A method for beamforming design of an intelligent reflecting surface assisted wireless communication system

Abstract

The application discloses a beamforming design method of an intelligent reflecting surface assisted wireless communication system, a transmitting beamforming vector and a phase shift matrix are obtained by establishing a model of the intelligent reflecting surface assisted wireless communication system; based on the transmitting beamforming vector and the phase shift matrix, an optimization objective function of maximizing the weighted sum rate of users is established, and a first auxiliary variable and a second auxiliary variable are introduced to optimize and solve the optimization objective function; the block coordinate descent method is used to iteratively update the optimization objective function, and initial optimal solutions of the first auxiliary variable and the second auxiliary variable are obtained; the initial optimal solutions are substituted into the optimization objective function to obtain a new optimization objective function; based on the new optimization objective function, the transmitting beamforming vector and the phase shift matrix are sequentially optimized until the weighted sum rate of users converges. By jointly optimizing the transmitting beamforming vector of the BS and the phase shift matrix of the RIS, the weighted sum rate of the UE is maximized.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and particularly relates to a beamforming design method for an intelligent reflector-assisted wireless communication system. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Intelligent reflectors have attracted much attention due to their ability to flexibly control wireless signals and their potential to improve the capacity and coverage of wireless networks. Furthermore, their advantages of low cost, low power consumption, and high reliability have led to their widespread recognition as a promising technology for sixth-generation communication networks. However, severe path loss in RIS-assisted millimeter-wave communication systems limits signal coverage. Beamforming design can effectively improve this, but the challenge lies in the high coupling between the base station's (BS) transmit beamforming and the phase shift matrix at the RIS. The problem to be solved is typically an NP-hard non-convex optimization problem, with high algorithm complexity and difficulty in obtaining closed-form solutions.

[0004] Existing technologies address the problem of maximizing the total data transmission rate in RIS-assisted multi-user (MU) systems. This is achieved by employing fractional programming (FP) to jointly optimize transmit beamforming and reflection phase shift matrices. However, these schemes suffer from poor beamforming robustness, low total user equipment (UE) data transmission rate, and high computational complexity. Therefore, improving the total data transmission rate while reducing computational complexity remains a pressing issue. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a beamforming design method for an intelligent reflector-assisted wireless communication system, and provides a low complexity-block coordinate descent (LC-BCD) method, which maximizes the weighted sum-rate (WSR) of the UE by jointly optimizing the transmit beamforming vector of the BS and the phase shift matrix of the RIS.

[0006] To achieve the above objectives, 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 reflector-assisted wireless communication system, comprising:

[0008] A wireless communication system model assisted by a smart reflector is established, and the transmit beamforming vector of the base station and the phase shift matrix of the smart reflector are obtained based on the system model.

[0009] Based on the transmitted beamforming vector and the phase shift matrix, an optimization objective function is established to maximize the weighted sum rate of the user; a first auxiliary variable and a second auxiliary variable are introduced into the optimization objective function, and the optimization solution is obtained based on the first auxiliary variable and the second auxiliary variable;

[0010] The block coordinate descent method is used to iteratively update the first auxiliary variable, the second auxiliary variable, the transmitted 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; the initial optimal solutions of the first auxiliary variable and the second auxiliary variable are substituted into the optimization objective function to obtain the new optimization objective function;

[0011] Based on the new optimization objective function, the transmit beamforming vector and phase shift matrix are optimized sequentially, and this optimization is iterated until the user's weighted sum rate converges, thus completing the joint optimization of the transmit beamforming vector and phase shift matrix.

[0012] A further technical solution utilizes a channel model to establish a wireless communication system model assisted by a smart reflector. The base station sends pilot signals that reach the user through the smart reflector. The base station BS is configured with M antennas, and the smart reflector RIS is equipped with N reflection elements. There are a total of K single-antenna users UE in the system model.

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

[0014]

[0015] in,(·) H This indicates the conjugate transpose. as well as These represent the baseband equivalent channels from the base station to user k, from the smart reflector to user k, and from the base station to the smart reflector, respectively.

