A joint optimization method for an intelligent reflective surface-assisted secure communication system

Abstract

This invention discloses a joint optimization method for an intelligent reflector-assisted secure communication system, belonging to the field of communication security. The joint optimization method includes: constructing an IRS-assisted secure communication system and a joint optimization problem, and decomposing the optimization problem into three sub-problems: solving for the base station transmission beamforming vector, optimizing the IRS reflection matrix, and optimizing the IRS location; using the generalized Rayleigh quotient to solve for the optimal closed-form solution of the base station transmission beamforming vector; transforming the IRS reflection matrix optimization problem into a tractable form and solving it using a semidefinite relaxation method; and optimizing the IRS location based on the solved base station transmission beamforming vector and IRS reflection matrix using a microbial community optimization algorithm, thereby significantly improving the security rate.

Description

Technical Field

[0001] This invention belongs to the field of communication security, and specifically relates to a joint optimization method for an intelligent reflective surface-assisted secure communication system. Background Technology

[0002] In recent years, transmission technologies for physical layer security have been studied in depth, such as cooperative jamming, artificial noise-assisted beamforming, and cooperative relay schemes. However, cooperative jamming and artificial noise-assisted beamforming require additional power. In addition, a large number of active relays are required. Intelligent reflectors (IRS) have attracted widespread attention as a potential technology for sixth-generation communication.

[0003] In wireless communication systems, IRS (Infrared Relays) are typically used as relays to assist transmission. Unlike traditional relays, IRS is a passive array structure controlled by a digital platform, which can dynamically adjust reflective elements to achieve beamforming. Furthermore, because IRS can increase the signal power of legitimate receivers while reducing the signal power of eavesdroppers through reflection, it has been widely used to address physical layer security issues.

[0004] In existing IRS-assisted secure transmission systems, the IRS is usually deployed in a fixed ground location, which limits performance improvement; in fact, optimizing the IRS location provides a new degree of freedom to improve transmission performance, which has been rarely studied. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a joint optimization method for an intelligent reflective surface-assisted security communication system.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A joint optimization method for an intelligent reflective surface-assisted secure communication system includes the following steps:

[0008] Construct a secure communication system assisted by IRS and a joint optimization problem, and decompose the optimization problem into three sub-problems: base station transmission beamforming vector solution, IRS reflection matrix optimization, and IRS location optimization.

[0009] The optimal closed-form solution for the base station transmission beamforming vector is obtained using the generalized Rayleigh quotient.

[0010] The IRS reflection matrix optimization problem is transformed into a tractable form and solved using a semidefinite relaxation method;

[0011] Based on the solved base station transmission beamforming vector and IRS reflection matrix, the location of the IRS is optimized using a microbial community-based optimization algorithm.

[0012] Furthermore, the secure communication system includes: an IRS with several reflective elements, and a base station with several transmitting antennas, the base station providing services to the User and Eve.

[0013] Furthermore, the optimization problem is:

[0014]

[0015] In the formula, [H min H max ] is the height range of the IRS, P is the transmission power of the BS, w is the transmission beamforming vector, Θ is the reflection matrix, and g is the position of the IRS.

[0016] Furthermore, the steps for finding the optimal closed-form solution of the base station transmission beamforming vector include:

[0017] S21, given the fixed position of the IRS and reflection matrix in problem P1, all transmitted power is allocated to beamforming, i.e., ||w|| 2 =P, in problem P1, the numerator and denominator of the objective function satisfy the following equation:

[0018]

[0019] In the formula, It is an identity matrix, and in,

[0020] S22, Substituting equation (9) into equation (8), we obtain a generalized Rayleigh quotient:

[0021]

[0022] And the optimal closed-form solution of the transmission beam vector is obtained:

[0023]

[0024] in,

[0025] Furthermore, the steps to solve for the IRS reflection matrix include:

[0026] S31, using the given IRS location and transmission beam, the reflection matrix problem is equivalently rewritten as:

[0027]

[0028] S32, let Based on v H v = N, and the numerator and denominator of the objective function in problem P2 are:

[0029]

[0030] Substitute (11) into problem P2 and apply the Charnes Cooper transformation method to transform problem P2 into:

[0031]

[0032] In equation (12),

[0033] S33, P3, a problem without rank-1 constraints, is a standard semidefinite programming problem that can be solved using convex optimization tools. When the obtained suboptimal solution is rank-1, eigenvalue decomposition can be used to restore the rank-1 solution. When the obtained suboptimal solution is not rank-1, Gaussian randomization can be used to restore the rank-1 solution.

