Anti-eavesdropping and anti-interference active RIS-ISAC system beam forming method

Abstract

The invention discloses an anti-eavesdropping and anti-interference active RIS-ISAC system beam forming method, which comprises the following steps: establishing a system model comprising a radar communication dual-function base station, an active RIS, a legal user, an illegal eavesdropper and a jammer, and establishing communication, perception and power consumption performance indexes; then, constructing an optimization problem which aims at minimizing the total power consumption of the system, meets communication and perception constraints at the same time, and jointly optimizes a base station transmitting beam forming vector and an active RIS phase shift matrix; splitting the non-convex optimization problem into two sub-problems, and respectively solving the optimal configuration of the base station and the RIS; and finally, an optimal solution is obtained by using an alternating optimization algorithm. The method has the advantages that under the condition that an illegal eavesdropper and a malicious enemy target exist at the same time, the communication performance is improved, eavesdropping and interference are suppressed and the total power consumption of the system is reduced by jointly designing the transmitting beam forming vector of the base station and the phase shift matrix of the active RIS.

Description

Technical Field

[0001] The invention relates to an active RIS-ISAC (Reconfigurable Intelligent Surface Integrated Sensing and Communication) system, and in particular to an active RIS-ISAC system beamforming method for preventing eavesdropping and interference. In the case of being attacked by eavesdropping and malicious interference at the same time, the method takes minimizing the total power consumption of the system as the goal, considers that the channel state information (CSI) of illegal nodes (illegal eavesdroppers and enemy targets, i.e., jammers) is imperfect, and under the constraints of the communication reachable rate of legal users, the eavesdropping rate of illegal eavesdroppers, and the beam gain generated in the direction of the active RIS (reconfigurable intelligent surface) to the enemy target, designs the transmission beamforming vector of the base station and the phase shift matrix of the active RIS, so as to achieve the purpose of the base station performing secure communication and target perception at the same time. Background Art

[0002] The popularity of wireless communications and radar systems has exacerbated the problem of frequency band saturation and brought about concerns about overcrowding and congestion of the spectrum. The next generation of wireless communication technology requires not only ultra-high-speed communication rates, but also high-precision perception services. To solve this problem, ISAC (Integrated Perception and Communication) has emerged as a revolutionary paradigm. Its concept is increasingly attracting the attention of academia, industry, and standardization organizations, and has been formally included in the six major usage scenarios of 6G by ITU-R. ISAC technology is to share spectrum, hardware platforms, and even waveforms and signal processing between communication and perception, thereby improving the integration gain of the system. The ISAC system can perform tasks such as target perception, positioning and tracking while meeting traditional communication needs.

[0003] Due to the broadcast characteristics of wireless channels and the spectrum sharing characteristics of communication perception, ISAC systems are vulnerable to attacks from illegal systems, such as interference from enemy targets (jammers), eavesdropping and deception by illegal users, etc. Existing research on physical layer security issues in ISAC systems mostly focuses on the eavesdropping of legitimate data by communication eavesdroppers, such as resisting this eavesdropping threat by emitting artificial noise. However, in actual electromagnetic environments, there may still be some malicious jammers that send interference signals to destroy legitimate communications and perceptions, and existing beamforming design solutions cannot solve this more challenging scenario. Therefore, when there are both illegal eavesdroppers and jammers in the ISAC system, it is crucial to design a synaesthesia integrated beamforming method that can prevent both eavesdropping and interference.

[0004] RIS (Reconfigurable Intelligent Surface) is a revolutionary low-cost technology that can assist wireless communication systems and perception systems to achieve an intelligent and reconfigurable propagation environment. It is considered to be a key enabling technology in the ISAC system, solving problems such as limited coverage and link obstruction in the ISAC system.

[0005] As an extension of traditional passive RIS, active RIS can amplify the signal while performing phase control, enhance the signal reception strength, and solve the signal attenuation problem caused by "multiplicative fading" in passive RIS. In addition, due to the potential unknowability of illegal nodes, the CSI (channel state information) related to illegal eavesdroppers and jammers may not be fully available to legal systems, and the existing physical layer security research of active RIS-ISAC systems mostly assumes that the system can obtain perfect CSI, which may be limited in practical applications. Therefore, the complex model when the CSI related to illegal nodes (illegal eavesdroppers and jammers) cannot be perfectly obtained should also be considered to design a robust beamforming method. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide an active RIS-ISAC system beamforming method for preventing eavesdropping and interference. In the case where there are illegal eavesdroppers and malicious enemy targets at the same time, and considering the imperfect channel state information of illegal nodes, the communication performance is improved, the eavesdropping and interference are suppressed, and the total power consumption of the system is reduced by jointly designing the base station's transmit beamforming vector and the active RIS phase shift matrix.

[0007] The technical solution adopted by the present invention to solve the above technical problems is: an active RIS-ISAC system beamforming method for preventing eavesdropping and interference, characterized by comprising the following steps:

[0008] Step 1: Establish an active RIS-ISAC system model, in which a base station with dual functions of radar communication, an active RIS, a legal user, an illegal eavesdropper, and an enemy target, namely a jammer, are set up. The base station communicates with the legal user in the downlink with the assistance of the active RIS. The illegal eavesdropper attempts to steal the signal sent by the base station to the legal user. The base station senses the enemy target, and the enemy target will actively send interference signals to the legal user to interfere with the legal user and destroy the communication between the base station and the legal user;

[0009] Step 2: Establish the communication performance indicators, perception performance indicators and power consumption performance indicators of the active RIS-ISAC system model. The communication performance indicators include the communication achievable rate of legitimate users and the eavesdropping rate of illegal eavesdroppers. The perception performance indicators include the beam gain generated in the direction of active RIS to the enemy target. The power consumption performance indicators include the total power consumption of the system.

[0010] Step 3: With the goal of minimizing the total power consumption of the system, the communication achievable rate of the legitimate user, the eavesdropping rate of the illegal eavesdropper, and the beam gain generated in the direction of the active RIS to the enemy target are constrained. The base station's transmit beamforming vector and the phase shift matrix of the active RIS are jointly optimized to construct an optimization problem.

