective function is established based on the system model, and an RIS reflection coefficient optimization model under perfect CSI is constructed. The RIS reflection coefficient optimization model under perfect CSI is used to optimize the RIS reflection coefficient, enabling the RIS to achieve the maximum gain in wireless communication and improving the communication quality between the base station and the user. Its function is to effectively improve the channel conditions and enhance the user signal quality by intelligently adjusting the RIS phase shift. The functions of this model are specifically reflected in formulas such as the objective function, auxiliary variable transformation, and RIS phase shift optimization, making it of great application value in scenarios such as 6G communication, millimeter-wave channel enhancement, and signal coverage optimization.

[0071] The optimization objective is to maximize the WSR of the system, that is, the problem of maximizing the weighted sum rate of users. The problem (objective function) can be expressed as:

[0072]

[0073] where, is the transmit beamforming matrix of the base station; θ is the phase shift vector of the RIS, and Ξ k is the weighted coefficient of the k-th user, representing the user's priority; P T is the maximum transmit power of the BS.

[0074] In the non-convex objective function , since the transmit beamforming matrix W and the phase shift vector θ of the RIS are highly coupled and difficult to directly optimize and solve, therefore, the present invention uses Lagrangian dual transformation and introduces the first auxiliary variable α = [α 1 , α 2 , ···, α K T to decouple the logarithm, expressed as:

[0075]

[0076] For the convenience of the subsequent optimization process, an auxiliary function is introduced. Introducing this auxiliary function for variable substitution makes the optimization problem more linear and decomposes the optimization problem into two independent sub-problems, facilitating the subsequent use of the proposed algorithm for optimization. Therefore, after introducing the first auxiliary variable α, the problem becomes the problem expressed as:

[0077]

[0078] The expression of the new objective function f 2 (α, W, θ) is:

[0079]

[0080] Among them, α k represents the first auxiliary variable corresponding to user k.

[0081] Using the quadratic transformation in FP, introduce the second auxiliary variable (also called the complex-valued auxiliary variable) β = [β 1 , β 2 , ···, β K T , and reconstruct the auxiliary function z(W, Θ) as:

[0082]

[0083] Among them, the auxiliary variable

[0084] Using the Cauchy-Schwarz inequality, then z(W, Θ) is equivalent to:

[0085]

[0086] Among them, represents the real part operation, (·) * represents the conjugate, and β k represents the second auxiliary variable corresponding to user k.

[0087] To make equation (11) hold with equality, equation (12) should be satisfied:

[0088]

[0089] After substituting equation (12) into equation (11), the problem becomes the problem as shown in equation (13):

[0090]

[0091] The expression of the new objective function f 3 (α, β, W, θ) is equation (14):

[0092]

[0093] When solving the optimization problem, introducing the first auxiliary variable and the second auxiliary variable is based on the application background of the fractional programming (FP) method. This variable definition enables the original fractional optimization problem to be decomposed and optimized and solved through Lagrangian dual transformation and quadratic transformation.

[0094] ​S3: Use the block coordinate descent method to iteratively update the first auxiliary variable, the second auxiliary variable, the transmit beamforming vector, and the phase shift matrix in the optimization objective function to obtain the initial optimal solutions of the first auxiliary variable and the second auxiliary variable; substitute the initial optimal solutions of the first auxiliary variable and the second auxiliary variable into the optimization objective function to obtain a new optimization objective function.

[0095] In this embodiment, the low complexity-block coordinate descent method (LC-BCD) is used to iteratively update α, β, W, and θ. By taking the derivatives of equation (14) with respect to the auxiliary variables α and β respectively and setting them equal to zero, the initial optimal solutions α opt , β opt are obtained, which are respectively expressed as:

[0096]

[0097]

[0098] where, represents the initial optimal solution of the first auxiliary variable corresponding to user k, represents the initial optimal solution of the complex-valued auxiliary variable corresponding to user k.

[0099] S4: Based on the new optimization objective function, optimize the transmit beamforming vector and the phase shift matrix in sequence, and iteratively optimize until the weighted sum rate of the users converges to complete the joint optimization of the transmit beamforming vector and the phase shift matrix.

[0100] In this embodiment, for the base station transmit beamforming matrix W, based on the first-order Taylor expansion of the current iteration point W (t) , optimize the transmit beamforming vector w k to construct a locally convex approximation function to update W (t+1) .

[0101] Update the transmit beamforming matrix W by solving the following problem, as shown in equation (17):

[0102]

[0103] where, represents the optimal solution obtained for the phase shift vector of the RIS after the previous iteration.

[0104] Calculate the gradient of f 4 (W) with respect to w k to obtain equation (18), which is expressed as: ​

[0105]

[0106] At neighborhood, use the first-order Taylor expansion to construct an auxiliary function as shown in Equation (19):

[0107]

[0108] where is the function value of the previous iteration point and is expressed as the gradient of with respect to represents the beamforming vector of the previous iteration, and η represents the step-size control parameter.