[0016] make The signal y received by the k-th user k It can be represented as:

[0017]

[0018] Where, θ H H represents the conjugate transpose of the phase shift vector.r,k This represents the channel matrix of the intelligent reflector cascade. s represents the transmit beamforming vector that the base station transmits to the k-th user in the downlink. k This represents the transmission symbol sent to the k-th user. This represents the additive white Gaussian noise received at user k.

[0019] The signal-to-interference-plus-noise ratio γ at the k-th user location k It can be represented as:

[0020]

[0021] in, This represents the noise power of additive white Gaussian noise.

[0022] A further technical solution is that the optimization objective function is expressed as:

[0023]

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

[0025] A further technical solution involves introducing a first auxiliary variable α = [α1, α2, ..., α] into the optimization objective function using a Lagrange dual transformation. K ] T By utilizing the quadratic transformation in fractional programming, a second auxiliary variable β = [β1, β2, ..., β] is introduced. K ] T The optimization objective function after introducing auxiliary variables is obtained, expressed as:

[0026]

[0027] Where, α k This represents the first auxiliary variable corresponding to user k. This indicates the operation of taking the real part, (·). * Indicates conjugation, β k This represents the second auxiliary variable corresponding to user k.

[0028] A further technical solution yields the initial optimal solution for the first auxiliary variable and the initial optimal solution for the second auxiliary variable, expressed as follows:

[0029]

[0030] in, Let represent the initial optimal solution of the first auxiliary variable corresponding to user k. This represents the initial optimal solution for the complex-valued auxiliary variable corresponding to user k.

[0031] A further technical solution involves optimizing the transmit beamforming vector 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 transmitted beamforming vector w k To optimize, a locally convex approximation function is constructed to update W. (t+1) .

[0032] A further technical solution involves optimizing the phase shift matrix using a second-order Taylor expansion.

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

[0034] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the beamforming design method of the intelligent reflector-assisted wireless communication system as described in the first aspect.

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

[0036] This invention addresses the optimization objective of maximizing WSR by introducing a first auxiliary variable α using Lagrange dual transformation and a second auxiliary variable β using the FP method, and then finds the initial optimal solutions for α and β. and Then, substitute it into the objective function, and use a first-order Taylor expansion to optimize the transmit beamforming vector W, and use a second-order Taylor expansion to optimize the RIS phase shift vector θ. Then iteratively update the optimization variables α, β, W, and θ to find the optimal solution, thereby maximizing WSR.

[0037] The method of this invention can reduce computational complexity, save computing resources, and improve beamforming accuracy, and is suitable for wireless communication systems assisted by RIS technology in 6G. Attached Figure Description

[0038] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0039] Figure 1 This is a flowchart of the beamforming design method according to an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of a wireless communication system assisted by an intelligent reflective surface according to an embodiment of the present invention;

[0041] Figure 3 In this embodiment of the invention, WSR follows the number of running rounds n loop The curve of change;

[0042] Figure 4 The WSR in this embodiment of the invention varies with the maximum transmit power P. T The curve of change;

[0043] Figure 5 This is the curve showing the change of WSR with the number of reflecting elements N when the number of antennas M = 16 in this embodiment of the invention;

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

[0045] Figure 7 The running time T of this embodiment of the invention varies with n loop The curve showing the change. Detailed Implementation

[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

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

[0049] Example 1

[0050] like Figure 1 As shown in the figure, this embodiment discloses a beamforming design method for an intelligent reflector-assisted wireless communication system, which includes the following steps:

[0051] S1: Establish a wireless communication system model assisted by a smart reflector, and obtain the base station's transmit beamforming vector and the phase shift matrix of the smart reflector based on the system model;

[0052] This invention only considers beamforming methods, so it assumes that channel estimation can achieve perfect CSI.

[0053] In this embodiment, as Figure 2 As shown, a downlink intelligent reflector (RIS)-assisted millimeter-wave multiple-input single-output (MIMO) system model is first established using the Saleh-Valenzuela channel model, assuming beamforming design under perfect CSI conditions. The base station (BS) transmits pilot signals that reach the user (UE) via the RIS. The base station (BS) is configured with M antennas, the RIS has N reflecting elements, and there are K single-antenna users in the system.