[0011] Step 4: split the optimization problem into sub-problem 1 and sub-problem 2. Sub-problem 1 is used to optimize the base station's transmit beamforming vector when the phase shift matrix of the active RIS is fixed, and sub-problem 2 is used to optimize the active RIS's phase shift matrix when the base station's transmit beamforming vector is fixed. Then, sub-problem 1 is transformed into convex problem 1, and sub-problem 2 is transformed into convex problem 2.

[0012] Step 5: Use the alternating optimization algorithm to iteratively solve convex problem 1 and convex problem 2 to obtain the optimal solutions for the base station's transmit beamforming vector and the active RIS's phase shift matrix.

[0013] In step 1, the base station is equipped with a uniform linear array with N antennas; the number of reflective elements of the active RIS is M; and both the legitimate user and the illegal eavesdropper are equipped with a single antenna.

[0014] In step 2, the process of obtaining the achievable rate of the legitimate user is as follows:

[0015] Step 2.1a: Transform the legitimate user’s received signal y u Expressed as The superscript "H" indicates the conjugate transpose operation, h R represents the channel from active RIS to legitimate users, Φ represents the phase shift matrix of active RIS, diag(·) is used to extract or construct the diagonal elements of a matrix, φ m represents the coefficient of the mth reflective element of the active RIS, m=1,2,…,M, “|·|” is the modulo operator, ξ m represents the amplification of the mth reflective element of the active RIS, ξ m |≤p max , p max represents the maximum amplified power of the reflective element of the active RIS, e is a natural constant, j is an imaginary number, and v m represents the phase of the mth reflection element of the active RIS, F represents the channel from the base station to the active RIS, and h B represents the channel from the base station to the legitimate user, x represents the signal sent by the base station, x = ws, s represents the communication information flow between the base station and the legitimate user, s obeys a complex Gaussian distribution with a mean of 0 and a variance of 1, w represents the transmit beamforming vector of the base station, P J represents the transmitting power of the enemy target, hJR represents the channel from the enemy target to the active RIS, h JU represents the channel from the enemy target to the legitimate user, n I represents the thermal noise generated by active RIS, which has a mean of 0 and a covariance matrix of I M represents the unit matrix of order M, n u represents the receiving noise of the legitimate user, which has a mean of 0 and a variance of

[0016] Step 2.1b: According to y u , get the SINR of the legitimate user u , Then we can get the communication rate R of the legitimate users: u , R u =log2(1+SINR u ), where “||·||2” is the binary norm operator.

[0017] In step 2, the process of obtaining the eavesdropping rate of the illegal eavesdropper is as follows:

[0018] Step 2.2a: Assuming that the illegal eavesdropper can eliminate the interference signal sent by the enemy target, the received signal y e Expressed as Among them, g R represents the channel from active RIS to illegal eavesdroppers, g B represents the channel from the base station to the illegal eavesdropper, n e represents the receiving noise of the illegal eavesdropper, whose mean is 0 and variance is

[0019] Step 2.2b: According to y e , get the signal to interference and noise ratio SINR of the illegal eavesdropper e , Then we can get the eavesdropping rate R of the illegal eavesdropper: e , R e =log2(1+SINR e ).

[0020] In step 2, the beam gain B(θ) generated in the direction of the active RIS to the enemy target is expressed as Where θ represents the angle of the enemy target relative to the active RIS, a(θ) represents the guidance vector of the active RIS, and a(θ) = [1, e jπsin(θ) ,...,e jπ(M-1)sin(θ) ] T , the superscript “T” indicates the transpose operation.

[0021] In step 2, the total system power consumption P total Represented as P total =P BS +P RIS , where P BS Indicates the power consumed by the base station, P RIS represents the power consumed by active RIS, “||·|| F " is the F norm operator symbol, P SW Represents the power consumed by phase conversion and control, P DC Indicates the DC circuit power.

[0022] h B 、h R and F is modeled as the Rice channel model, in, Indicates the line-of-sight link from the base station to the legitimate user, Indicates the non-line-of-sight link from the base station to the legitimate user, Indicates the line-of-sight link from the active RIS to the legitimate user. Indicates a non-line-of-sight link from an active RIS to a legitimate user. and All obey a complex Gaussian distribution with a mean of 0 and a variance of 1, β B represents the path loss from the base station to the legitimate user, C0 represents the path loss at the reference distance d0, d B represents the path distance from the base station to the legitimate user, α B represents the path loss index from the base station to the legitimate user, κ B represents the Rice factor from the base station to the legitimate user, β R represents the path loss from active RIS to legitimate users, d R represents the path distance from the active RIS to the legitimate user, α R represents the path loss index from active RIS to legitimate users, κ R The Rice factor F represents the active RIS to the legitimate user. LoS represents the line-of-sight link from the base station to the active RIS, F NLoS represents the non-line-of-sight link from the base station to the active RIS, F NLoS It follows a complex Gaussian distribution with mean 0 and variance 1, β F represents the path loss from the base station to the active RIS, d F represents the path distance from the base station to the active RIS, α F represents the path loss index from the base station to the active RIS, κ Frepresents the Rice factor from the base station to the active RIS;

[0023] in, express The estimated value of express The estimation error of express The upper bound of the estimation error is δ represents the error percentage, Indicates g i The estimated value of Δg i Indicates g i The estimation error, ||Δg i ||2≤∈ i ,∈ i Indicates g i The upper bound of the estimation error is and Modeled as the Rice channel model, For JR Indicates the line-of-sight link from the enemy target to the active RIS, Indicates the non-line-of-sight link from the enemy target to the active RIS, It follows a complex Gaussian distribution with mean 0 and variance 1, β JR represents the path loss from the enemy target to the active RIS, d JR represents the path distance from the enemy target to the active RIS, α JR represents the path loss index from the enemy target to the active RIS, κ JR Represents the Rice factor of the enemy target to the active RIS, For JU represents the line-of-sight link from the enemy target to the legitimate user, represents the non-line-of-sight link from the enemy target to the legitimate user, It follows a complex Gaussian distribution with mean 0 and variance 1, β JU represents the path loss from the enemy target to the legitimate user, d JU represents the path distance from the enemy target to the legitimate user, α JU represents the path loss index from the enemy target to the legitimate user, κ JU Represents the Rice factor from the enemy target to the legitimate user, when i is B represents the line-of-sight link from the base station to the illegal eavesdropper, Indicates the non-line-of-sight link from the base station to the illegal eavesdropper, It follows a complex Gaussian distribution with mean 0 and variance 1. represents the path loss from the base station to the illegal eavesdropper, represents the path distance from the base station to the illegal eavesdropper, represents the path loss index from the base station to the illegal eavesdropper, Represents the Rice factor from the base station to the illegal eavesdropper, when i is R Indicates the line-of-sight link from the active RIS to the illegal eavesdropper. Indicates a non-line-of-sight link from an active RIS to an illegal eavesdropper. It follows a complex Gaussian distribution with mean 0 and variance 1. represents the path loss from the active RIS to the illegal eavesdropper, represents the path distance from the active RIS to the illegal eavesdropper, It represents the path loss index from active RIS to illegal eavesdropper, Represents the Rice factor of active RIS to illegal eavesdroppers.