[0109] The auxiliary function should satisfy Equation (20):

[0110]

[0111] where, at the equal sign holds.

[0112] The update of the transmit beamforming matrix W satisfies Equation (21):

[0113]

[0114] where the step-size control parameter η > 0, and the value of η is determined by the Armijo criterion and should satisfy Equation (20).

[0115] In this embodiment, the phase shift vector θ of the RIS is optimized using the second-order Taylor expansion.

[0116] Expand Equation (14), and after ignoring the constant terms independent of the phase shift vector θ, a typical quadratic term θ H Uθ can be extracted. Update the phase shift vector θ by solving the following problem, as shown in Equation (22):

[0117]

[0118] where

[0119] To facilitate the subsequent optimization process, a third auxiliary variable U and a fourth auxiliary variable V are introduced. By introducing U, the originally complex optimization problem is transformed into a standard Quadratically Constrained Quadratic Programming (QCQP) problem, which is convenient for solution; V acts as the linear term of the optimization objective, that is, the gradient direction, enabling the algorithm to perform efficient iterations based on the gradient direction and improving the convergence speed. They are respectively expressed as:

[0120]

[0121] Regarding the objective function f 6 (θ), the first-order gradient and Hessian matrix are respectively expressed as:

[0122]

[0123] In the domain, using the second-order Taylor expansion, construct the auxiliary function expressed as:

[0124]

[0125] where is the function value at the previous iteration point .

[0126] The auxiliary function should satisfy equation (28), expressed as:

[0127]

[0128] where, at , the equal sign holds.

[0129] The update of the phase shift vector θ satisfies equation (29), expressed as:

[0130]

[0131] where the step size control parameter L > 0, and the value of L is determined by the Armijo criterion and should satisfy equation (28).

[0132] Iteratively optimize α, β, W, θ until the user's WSR converges, thus completing the joint optimization of the BS's transmit beamforming vector W and the RIS phase shift matrix Θ.

[0133] This method can reduce the computational complexity while saving computational resources and improving the beamforming accuracy, and is applicable to the RIS technology-assisted wireless communication system in 6G.

[0134] The method of the present invention (LC-BCD) is compared with five other methods: the Alternating Optimization (AO) method, the Accelerated Projected Gradient (APG) method, the Alternating Direction Method of Multipliers (ADMM), the Semi-Definite Relaxation (SDR) method, and the Gradient Descent Approach (GDA).

[0135] To prove that the present invention can improve the effect of beamforming design, Figure 3 and Figure 4 the comparison results of the weighted sum rate of the present invention in six different cases of the AO method, the APG method, channel errors e being 0.1 and 0.5 respectively, random phase setting (Random Phase), and without using RIS (WithoutRIS) are shown. Figure 3 For the curve of WSR versus the number of running rounds n loop It can be seen from Figure 3 that the LC-BCD algorithm is close to the AO algorithm, the APG algorithm and other algorithms in performance, and the WSR basically tends to be stable at the 15th iteration, and the algorithm converges to at least a local optimal solution. Figure 4 For the curve of WSR versus the maximum transmit power P T It can be seen from Figure 4 that the WSR of the method of the present invention is higher than that of other methods. Therefore, the present invention has a better beamforming effect. Applying this method can improve the performance of the RIS-assisted wireless communication system and reduce the computational complexity of the beamforming design of the wireless communication system.

[0136] To prove that the present invention has higher beamforming accuracy, Figure 5 and Figure 6 the curves of WSR versus the number of reflecting elements N when the number of BS antennas M = 16 and 64 for the present invention and the APG, ADMM, SDR, and GDA methods are shown respectively. It can be seen from Figure 5 and Figure 6 that the present invention has a higher WSR in the case of different numbers of BS antennas. Therefore, the present invention has higher beamforming accuracy.

[0137] To prove that the present invention has lower computational complexity, Figure 7 the curves of the running time T versus the number of iteration rounds n loop for the present invention and the APG, ADMM, SDR, and GDA methods are shown. It can be seen from Figure 7It can be seen that under the same operating conditions, the beamforming design method of the present invention has a lower running time, indicating a lower time complexity.

[0138] Embodiment 2

[0139] The purpose of this embodiment is to provide a computing device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method in Embodiment 1 are implemented.

[0140] Embodiment 3

[0141] The purpose of this embodiment is to provide a computer-readable storage medium. A computer-readable storage medium has a computer program stored thereon. When the program is executed by a processor, the steps of the method in Embodiment 1 are executed.

