Method for optimizing secrecy rate based on distributed active intelligent reflecting surface

Abstract

The application relates to the technical field of communication, in particular to a security rate optimization method based on a distributed active intelligent reflecting surface. By constructing a secure communication system model containing a distributed active reconfigurable intelligent reflecting surface auxiliary architecture, based on the secure communication system model, taking the base station beam forming vector and the phase shift matrix of the distributed active RIS as an optimization problem, taking the maximum security rate of the legal user as the target, constructing an optimization target problem expression set, taking the optimization target problem expression set as a constraint, selecting any two of the first preset active reconfigurable intelligent reflecting surface phase shift matrix, the second preset active reconfigurable intelligent reflecting surface phase shift matrix and the base station transmitting beam forming vector three functions, optimizing the other function, and determining the maximum security rate of the secure communication system by jointly optimizing the three functions. The problem of how to optimize the security rate of the communication system through the distributed active intelligent reflecting surface is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to a secrecy rate optimization method based on distributed active intelligent reflecting surface. BACKGROUND

[0002] Reconfigurable Intelligent Surface (RIS) is one of the key technologies of 6G. By integrating a large number of low-cost passive reflecting elements on a plane and adaptively adjusting the phase offset of the reflecting elements, the performance of wireless communication networks is significantly improved. Since RIS can improve the signal quality of legitimate users while trying to weaken the signal quality of eavesdropping users, it has shown outstanding ability in enhancing PLS, so the combination of these two technologies has attracted extensive attention and research.

[0003] However, due to the "double fading" effect, that is, the path loss of the RIS reflection link is proportional to the square of the product of the path loss of the two paths from the base station to the RIS and from the RIS to the user, which limits the gain that RIS can provide. In order to overcome the "double fading" effect, some scholars have proposed active RIS. Unlike traditional RIS, which only reflects signals through adjustable phase offset, each reflecting element of active RIS is assisted by an active negative impedance load, which not only reflects the incident signal but also amplifies the signal. In addition, active RIS is superior to active relay in terms of hardware overhead and performance, only requiring simple power amplification and diodes, while active relay requires complex equipment and also needs to deal with self-interference problems.

[0004] Most of the current research on secure communication is based on the centralized RIS deployment model, which does not fully utilize the advantages of distributed RIS in multi-path enhancement and security performance improvement.

[0005] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0006] The main purpose of the present application is to provide a secrecy rate optimization method based on distributed active intelligent reflecting surface, aiming to solve the problem of how to optimize the secrecy rate of the communication system through distributed active intelligent reflecting surface.

[0007] To achieve the above purpose, the present application provides a secrecy rate optimization method based on distributed active intelligent reflecting surface, the method comprising:

[0008] S1, constructing a secure communication system model comprising a distributed active reconfigurable intelligent reflecting surface assisted architecture, the secure communication system model comprising a first preset active reconfigurable intelligent reflecting surface phase shift matrix, a second preset active reconfigurable intelligent reflecting surface phase shift matrix, and a base station transmit beamforming vector;

[0009] S2, based on the secure communication system model, taking the base station beamforming vector and the phase shift matrix of the distributed active RIS as an optimization problem, and taking maximizing the secrecy rate of the legitimate user as an objective, constructing a set of problem expressions of the optimization objective;

[0010] S3, taking the set of problem expressions of the optimization objective as a constraint, selecting any two of the first preset active reconfigurable intelligent reflecting surface phase shift matrix, the second preset active reconfigurable intelligent reflecting surface phase shift matrix, and the base station transmit beamforming vector, and optimizing the other function, to obtain an optimized base station transmit beamforming vector, an optimized first preset active reconfigurable intelligent reflecting surface phase shift matrix, and an optimized second preset active reconfigurable intelligent reflecting surface phase shift matrix, respectively;

[0011] S4, jointly determining the maximum secrecy rate of the secure communication system based on the optimized base station transmit beamforming vector, the optimized first preset active reconfigurable intelligent reflecting surface phase shift matrix, and the optimized second preset active reconfigurable intelligent reflecting surface phase shift matrix.