[0034] Furthermore, the Gaussian randomization method first performs eigenvalue decomposition on the suboptimal solution to obtain:

[0035] V opt =LΛL H

[0036] in, It is a unitary matrix, Λ=[λ1,…,λ N [] is a diagonal matrix of eigenvalue vectors;

[0037] To satisfy the constant modulus constraint |v n |=1, the elements of IRS are taken in,

[0038] Further steps to optimize the IRS location include:

[0039] S41, construct the IRS location optimization problem, specifically:

[0040]

[0041] In question P4, the channel Both h are determined by the location of the IRS; and the BSO algorithm is used to solve problem P4;

[0042] S42, each bacterium is represented by the position of its IRS, i.e., g = [x, y, H] T In problem P4, the objective function value represents the fitness of each bacterium, denoted as R; the number of bacteria is denoted as η. max The number of steps for chemotaxis, reproduction, and diffusion is denoted as N. j N k N l Therefore, during chemotaxis, the renewal of the ηth bacterium follows the following rules:

[0043]

[0044] In equation (14), C(η) is the number of steps the bacteria take, Δ(η) is a random direction vector with elements ranging from [-1, 1]; and the fitness value of the ηth bacterium. The update is based on the updated bacteria, i.e., the IRS location;

[0045] S43, after each bacterium undergoes chemotaxis, it mutates using a particle swarm optimization operator. At this point, the swimming speed V and position g of the ηth bacterium follow the following rules:

[0046]

[0047]

[0048] In the formula, ω and c1 are the inertia weight and learning factor, respectively, ε1 is a random number within [0,1], and g b It records the location of the bacteria with the highest fitness in the entire bacterial community, and s is the number of iterations of the PSO operator;

[0049] S44, bacteria reproduce while maintaining information;

[0050] S45, bacteria will spread with probability P ed The bacteria migrate to other random locations, and the fitness value obtained at each random location is compared with the fitness values ​​of existing bacteria. The maximum fitness value and the location of the bacteria are recorded, and the bacteria then undergo chemotaxis, reproduction, and dispersal again until the cycle ends after N steps. j N k N l .

[0051] A smart reflector-assisted secure communication system includes an IRS with several reflective elements and a base station with several transmitting antennas. The base station provides services to Users and Eves. The system also uses the aforementioned joint optimization method to jointly optimize the base station's transmission beamforming vector, the IRS reflection matrix, and the IRS's location.

[0052] A device for optimizing a smart reflector-assisted security communication system, comprising:

[0053] Model and Problem Building Module: Constructs a secure communication system assisted by IRS and a joint optimization problem, and decomposes the optimization problem into three sub-problems: base station transmission beamforming vector solution, IRS reflection matrix optimization, and IRS location optimization;

[0054] Transmission beamforming vector solution module: Using the generalized Rayleigh quotient, solve for the optimal closed-form solution of the base station transmission beamforming vector;

[0055] The reflection matrix optimization module transforms the IRS reflection matrix optimization problem into a tractable form and solves it using a semidefinite relaxation method.

[0056] In addition, the IRS location optimization module optimizes the IRS location based on the solved base station transmission beamforming vector and IRS reflection matrix, using a microbial community-based optimization algorithm.

[0057] The beneficial effects of this invention are:

[0058] 1. Introducing an IRS (Interceptor Signal Reflector) into a communication system can enhance the communication link and improve signal reception at the receiving end through effective signal reflection. Furthermore, in the presence of eavesdroppers in the communication system, the phase shift of the IRS can be adjusted to reduce the eavesdropper's received signal, thus ensuring communication security.

[0059] 2. In IRS-assisted secure communication systems, the location of the IRS is rarely considered. It is generally assumed to be fixed on the building wall, and then the base station's transmission beam vector and the IRS's reflection matrix are jointly optimized, which limits performance improvement. In fact, IRS location optimization can provide a new degree of freedom to the communication system. This invention considers the joint optimization of the base station's transmission beam vector, the IRS reflection matrix, and the IRS's three-dimensional coordinate position. The IRS can be placed on an indoor ceiling or an outdoor aerial platform. Compared with the case of a fixed IRS location, optimizing the IRS location can effectively improve security performance.

