Secrecy rate optimization method based on distributed active intelligent reflecting surface

Abstract

The invention relates to the technical field of communication, in particular to a secrecy rate optimization method based on a distributed active intelligent reflecting surface. The method comprises the following steps: constructing a secure communication system model comprising a distributed active reconfigurable intelligent reflecting surface auxiliary framework, based on the secure communication system model, taking a base station beam forming vector and a phase shift matrix of a distributed active RIS as optimization problems, and taking maximization of a secrecy rate of a legal user as a target, constructing a problem expression set of an optimization target, and establishing a problem expression set of the optimization target; taking the problem expression set of the optimization target as a constraint, selecting any two of three functions, namely a first preset active reconfigurable intelligent reflecting surface shift matrix, a second preset active reconfigurable intelligent reflecting surface shift matrix and a base station transmitting beam forming vector, and optimizing the other function, and determining the maximum secrecy rate of the secure communication system by combining the three optimized functions. The objective of the invention is to solve the problem of how to optimize the secrecy rate of a communication system through a distributed active intelligent reflecting surface.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method for optimizing a confidentiality rate based on a distributed active intelligent reflection surface. Background Art

[0002] As one of the key technologies of 6G, Reconfigurable Intelligent Surface (RIS) integrates a large number of low-cost passive reflective elements on a plane and adaptively adjusts the phase offset of its reflective unit, thereby significantly improving the performance of wireless communication networks. Since RIS can improve the signal quality of legitimate users while minimizing the signal quality of eavesdropping users, it has outstanding capabilities in enhancing PLS, so the combination of these two technologies has received extensive attention and research.

[0003] However, due to the "double fading" effect, the path loss of the RIS reflection link is proportional to the square of the product of the path losses from the base station to the RIS and from the RIS to the user, which limits the gain that the RIS can provide. In order to overcome the "double fading" effect, some scholars have proposed active RIS. Different from the traditional RIS, which only reflects signals through adjustable phase shift, each reflection unit of the active RIS is assisted by an active negative impedance load, which can not only reflect the incident signal, but also amplify the signal; in addition, active RIS is superior to active relay in hardware overhead and performance, requiring only simple power amplifiers and diodes, while active relays require complex equipment and have to deal with self-interference problems.

[0004] At present, most of the research on secure communications is based on the centralized RIS deployment model, which does not fully utilize the advantages of distributed RIS in multipath enhancement and security performance improvement.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of this application is to provide a method for optimizing the confidentiality rate based on a distributed active intelligent reflection surface, aiming to solve the problem of how to optimize the confidentiality rate of a communication system through a distributed active intelligent reflection surface.

[0007] To achieve the above-mentioned purpose, the present application provides a method for optimizing the confidentiality rate based on a distributed active intelligent reflective surface, the method comprising:

[0008] S1, constructing a secure communication system model including a distributed active reconfigurable intelligent reflector auxiliary architecture, wherein the secure communication system model includes a first preset active reconfigurable intelligent reflector phase shift matrix, a second preset active reconfigurable intelligent reflector 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 the optimization problem, taking maximizing the confidentiality rate of the legitimate user as the goal, constructing a problem statement set of the optimization goal;

[0010] S3, taking the problem statement set of the optimization target as a constraint, selecting any two of the three functions of the first preset active reconfigurable intelligent reflection surface phase shift matrix, the second preset active reconfigurable intelligent reflection surface phase shift matrix and the base station transmit beamforming vector, optimizing another function, and obtaining the optimized base station transmit beamforming vector, the optimized first preset active reconfigurable intelligent reflection surface phase shift matrix and the optimized second preset active reconfigurable intelligent reflection surface phase shift matrix respectively;

[0011] S4, jointly optimizing the base station transmit beamforming vector, optimizing the first preset active reconfigurable intelligent reflection surface phase shift matrix, and optimizing the second preset active reconfigurable intelligent reflection surface phase shift matrix to determine the maximum confidentiality rate of the secure communication system.