[0024] In step 3, the optimization problem is described as:

[0025]

[0026] C2:R u ≥γ u ,

[0027] C3:R e ≤γ e ,

[0028] C4:φ m |≤p max ,m=1,...,M.

[0029] in, represents the estimated value of θ, Δθ represents the estimated error of θ, and γ t represents the beam gain threshold, γ u represents the communication achievable rate threshold, γ e Indicates the eavesdropping rate threshold.

[0030] In step 4, sub-problem 1 is described as: Sub-problem 2 is described as:

[0031] stC1,C2,C3,C4

[0032] In step 4, the specific process of transforming subproblem 1 into convex problem 1 is:

[0033] Step 4.1a: Order Transform the objective function of subproblem 1 into Where Tr(·) means finding the trace of the matrix;

[0034] Rewrite constraint C1 of subproblem 1 as constraint C1':

[0035] make Rewrite constraint C2 of subproblem 1 as constraint C2': in, Then define the cascade channel h from the enemy target to the active RIS to the legitimate user JRU for The uncertainty model is Then the joint channel from the enemy target to the legitimate user Expressed as in, Indicates h JRU The estimated value of Δh JRU Indicates h JRU The estimation error of express The estimated value of express The estimation error of Next, define Combined with Convert the left side of constraint C2' to Constraint C2” is obtained, where φ represents the column vector composed of the coefficients of all reflective elements of the active RIS, φ=[φ1,φ2,…,φ M ] T , Re{·} represents taking the real part; then the constraint C2" is transformed into the form of a linear matrix inequality using the S lemma, that is, the constraint C2"' is obtained: Among them, λ represents a non-negative auxiliary variable, I M+1 represents the unit matrix of order M+1,

[0036] Define the cascade channel G from the base station to the active RIS to the illegal eavesdropper BRE for The uncertainty model is Then the joint channel G from the base station to the illegal eavesdropper is expressed as in, Represents G BRE The estimated value of ΔG BRE Represents G BRE The estimation error of represents the estimated value of G, ΔG represents the estimation error of G, ||ΔG||2≤∈G , Then transform constraint C3 of subproblem 1 into constraint C3': in, Then according to the properties of matrix trace: Tr(X H Y)=vec H (X)vec(Y), we have: Among them, vec(·) represents vectorized operation, is the Crocker inner product operator, g = vec(ΔG H ), Then transform constraint C3' into constraint C3": Then, the S lemma is used to transform the constraint C3” into a linear matrix inequality, that is, the constraint C3”’ is obtained: Among them, μ represents a non-negative auxiliary variable, I (M+1)N represents the unit matrix of (M+1)N order;

[0037] Step 4.1b: Formulate the convex problem 1 as:

[0038] The specific process of transforming subproblem 2 into convex problem 2 is:

[0039] Step 4.2a: Introduce the auxiliary variable η and transform subproblem 2 into:

[0040]

[0041] C1,C2,C3,C4

[0042] Step 4.2b: Rewrite constraint C5 as constraint C5': in, Then Substituting into constraint C5', we get Then there is constraint C5": Then, we use the S lemma to transform the constraint C5" into a linear matrix inequality, that is, we get the constraint in, represents non-negative auxiliary variables;

[0043] Convert constraint C4 to constraint C4': in, express The mth element of ;

[0044] Step 4.2c: Formulate convex problem 2 as: in, I Mrepresents the unit matrix of order M, the superscript “*” represents the conjugate operation, Indicated by The submatrix consisting of the 1st to Mth rows and the 1st to Mth columns of .

[0045] Compared with the prior art, the advantages of the present invention are:

[0046] 1) The method of the present invention obtains a beamforming method that minimizes the total power consumption of the system by jointly optimizing the transmission beamforming of the dual-function base station and the phase shift matrix of the active reconfigurable smart surface, thereby achieving a green and energy-saving communication effect. Compared with the traditional method, the present invention solves the problems of blocked communication links and limited synaesthesia coverage in the traditional method by introducing an active reconfigurable smart surface. Compared with the traditional passive reconfigurable smart surface, the phase shift matrix optimization method of the active reconfigurable smart surface proposed in the present invention can better reduce the power consumption of the system. The present invention adopts an alternating optimization algorithm to effectively solve the variable coupling problem, and uses matrix theory methods such as the S lemma to solve the actual application scenario where the channel state information of illegal nodes cannot be perfectly obtained, and finally derives the optimization problem into a convex form for solution. This beamforming method provides an efficient and environmentally friendly solution for modern wireless communication systems, which helps to achieve more stable and efficient communication services in complex electromagnetic environments such as cities and campuses.

[0047] 2) The present invention is particularly aimed at a challenging communication-perception integrated application scenario, in which the system needs to complete the dual tasks of user communication and target perception in a complex environment where illegal eavesdroppers and malicious jammers coexist. By establishing the communication performance indicators, perception performance indicators and power consumption performance indicators of the system model, a system total power consumption minimization problem is formulated. While formulating the optimization problem, it is ensured that the transmission rate of the communication user meets the requirements and that its data will not be illegally obtained by illegal eavesdroppers. At the same time, the present invention also takes into account the imperfect channel state information related to illegal nodes. When there are errors in the acquisition of the channel, the designed beamforming method has a certain degree of robustness. The system also has effective anti-interference capabilities and can resist the interference signals of malicious jammers, thereby achieving coordinated optimization of communication, perception and physical layer security. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flowchart of the overall implementation process of the method of the present invention;

[0049] Figure 2 A simple schematic diagram of the active RIS-ISAC system model in the method of the present invention;

[0050] Figure 3 The communication achievable rate threshold γ of the method of the present invention and the comparative method uSchematic diagram of the comparison curve of the total power consumption of the system when it changes;

[0051] Figure 4 is the communication achievable rate threshold γ u =4bps / Hz, the transmission power P of the jammer is J Schematic diagram of the comparison curve of the change in total system power consumption when changes. DETAILED DESCRIPTION

[0052] The present invention is further described in detail below with reference to the accompanying drawings.