[0012] Optionally, in the S1, the construction process of the secure communication system model comprises:

[0013] S1.1, assuming that the secure communication system model comprises a base station, a first active reconfigurable intelligent reflecting surface, a second active reconfigurable intelligent reflecting surface, a legitimate user, and an eavesdropping user, wherein the base station is obtained by uniformly arranging and combining M1 and M2 reflecting units, and the legitimate user and the eavesdropping user are both single-antenna;

[0014] S1.2, assuming that the channel gain from the base station to the first active reconfigurable intelligent reflecting surface is the channel gain from the base station to the second active reconfigurable intelligent reflecting surface is the channel gain from the first active reconfigurable intelligent reflecting surface to the legitimate user and the eavesdropping user is the channel gain from the second active reconfigurable intelligent reflecting surface to the legitimate user and the eavesdropping user is

[0015] S1.3, assuming that the expression of the phase shift matrix of the first active reconfigurable intelligent reflecting surface and the second active reconfigurable intelligent reflecting surface is:

[0016]

[0017] where m k ∈1...M k , denotes the phase shift coefficient of the m k th element of the kth active reconfigurable intelligent surface, denotes the amplification coefficient of the m k th element of the kth active RIS, is greater than 1;

[0018] S1.4, assuming the expressions of the received signal y B at the legitimate user and the received signal y E at the eavesdropper are respectively:

[0019]

[0020] where is the base station’s transmit beamforming vector, x∈C 1×1 is the signal transmitted by the base station to the legitimate user and satisfies k∈{1,2} denotes the thermal noise at the kth active reconfigurable intelligent surface, denotes the additive white Gaussian noise at the legitimate user, denotes the additive white Gaussian noise at the eavesdropper;

[0021] S1.5, assuming the expressions of the information rate R B at the legitimate user and the information rate R E at the eavesdropper are respectively:

[0022]

[0023] The expression of the secrecy rate R sec is obtained as:

[0024] R sec = max{0, R B - R E}.

[0025] Optionally, in the S2, the problem formulation set of the optimization objective includes:

[0026]

[0027] where C1 denotes the constraint of the transmit power P BS at the base station, C2 denotes the constraint of the amplification power P RIS1 at the first active reconfigurable intelligent surface, and C3 denotes the constraint of the amplification power P RIS2C4 represents the amplification factor of the first active reconfigurable intelligent surface C5 represents the amplification factor of the first active reconfigurable intelligent surface C4 represents the amplification factor of the first active reconfigurable intelligent surface

[0028] Optionally, in the S3, the optimization step of the base station transmit beamforming vector specifically includes:

[0029] S3.1, given the first preset active reconfigurable intelligent surface phase shift matrix Θ1 and the second preset active reconfigurable intelligent surface phase shift matrix Θ2, define:

[0030]

[0031] S3.2, change the problem P1 in the problem expression set of the optimization target to:

[0032]

[0033] s.t.C1:||w|| 2 ≤P BS

[0034] S3.3, the optimization base station transmit beamforming vector w is obtained by arranging: opt :

[0035]

[0036] In the formula, u max [] represents the normalized vector corresponding to the maximum eigenvalue of the matrix, and I represents the unit matrix of N t ×N t .

[0037] Optionally, in the S3, the optimization step of the first preset active reconfigurable intelligent surface phase shift matrix specifically includes:

[0038] S3.4, given the base station transmit beamforming vector w and the second preset active reconfigurable intelligent surface phase shift matrix Θ2, define:

[0039] W=ww H

[0040]

[0041] The original problem P1 is rewritten as:

[0042]

[0043] The constraints C2 and C4 are equivalently converted into constraints C6 and C7, and the objective functions Z1 and Z2 are respectively expressed as:

[0044]

[0045] where, j∈{B,E};

[0046] S3.5, simplify the above problem, rewrite the problem as:

[0047]

[0048] s.t.C8:Tr(H p1 U1)≤P RIS1

[0049]

[0050] C11:Rank(U1)=1U1≥0

[0051] where,

[0052]

[0053] S3.6, ignore the constant term in Z 11 and Z 21 , the expression of the lower bound approximation of the objective function is:

[0054]

[0055] S3.7, according to the lower bound approximation of the objective function, redefine the problem P4 as P5:

[0056]

[0057] S3.8, Gaussian randomization is adopted to obtain an approximate solution U1 that satisfies the constraint condition, and the optimization of the first preset active reconfigurable intelligent reflecting surface phase shift matrix is obtained:

[0058]

[0059] Optionally, in the S3, the optimization step of the second preset active reconfigurable intelligent reflecting surface phase shift matrix specifically includes:

[0060] S3.9, given the base station transmit beamforming vector w and the phase shift matrix Θ1 of the active RIS1, define:

[0061]

[0062] Rewrite the original problem P1 as:

[0063]

[0064] The constraint C2, C4 is equivalent to constraint C12, C13, and the objective functions Z'1 and Z'2 are respectively represented as:

[0065]

[0066] wherein, j∈{B,E};

[0067] S3.10, the above problem is simplified, and the problem is rewritten as:

[0068]

[0069] s.t.C14:Tr(H p2 U2)≤P RIS2

[0070]

[0071] C17:Rank(U2)=1 U2≥0

[0072] wherein,

[0073]

[0074] S3.11, ignoring the constant terms in Z' 21 and Z' 21 , the expression of the lower bound approximation of the objective function is obtained as:

[0075]

[0076] S3.12, according to the lower bound approximation of the objective function, the problem P4 is redefined as P5:

[0077]

[0078] S3.13, the approximate solution satisfying the constraint condition is obtained by using Gaussian randomization, and the optimization second preset active reconfigurable intelligent reflecting surface phase shift matrix is obtained:

[0079]

[0080] Optionally, the S4 specifically comprises:

[0081] S4.1, initializing noise, path loss parameters, simulated channel information, base stations, distributed reconfigurable intelligent reflecting surfaces, coordinate positions of users, iteration number T, maximum iteration number K and setting convergence precision ε;

[0082] S4.2, solving P1 to obtain the optimal solution of the problem w (T+1)

[0083] 、 and

[0084] S4.3, iteratively calculating the secrecy rate of the secure communication system, when the iteration is stopped, and the secrecy rate obtained at present is taken as the maximum secrecy rate.

[0085] The present application has at least the following beneficial effects: compared with a single active RIS assisted scheme, a distributed passive RIS assisted scheme and a single passive RIS assisted scheme, the algorithm can significantly improve the security performance of the system. BRIEF DESCRIPTION OF DRAWINGS

[0086] Figure 1 A model diagram of an active RIS assisted secure communication system related to an embodiment of the present application;

[0087] Figure 2 A diagram showing the change of the secrecy rate with the base station transmit power related to an embodiment of the present application;

[0088] Figure 3 A diagram showing the change of the secrecy rate with the iteration number related to an embodiment of the present application;

[0089] Figure 4 A diagram showing the change of the secrecy rate with the location of the legitimate user related to an embodiment of the present application;

[0090] Figure 5 A diagram showing the change of the secrecy rate with the number of RIS elements related to an embodiment of the present application;

[0091] Figure 6 A schematic diagram of the architecture of the hardware running environment of a computer system related to an embodiment of the present application;

[0092] Figure 7 A flowchart of a first embodiment of the secrecy rate optimization method based on a distributed active intelligent reflecting surface related to an embodiment of the present application.

[0093] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0094] In order to better understand the above technical solutions, the exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0095] First embodiment

[0096] As Figure 1 shown, a model of active RIS-aided secure communication system is constructed, considering a downlink multiple-input single-output (MISO) communication system model. The system consists of a base station, an active RIS1, an active RIS2, a legitimate user (Bob) and an eavesdropping user (Eve). It is assumed that the base station (BS) consists of a root uniform linear array of antennas, the active RIS1 and the active RIS2 are composed of M1, M2 reflecting elements in a uniform planar array (UPA) respectively, and Bob and Eve are both single-antenna. Considering a more adverse case, the eavesdropping user is located near the legitimate user's position and can overhear the transmission signal of the legitimate user.

[0097] It is assumed that the channel gains from the BS to the active RIS1 and from the BS to the active RIS2 are denoted as The channel gains from the active RIS1 and the active RIS2 to Bob and Eve are denoted as The reflection coefficient matrix of the active RIS is modeled as m k ∈1...M k , k ∈ {1, 2}, where denotes the phase shift coefficient of the m k th element of the kth active RIS, denotes the amplification coefficient of the m k th element of the kth active RIS, which can be greater than 1. In addition, due to the characteristics of the active RIS, its thermal noise cannot be ignored. Based on the above assumptions, the received signals at the legitimate user and the eavesdropping user can be expressed as

[0098]

[0099] where is the BS transmit beamforming vector, x ∈ C 1×1 is the signal transmitted by the BS to Bob and satisfies k ∈ {1, 2}, denotes the thermal noise of the kth active RIS, and denote the additive white Gaussian noise at Bob and Eve, respectively.