[0060] 3. In IRS-assisted secure communication systems, the three-dimensional location optimization of the IRS is considered, making the optimization problem different from other problems. The severe coupling of variables in the optimization problem leads to complexity and difficulty in solving the problem. Therefore, an alternating optimization method is adopted to jointly optimize the base station transmission beam vector, IRS reflection matrix, and IRS location. Among them, the IRS location optimization problem is difficult to transform into a convex problem. Therefore, the BSO algorithm is used to solve it. BSO is a hybrid method of Bacterial Foraging Algorithm (BFOA) and Particle Swarm Optimization (PSO). It combines the local search capability of BFOA and the global search capability of PSO, which can avoid premature convergence and erroneous search. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a flowchart of the optimized method of the present invention;

[0063] Figure 2This is a structural diagram of the IRS-assisted secure communication system of the present invention;

[0064] Figure 3 These are security performance diagrams for different schemes of the present invention as the base station transmit power P increases;

[0065] Figure 4 These are security performance diagrams for different schemes of the present invention as the number of reflective elements increases;

[0066] Figure 5 This is a three-dimensional view of the optimized IRS position when Eve moves from position E1 to position E5 according to the present invention;

[0067] Figure 6 This is a top view of the optimized IRS position when Eve moves from position E1 to position E5 according to the present invention. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] like Figure 1 As shown, a joint optimization method for an intelligent reflective surface-assisted secure communication system includes the following steps:

[0070] S1, construct a secure communication system and optimization problem assisted by intelligent reflector (IRS), and decompose the optimization problem into three sub-problems: BS transmission beamforming vector solution, IRS reflection matrix optimization, and IRS position optimization;

[0071] like Figure 2 As shown, the IRS in this system has N reflective elements to improve transmission security; at the same time, the base station (BS) with M transmit antennas provides services to legitimate single-antenna users (User) and illegitimate single-antenna users (Eve); due to severe congestion or path loss, the direct connection between the BS and the User and Eve is ignored.

[0072] Figure 2 A Cartesian coordinate system was established, with BS located at the origin [0,0,0]. T Assume User and Eve are located within a rectangular region Ω; the center of this region has a length and width of O = [x0, 0, 0]. T L x and L y The coordinates of User and Eve are u = [xu ,y u ,0] T and e = [x e ,y e ,0] T ,in, The coordinates of the first element from the left in the IRS are represented as g = [x, y, H]. T Therefore, the distances from BS to IRS, IRS to User, and IRS to Eve are respectively d BI =||g||,d IU =||gu||, and d IE =||ge||.

[0073] Therefore, the channel coefficient β of the BS-IRS (BI), IRS-user (IU), and IRS-Eve (IE) signal links i for:

[0074]

[0075] In the formula, α is the path fading index, and β0 represents the signal loss at a propagation distance d0 = 1m.

[0076] The corresponding BI link channel is:

[0077]

[0078] In the formula, λ is the carrier length. and These represent the angle of departure (AoD) and angle of arrival of the BI link signal, respectively. and These represent the transmission array response at the BS and the receiving array response at the IRS, respectively.

[0079]

[0080]

[0081] in, d1 and d2 are the distances between the BS transmission antenna and the IRS passive reflector.

[0082] and These are the departure angles of the IU and IE links, respectively, and the channels of these two links are:

[0083]

[0084] In the formula, This is the IRS transmit array response, where,

[0085] The signals received by User and Eve are:

[0086]

[0087] In the formula, The variance is Additive white Gaussian noise, the phase shift matrix of IRS is represented as in, The transmission beamforming and signal at BS are represented as follows: and s.

[0088] Therefore, the system's security rate is:

[0089]

[0090] This invention focuses on jointly optimizing the transmission beamforming vector w, the reflection matrix Θ, and the position g of the IRS to maximize the system's security. Therefore, the optimization problem is as follows:

[0091]

[0092] In the formula, [H min H max [] represents the height range of the IRS, and P represents the transmission power of the BS.

[0093] Equation (8) is difficult to solve due to interference terms and coupling variables in the objective function; this invention transforms the original problem P1 into three subproblems and solves them alternately; in (8), the constraints of each variable are independent of the other two variables. This prompts the use of the Alternating Optimization (AO) method to decouple the optimization variables. Specifically, the solution of the transmission beamforming vector is first described in closed form. Then, the optimized reflection matrix is ​​obtained through SDR. Finally, based on the obtained beamforming vector and reflection matrix, a BSO-based algorithm (the overall algorithm is simply referred to as the JOBP algorithm) is proposed to obtain the position of the IRS with three-dimensional coordinates.