[0053] The present invention proposes an active RIS-ISAC system beamforming method for preventing eavesdropping and interference, and its overall implementation flow chart is as follows: Figure 1 As shown, it includes the following steps:

[0054] Step 1: Establish an active RIS-ISAC (Reconfigurable Intelligent Surface Assisted Communication-Awareness Integration) system model, such as Figure 2 As shown in the figure, the model is provided with a base station (BS) with dual functions of radar communication, an active RIS (Active-RIS), a legitimate user (User), an illegal eavesdropper (Eve), and an enemy target, namely a jammer (Jam / Tar). The base station communicates with the legitimate user in the downlink with the assistance of the active RIS. The illegal eavesdropper attempts to steal the signal sent by the base station to the legitimate user. The base station senses the enemy target, and the enemy target will actively send interference signals to the legitimate user to interfere with the legitimate user and destroy the communication between the base station and the legitimate user.

[0055] It is further defined that the base station is equipped with a uniform linear array, the number of antennas is N, and in this embodiment, N=12; the number of reflective elements of the active RIS is M, and in this embodiment, M=30; and both the legitimate user and the illegal eavesdropper are equipped with a single antenna.

[0056] Step 2: Establish the communication performance indicators, perception performance indicators and power consumption performance indicators of the active RIS-ISAC system model. The communication performance indicators include the communication achievable rate of legitimate users and the eavesdropping rate of illegal eavesdroppers. The perception performance indicators include the beam gain generated in the direction of active RIS to the enemy target. The power consumption performance indicators include the total power consumption of the system.

[0057] In this embodiment, in step 2, the process of obtaining the achievable rate of the legitimate user is as follows:

[0058] Step 2.1a: Transform the legitimate user’s received signal y u Expressed as The superscript "H" indicates the conjugate transpose operation, h RIndicates the channel from active RIS to legitimate users. Φ represents the phase shift matrix of active RIS, To define the notation, diag(·) is used to extract or construct the diagonal elements of a matrix, φ m represents the coefficient of the mth reflective element of the active RIS, m=1,2,…,M, “|·|” is the modulo operator, ξ m represents the amplification of the mth reflective element of the active RIS, |ξ m |≤p max , p max represents the maximum amplified power of the reflective element of the active RIS, which is p in this embodiment. max =40dB, e is a natural constant, e=2.7…, j is an imaginary number, v m represents the phase of the mth reflection element of the active RIS, F represents the channel from the base station to the active RIS, h B represents the channel from the base station to the legitimate user, x represents the signal sent by the base station, x = ws, s represents the communication information flow between the base station and the legitimate user, and s obeys a complex Gaussian distribution with a mean of 0 and a variance of 1, that is, represents a complex Gaussian distribution with a mean of 0 and a variance of 1, w represents the transmit beamforming vector of the base station, P J represents the transmitting power of the enemy target, h JR Indicates the channel from the enemy target to the active RIS, h JU represents the channel from the enemy target to the legitimate user, n I represents the thermal noise generated by active RIS, which has a mean of 0 and a covariance matrix of I M represents the unit matrix of order M, n u represents the receiving noise of the legitimate user, which has a mean of 0 and a variance of In this embodiment,

[0059] Step 2.1b: According to y u , get the SINR of the legitimate user u , Then we can get the communication rate R of the legitimate users: u , R u =log2(1+SINR u ), where “||·||2” is the binary norm operator.

[0060] In this embodiment, in step 2, the process of obtaining the eavesdropping rate of the illegal eavesdropper is as follows:

[0061] Step 2.2a: Since there is cooperation between the illegal eavesdropper and the enemy target, it can be assumed that the illegal eavesdropper can eliminate the interference signal sent by the enemy target, and the received signal y of the illegal eavesdropper is converted to e Expressed as Among them, g R Indicates the channel from active RIS to illegal eavesdroppers. g B represents the channel from the base station to the illegal eavesdropper, n e represents the receiving noise of the illegal eavesdropper, whose mean is 0 and variance is In this embodiment,

[0062] Step 2.2b: According to y e , get the signal to interference and noise ratio SINR of the illegal eavesdropper e , Then we can get the eavesdropping rate R of the illegal eavesdropper: e , R e =log2(1+SINR e ).

[0063] In this embodiment, in step 2, it is assumed that the direct link from the base station to the enemy target (jammer) is completely blocked by buildings, trees, etc. In this case, deploying active RIS can create a virtual line-of-sight link between the base station and the enemy target to achieve the purpose of enemy target perception. Here, the beam gain generated by active RIS on the enemy target is used as an indicator to measure the perception performance, and the beam gain B(θ) generated in the direction from active RIS to the enemy target is expressed as Where θ represents the angle of the enemy target relative to the active RIS, a(θ) represents the guidance vector of the active RIS, and a(θ) = [1, e jπsin(θ) ,...,e jπ(M-1)sin(θ) ] T , where the superscript “T” indicates a transposition operation. In an actual system, there is uncertainty in the angle θ of the enemy target relative to the active RIS. The present invention assumes that the true position of the enemy target is limited to a certain range, i.e. in, represents the estimated value of θ, Δθ represents the estimated error of θ, and in this embodiment, Δθ=5°.

[0064] In this embodiment, in step 2, unlike the passive RIS, the active RIS is equipped with a set of reflective elements that can amplify the signal, and its power consumption is not negligible for the entire system. Therefore, the total power consumption of the system should be equal to the sum of the power consumption of the base station and the active RIS. total Represented as P total =P BS +P RIS , where P BS Indicates the power consumed by the base station, P RIS represents the power consumed by active RIS, “‖·‖ F " is the F norm operator symbol, P SW Represents the power consumed by phase conversion and control, P DC Indicates the DC circuit power. In this embodiment, P SW =-10dBm, P DC =-5dBm.