[0100] The formula of the limit transmission rate of the channel is given by the Shannon theorem, and the information rates at Bob and Eve are expressed as

[0101]

[0102] Therefore, the secrecy rate of the system can be expressed as

[0103] R sec = max{0, R B -R E}.

[0104] The application effect of the application is described in detail below in combination with simulation.

[0105] (1) Simulation conditions

[0106] In the simulation system scenario, the base station is located at (0m, 20m), the active RIS1 is located at (85m, 10m), the active RIS2 is located at (85m, 30m), the legitimate user is located at (90m, 20m), and the eavesdropping user is located at (100m, 20m). The number of base station antennas N = 5, the legitimate user is a single antenna, and the number of elements of the active RIS M1 = M2 = 10. The noise power is set to Convergence accuracy ε = 10 -3 .

[0107] All channels involved in the system adopt a Rician fading channel model

[0108]

[0109] wherein ρ0 = -30dB represents a path loss factor at a reference distance d0 = 1m, d H represents the distance from the base station to the active RIS and from the active RIS to the user, α H represents the path loss exponent corresponding to the environment, wherein α H1 = α H2 = 2.8, α I1B = α I2B = 2.6, α I1E = α I2E = 2.6, and P RIS = 10 represents a Rician factor. H Los represents the line-of-sight (LOS) part of the communication link, which is set to a unit matrix for the sake of simplifying the experiment. H NLOS represents the non-line-of-sight (NLOS) part, which is modeled as Rayleigh fading. In addition, in order to compare the active RIS with the passive RIS in the greatest fairness, the total system power P total = P BS + P RIS is set, and the active RIS is compared with the passive RIS under the condition of the total system power.

[0110] (2) Simulation results

[0111] Figure 2The graph shows the relationship between the average security rate and the total system power under different schemes. As the total system power increases, the average security rate under all schemes shows an upward trend. The graph shows that the average security rate under the scheme presented in this paper is generally higher than the average security rate under the passive RIS-assisted scheme, indicating that the active RIS effectively overcomes the "double fading" effect. When P... total At 25dBm, the dual active RIS-assisted scheme achieves an improvement of approximately 32% compared to the single active RIS-assisted scheme and 86% compared to the dual passive RIS-assisted scheme. This demonstrates that the dual active RIS-assisted scheme has a greater advantage in enhancing signal transmission and secure communication compared to other schemes. Furthermore, as shown in the figure, increasing the amplification factor of the active RIS in this scheme can achieve even better security performance.

[0112] Figure 3 When the total power P of the system total =20dBm, iterative convergence graphs of the AO algorithm under different schemes. Overall, the dual-active RIS scheme outperforms other schemes in average security rate after iterations. It is worth noting that since the RIS phase used in this paper is randomly initialized, the initial security rate in the first iteration is usually random, and multiple iterations are required to converge to the target optimal solution. Furthermore, the dual-active RIS-assisted scheme converges more slowly than other schemes. The distributed dual-active RIS-assisted scheme proposed in this paper gradually converges within 5 iterations as the number of iterations increases, while other schemes generally achieve numerical convergence within 3 iterations. This is because the dual-active RIS introduces more parameters and variables, making the optimization process more complex. Additionally, the nested MM algorithm within the AO algorithm requires multiple iterations to converge, resulting in a slower convergence speed.

[0113] Figure 4 In the total power P of the system total=20dBm. The graph shows the average security rate trend of each scheme by changing the positions of the legitimate user and the eavesdropping user. When the legitimate user's position is (x, 0), the eavesdropping user's position is (x+10, 0). Specifically, as the distance between the legitimate user and the base station gradually increases from 50m to 90m, the average security rate increases with distance. This is because when the legitimate user approaches the RIS (Radio Router Assist), the RIS can effectively enhance the legitimate user's information transmission rate through reflected beamforming. As can be seen from the graph, the proposed distributed active RIS-assisted scheme shows better security performance than the scheme with only active RIS assistance and the passive RIS-assisted scheme. When the legitimate user is 90m from the base station, the dual active RIS-assisted scheme improves the security rate by approximately 33% compared to the single active RIS-assisted scheme and by 93% compared to the dual passive RIS-assisted scheme. Therefore, the distributed dual active RIS-assisted scheme can significantly improve the system's security rate in long-distance communication, exhibiting superior performance compared to the single active RIS and dual passive RIS-assisted schemes.