[0012] Optionally, in S1, the process of constructing the secure communication system model includes:

[0013] S1.1, assuming that the secure communication system model consists 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 obtained by evenly arranging and combining M1 and M2 reflecting units in a plane, and the legitimate user and the eavesdropping user are both single antennas;

[0014] S1.2, assuming that the channel gain from the base station to the first active reconfigurable smart reflector is The channel gain from the base station to the second active reconfigurable smart reflector is The channel gains of the first active reconfigurable intelligent reflective surface to the legitimate user and the eavesdropping user are respectively The channel gains of the second active reconfigurable intelligent reflective surface to the legitimate user and the eavesdropping user are respectively

[0015] S1.3, assuming that the phase shift matrix of the first active reconfigurable smart reflective surface and the second active reconfigurable smart reflective surface is expressed as:

[0016]

[0017] In the formula, m k ∈1...M k , represents the mth active reconfigurable smart reflector k The phase shift coefficient of each element is represents the mth active RIS of the kth active RIS k The amplification factor of each element, Greater than 1;

[0018] S1.4, let the received signal y of the legal user B and the received signal y of the eavesdropping user E The expressions are:

[0019]

[0020] In the formula, is the transmit beamforming vector at the base station, x∈C 1×1 The signal transmitted by the base station to the legitimate user and meets k∈{1,2}, represents the thermal noise of the kth active reconfigurable smart reflector, represents the additive Gaussian white noise at the legitimate user, represents the additive white Gaussian noise at the eavesdropping user;

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

[0022]

[0023] The confidentiality rate R is obtained by sorting sec The expression is:

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

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

[0026]

[0027] Where C1 represents the transmission power P at the base station BS Constraints, C2 represents the amplified power P of the first active reconfigurable smart reflector RIS1 Constraints, C3 represents the amplified power P of the second active reconfigurable smart reflector RIS2Constraints, C4 represents the magnification factor of the first active reconfigurable smart reflector Constraints, C5 represents the magnification factor of the first active reconfigurable smart reflector constraints.

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

[0029] S3.1, given a first preset active reconfigurable smart reflector phase shift matrix Θ1, and a second preset active reconfigurable smart reflector phase shift matrix Θ2, define:

[0030]

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

[0032]

[0033] stC1:||w|| 2 ≤P BS

[0034] S3.3, sort out and obtain the optimized base station transmit beamforming vector w opt :

[0035]

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

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

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

[0039] W=ww H

[0040]

[0041] Rewrite the original problem P1 as:

[0042]

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

[0044]

[0045] in, j∈{B,E};

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

[0047]

[0048] stC8:Tr(H p1 U1)≤P RIS1

[0049]

[0050] C11:Rank(U1)=1U1≥0

[0051] in,

[0052]

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

[0054]

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

[0056]

[0057] S3.8, using Gaussian randomization to obtain an approximate solution U1 that satisfies the constraint conditions, and sorting out the optimized first preset active reconfigurable smart reflector phase shift matrix:

[0058]

[0059] Optionally, in S3, the step of optimizing the second preset active reconfigurable smart reflection 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 constraints C2 and C4 are equivalently converted to constraints C12 and C13, and the objective functions Z'1 and Z'2 are expressed as:

[0065]

[0066] in, j∈{B,E};

[0067] S3.10, simplify the above problem and rewrite it as:

[0068]

[0069] stC14:Tr(H p2 U2)≤P RIS2

[0070]

[0071] C17:Rank(U2)=1 U2≥0

[0072] in,

[0073]

[0074] S3.11, ignore Z' 21 and Z' 21 The constant term in , the approximate expression of the lower bound of the objective function is:

[0075]

[0076] S3.12, based on the lower bound approximation of the objective function, redefine problem P4 as P5:

[0077]

[0078] S3.13, using Gaussian randomization to obtain an approximate solution that satisfies the constraint conditions, and sorting out the optimized second preset active reconfigurable smart reflector phase shift matrix:

[0079]

[0080] Optionally, the S4 specifically includes:

[0081] S4.1, initialize noise, path loss parameters, simulated channel information, base station, distributed reconfigurable intelligent reflector, user coordinate position, iteration number T, maximum iteration number K and set convergence accuracy ε;

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

[0084] S4.3, iteratively calculate the confidentiality rate of the secure communication system, when When , the iteration is stopped and the currently obtained confidentiality rate is used as the maximum confidentiality rate.

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

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

[0087] Figure 2 This is a diagram showing how the confidentiality rate varies with the base station transmission power involved in an embodiment of the present application;

[0088] Figure 3 A diagram showing how the confidentiality rate varies with the number of iterations involved in an embodiment of the present application;

[0089] Figure 4 A diagram showing how the confidentiality rate varies with the location of a legitimate user involved in an embodiment of the present application;

[0090] Figure 5 A diagram showing how the confidentiality rate varies with the number of RIS elements involved in an embodiment of the present application;

[0091] Figure 6 A schematic diagram of the architecture of the hardware operating environment of the computer system involved in the embodiment of the present application;

[0092] Figure 7 This is a flow chart of the first embodiment of the confidentiality rate optimization method based on distributed active intelligent reflective surface involved in the embodiments of the present application.