[0065] In this specific embodiment, h B 、h R and F is modeled as the Rice channel model, F is determined by both large-scale fading and small-scale fading. in, Indicates the line-of-sight link from the base station to the legitimate user, Indicates the non-line-of-sight link from the base station to the legitimate user, Indicates the line-of-sight link from the active RIS to the legitimate user. Indicates a non-line-of-sight link from an active RIS to a legitimate user. and All obey a complex Gaussian distribution with a mean of 0 and a variance of 1, β B represents the path loss from the base station to the legitimate user, C0 represents the path loss at the reference distance d0. In this embodiment, C0=-30dB, d0=1 meter, B represents the path distance from the base station to the legitimate user, α B represents the path loss index from the base station to the legitimate user, κ B Represents the Rice factor from the base station to the legitimate user, which represents and The intensity ratio, β R represents the path loss from active RIS to legitimate users, d R represents the path distance from the active RIS to the legitimate user, α R represents the path loss index from active RIS to legitimate users, κ RThe Rice factor representing active RIS to legitimate users, which represents and In this embodiment, we take κ B =0, α B =3.5,κ R =1, α R =2.2, F LoS represents the line-of-sight link from the base station to the active RIS, F NLoS represents the non-line-of-sight link from the base station to the active RIS, F NLoS It follows a complex Gaussian distribution with mean 0 and variance 1, β F represents the path loss from the base station to the active RIS, d F represents the path distance from the base station to the active RIS, α F represents the path loss index from the base station to the active RIS, κ F The Rice factor from the base station to the active RIS represents F LoS With F NLoS In this embodiment, we take κ F =1, α F =2.2.

[0066] For a legal system, illegal nodes (illegal eavesdroppers and enemy targets) have some uncertainty, and their related channels are difficult to obtain perfectly in practice. Therefore, the present invention adopts a bounded channel error model to describe this uncertainty. in, express The estimated value of express The estimation error of express The upper bound of the estimation error is δ represents the error percentage, and in this specific embodiment, δ=0.01. Indicates g i The estimated value of Δg i Indicates g i The estimation error, ‖Δg i ‖2≤∈ i ,∈ i Indicates g i The upper bound of the estimation error is and Modeled as the Rice channel model, For JR Indicates the line-of-sight link from the enemy target to the active RIS, Indicates the non-line-of-sight link from the enemy target to the active RIS, It follows a complex Gaussian distribution with mean 0 and variance 1, β JR represents the path loss from the enemy target to the active RIS, d JR represents the path distance from the enemy target to the active RIS, α JR represents the path loss index from the enemy target to the active RIS, κ JR The Rice factor of the enemy target to the active RIS, which represents and The strength ratio, For JU represents the line-of-sight link from the enemy target to the legitimate user, represents the non-line-of-sight link from the enemy target to the legitimate user, It follows a complex Gaussian distribution with mean 0 and variance 1, β JU represents the path loss from the enemy target to the legitimate user, d JU represents the path distance from the enemy target to the legitimate user, α JU represents the path loss index from the enemy target to the legitimate user, κ JU Represents the Rice factor from the enemy target to the legitimate user, which represents and In this embodiment, we take κ JR =1, α JR =2.2,κ JU =0, α JU =3.5, when i is B Indicates the line-of-sight link from the base station to the illegal eavesdropper, Indicates the non-line-of-sight link from the base station to the illegal eavesdropper, It follows a complex Gaussian distribution with mean 0 and variance 1. represents the path loss from the base station to the illegal eavesdropper, represents the path distance from the base station to the illegal eavesdropper, represents the path loss index from the base station to the illegal eavesdropper, The Rice factor from the base station to the illegal eavesdropper is represented by and The intensity ratio, i is R Indicates the line-of-sight link from the active RIS to the illegal eavesdropper. Indicates a non-line-of-sight link from an active RIS to an illegal eavesdropper. It follows a complex Gaussian distribution with mean 0 and variance 1. represents the path loss from the active RIS to the illegal eavesdropper, represents the path distance from the active RIS to the illegal eavesdropper, It represents the path loss index from active RIS to illegal eavesdropper, The Rice factor representing active RIS to illegal eavesdroppers, which represents and In this embodiment, the intensity ratio is

[0067] Step 3: With the goal of minimizing the total power consumption of the system, and with the communication achievable rate of legitimate users, the eavesdropping rate of illegal eavesdroppers, and the beam gain generated in the direction of active RIS to the enemy target as constraints, the base station's transmit beamforming vector and the phase shift matrix of active RIS are jointly optimized to construct an optimization problem.

[0068] Further limiting, the optimization problem is described as:

[0069]

[0070] C2:R u ≥γ u ,

[0071] C3:R e ≤γ e ,

[0072] C4:φ m |≤p max ,m=1,...,M.

[0073] Among them, min is the minimum function, st means "subject to...", γ t represents the beam gain threshold, γ u represents the communication achievable rate threshold, γ e represents the eavesdropping rate threshold, in this embodiment, γ t =20dB,γ u =5bps / Hz, γ e =1bps / Hz, constraint C1 indicates that the beam gain generated by active RIS in the direction of the enemy target is expected to be no less than γ t , constraint C2 means that the communication rate of legitimate users is not less than γ u , constraint C3 means that the eavesdropping rate of the illegal eavesdropper is no greater than γ e , constraint C4 represents the coefficient constraint of each reflective element of the active RIS.

[0074] Step 4: Since the optimization problem constructed in step 3 is a non-convex variable coupling optimization problem and is difficult to solve, the optimization problem is split into sub-problem 1 and sub-problem 2. Sub-problem 1 is used to optimize the base station's transmit beamforming vector when the phase shift matrix of the active RIS is fixed, and sub-problem 2 is used to optimize the phase shift matrix of the active RIS when the transmit beamforming vector of the base station is fixed; then sub-problem 1 is transformed into convex problem 1, and sub-problem 2 is transformed into convex problem 2.

[0075] Further limiting, sub-problem 1 is described as: Sub-problem 2 is described as:

[0076] stC1,C2,C3,C4

[0077] Further limiting, the specific process of transforming subproblem 1 into convex problem 1 is:

[0078] Step 4.1a: Order Transform the objective function of subproblem 1 into Where Tr(·) means finding the trace of the matrix.