[0094] S2, using the generalized Rayleigh quotient, solve for the optimal closed-form solution of the BS transmission beamforming vector;

[0095] The solution steps are as follows:

[0096] S21, to solve the subproblem of transmit beamforming, the position of the IRS and the reflection matrix in problem P1 are fixed; to achieve optimal transmission, all transmission power is allocated to beamforming, i.e., ||w|| 2 =P, therefore, in problem P1, the numerator and denominator of the objective function satisfy the equation:

[0097]

[0098] In the formula, It is an identity matrix, and in,

[0099] S22, Substituting equation (9) into equation (8), we can obtain a generalized Rayleigh quotient:

[0100]

[0101] And the optimal closed-form solution of the transmission beam vector is obtained:

[0102]

[0103] in,

[0104] S3 transforms the IRS reflection matrix optimization problem into a tractable form and solves it using the semidefinite relaxation method (SDR).

[0105] The specific steps are as follows:

[0106] S31, using the given IRS location and transmission beam, the reflection matrix problem is equivalently rewritten as:

[0107]

[0108] S32, Due to the interference terms in the objective function, problem P2 is difficult to solve; let Based on v H v = N, and the numerator and denominator of the objective function in problem P2 are:

[0109]

[0110] Substituting (11) into problem P2 and applying the Charnes Cooper transformation method, problem P2 is transformed into:

[0111]

[0112] In equation (12),

[0113] S33, the problem P3 without rank-1 constraints is a standard semidefinite programming (SDP) problem, which can be solved using convex optimization tools such as CVX; however, the suboptimal solution obtained is not necessarily rank-1. When the suboptimal solution obtained is rank-1, eigenvalue decomposition (EVD) can be used to restore the rank-1 solution. When the suboptimal solution obtained is not rank-1, Gaussian randomization can be used to restore the rank-1 solution.

[0114] The Gaussian randomization method first performs eigenvalue decomposition on the suboptimal solution to obtain V. opt =LΛL H Where L = [e1,…,e N ] is a unitary matrix, Λ=[λ1,…,λ N ] is a diagonal matrix of eigenvalue vectors; in order to satisfy the constant modulus constraint |v n |=1, the elements of IRS are taken in,

[0115] S4, based on the BS transmission beam vector and IRS reflection matrix obtained from S2 and S3, uses the microbial community optimization (BSO) algorithm to optimize the position of the IRS;

[0116] The specific steps include:

[0117] S41, construct the IRS location optimization problem, specifically:

[0118]

[0119] In problem P4, the channel r k H Both h and are determined by the position of the IRS, therefore, problem P4 is very complex and difficult to solve. A heuristic algorithm is used to solve problem P4. The BSO algorithm is the process of bacteria swimming to find a solution, and it is optimized based on three behaviors: chemotaxis, reproduction and diffusion.

[0120] S42, each bacterium is represented by the position of its IRS, i.e., g = [x, y, H] T To measure the optimality of the bacteria, the objective function value in problem P4 is chosen to represent the fitness of each bacterium, denoted as R; the number of bacteria is denoted as η. max The number of steps for chemotaxis, reproduction, and diffusion is denoted as N. j N k N l Therefore, during chemotaxis, the renewal of the ηth bacterium follows the following rules:

[0121]

[0122] In equation (14), C(η) is the number of steps the bacteria take, Δ(η) is a random direction vector with elements ranging from [-1, 1]; and the fitness value of the ηth bacterium. The update is based on the updated bacteria, i.e., the IRS location.

[0123] S43, after chemotaxis, each bacterium undergoes mutation via a particle swarm optimization (PSO) operator. During this process, the swimming velocity V and position g of the ηth bacterium follow the following rules:

[0124]

[0125]

[0126] In the above formula, ω and c1 are the inertia weight and learning factor, ε1 is a random number within [0,1], and g b It records the location of the bacteria with the highest fitness in the entire bacterial community, and s is the number of iterations of the PSO operator.

[0127] S44, then, to avoid the blindness of population renewal, the bacteria reproduce and keep the information unchanged.

[0128] S45, Finally, the bacteria in this area will spread with probability P ed The bacteria migrate to other random locations, and the fitness value obtained at each random location is compared with the fitness values ​​of existing bacteria. The maximum fitness value and the location of the bacteria are recorded, and the bacteria then undergo chemotaxis, reproduction, and dispersal again until the cycle ends after N steps. j N k N l .