[0142] The steps involved in the devices in Embodiments 2 and 3 above correspond to those in Method Embodiment 1. For specific implementation manners, reference may be made to the relevant description part of Embodiment 1. The term "computer-readable storage medium" should be understood to include a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode, or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0143] Those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computer device. Optionally, they can be implemented by program codes executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0144] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0145] Although the specific implementation manners of the present invention have been described above in conjunction with the accompanying drawings, it is not a limitation on the protection scope of the present invention. Those skilled in the art should understand that based on the technical solutions of the present invention, various modifications or deformations that can be made by those skilled in the art without creative labor are still within the protection scope of the present invention.

Claims

1. A beamforming design method for a smart reflector-assisted wireless communication system, characterized in that: include: Establishing a wireless communication system model assisted by an intelligent reflective surface, and obtaining a transmit beamforming vector of a base station and a phase shift matrix of the intelligent reflective surface based on the system model; Based on the transmit beamforming vector and the phase shift matrix, an optimization objective function for maximizing the weighted sum rate of the user is established; a first auxiliary variable and a second auxiliary variable are introduced into the optimization objective function, and the optimization objective function is optimized based on the first auxiliary variable and the second auxiliary variable; Iteratively updating the first auxiliary variable, the second auxiliary variable, the transmit beamforming vector, and the phase shift matrix in the optimization objective function by using a block coordinate descent method to obtain an initial optimal solution of the first auxiliary variable and an initial optimal solution of the second auxiliary variable; Substituting the initial optimal solution of the first auxiliary variable and the initial optimal solution of the second auxiliary variable into the optimization objective function to obtain a new optimization objective function; Based on the new optimization objective function, the transmit beamforming vector and the phase shift matrix are optimized in sequence, and the optimization is iteratively performed until the weighted sum rate of the user converges, thereby completing the joint optimization of the transmit beamforming vector and the phase shift matrix.

2. The beamforming design method for a smart reflector-assisted wireless communication system according to claim 1, characterized in that: The channel model is used to establish a wireless communication system model assisted by an intelligent reflector. The base station sends a pilot signal to the user through the intelligent reflector. The base station BS is equipped with M antennas, and the intelligent reflector RIS is equipped with N reflection units. There are K single-antenna users UE in the system model.

3. The beamforming design method for a smart reflector-assisted wireless communication system according to claim 2, characterized in that: The signal-to-interference-to-noise ratio at the user is calculated based on the system model: the channel between the base station and each user includes a BS-UE link and a BS-RIS-UE cascade link, and the channel h from the base station to the kth user k It can be expressed as: in,(·) H represents the conjugate transpose, as well as represent the baseband equivalent channels from the base station to user k, from the smart reflection surface to user k, and from the base station to the smart reflection surface respectively; make The signal y received by the kth user k It can be expressed as: Among them, θ H represents the conjugate transposition of the phase shift vector, H r,k represents the smart reflector cascade channel matrix, represents the transmit beamforming vector transmitted by the base station to the kth user in the downlink, s k represents the transmission symbol sent to the kth user, represents the additive Gaussian white noise received by user k; The signal-to-interference-to-noise ratio γ at the kth user k It can be expressed as: in, Represents the noise power of additive white Gaussian noise.

4. The beamforming design method for a smart reflector-assisted wireless communication system according to claim 1, wherein: The optimization objective function is expressed as: in, is the transmit beamforming matrix of the base station; θ is the phase shift vector of RIS, k is the weighted coefficient of the kth user, P T is the maximum transmit power of the BS.

5. The beamforming design method for a smart reflector-assisted wireless communication system according to claim 1, wherein: In the optimization objective function, the first auxiliary variable α=[α1,α2,···,α K ] T , using the quadratic transformation in fractional programming, introduce the second auxiliary variable β = [β1,β2,···,β K ] T , the optimization objective function after introducing auxiliary variables is obtained, which is expressed as: Among them, α k represents the first auxiliary variable corresponding to user k, represents the real part operation, (·) * represents conjugation, β k Represents the second auxiliary variable corresponding to user k.

6. The beamforming design method for a smart reflector-assisted wireless communication system according to claim 1, characterized in that: The initial optimal solution of the first auxiliary variable and the initial optimal solution of the second auxiliary variable are expressed as: in, represents the initial optimal solution of the first auxiliary variable corresponding to user k, represents the initial optimal solution of the complex-valued auxiliary variable corresponding to user k.

7. The beamforming design method for a smart reflector-assisted wireless communication system according to claim 1, characterized in that: The optimization of the transmit beamforming vector is as follows: for the base station transmit beamforming matrix W, based on the current iteration point W (t) The first-order Taylor expansion of the transmit beamforming vector w k Optimize and construct a local convex approximation function to update W (t+1) .

8. The beamforming design method for a smart reflector-assisted wireless communication system according to claim 1, wherein: The second-order Taylor expansion is used to optimize the phase shift matrix.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the beamforming design method for an intelligent reflector-assisted wireless communication system as described in any one of claims 1 to 8 are implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the beamforming design method for the smart reflector-assisted wireless communication system according to any one of claims 1 to 8 are implemented.

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