[0114] Figure 5 The total power P of the system is given. total =20dBm, the trend of average security rate as a function of the total number of active RIS reflective elements. The total number of reflective elements refers to the sum of the number of reflective elements in two active RISs, i.e., M1 + M2; while when only a single active RIS is used, the total number of elements is the number of elements in that single active RIS. The simulation results show that the average security rate gradually increases with the increase of the number of reflective elements. This is because the more reflective elements there are, the more concentrated the beam becomes, and the stronger the reflected signal from the RIS can be received by the user, which enhances the signal-to-noise ratio of legitimate users and thus improves communication security. The figure shows that the overall average security rate under dual active RIS assistance is significantly higher than that under a single RIS assistance. When the total number of active RIS reflective elements is 24, with dual active RIS assistance and the active RIS amplification factor... In the case of dual active RIS assistance and active RIS amplification factor, its average security rate is improved by 47% compared to the average security rate of a single active RIS-assisted system; In this case, its average security rate is 36% higher than that of a single active RIS-assisted system. This demonstrates that, with the same total number of reflective elements, the distributed dual active RIS system can perform signal control and optimization more flexibly than a single active RIS system, significantly improving signal enhancement for legitimate users and interference capabilities against eavesdroppers, thereby better enhancing system security performance.

[0115] Second Embodiment

[0116] As one implementation scheme,Figure 6 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0117] like Figure 6 As shown, the computer system may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0118] Those skilled in the art will understand that Figure 6 The computer system architecture shown does not constitute a limitation on the computer system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0119] like Figure 6 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a security rate optimization program based on a distributed active intelligent reflective surface. The operating system is a program that manages and controls the hardware and software resources of the computer system, and the security rate optimization program based on the distributed active intelligent reflective surface, along with other software or programs, is responsible for their operation.

[0120] exist Figure 6 In the computer system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; and the processor 1001 can be used to call the security rate optimization program based on the distributed active intelligent reflective surface stored in the memory 1005.

[0121] In this embodiment, the computer system includes: a memory 1005, a processor 1001, and a security rate optimization program based on a distributed active smart reflective surface stored in the memory and executable on the processor, wherein:

[0122] When processor 1001 calls the security rate optimization program based on distributed active smart reflective surface stored in memory 1005, it performs the following operations:

[0123] S1, constructing a secure communication system model comprising a distributed active reconfigurable intelligent reflecting surface assisted architecture, the secure communication system model comprising a first preset active reconfigurable intelligent reflecting surface phase shift matrix, a second preset active reconfigurable intelligent reflecting surface phase shift matrix, and a base station transmit beamforming vector;

[0124] S2, based on the secure communication system model, taking the base station beamforming vector and the phase shift matrix of the distributed active RIS as an optimization problem, and taking maximizing the secrecy rate of the legitimate user as the target, constructing a set of problem expressions of the optimization target;

[0125] S3, taking the set of problem expressions of the optimization target as a constraint, selecting any two of the first preset active reconfigurable intelligent reflecting surface phase shift matrix, the second preset active reconfigurable intelligent reflecting surface phase shift matrix, and the base station transmit beamforming vector, and optimizing the other function, to obtain an optimized base station transmit beamforming vector, an optimized first preset active reconfigurable intelligent reflecting surface phase shift matrix, and an optimized second preset active reconfigurable intelligent reflecting surface phase shift matrix, respectively;

[0126] S4, jointly determining the maximum secrecy rate of the secure communication system based on the optimized base station transmit beamforming vector, the optimized first preset active reconfigurable intelligent reflecting surface phase shift matrix, and the optimized second preset active reconfigurable intelligent reflecting surface phase shift matrix.

[0127] When the processor 1001 invokes the secrecy rate optimization program based on the distributed active intelligent reflecting surface stored in the memory 1005, the following operations are performed:

[0128] S1.1, assuming that the secure communication system model comprises a base station, a first active reconfigurable intelligent reflecting surface, a second active reconfigurable intelligent reflecting surface, a legitimate user, and an eavesdropping user, wherein the base station is composed of M1, M2 reflecting elements arranged in a uniform plane, and the legitimate user and the eavesdropping user are both single-antenna;

[0129] S1.2, assuming that the channel gain from the base station to the first active reconfigurable intelligent reflecting surface is H1∈C M1×Nt , the channel gain from the base station to the second active reconfigurable intelligent reflecting surface is H2∈C M2×Nt , the channel gain from the first active reconfigurable intelligent reflecting surface to the legitimate user and the eavesdropping user is , and the channel gain from the second active reconfigurable intelligent reflecting surface to the legitimate user and the eavesdropping user is