[0129] Based on the above, the secrecy rate is maximized by alternately searching variables (w, Θ, g). The algorithm flow is as follows: Figure 1 As shown. Notably, during BSO initialization, the initial positions of all bacteria except the first one are... It is a random number that satisfies the constraints in (13).

[0130] because The non-decreasing and upper-bounded nature of the algorithm guarantees convergence; the complexity of solving the transmitted beam vector and the IRS phase shift matrix are respectively... The complexity of the BSO algorithm is S pop Let be the number of bacteria, and D be the dimension of each bacterium. Therefore, the overall complexity of the algorithm is O(n log n). Where, N iter It represents the number of iterations.

[0131] Numerical simulation and results:

[0132] Comprehensive simulations were performed on the following benchmark schemes:

[0133] JOBP (JBF scheme) when the IRS height H is fixed: The IRS is fixed at a height H = 100m to serve the system, and the planar position of the IRS is obtained by the BSO algorithm.

[0134] When the IRS height H is fixed, the JOBP (JPF scheme) based on the PSO algorithm: the planar position of the IRS is obtained by the PSO algorithm.

[0135] JOBP (JFG scheme) with a fixed IRS location: The IRS is fixed at... Alternate optimization (w,Θ).

[0136] The base station has M = 16 transmission antennas, and the terrestrial channel propagates with a path fading index α = 3. The center, length, and width of the region Ω are O = [600m, 0, 0]. T L x =1000m and L y =600m. The height range of the IRS is [H min H max [70m, 120m]. The noise power for User and Eve is... The path loss is β0 = -40 dB. Furthermore, the distances between the antenna and the reflector are d1 = λ / 2 and d2 = λ / 10, respectively.

[0137] Figure 3 The data shows the security performance under different schemes as the base station transmit power P increases. The number of reflecting elements is N = 200. The positions of User and Eve are u = [200m, 10m, 0], respectively. T And e = [400m, -20m, 0] T .Depend on Figure 3 It can be seen that the confidentiality rate increases monotonically with increasing P, and JOBP achieves the best performance because the height of the IRS is optimized. Furthermore, JBF outperforms JPF, indicating that the BSO algorithm outperforms the PSO algorithm in this system. When P exceeds 36 dBm, JFG exhibits a slow increase and a limited confidentiality rate. However, the proposed JOBP maintains good confidentiality performance by adaptively adjusting the position of the IRS.

[0138] Figure 4 This demonstrates the security performance of different schemes as the number of reflective elements increases. The transmission power is P = 35 dBm. The positions of User and Eve are shown. Figure 3 The scheme is the same as in the previous one. It can be found that increasing the number of reflection elements can improve the security rate. In addition, as the number of reflection elements increases, the gap between JOBP and JPF increases, indicating that the algorithm performs better when using a larger-sized IRS.

[0139] Figure 5 and Figure 6 The images show a 3D view and a top view of the optimized IRS position as Eve moves from position E1 to position E5. The transmit power is P = 35 dBm, and the number of reflectors is N = 200. The User's position is u = [600m, 0, 0]. TIn the diagram, when Eve is located at position E3, the optimized IRS position R3 is significantly higher than other IRS positions. This indicates that when Eve is close to the user and its height is close to the IRS, the IRS needs to increase its height to improve confidentiality. Figure 6 In this configuration, all IRSs are located between the User and the Base Station (BS) on the xy-plane. This is because the proximity of the IRS to the User and BS ensures a stronger channel, facilitating secure communication.

[0140] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0141] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A joint optimization method for an intelligent reflective surface-assisted secure communication system, characterized in that, Includes the following steps: Construct a secure communication system assisted by IRS and a joint optimization problem, and decompose the optimization problem into three sub-problems: base station transmission beamforming vector solution, IRS reflection matrix optimization, and IRS location optimization. The optimal closed-form solution for the base station transmission beamforming vector is obtained using the generalized Rayleigh quotient. The IRS reflection matrix optimization problem is transformed into a tractable form and solved using a semidefinite relaxation method; Based on the solved base station transmission beamforming vector and IRS reflection matrix, and using a microbial community-based optimization algorithm, the location of the IRS is optimized. The optimization problem is: （8） In the formula, It is the height range of the IRS. It is the transmission power of BS. To form a transmission beam vector, For the reflection matrix, Location of the IRS; The steps to optimize IRS location include: S41, construct the IRS location optimization problem, specifically: (13) In question P4, the channel and All of these are determined by the location of the IRS; and the BSO algorithm is used to solve problem P4; S42, each bacterium is indicated by the location of the IRS, i.e. In problem P4, the objective function value represents the fitness of each bacterium, denoted as... The number of bacteria is expressed as The number of steps for chemotaxis, reproduction, and diffusion are respectively expressed as: ; Therefore, in the process of chemotaxis, the first The updating of each bacterium follows these rules: （14） In equation (14), It is the number of steps the bacteria take to swim. It is a random direction vector, and its elements range from within; No. Fitness value of each bacterium The update is based on the updated bacteria, i.e., the IRS location; S43, each bacterium undergoes chemotaxis and is mutated using a particle swarm optimization operator; S44, bacteria reproduce while maintaining information; S45, bacteria will spread with probability The bacteria migrate to other random locations, and the fitness value obtained at each random location is compared with the fitness values ​​of existing bacteria. The maximum fitness value and the location of the bacteria are recorded, and the bacteria then undergo chemotaxis, reproduction, and dispersal again until their turn is reached. .