[0093] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0094] In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying 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] like Figure 1 As shown in the figure, a secure communication system model assisted by active RIS is constructed, and a downlink multiple-input single-output (MISO) communication system model is considered. The system consists of a base station, active RIS1, active RIS2, a legitimate user (Bob) and an eavesdropping user (Eve). Assume that the base station (BS) is composed of antennas arranged uniformly linearly, active RIS1 and active RIS2 are respectively composed of M1 and M2 reflection units arranged uniformly in a planar manner (UPA), and Bob and Eve are both single antennas. Considering a worse case, the eavesdropping user is located near the legitimate user and can eavesdrop on the legitimate user's transmission signal.

[0097] Assume that the channel gains from BS to active RIS1 and from BS to active RIS2 are expressed as The channel gains from active RIS1 and active RIS2 to Bob and Eve are expressed as The reflection coefficient matrix modeling of active RIS is expressed as m k ∈1...M k , k∈{1,2}, where represents the mth active RIS of the kth active RIS k The phase shift coefficient of each element is represents the mth active RIS of the kth active RIS k The amplification factor of each element, can be greater than 1. In addition, due to the characteristics of 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] in, is the transmit beamforming vector at the BS, x∈C 1×1 is the signal transmitted by BS to Bob and satisfies k∈{1,2}, represents the thermal noise of the kth active RIS, and denote the additive white Gaussian noise at Bob and Eve respectively.

[0100] Shannon's theorem gives the formula for the channel's limiting transmission rate. The information rate at Bob and Eve is expressed as

[0101]

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

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

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

[0105] (1) Simulation conditions

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

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

[0108]

[0109] Where ρ0 = -30 dB represents the path loss factor at the reference distance d0 = 1 m, 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 index of the corresponding environment, where α H1 =α H2 =2.8,α I1B =α I2B =2.6,α I1E =α I2E =2.6, P RIS =10 represents the Rice factor. Los H represents the line-of-sight (LOS) part of the communication link, and is set to the unit matrix to simplify the experiment. NLOS represents the non-line-of-sight (NLOS) part, which is modeled as Rayleigh fading. In addition, in order to compare with passive RIS to the greatest extent possible, the total system power P is set total =P BS +P RIS , passive RIS is compared with active RIS under the condition of total system power.

[0110] (2) Simulation results

[0111] Figure 2The relationship between the average confidentiality rate and the total system power under different schemes is shown in the figure. As the total system power increases, the average confidentiality rate under different schemes shows an upward trend. It can be seen from the figure that the average confidentiality rate under the scheme of this paper is generally higher than the average confidentiality rate under the passive RIS assistance. It can be seen that the active RIS has overcome the "double fading" effect very well. When P total =25dBm, the dual active RIS assisted solution is improved by about 32% compared with the single active RIS assisted solution, and by 86% compared with the dual passive RIS assisted solution, which shows that the dual active RIS assisted solution has greater advantages than other solutions in enhancing signal transmission and secure communication. In addition, it can be seen from the figure that better confidentiality performance can be obtained by increasing the amplification factor of the active RIS in the solution of this paper.

[0112] Figure 3 When the total system power P total =20dBm, iterative convergence diagram of AO algorithm under different schemes. Overall, the average confidentiality rate of the dual active RIS scheme after iteration is better than that of other schemes. It is worth noting that since the RIS phase used in this paper is randomly initialized, the initial confidentiality rate in the first iteration is usually random, and it takes multiple iterations to converge to the target optimal solution. In addition, the scheme assisted by dual active RIS converges slower than other schemes. The distributed dual active RIS assisted scheme proposed in this paper can gradually converge with the increase of the number of iterations within 5 iterations, while other schemes can basically reach numerical convergence within 3 times. The reason is that dual active RIS introduces more parameters and variables, making the optimization process more complicated. In addition, since the MM algorithm nested in the AO algorithm requires multiple iterations to converge, the convergence speed will be slower.