[0130] S1.3, assuming the expression of the phase shift matrix of the first active reconfigurable intelligent surface and the second active reconfigurable intelligent surface is:

[0131]

[0132] wherein, m k ∈1...M k , denotes the phase shift coefficient of the m k th element of the kth active reconfigurable intelligent surface, denotes the amplification coefficient of the m k th element of the kth active RIS, is greater than 1;

[0133] S1.4, assuming the expression of the received signal y B of the legitimate user and the received signal y E of the eavesdropper are respectively:

[0134]

[0135] wherein, is the transmit beamforming vector at the base station, x∈C 1×1 is the signal transmitted by the base station to the legitimate user and satisfies k∈{1,2} denotes the thermal noise of the kth active reconfigurable intelligent surface, denotes the additive white Gaussian noise at the legitimate user, denotes the additive white Gaussian noise at the eavesdropper;

[0136] S1.5, assuming the expression of the information rate R B at the legitimate user and the information rate R E at the eavesdropper are respectively:

[0137]

[0138] The expression of the secrecy rate R sec is obtained as:

[0139] R sec = max{0, R B - R E}.

[0140] When the processor 1001 invokes the secrecy rate optimization program based on the distributed active intelligent surface stored in the memory 1005, the following operations are performed:

[0141] S3.1, given the first preset active reconfigurable intelligent surface phase shift matrix Θ1 and the second preset active reconfigurable intelligent surface phase shift matrix Θ2, define:

[0142]

[0143] S3.2, change the problem P1 in the optimization target problem expression set to:

[0144]

[0145] S3.3, obtain the optimization base station transmit beamforming vector w opt :

[0146]

[0147] wherein u max [] represents the normalized vector corresponding to the maximum eigenvalue of the matrix, and I represents an N t ×N t unit matrix. When the processor 1001 invokes the distributed active intelligent reflector based secrecy rate optimization program stored in the memory 1005, the following operations are performed:

[0148] When the processor 1001 invokes the distributed active intelligent reflector based secrecy rate optimization program stored in the memory 1005, the following operations are performed:

[0149] S3.4, given the base station transmit beamforming vector w and the second preset active reconfigurable intelligent reflector phase shift matrix Θ2, define:

[0150] W=ww H

[0151]

[0152] Rewrite the original problem P1 as:

[0153]

[0154] Convert the constraints C2 and C4 equivalently to the constraints C6 and C7, and the objective functions Z1 and Z2 are respectively represented as:

[0155]

[0156] wherein, j∈{B,E};

[0157] S3.5, simplify the above problem, and rewrite the problem as:

[0158]

[0159] s.t.C8:Tr(H p1 U1)≤PRIS1

[0160]

[0161] C11:Rank(U1)=1U1≥0

[0162] in,

[0163]

[0164]

[0165] S3.6, Ignore Z 11 and Z 21 The constant term in the expression yields the following approximate expression for the lower bound of the objective function:

[0166]

[0167]

[0168] S3.7, Based on the lower bound approximation of the objective function, redefine problem P4 as P5:

[0169]

[0170] S3.8, Gaussian randomization is used to obtain an approximate solution U1 that satisfies the constraints, and the resulting solution is rearranged to obtain the optimized first preset active reconfigurable smart reflector phase shift matrix:

[0171]

[0172] When processor 1001 calls the security rate optimization program based on distributed active smart reflective surface stored in memory 1005, it performs the following operations:

[0173] S3.9, given the base station transmit beamforming vector w and the phase shift matrix Θ1 of the active RIS1, define:

[0174]

[0175] The original problem P1 is rewritten as:

[0176]

[0177] Constraints C2 and C4 are equivalently transformed into constraints C12 and C13, and the objective functions Z'1 and Z'2 are expressed as follows:

[0178]

[0179]

[0180] in, j∈{B,E};

[0181] S3.10, Simplify the above problem and rewrite it as follows:

[0182]

[0183] stC14:Tr(H p2 U2)≤P RIS2

[0184]

[0185] C17: Rank(U2) = 1 U2 ≥ 0

[0186] in,

[0187]

[0188] S3.11, Ignore Z' 21 and Z' 21 The constant term in the expression yields the following approximate expression for the lower bound of the objective function:

[0189]

[0190] S3.12, Based on the lower bound approximation of the objective function, redefine problem P4 as P5:

[0191]

[0192] S3.13, Gaussian randomization is used to obtain an approximate solution that satisfies the constraints, and the solution is rearranged to obtain the optimized second preset active reconfigurable smart reflector phase shift matrix:

[0193]

[0194] When processor 1001 calls the security rate optimization program based on distributed active smart reflective surface stored in memory 1005, it performs the following operations:

[0195] S4.1 Initialize noise, path loss parameters, simulated channel information, base station, distributed reconfigurable smart reflector, user coordinates, iteration count T, maximum iteration count K, and set convergence accuracy ε;

[0196] S4.2, Solve P1 to obtain the optimal solution to the problem. w (T+1) , and

[0197] S4.3, Iteratively calculate the confidentiality rate of the secure communication system, when When the iteration is stopped, the current obtained secrecy rate is taken as the maximum secrecy rate.

[0198] In addition, those skilled in the art can understand that all or part of the processes in the method for implementing the above embodiments can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in a computer system to implement the process steps of the above-mentioned embodiments of the method.

[0199] Therefore, the present application also provides a computer readable storage medium, which stores a distributed active intelligent reflecting surface based secrecy rate optimization program. When the distributed active intelligent reflecting surface based secrecy rate optimization program is executed by a processor, each step of the distributed active intelligent reflecting surface based secrecy rate optimization method according to the above embodiments is implemented.

[0200] The distributed active intelligent reflecting surface based secrecy rate optimization method includes the following steps: Figure 7 The distributed active intelligent reflecting surface based secrecy rate optimization method includes the following steps:

[0201] S1, a secure communication system model including a distributed active reconfigurable intelligent reflecting surface auxiliary architecture is constructed, and the secure communication system model includes a first preset active reconfigurable intelligent reflecting surface phase shift matrix, a second preset active reconfigurable intelligent reflecting surface phase shift matrix, and a base station transmit beamforming vector;

[0202] S2, based on the secure communication system model, taking the base station beamforming vector and the phase shift matrix of the distributed active RIS as an optimization problem, and taking the maximum secrecy rate of the legitimate user as the target, a set of problem expressions of the optimization target is constructed;

[0203] S3, taking the set of problem expressions of the optimization target as a constraint, selecting any two of the first preset active reconfigurable intelligent reflecting surface phase shift matrix, the second preset active reconfigurable intelligent reflecting surface phase shift matrix, and the base station transmit beamforming vector, and optimizing the other function, to obtain an optimized base station transmit beamforming vector, an optimized first preset active reconfigurable intelligent reflecting surface phase shift matrix, and an optimized second preset active reconfigurable intelligent reflecting surface phase shift matrix, respectively.

[0204] S4, the optimized base station transmit beamforming vector, the optimized first preset active reconfigurable intelligent reflecting surface phase shift matrix, and the optimized second preset active reconfigurable intelligent reflecting surface phase shift matrix are combined to determine the maximum secrecy rate of the secure communication system.

[0205] The computer readable storage medium can be a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk, and the like.

[0206] It should be noted that the storage medium provided by the embodiments of the present application is a storage medium used to implement the method of the embodiments of the present application. Therefore, based on the method introduced in the embodiments of the present application, the specific structure and modification of the storage medium can be understood by those skilled in the art, and therefore will not be described here. Any storage medium used by the method of the embodiments of the present application belongs to the scope of protection of the present application.

[0207] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0208] The present application is described with reference to flowcharts and / or block diagrams according to the method, device (system) and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0209] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0210] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a means for implementing the functions specified in the flowcharts and / or block diagrams.Figure 1 one or more processes and / or functions specified in one or more blocks. Figure 1 one or more blocks.

[0211] It is noted that in the claims the word "comprising" does not exclude not having other parts than those specified in the claim. The word "a" or "an" preceding the citation of a generic term does not exclude a plurality of those generic terms. This application can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unitary claim, the features of the different embodiments can be combined with each other if this is explicitly stated in the claims. The use of the word "a" or "an" preceding the citation of a generic term is not to be construed in the exclusion of a plurality of those generic terms. The use of the term "about" followed by a value and / or term is intended to describe a quantity, dimension, or other measure that can vary between about +10% and about -10% of the recited value and / or term. The use of the term "about" followed by a value and / or term is intended to describe a quantity, dimension, or other measure that can vary between about +10% and about -10% of the recited value and / or term.