2. The joint optimization method for an intelligent reflective surface-assisted security communication system according to claim 1, characterized in that, The secure communication system includes an IRS with several reflective elements and a base station with several transmitting antennas, the base station providing services to the User and Eve.

3. The joint optimization method for an intelligent reflective surface-assisted security communication system according to claim 1, characterized in that, The steps to find the optimal closed-form solution for the base station transmission beamforming vector include: S21, given the fixed position of the IRS and reflection matrix in problem P1, all transmitted power is allocated to beamforming, i.e. In problem P1, the numerator and denominator of the objective function satisfy the following equation: （9） In the formula, It is an identity matrix, and ,in, ; S22, Substituting equation (9) into equation (8), we obtain a generalized Rayleigh quotient: ， And the optimal closed-form solution of the transmission beam vector is obtained: ， in, .

4. The joint optimization method for an intelligent reflective surface-assisted security communication system according to claim 1, characterized in that, The steps to solve for the IRS reflection matrix include: S31, using the given IRS location and transmission beam, the reflection matrix problem is equivalently rewritten as: （10）； S32, let ,based on In problem P2, the numerator and denominator of the objective function are: （11）； Substitute (11) into problem P2 and apply the Charnes Cooper transformation method to transform problem P2 into: （12） In equation (12), ； S33, P3 is a standard semidefinite programming problem without rank-1 constraints. Convex optimization tools are used. When the obtained suboptimal solution is rank-1, eigenvalue decomposition is used to restore the rank-1 solution. When the obtained suboptimal solution is not rank-1, Gaussian randomization is used to restore the rank-1 solution.

5. The joint optimization method for an intelligent reflective surface-assisted security communication system according to claim 4, characterized in that, The Gaussian randomization method first performs eigenvalue decomposition on the suboptimal solution, obtaining: in, It is a unitary matrix. It is a diagonal matrix of vectors containing eigenvalues; To satisfy the constant modulus constraint The elements of the IRS are taken ,in, , .

6. The joint optimization method for an intelligent reflective surface-assisted security communication system according to claim 1, characterized in that, In S43, the first The swimming speed of a bacterium and location Follow these rules: （15） （16） In the formula, and These are inertia weights and learning factors. yes Internal random number, It records the location of the most adaptable bacteria in the entire bacterial community. It is the number of iterations of the PSO operator.

7. A smart reflective surface-assisted security communication system, characterized in that, It includes an IRS with several reflective elements and a base station with several transmit antennas, the base station providing services to Users and Eve; and, it is possible to jointly optimize the base station transmission beamforming vector, the IRS reflection matrix and the location of the IRS using the optimization method described in any one of claims 1-6.

8. A device for optimizing a smart reflective surface-assisted security communication system, comprising executing the optimization method according to any one of claims 1-6, characterized in that, include: Model and Problem Building Module: Constructs a secure communication system assisted by IRS and a joint optimization problem, and decomposes the optimization problem into three sub-problems: base station transmission beamforming vector solution, IRS reflection matrix optimization, and IRS location optimization; Transmission beamforming vector solution module: Using the generalized Rayleigh quotient, solve for the optimal closed-form solution of the base station transmission beamforming vector; The reflection matrix optimization module transforms the IRS reflection matrix optimization problem into a tractable form and solves it using a semidefinite relaxation method. In addition, the IRS location optimization module optimizes the IRS location based on the solved base station transmission beamforming vector and IRS reflection matrix, using a microbial community-based optimization algorithm.

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