[0054] The transmitted signal x of base station BS is represented as:

[0055]

[0056] Where K represents the number of users per antenna. s represents the transmit beamforming vector that the base station transmits to the k-th user in the downlink. k This represents the transmission symbol sent to the k-th user.

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

[0058] Θ = diag(θ1, θ2, ..., θ) N (2)

[0059] in, This represents the phase shift of the nth RISC unit, where j represents the imaginary unit. Let the phase shift angle of the nth RIS reflector unit satisfy the following condition: θ = [θ1, θ2, ..., θ N ] H Represents the phase shift vector; β n Let β represent the amplitude of the nth RIS unit, satisfying β n ∈[0,1]. In this embodiment, β is set to [0,1]. n =1 to maximize RIS signal reflection.

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

[0061]

[0062] in,(·) H This indicates the conjugate transpose. as well as These represent the baseband equivalent channels from the base station to user k, from the smart reflector to user k, and from the base station to the smart reflector, respectively.

[0063] make The signal y received by the k-th user k It can be represented as:

[0064]

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

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

[0067]

[0068] in, This represents the noise power of additive white Gaussian noise.

[0069] S2: Based on the transmitted beamforming vector and the phase shift matrix, establish an optimization objective function that maximizes the weighted sum rate of the user; 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 simpler; by introducing the second auxiliary variable, the fractional optimization problem is decoupled, simplifying the calculation.

[0070] In this embodiment, an optimization objective function is established based on the system model, and a 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, maximizing the gain of RIS in wireless communication and improving the communication quality between the base station and the user. Its function is to effectively improve channel conditions and enhance user signal quality by intelligently adjusting the RIS phase shift. The model's function is specifically reflected in the objective function, auxiliary variable transformation, and RIS phase shift optimization formulas, making it valuable for applications in 6G communication, millimeter-wave channel enhancement, and signal coverage optimization.

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

[0072]

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

[0074] In a nonconvex objective function In this process, due to the high coupling between the transmit beamforming matrix W and the phase shift vector θ of RIS, it is difficult to directly optimize the solution. Therefore, this invention employs a Lagrange dual transformation and introduces a first auxiliary variable α = [α1, α2, ..., α...]. K ] T To decouple the logarithm, it is represented as:

[0075]

[0076] To facilitate the subsequent optimization process, an auxiliary function is introduced. Introducing this auxiliary function for variable substitution makes the optimization problem more linear and decomposes it into two independent subproblems, facilitating subsequent optimization using the proposed algorithm. Therefore, after introducing the first auxiliary variable α, the problem... Become a problem Represented as:

[0077]

[0078] The expression for the new objective function f2(α,W,θ) is:

[0079]

[0080] Where, α k This represents the first auxiliary variable corresponding to user k.

[0081] Using the quadratic transformation in functional programming, a second auxiliary variable (also called a complex-valued auxiliary variable) β = [β1, β2, ..., β2] is introduced. K ] T The auxiliary function z(W,Θ) is reconstructed as follows:

[0082]

[0083] Among them, auxiliary variables

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

[0085]

[0086] in, This indicates the operation of taking the real part, (·). * Indicates conjugation, β k This represents the second auxiliary variable corresponding to user k.

[0087] For equation (11) to be equal, equation (12) must be satisfied:

[0088]

[0089] Substituting equation (12) into equation (11), the problem... Become a problem As shown in equation (13):

[0090]

[0091] The expression for the new objective function f3(α,β,W,θ) is given by equation (14):

[0092]

[0093] The introduction of first and second auxiliary variables when solving optimization problems is based on the application background of fractional programming (FP) methods. This variable definition enables the original fractional optimization problem to be decomposed and optimized through Lagrange dual transformation and quadratic transformation.