[0113] Figure 4 is the total system power P total=20dBm, by changing the positions of the legitimate user and the eavesdropping user, the average confidentiality rate variation trend diagram of each scheme is obtained. When the legitimate user position is (x, 0), the eavesdropping user position is (x+10, 0). In particular, when the distance between the legitimate user and the base station gradually increases from 50m to 90m, the average confidentiality rate increases with the increase of distance. This is because when the legitimate user approaches the RIS, the RIS can effectively enhance the information transmission rate of the legitimate user through reflection beamforming. It can be seen from the figure that the proposed distributed active RIS-assisted scheme shows better confidentiality performance than the only active RIS-assisted scheme and the passive RIS-assisted scheme. When the legitimate user is 90m away from the base station, the dual active RIS-assisted scheme is improved by about 33% compared with the single active RIS-assisted scheme and 93% compared with the dual passive RIS-assisted scheme. It can be seen that the distributed dual active RIS-assisted scheme can significantly improve the confidentiality rate of the system in long-distance communication, and has better performance than the single active RIS and dual passive RIS-assisted schemes.

[0114] Figure 5 Given the total system power P total =20dBm, the trend graph of the average confidentiality rate changing with the total number of active RIS reflective elements. The total number of reflective elements refers to the sum of the number of reflective elements of the two active RIS, that is, M1+M2; and when only a single active RIS is assisted, the total number of elements is the number of elements of a single active RIS. It can be seen from the simulation results that with the increase in the number of reflective surface elements, the average confidentiality rate will gradually increase. This is because the more reflective surface elements there are, the more concentrated the beam is, and the user can receive a stronger reflected signal from the RIS, which enhances the signal-to-noise ratio of legitimate users and thus improves the security of communications. It can be seen from the figure that the overall average confidentiality rate under the assistance of dual active RIS is significantly higher than the overall average confidentiality rate under the assistance of only a single RIS. When the total number of active RIS reflective elements is 24, with the assistance of dual active RIS and the active RIS amplification factor is In the case of dual active RIS assistance and active RIS magnification, the average confidentiality rate is increased by 47% compared with the average confidentiality rate of single active RIS assistance; In the case of , its average confidentiality rate is 36% higher than that of the single active RIS. It can be seen that under the same total number of reflective elements, the distributed dual active RIS can perform signal control and optimization more flexibly than the single active RIS, significantly improving the signal enhancement effect for legitimate users and the interference ability for eavesdroppers, thereby better improving the confidentiality performance of the system.

[0115] Second embodiment

[0116] As an implementation plan, Figure 6 This is a schematic diagram of the architecture of the hardware operating environment of the computer system involved in the embodiment of the present 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. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. 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 in the figure does not constitute a limitation of the computer system, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0119] like Figure 6 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a confidentiality rate optimization program based on a distributed active intelligent reflective surface. The operating system is a program for managing and controlling the hardware and software resources of a computer system, and the confidentiality rate optimization program based on a distributed active intelligent reflective surface and other software or programs are executed.

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

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

[0122] When the processor 1001 calls the confidentiality rate optimization program based on the distributed active intelligent reflective surface stored in the memory 1005, the following operations are performed:

[0123] S1, constructing a secure communication system model including a distributed active reconfigurable intelligent reflector auxiliary architecture, wherein the secure communication system model includes a first preset active reconfigurable intelligent reflector phase shift matrix, a second preset active reconfigurable intelligent reflector 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 the optimization problem, taking maximizing the confidentiality rate of the legitimate user as the goal, constructing a problem statement set of the optimization goal;

[0125] S3, taking the problem statement set of the optimization target as a constraint, selecting any two of the three functions of the first preset active reconfigurable intelligent reflection surface phase shift matrix, the second preset active reconfigurable intelligent reflection surface phase shift matrix and the base station transmit beamforming vector, optimizing another function, and obtaining the optimized base station transmit beamforming vector, the optimized first preset active reconfigurable intelligent reflection surface phase shift matrix and the optimized second preset active reconfigurable intelligent reflection surface phase shift matrix respectively;

[0126] S4, jointly optimizing the base station transmit beamforming vector, optimizing the first preset active reconfigurable intelligent reflection surface phase shift matrix, and optimizing the second preset active reconfigurable intelligent reflection surface phase shift matrix to determine the maximum confidentiality rate of the secure communication system.