[0212] Although the preferred embodiments of the application have been described, those skilled in the art will recognize that many modifications and variations of the preferred embodiments could be made without departing from the spirit and scope of the application. It is therefore intended that the appended claims cover all such modifications and variations as fall within the true scope of the application.

[0213] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1.A method for secrecy rate optimization based on distributed active intelligent reflecting surface, characterized in that, The method comprises the following steps: S1, constructing a secure communication system model comprising a distributed active reconfigurable intelligent reflecting surface auxiliary architecture, the secure communication system model comprising a first preset active reconfigurable intelligent reflecting surface phase shift matrix, a second preset active reconfigurable intelligent reflecting surface phase shift matrix, and a base station transmit beamforming vector; S2, based on the secure communication system model, taking the base station beamforming vector and the phase shift matrix of the distributed active RIS as an optimization problem, and taking the maximum secrecy rate of the legitimate user as the target, constructing a problem expression set of the optimization target; S3, taking the problem expression set of the optimization target as a constraint, selecting any two of the first preset active reconfigurable intelligent reflecting surface phase shift matrix, the second preset active reconfigurable intelligent reflecting surface phase shift matrix, and the base station transmit beamforming vector, and optimizing the other function, to obtain an optimized base station transmit beamforming vector, an optimized first preset active reconfigurable intelligent reflecting surface phase shift matrix, and an optimized second preset active reconfigurable intelligent reflecting surface phase shift matrix, respectively; S4, jointly determining the maximum secrecy rate of the secure communication system by using the optimized base station transmit beamforming vector, the optimized first preset active reconfigurable intelligent reflecting surface phase shift matrix, and the optimized second preset active reconfigurable intelligent reflecting surface phase shift matrix; In the S1, the construction process of the secure communication system model comprises: S1.1, assuming that the secure communication system model is composed of a base station, a first active reconfigurable intelligent reflecting surface, a second active reconfigurable intelligent reflecting surface, a legitimate user and an eavesdropping user, wherein the base station is composed of , a plurality of reflecting units are uniformly arranged in a plane to form a reflecting surface, and the legitimate user and the eavesdropping user are both single-antenna users. S1.2, set the channel gain from the base station to the first active reconfigurable intelligent surface as , the channel gain from the base station to the second active reconfigurable intelligent surface as , the channel gains from the first active reconfigurable intelligent surface to the legitimate user and the eavesdropping user respectively as 、 , the channel gains from the second active reconfigurable intelligent surface to the legitimate user and the eavesdropping user respectively as 、 ; S1.3, assuming that the expressions of the phase shift matrices of the first active reconfigurable intelligent reflecting surface and the second active reconfigurable intelligent reflecting surface are: ； wherein , , denotes the phase shift coefficient of the element of the active reconfigurable intelligent surface, denotes the amplification coefficient of the element of the active RIS, is greater than 1; S1.4, let the expression of the received signal of the legitimate user and the received signal of the eavesdropping user be respectively ； ； wherein is a base station transmit beamforming vector, is a signal transmitted by the base station to a legitimate user and satisfies , , represents thermal noise at the th active reconfigurable intelligent surface, represents additive white Gaussian noise at the legitimate user, represents additive white Gaussian noise at the eavesdropping user; S1.5, the information rate at the legitimate user and the information rate at the eavesdropper are given by the expressions: ； ； The secret rate is obtained by arranging The expression is: 。 2. The method of claim 1, wherein, In the S2, the problem expression set of the optimization target comprises: ； C1 represents a constraint on the transmission power at the base station C2 represents a constraint on the amplification power of the first active reconfigurable intelligent surface C3 represents a constraint on the amplification power of the second active reconfigurable intelligent surface C4 represents a constraint on the amplification coefficient of the first active reconfigurable intelligent surface C5 represents a constraint on the amplification coefficient of the first active reconfigurable intelligent surface C6 represents a constraint on the amplification coefficient of the second active reconfigurable intelligent surface 3. The method of claim 2, wherein, The S4 specifically comprises: S4.1, initializing noise, path loss parameters, simulated channel information, coordinates of base stations, distributed reconfigurable intelligent reflecting surfaces, users, number of iterations , maximum number of iterations and setting the convergence accuracy ; S4.2, solve P1 to get the optimal solution of the problem , , and ; S4.3, iteratively calculate the secrecy rate of the secure communication system, when the iteration is stopped, and the current obtained secrecy rate is taken as the maximum secrecy rate.

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