[0079] Rewrite constraint C1 of subproblem 1 as constraint C1':

[0080] make Rewrite constraint C2 of subproblem 1 as constraint C2': in, Then define the cascade channel h from the enemy target to the active RIS to the legitimate user JRU for Its uncertainty can be modeled as Then the joint channel from the enemy target to the legitimate user (including cascade channels and direct channels) is expressed as in, Indicates h JRU The estimated value of Δh JRU Indicates h JRU The estimation error of express The estimated value of express The estimation error of Next, define Combined with Convert the left side of constraint C2' to Constraint C2” is obtained, where φ represents the column vector composed of the coefficients of all reflective elements of the active RIS, φ=[φ1,φ2,…,φ M ] T, Re{·} represents the real part; Due to the introduction of the bounded channel error model, constraint C2” consists of an infinite number of non-convex constraints. Therefore, in order to solve this problem, the S lemma is used to transform constraint C2” into the form of a linear matrix inequality, that is, constraint C2”’ is obtained: Among them, λ represents a non-negative auxiliary variable, I M+1 represents the unit matrix of order M+1,

[0081] In order to transform constraint C3 into a convex form, we define the cascade channel G from the base station to the active RIS to the illegal eavesdropper. BRE for Its uncertainty can be modeled as Then the joint channel G (including direct channel and cascade channel) from the base station to the illegal eavesdropper is expressed as in, Represents G BRE The estimated value of ΔG BRE Represents G BRE The estimation error of represents the estimated value of G, ΔG represents the estimated error of G, ‖ΔG‖2≤∈ G , Then transform constraint C3 of subproblem 1 into constraint C3': in, Then according to the properties of matrix trace: Tr(X H Y)=vec H (X)vec(Y), we have: Among them, vec(·) represents vectorized operation, is the Crocker inner product operator, g = vec(ΔG H ), Then transform constraint C3' into constraint C3": Then, the S lemma is used to transform the constraint C3” into a linear matrix inequality, that is, the constraint C3”’ is obtained: Among them, μ represents a non-negative auxiliary variable, I (M+1)N represents the unit matrix of (M+1)N order.

[0082] Step 4.1b: Formulate the convex problem 1 as:

[0083] The specific process of transforming subproblem 2 into convex problem 2 is:

[0084] Step 4.2a: Introduce the auxiliary variable η and transform subproblem 2 into:

[0085]

[0086] C1,C2,C3,C4

[0087] Step 4.2b: Rewrite constraint C5 as constraint C5': in, Then Substituting into constraint C5', we get Then there is constraint C5": Then, we use the S lemma to transform the constraint C5" into a linear matrix inequality, and obtain the constraint C5"': Where ζ represents a non-negative auxiliary variable.

[0088] Convert constraint C4 to constraint C4': in, express The mth element of .

[0089] Step 4.2c: Formulate convex problem 2 as: in, I M represents the unit matrix of order M, the superscript “*” represents the conjugate operation, Indicated by The submatrix consisting of the 1st to Mth rows and the 1st to Mth columns of .

[0090] Step 5: Use the alternating optimization algorithm to iteratively solve convex problem 1 and convex problem 2 to obtain the optimal solutions for the base station's transmit beamforming vector and the active RIS's phase shift matrix.

[0091] Here, the alternating optimization algorithm is an existing commonly used algorithm, which is used to alternately iterate and solve multiple optimization variables. When the iteration accuracy reaches a preset accuracy threshold or the number of iterations reaches a preset maximum number of iterations, the iteration is stopped to obtain the optimal solution of each optimization variable. In this embodiment, the preset accuracy threshold can be set to 10 -3 , the preset maximum number of iterations can be set to 30. In each iteration, a convex optimization solver such as CVX is used to solve convex problems 1 and convex problems 2.

[0092] The feasibility and effectiveness of the method of the present invention are further illustrated by the following simulation.

[0093] In the simulation, the comparison methods include the random RIS phase shift method (the phase shift matrix of the active RIS is not optimized and is randomly given) and the passive RIS method (the passive RIS can only adjust the signal phase and does not have the ability to amplify the signal). The performance curves of the method of the present invention under perfect CSI (assuming that the channel state information of illegal nodes (illegal eavesdroppers and enemy targets) can be perfectly obtained) are compared.

[0094] Figure 3The results of the method of the present invention (with an error percentage of 0.01) and the method of the present invention under perfect CSI conditions and the comparative method under the communication achievable rate threshold γ are given. u The comparison curve of the total power consumption of the system when changes. Figure 3 As can be seen from the figure, when the demand for communication continues to increase, that is, the communication rate threshold γ u When the communication rate threshold γ is increased, the total power consumption of the system increases, but the total power consumption of the system in the method of the present invention is still maintained at a low level, especially in the case of perfect CSI, the total power consumption of the system is basically maintained below 10W (watts). In addition, the method of the present invention is superior to the passive RIS method and the random RIS phase shift method in reducing the total power consumption of the system. u As θ increases, the difference in total system power consumption between the method of the present invention and the comparative method also increases, which verifies the effectiveness of the method of the present invention in green and energy-saving communications.

[0095] Figure 4 Given the communication achievable rate threshold γ u =4bps / Hz, the method of the present invention (error percentage is 0.01) and the method of the present invention under perfect CSI conditions and the comparison method under the jammer's transmission power P J The comparison curve of the total power consumption of the system when changes. Figure 4 It can be seen that with the continuous increase in the jammer power, the total system power consumption shows an upward trend, and the total system power consumption of the method of the present invention is lower than that of the passive RIS method and the random RIS phase shift method. Compared with the passive RIS-assisted ISAC system, the active RIS can not only adjust the phase of the incident signal, but also has the ability to amplify the signal. Even if its reflective element itself will bring some power consumption, the deployment of active RIS will greatly improve the overall performance of the system. Compared with the random RIS phase shift method, the RIS phase shift optimization method proposed in the present invention is very effective and can greatly reduce the total system power consumption. It can achieve the effect of green and energy-saving communication while ensuring safe anti-interference transmission and target perception.