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

[0128] S1.1, assuming that the secure communication system model consists 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 obtained by evenly arranging and combining M1 and M2 reflecting units in a plane, and the legitimate user and the eavesdropping user are both single antennas;

[0129] S1.2, assuming that the channel gain from the base station to the first active reconfigurable smart reflector is H1∈C M1×Nt , the channel gain from the base station to the second active reconfigurable smart reflector is H2∈C M2×Nt , the channel gains of the first active reconfigurable intelligent reflector to the legitimate user and the eavesdropping user are respectively The channel gains of the second active reconfigurable intelligent reflective surface to the legitimate user and the eavesdropping user are respectively

[0130] S1.3, assuming that the phase shift matrix of the first active reconfigurable smart reflective surface and the second active reconfigurable smart reflective surface is expressed as:

[0131]

[0132] In the formula, m k ∈1...M k , represents the mth active reconfigurable smart reflector k The phase shift coefficient of each element is represents the mth active RIS of the kth active RIS k The amplification factor of each element, Greater than 1;

[0133] S1.4, let the received signal y of the legal user B and the received signal y of the eavesdropping user E The expressions are:

[0134]

[0135] In the formula, is the transmit beamforming vector at the base station, x∈C 1×1 The signal transmitted by the base station to the legitimate user and meets k∈{1,2}, represents the thermal noise of the kth active reconfigurable smart reflector, represents the additive Gaussian white noise at the legitimate user, represents the additive white Gaussian noise at the eavesdropping user;

[0136] S1.5, assuming the information rate R at the legitimate user B and the information rate R at the eavesdropping user E The expressions are:

[0137]

[0138] The confidentiality rate R is obtained by sorting sec The expression is:

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

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

[0141] S3.1, given a first preset active reconfigurable smart reflector phase shift matrix Θ1, and a second preset active reconfigurable smart reflector phase shift matrix Θ2, define:

[0142]

[0143] S3.2, change the problem P1 in the problem statement set of the optimization objective to:

[0144]

[0145] S3.3, sort out and obtain the optimized base station transmit beamforming vector w opt :

[0146]

[0147] In the formula, u max [] represents the normalized vector corresponding to the maximum eigenvalue of the matrix, and I represents N t ×N t When the processor 1001 calls the confidentiality rate optimization program based on the distributed active intelligent reflection surface stored in the memory 1005, the following operations are performed:

[0148] When the processor 1001 calls the confidentiality rate optimization program based on the distributed active intelligent reflective surface 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 smart reflection surface phase shift matrix Θ2, define:

[0150] W=ww H

[0151]

[0152] Rewrite the original problem P1 as:

[0153]

[0154] The constraints C2 and C4 are equivalently converted to constraints C6 and C7, and the objective functions Z1 and Z2 are expressed as:

[0155]

[0156] in, j∈{B,E};

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

[0158]

[0159] stC8: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 approximate expression of the lower bound of the objective function is:

[0166]

[0167]

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

[0169]

[0170] S3.8, using Gaussian randomization to obtain an approximate solution U1 that satisfies the constraint conditions, and sorting out the optimized first preset active reconfigurable smart reflector phase shift matrix:

[0171]

[0172] When the processor 1001 calls the confidentiality rate optimization program based on the distributed active intelligent reflective surface stored in the memory 1005, the following operations are performed:

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

[0174]

[0175] Rewrite the original problem P1 as:

[0176]

[0177] The constraints C2 and C4 are equivalently converted to constraints C12 and C13, and the objective functions Z'1 and Z'2 are expressed as:

[0178]

[0179]

[0180] in, j∈{B,E};

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

[0182]

[0183] stC14:Tr(H p2 U2)≤P RIS2

[0184]

[0185] C17:Rank(U2)=1U2≥0

[0186] in,

[0187]

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

[0189]

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

[0191]

[0192] S3.13, using Gaussian randomization to obtain an approximate solution that satisfies the constraint conditions, and sorting out the optimized second preset active reconfigurable smart reflector phase shift matrix:

[0193]

[0194] When the processor 1001 calls the confidentiality rate optimization program based on the distributed active intelligent reflective surface stored in the memory 1005, the following operations are performed:

[0195] S4.1, initialize noise, path loss parameters, simulated channel information, base station, distributed reconfigurable intelligent reflector, user coordinate position, iteration number T, maximum iteration number K and set convergence accuracy ε;

[0196] S4.2, solve P1 to get the optimal solution of 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 and the currently obtained confidentiality rate is used as the maximum confidentiality rate.

[0198] In addition, it can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment 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 embodiment of the above method.