[0094] S3: The block coordinate descent method is used to iteratively update the first auxiliary variable, the second auxiliary variable, the transmitted 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; the initial optimal solution of the first auxiliary variable and the initial optimal solution of the second auxiliary variable are substituted into the optimization objective function to obtain the new optimization objective function;

[0095] In this embodiment, the Low Complexity-Block Coordinate Descent (LC-BCD) method is used to iteratively update α, β, W, and θ. By differentiating Equation (14) with respect to the auxiliary variables α and β respectively and setting them equal to zero, the initial optimal solution α of α and β is obtained. opt β opt , respectively represented as:

[0096]

[0097]

[0098] in, Let represent the initial optimal solution of the first auxiliary variable corresponding to user k. This represents the initial optimal solution for the complex-valued auxiliary variable corresponding to user k.

[0099] S4: Based on the new optimization objective function, the transmit beamforming vector and phase shift matrix are optimized sequentially, and this optimization is iterated until the user's weighted sum rate converges, thus completing the joint optimization of the transmit beamforming vector and phase shift matrix.

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

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

[0102]

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

[0104] Calculate f4(W) with respect to w k gradient Equation (18) is obtained, which can be expressed as:

[0105]

[0106] exist Using a first-order Taylor expansion, construct an auxiliary function for the neighborhood of [the neighborhood]. As shown in equation (19):

[0107]

[0108] in, It is the previous iteration point The function value, Represented as right gradient, η represents the beamforming vector of the previous iteration, and η represents the step size control parameter.

[0109] Auxiliary functions It should satisfy equation (20):

[0110]

[0111] Among them, At this point, the equality holds.

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

[0113]

[0114] Among them, 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, a second-order Taylor expansion is used to optimize the phase shift vector θ of the RIS.

[0116] Expanding equation (14), after ignoring the constant terms that are independent of the phase shift vector θ, we can extract a typical quadratic term θ. H Uθ is updated by solving the following problem, as shown in equation (22):

[0117]

[0118] in,

[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 easier to solve. V serves as the linear term of the optimization objective, i.e., the gradient direction, allowing the algorithm to perform efficient iterations based on the gradient direction, thus improving the convergence speed. These are expressed as follows:

[0120]

[0121] The first-order gradient and Hessian matrix of the objective function f6(θ) are expressed as follows:

[0122]

[0123] exist In the domain, a second-order Taylor expansion is used to construct an auxiliary function. Represented as:

[0124]

[0125] in, It is the previous iteration point The function value.

[0126] Auxiliary functions Equation (28) should be satisfied, expressed as:

[0127]

[0128] Among them, At this point, the equality holds.

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

[0130]

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

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

[0133] This method can reduce computational complexity, save computational resources, and improve beamforming accuracy, making it suitable for RIS technology-assisted wireless communication systems in 6G.

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

[0135] To demonstrate that this invention can improve the effectiveness of beamforming design, Figure 3 and Figure 4 The invention presents the weighted sum rate comparison results under six different conditions: AO method, APG method, channel error e of 0.1 and 0.5 respectively, random phase shift setting, and without RIS. Figure 3 For WSR, the number of running rounds n loop The change curve, from Figure 3 It can be seen that the LC-BCD algorithm has performance similar to that of the AO algorithm, APG algorithm, etc., and the WSR basically stabilizes by the 15th iteration, indicating that the algorithm converges to at least a local optimum. Figure 4 WSR with maximum transmit power P T The change curve, from Figure 4As can be seen, the beamforming performance (WSR) of the method of this invention is higher than that of other methods; therefore, this invention has better beamforming effect. Applying this method can improve the performance of RIS-assisted wireless communication systems and reduce the computational complexity of beamforming design in wireless communication systems.

[0136] To demonstrate that the present invention has higher beamforming accuracy, Figure 5 and Figure 6 The WSR curves of this invention, along with those of APG, ADMM, SDR, and GDA methods, are shown for BS antenna counts M=16 and 64, respectively, as a function of the reflecting element N. Figure 5 and Figure 6 As can be seen, the present invention has higher beamforming accuracy under different numbers of BS antennas.

[0137] To demonstrate that the present invention has lower computational complexity, Figure 7 The running time T of this invention, along with the APG, ADMM, SDR, and GDA methods, is shown as a function of the number of iterations n. loop A changing curve. (From...) Figure 7 It is evident that, under the same operating conditions, the beamforming design method of the present invention has a shorter operating time, indicating lower time complexity.