Claims

1. An active RIS-ISAC system beamforming method for preventing eavesdropping and interference, characterized in that The following steps are involved: Step 1: Establish an active RIS-ISAC system model, in which a base station with dual functions of radar communication, an active RIS, a legal user, an illegal eavesdropper, and an enemy target, i.e., a jammer, are set up. The base station communicates with the legal user in the downlink with the assistance of the active RIS. The illegal eavesdropper attempts to steal the signal sent by the base station to the legal user. The base station senses the enemy target, and the enemy target will actively send interference signals to the legal user to interfere with the legal user and destroy the communication between the base station and the legal user; Step 2: Establish the communication performance indicators, perception performance indicators and power consumption performance indicators of the active RIS-ISAC system model. The communication performance indicators include the communication achievable rate of legitimate users and the eavesdropping rate of illegal eavesdroppers. The perception performance indicators include the beam gain generated in the direction of active RIS to the enemy target. The power consumption performance indicators include the total power consumption of the system. Step 3: With the goal of minimizing the total power consumption of the system, the communication achievable rate of the legitimate user, the eavesdropping rate of the illegal eavesdropper, and the beam gain generated in the direction of the active RIS to the enemy target are constrained. The base station's transmit beamforming vector and the phase shift matrix of the active RIS are jointly optimized to construct an optimization problem. Step 4: split the optimization problem into sub-problem 1 and sub-problem 2. Sub-problem 1 is used to optimize the base station's transmit beamforming vector when the phase shift matrix of the active RIS is fixed, and sub-problem 2 is used to optimize the active RIS's phase shift matrix when the base station's transmit beamforming vector is fixed. Then, sub-problem 1 is transformed into convex problem 1, and sub-problem 2 is transformed into convex problem 2. Step 5: Use the alternating optimization algorithm to iteratively solve convex problem 1 and convex problem 2 to obtain the optimal solutions for the base station's transmit beamforming vector and the active RIS's phase shift matrix.

2. The active RIS-ISAC system beamforming method for preventing eavesdropping and interference according to claim 1 is characterized in that In step 1, the base station is equipped with a uniform linear array, the number of antennas is N; the number of reflective elements of the active RIS is M; Both the legitimate user and the illegal eavesdropper are equipped with a single antenna.

3. The active RIS-ISAC system beamforming method for preventing eavesdropping and interference according to claim 2 is characterized in that In step 2, the process of obtaining the achievable rate of the legitimate user is as follows: Step 2.1a: Transform the legitimate user’s received signal y u Expressed as The superscript "H" indicates the conjugate transpose operation, h R represents the channel from active RIS to legitimate users, Φ represents the phase shift matrix of active RIS, diag(·) is used to extract or construct the diagonal elements of a matrix, φ m represents the coefficient of the mth reflective element of the active RIS, m=1,2,…,M, "|·|" is the modulus operator, ξ m represents the amplification of the mth reflective element of the active RIS, ξ m |≤p max , p max represents the maximum amplified power of the reflective element of the active RIS, e is a natural constant, j is an imaginary number, and v m represents the phase of the mth reflection element of the active RIS, F represents the channel from the base station to the active RIS, and h B represents the channel from the base station to the legitimate user, x represents the signal sent by the base station, x = ws, s represents the communication information flow between the base station and the legitimate user, s obeys a complex Gaussian distribution with a mean of 0 and a variance of 1, w represents the transmit beamforming vector of the base station, P J represents the transmitting power of the enemy target, h JR represents the channel from the enemy target to the active RIS, h JU represents the channel from the enemy target to the legitimate user, n I represents the thermal noise generated by active RIS, which has a mean of 0 and a covariance matrix of I M represents the unit matrix of order M, n u represents the receiving noise of the legitimate user, which has a mean of 0 and a variance of Step 2.1b: According to y u , get the SINR of the legitimate user u , Then we can get the communication rate R of the legitimate users: u , R u =log2(1+SINR u ), where "||·||2" is the binary norm operator.

4. The active RIS-ISAC system beamforming method for preventing eavesdropping and interference according to claim 3 is characterized in that In step 2, the process of obtaining the eavesdropping rate of the illegal eavesdropper is as follows: Step 2.2a: Assuming that the illegal eavesdropper can eliminate the interference signal sent by the enemy target, the received signal y e Expressed as Among them, g R represents the channel from active RIS to illegal eavesdroppers, g B represents the channel from the base station to the illegal eavesdropper, n e represents the receiving noise of the illegal eavesdropper, whose mean is 0 and variance is Step 2.2b: According to y e , get the signal to interference and noise ratio SINR of the illegal eavesdropper e , Then we can get the eavesdropping rate R of the illegal eavesdropper: e , R e =log2(1+SINR e ).

5. The active RIS-ISAC system beamforming method for preventing eavesdropping and interference according to claim 4 is characterized in that In step 2, the beam gain B(θ) generated in the direction of the active RIS to the enemy target is expressed as Where θ represents the angle of the enemy target relative to the active RIS, a(θ) represents the guidance vector of the active RIS, and a(θ) = [1, e jπsin(θ) ,...,e jπ(M-1)sin(θ) ] T , the superscript "T" indicates the transpose operation.

6. The active RIS-ISAC system beamforming method for preventing eavesdropping and interference according to claim 5 is characterized in that In step 2, the total system power consumption P total Represented as P total =P BS +P RIS , where P BS represents the power consumed by the base station, P RIS represents the power consumed by active RIS, "||·|| F " is the F norm operator symbol, P SW Represents the power consumed by phase conversion and control, P DC Indicates the DC circuit power.