[0199] Therefore, the present application also provides a computer-readable storage medium, which stores a confidentiality rate optimization program based on a distributed active intelligent reflecting surface. When the confidentiality rate optimization program based on a distributed active intelligent reflecting surface is executed by a processor, the confidentiality rate optimization program based on a distributed active intelligent reflecting surface implements the various steps of the confidentiality rate optimization method based on a distributed active intelligent reflecting surface as described in the above embodiment.

[0200] Among them, refer to Figure 7 The confidentiality rate optimization method based on distributed active intelligent reflective surface comprises the following steps:

[0201] S1, constructing a secure communication system model including a distributed active reconfigurable intelligent reflector auxiliary architecture, wherein the secure communication system model includes a first preset active reconfigurable intelligent reflector phase shift matrix, a second preset active reconfigurable intelligent reflector 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 the optimization problem, taking maximizing the confidentiality rate of the legitimate user as the goal, constructing a problem statement set of the optimization goal;

[0203] S3, taking the problem statement set of the optimization target as a constraint, selecting any two of the three functions of the first preset active reconfigurable intelligent reflection surface phase shift matrix, the second preset active reconfigurable intelligent reflection surface phase shift matrix and the base station transmit beamforming vector, optimizing another function, and obtaining the optimized base station transmit beamforming vector, the optimized first preset active reconfigurable intelligent reflection surface phase shift matrix and the optimized second preset active reconfigurable intelligent reflection surface phase shift matrix respectively;

[0204] S4, jointly optimizing the base station transmit beamforming vector, optimizing the first preset active reconfigurable intelligent reflection surface phase shift matrix, and optimizing the second preset active reconfigurable intelligent reflection surface phase shift matrix to determine the maximum confidentiality rate of the secure communication system.

[0205] The computer-readable storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which may store program codes.

[0206] It should be noted that since the storage medium provided in the embodiment of the present application is the storage medium used to implement the method of the embodiment of the present application, based on the method introduced in the embodiment of the present application, the person skilled in the art can understand the specific structure and deformation of the storage medium, so it is not repeated here. All storage media used in the method of the embodiment of the present application belong to the scope of protection of this application.

[0207] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may 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.) that contain computer-usable program code.

[0208] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0209] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0211] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0212] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0213] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing the confidentiality rate based on a distributed active intelligent reflective surface, characterized in that: The method comprises the following steps: S1, constructing a secure communication system model including a distributed active reconfigurable intelligent reflector auxiliary architecture, wherein the secure communication system model includes a first preset active reconfigurable intelligent reflector phase shift matrix, a second preset active reconfigurable intelligent reflector 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 the optimization problem, taking maximizing the confidentiality rate of the legitimate user as the goal, constructing a problem statement set of the optimization goal; S3, taking the problem statement set of the optimization target as a constraint, selecting any two of the three functions of the first preset active reconfigurable intelligent reflection surface phase shift matrix, the second preset active reconfigurable intelligent reflection surface phase shift matrix and the base station transmit beamforming vector, optimizing another function, and obtaining the optimized base station transmit beamforming vector, the optimized first preset active reconfigurable intelligent reflection surface phase shift matrix and the optimized second preset active reconfigurable intelligent reflection surface phase shift matrix respectively; S4, jointly optimizing the base station transmit beamforming vector, optimizing the first preset active reconfigurable intelligent reflection surface phase shift matrix, and optimizing the second preset active reconfigurable intelligent reflection surface phase shift matrix to determine the maximum confidentiality rate of the secure communication system.