[0138] Example 2

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

[0140] Example 3

[0141] The purpose of this embodiment is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method of Embodiment 1.

[0142] The steps and methods involved in the apparatuses of Embodiments 2 and 3 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0143] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0144] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0145] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for beamforming design of an intelligent reflecting surface-assisted wireless communication system, characterized in that, The method comprises the following steps: A smart reflector-assisted wireless communication system model is established, and a base station transmit beamforming vector and a smart reflector phase shift matrix are obtained based on the system model; An optimization objective function for maximizing the weighted sum rate of users is established based on the transmit beamforming vector and the phase shift matrix; first and second auxiliary variables are introduced into the optimization objective function, and the first and second auxiliary variables are optimized and solved based on the first and second auxiliary variables; The block coordinate descent method is used to iteratively update the first and second auxiliary variables, the transmit beamforming vector, and the phase shift matrix in the optimization objective function, to obtain an initial optimal solution of the first auxiliary variable and an initial optimal solution of the second auxiliary variable; The initial optimal solution of the first auxiliary variable and the initial optimal solution of the second auxiliary variable are substituted 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 sequentially optimized, and the optimization is iterated until the weighted sum rate of users converges, thereby completing the joint optimization of the transmit beamforming vector and the phase shift matrix; In the optimization objective function, a first auxiliary variable is introduced by using Lagrange dual transformation By using quadratic transformation in fractional programming, a second auxiliary variable is introduced The optimization objective function after introducing auxiliary variables is obtained and expressed as wherein, represents a user corresponding first auxiliary variable, represents a real part operation, represents a conjugate, represents a user corresponding second auxiliary variable; is a phase shift vector of the RIS; is a maximum transmit power of the BS; represents a phase shift of the th RIS element; is a weighting coefficient of the th user; represents a conjugate transpose, represents a baseband equivalent channel from the base station to the user ; represents an intelligent reflecting surface cascaded channel matrix; represents a noise power of an additive white Gaussian noise; The optimization of the transmit beamforming vector is specifically: for the base station transmit beamforming matrix , based on the first-order Taylor expansion of the current iteration point , the transmit beamforming vector is optimized, a local convex approximation function is constructed to update ; The second-order Taylor expansion is used to optimize the phase shift matrix.

2. The method of Claim 1, wherein, A channel model is used to establish a model of a wireless communication system assisted by a smart reflecting surface. A pilot signal transmitted by a base station reaches a user via the smart reflecting surface. The base station BS is configured with M antennas, the smart reflecting surface RIS is provided with N reflecting units, and there are a total of single-antenna users UE in the system model.

3. The method of Claim 2, wherein, The signal-to-interference-and-noise ratio at the user is calculated 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 concatenated link, and the channel from the base station to the first user may be represented as: wherein, denotes the conjugate transpose, and denote the baseband equivalent channels from the base station to the user , from the intelligent reflecting surface to the user , and from the base station to the intelligent reflecting surface, respectively. Let , the signal received by the th user can be expressed as: ​ wherein, denotes a conjugate transpose of a phase shift vector, denotes an intelligent reflecting surface cascaded channel matrix, denotes a transmit beamforming vector of a base station transmitting to the th user in a downlink, denotes a transmission symbol sent to the th user, denotes an additive white Gaussian noise received at the user . The first user's SINR may be expressed as: wherein denotes the noise power of the additive white Gaussian noise.

4. The method of Claim 1, wherein, The optimization objective function is expressed as: wherein, is a transmit beamforming matrix of the base station; is a phase shift vector of the RIS, is a weighting coefficient of the user, is a maximum transmit power of the BS.

5. The method of Claim 1, wherein, The initial optimal solution of the first auxiliary variable and the initial optimal solution of the second auxiliary variable are obtained by: wherein, representing a user the initial optimal solution of the corresponding first auxiliary variable, representing a user the initial optimal solution of the corresponding complex auxiliary variable.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps in the beamforming design method of the smart reflector-assisted wireless communication system according to any one of claims 1-5.

7. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps in the beamforming design method of the smart reflector-assisted wireless communication system according to any one of claims 1-5.

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