7. The active RIS-ISAC system beamforming method for preventing eavesdropping and interference according to claim 4 is characterized in that h B 、h R and F is modeled as the Rice channel model, in, Indicates the line-of-sight link from the base station to the legitimate user, Indicates the non-line-of-sight link from the base station to the legitimate user, Indicates the line-of-sight link from the active RIS to the legitimate user. Indicates a non-line-of-sight link from an active RIS to a legitimate user. and All obey a complex Gaussian distribution with a mean of 0 and a variance of 1, β B represents the path loss from the base station to the legitimate user, C0 represents the path loss at the reference distance d0, d B represents the path distance from the base station to the legitimate user, α B represents the path loss index from the base station to the legitimate user, κ B represents the Rice factor from the base station to the legitimate user, β R represents the path loss from active RIS to legitimate users, d R represents the path distance from the active RIS to the legitimate user, α R represents the path loss index from active RIS to legitimate users, κ R The Rice factor F represents the active RIS to the legitimate user. LoS represents the line-of-sight link from the base station to the active RIS, F NLoS represents the non-line-of-sight link from the base station to the active RIS, F NLoS It follows a complex Gaussian distribution with mean 0 and variance 1, β F represents the path loss from the base station to the active RIS, d F represents the path distance from the base station to the active RIS, α F represents the path loss index from the base station to the active RIS, κ F represents the Rice factor from the base station to the active RIS; in, Indicates h l The estimated value of Δh l Indicates h l The estimation error, ||Δh l ||2≤∈ l , l represents h l The upper bound of the estimation error is δ represents the error percentage, Indicates g i The estimated value of Δg i Indicates g i The estimation error, ||Δg i ||2≤∈ i ,∈ i Indicates g i The upper bound of the estimation error is and Modeled as the Rice channel model, When l is JR Indicates the line-of-sight link from the enemy target to the active RIS, Indicates the non-line-of-sight link from the enemy target to the active RIS, It follows a complex Gaussian distribution with mean 0 and variance 1, β JR represents the path loss from the enemy target to the active RIS, d JR represents the path distance from the enemy target to the active RIS, α JR represents the path loss index from the enemy target to the active RIS, κ JR Indicates the Rice factor from the enemy target to the active RIS, when l is JU represents the line-of-sight link from the enemy target to the legitimate user, represents the non-line-of-sight link from the enemy target to the legitimate user, It follows a complex Gaussian distribution with mean 0 and variance 1, β JU represents the path loss from the enemy target to the legitimate user, d JU represents the path distance from the enemy target to the legitimate user, α JU represents the path loss index from the enemy target to the legitimate user, κ JU Represents the Rice factor from the enemy target to the legitimate user, when i is B represents the line-of-sight link from the base station to the illegal eavesdropper, Indicates the non-line-of-sight link from the base station to the illegal eavesdropper, It follows a complex Gaussian distribution with mean 0 and variance 1. represents the path loss from the base station to the illegal eavesdropper, represents the path distance from the base station to the illegal eavesdropper, represents the path loss index from the base station to the illegal eavesdropper, Represents the Rice factor from the base station to the illegal eavesdropper, when i is R Indicates the line-of-sight link from the active RIS to the illegal eavesdropper. Indicates a non-line-of-sight link from an active RIS to an illegal eavesdropper. It follows a complex Gaussian distribution with mean 0 and variance 1. represents the path loss from the active RIS to the illegal eavesdropper, represents the path distance from the active RIS to the illegal eavesdropper, It represents the path loss index from active RIS to illegal eavesdropper, Represents the Rice factor of active RIS to illegal eavesdroppers.

8. The active RIS-ISAC system beamforming method for preventing eavesdropping and interference according to claim 6 is characterized in that In step 3, the optimization problem is described as: C2:R u ≥γ u , C3:R e ≤γ e , C4:φ m |≤p max ,m=1,...,M. in, represents the estimated value of θ, Δθ represents the estimated error of θ, and γ t represents the beam gain threshold, γ u represents the communication achievable rate threshold, γ e Indicates the eavesdropping rate threshold.

9. The active RIS-ISAC system beamforming method for preventing eavesdropping and interference according to claim 8 is characterized in that In step 4, sub-problem 1 is described as: Sub-problem 2 is described as: stC1,C2,C3,C4 10. The active RIS-ISAC system beamforming method for preventing eavesdropping and interference according to claim 9 is characterized in that In step 4, the specific process of transforming subproblem 1 into convex problem 1 is: Step 4.1a: Let Transform the objective function of subproblem 1 into Where Tr(·) means finding the trace of the matrix; Rewrite constraint C1 of subproblem 1 as constraint C1': make Rewrite constraint C2 of subproblem 1 as constraint C2': in, Then define the cascade channel h from the enemy target to the active RIS to the legitimate user JRU for The uncertainty model is Then the joint channel from the enemy target to the legitimate user Expressed as in, Indicates h JRU The estimated value of Δh JRU Indicates h JRU The estimation error of express The estimated value of express The estimation error of Next, define Combined with Convert the left side of constraint C2' to Constraint C2” is obtained, where φ represents the column vector composed of the coefficients of all reflective elements of the active RIS, φ=[φ1,φ2,…,φ M ] T , Re{·} represents taking the real part; then the constraint C2" is transformed into the form of a linear matrix inequality using the S lemma, that is, the constraint C2"' is obtained: Among them, λ represents a non-negative auxiliary variable, I M+1 represents the unit matrix of order M+1, Define the cascade channel G from the base station to the active RIS to the illegal eavesdropper BRE for The uncertainty model is Then the joint channel G from the base station to the illegal eavesdropper is expressed as in, Represents G BRE The estimated value of ΔG BRE Represents G BRE The estimation error of represents the estimated value of G, ΔG represents the estimation error of G, ||ΔG||2≤∈ G , Then transform constraint C3 of subproblem 1 into constraint C3': in, Then according to the properties of matrix trace: Tr(X H Y)=vec H (X)vec(Y), we have: Among them, vec(·) represents vectorized operation, is the Crocker inner product operator, g = vec(ΔG H ), Then transform constraint C3' into constraint C3": Then, the S lemma is used to transform the constraint C3” into a linear matrix inequality, that is, the constraint C3”’ is obtained: Among them, μ represents a non-negative auxiliary variable, I (M+1)N represents the unit matrix of (M+1)N order; Step 4.1b: Formulate the convex problem 1 as: The specific process of transforming subproblem 2 into convex problem 2 is: Step 4.2a: Introduce the auxiliary variable η and transform subproblem 2 into: C1,C2,C3,C4 Step 4.2b: Rewrite constraint C5 as constraint C5': in, Then Substituting into constraint C5', we get Then there is constraint C5": Then, we use the S lemma to transform the constraint C5" into a linear matrix inequality, and obtain the constraint C5"': Among them, ζ represents a non-negative auxiliary variable; Convert constraint C4 to constraint C4': in, express The mth element of ; Step 4.2c: Formulate convex problem 2 as: in, I M represents the unit matrix of order M, the superscript "*" represents the conjugate operation, Indicated by The submatrix consisting of the 1st to Mth rows and the 1st to Mth columns of .

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