2. The method according to claim 1, characterized in that In S1, the construction process of the secure communication system model includes: S1.1, assuming that the secure communication system model consists 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 obtained by evenly arranging and combining M1 and M2 reflecting units in a plane, and the legitimate user and the eavesdropping user are both single antennas; S1.2, assuming that the channel gain from the base station to the first active reconfigurable smart reflector is The channel gain from the base station to the second active reconfigurable smart reflector is The channel gains of the first active reconfigurable intelligent reflective surface to the legitimate user and the eavesdropping user are respectively The channel gains of the second active reconfigurable intelligent reflective surface to the legitimate user and the eavesdropping user are respectively S1.3, assuming that the phase shift matrix of the first active reconfigurable smart reflective surface and the second active reconfigurable smart reflective surface is expressed as: In the formula, m k ∈1...M k , represents the mth active reconfigurable smart reflector k The phase shift coefficient of each element is represents the mth active RIS of the kth active RIS k The amplification factor of each element, Greater than 1; S1.4, let the received signal y of the legal user B and the received signal y of the eavesdropping user E The expressions are: In the formula, is the transmit beamforming vector at the base station, x∈C 1×1 The signal transmitted by the base station to the legitimate user and meets k∈{1,2}, represents the thermal noise of the kth active reconfigurable smart reflector, represents the additive white Gaussian noise at the legitimate user, represents the additive white Gaussian noise at the eavesdropping user; S1.5, assuming the information rate R at the legitimate user B and the information rate R at the eavesdropping user E The expressions are: The confidentiality rate R is obtained by sorting sec The expression is: R sec =max{0,R B -R E }。 3. The method according to claim 2, characterized in that In S2, the problem statement set of the optimization objective includes: s.t.C1:‖w‖ 2 ≤P BS Where C1 represents the transmission power P at the base station BS Constraints, C2 represents the amplified power P of the first active reconfigurable smart reflector RIS1 Constraints, C3 represents the amplified power P of the second active reconfigurable smart reflector RIS2 Constraints, C4 represents the magnification factor of the first active reconfigurable smart reflector Constraints, C5 represents the magnification factor of the first active reconfigurable smart reflector constraints.

4. The method according to claim 3, characterized in that In S3, the step of optimizing the base station transmit beamforming vector specifically includes: S3.1, given a first preset active reconfigurable smart reflector phase shift matrix Θ1, and a second preset active reconfigurable smart reflector phase shift matrix Θ2, define: S3.2, change the problem P1 in the problem statement set of the optimization objective to: s.t.C1:||w|| 2 ≤P BS S3.3, sort out and obtain the optimized base station transmit beamforming vector w opt : In the formula, u max [] represents the normalized vector corresponding to the maximum eigenvalue of the matrix, and I represents N t ×N t The identity matrix of .

5. The method according to claim 3, characterized in that In S3, the optimization step of the first preset active reconfigurable smart reflection surface phase shift matrix specifically includes: S3.4, given the base station transmit beamforming vector w and the second preset active reconfigurable smart reflection surface phase shift matrix Θ2, define: W=ww H Rewrite the original problem P1 as: The constraints C2 and C4 are equivalently converted to constraints C6 and C7, and the objective functions Z1 and Z2 are expressed as: in, j∈{B,E}; S3.5, simplify the above problem and rewrite it as: s.t.C8:Tr(H p1 U1)≤P RIS1 C11:Rank(U1)=1U1≥0 in, j∈{B,E} S3.6, ignore Z 11 and Z 21 The constant term in , the approximate expression of the lower bound of the objective function is: S3.7, based on the lower bound approximation of the objective function, redefine problem P4 as P5: S3.8, using Gaussian randomization to obtain an approximate solution U1 that satisfies the constraint conditions, and sorting out the optimized first preset active reconfigurable smart reflector phase shift matrix:

6. The method according to claim 3, characterized in that In S3, the optimization step of the second preset active reconfigurable smart reflection surface phase shift matrix specifically includes: S3.9, given the base station transmit beamforming vector w and the phase shift matrix Θ1 of the active RIS1, define: Rewrite the original problem P1 as: The constraints C2 and C4 are equivalently converted to constraints C12 and C13, and the objective functions Z'1 and Z'2 are expressed as: in, j∈{B,E}; S3.10, simplify the above problem and rewrite it as: s.t.C14:Tr(H p2 U2)≤P RIS2 C17:Rank(U2)=1U2≥0 in, j∈{B,E}; S3.11, ignore Z' 21 and Z' 21 The constant term in , the approximate expression of the lower bound of the objective function is: S3.12, based on the lower bound approximation of the objective function, redefine problem P4 as P5: S3.13, using Gaussian randomization to obtain an approximate solution that satisfies the constraint conditions, and sorting out the optimized second preset active reconfigurable smart reflector phase shift matrix:

7. The method according to any one of claims 3 to 6, characterized in that The S4 specifically includes: S4.1, initialize noise, path loss parameters, simulated channel information, base station, distributed reconfigurable intelligent reflector, user coordinate position, iteration number T, maximum iteration number K and set convergence accuracy ε; S4.2, solve P1 to get the optimal solution of the problem w (T+1) , and S4.3, iteratively calculate the confidentiality rate of the secure communication system, when When , the iteration is stopped and the currently obtained confidentiality rate is used as the maximum confidentiality rate.

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