ISAC system secure transmission method for illegal RIS scene

Abstract

The invention discloses an ISAC system secure transmission method for an illegal RIS scene, and the method constructs an ISAC system based on RIS assistance to complete communication and perception tasks at the same time. The sensing signal can effectively interfere with an IRIS-assisted eavesdropping link while completing the sensing task. In order to more effectively cope with the challenge of system safety performance deterioration caused by IRIS, a beam forming matrix of radar signals is designed. In consideration of the uncertainty of the accurate position of an eavesdropper (Eve), the traversal safety rate is used to represent the safety performance of the system, and an approximate traversal eavesdropping rate is deduced. In order to efficiently solve the two non-convex optimization problems, a traversal objective function is properly converted, and then an efficient alternating optimization algorithm is proposed to solve each optimization variable. Through verification, the combination of the beam forming and the RIS reflection design is more effective in the eavesdropping process of the IRIS, and the key function of the radar beam forming design in the aspect of relieving the influence of the IRIS is revealed at the same time.

Description

Technical Field

[0001] The invention relates to an ISAC system security transmission method for illegal RIS scenarios, and belongs to the technical field of wireless communication. Background Art

[0002] In recent years, driven by the demand for high-precision applications, the industry has begun to explore the construction of an integrated perception and communication network. This has led to the emergence and development of a new concept: Integrated Sensing and Communication (ISAC). According to the main design focus, ISAC systems can be divided into three types: communication-centric (C&C) design, radar-centric (R&C) design, and joint waveform design. In C&C design, perception is integrated into the communication platform, giving priority to communication performance. In contrast, R&C design gives priority to perception, embedding information into the perception waveform to achieve communication without affecting perception performance too much. However, neither design achieves a balance between perception and communication, thus accelerating the development of a third design approach. This approach focuses on tailoring new ISAC waveforms that do not rely on existing communication or radar signals. Therefore, it provides a higher degree of freedom to achieve both communication and perception functions. In this ISAC system, the communication and radar systems share the same spectrum resources and can assist each other. This approach significantly improves the spectrum, energy, and hardware utilization of the system while providing additional integration and assistance gains.

[0003] Although ISAC brings performance improvements, it still faces challenges such as data transmission security. These challenges are mainly caused by the broadcast and superposition characteristics of wireless communication systems. Specifically, the security of wireless transmission systems depends on the difference in channel gain between the base station (BS) and the legitimate user / eavesdropper (Eve). There are existing technologies to enhance physical layer security (PLS) by amplifying this difference, such as transmit beamforming, cooperative jamming, and relaying. However, the use of a large number of active jammers and relays will lead to a large amount of energy consumption and high hardware costs. In addition, under adverse wireless propagation conditions, these technologies are not very effective in enhancing PLS. Fortunately, the recently emerged Reconfigurable Intelligent Surface (RIS) technology provides a potential pioneering solution. In essence, RIS is a planar array composed of many passive reflective elements, which can control the direction of the reflected signal by adjusting the phase shift of the incident signal. Therefore, the reflected signals from RIS can be combined constructively or destructively at the desired receiver.

[0004] Recently, many studies have introduced RIS into ISAC systems to improve their security performance. However, although RIS has advantages such as low cost and reconfigurability, it also brings serious potential risks. Specifically, potential attackers can reduce the security performance of legitimate communication links or enhance the eavesdropping effect of eavesdropping links by properly deploying RIS, thus posing a serious security threat to wireless communication systems. This has gradually led to the concept of illegal RIS (Illegal Reconfigurable Intelligent Surface, IRIS). Many recent studies have further explored the role of IRIS in facilitating jamming attacks. In addition to jamming attacks, IRIS can also be used to facilitate eavesdropping activities. It is worth noting that when the direct link of BS-Eve deteriorates or is blocked, the channel reconstruction capability of IRIS becomes crucial to assist the eavesdropping process. These studies highlight the security challenges currently faced by wireless communication systems. Although some scholars have proposed a countermeasure based on artificial noise (AN) to deal with the eavesdropping threat caused by IRIS, this scheme will significantly limit the potential performance of the system under power-limited conditions. Therefore, an AN-based jamming scheme is needed that can effectively interfere with IRIS while minimizing the "waste" of AN. The current ISAC system is very suitable for this task. It can use radar signals to locate IRIS and interfere with potential attackers at the same time, thereby improving the utilization of radar signals while enhancing system security. However, there is currently no relevant research to solve the signal leakage problem caused by IRIS in the ISAC system. Summary of the invention

[0005] The purpose of the present invention is to address the defects and shortcomings of the above-mentioned prior art and propose a secure transmission method for the ISAC system for illegal RIS scenarios. The method can utilize the sensing signal to achieve interference to the IRIS and its auxiliary equipment while completing target detection. The method can also analyze the echo signals from around the IRIS to complete the positioning of the IRIS, thereby effectively countering potential eavesdroppers assisted by the IRIS.

[0006] The technical solution adopted by the present invention to solve the technical problem is: a method for secure transmission of an ISAC system for illegal RIS scenarios, the method comprising the following steps:

[0007] Step 1: Establish a system model including BS, RIS, IRIS, users and sensing targets, where the BS is equipped with M transmitting and receiving antennas, and each antenna is a uniform linear array with half-wavelength spacing. With the help of RIS, the BS simultaneously completes the communication and sensing tasks.

[0008] Step 2: From the perspective of legitimate devices, construct a transmit / receive beamforming matrix associated with the BS and the associated optimization problem of the RIS phase shift to maximize the system security rate, while satisfying the system target detection requirements, the BS transmit power budget, and the unit constant modulus constraint of each RIS phase shift. From the perspective of eavesdropping, construct an optimization problem to maximize the eavesdropping rate by adjusting the IRIS reflection phase shift, and satisfy the unit constant modulus constraint of the IRIS phase shift.

[0009] Step 3: Based on the possible location range of Eve, derive the system's traversal safety rate. And use closed-form fractional programming (CFFP) to convert the optimization problem of legal angles into a form that is easy to solve. The same method can be used to deal with the optimization problem of eavesdropping angles.

[0010] Step 4: An alternating optimization method combining Taylor approximation, minimization-maximization algorithm (MM) and alternating optimization method of multipliers (ADMM) is used to solve the transmit / receive beamforming matrix of the BS and the RIS phase shift.

[0011] Step 5: Based on the algorithm proposed in step 4, simulate and analyze how to design the ISAC system to obtain better security performance.

[0012] Furthermore, the above step 1 of the present invention establishes an ISAC system consisting of an ISAC BS with M transmitting and receiving antennas, a single N-element RIS, a single Ne-element IRIS, a point-like sensing target, a single-antenna Eve, and K single-antenna users. The ISAC BS simultaneously completes the tasks of communicating with the user and sensing a point-like target. To avoid the location being monitored, Eve hides herself behind an obstacle and only completes the eavesdropping activity through the cascade link BS-IRIS-Eve constructed by IRIS. The dual-function signal transmitted by the system model BS of the present invention in the lth time slot is:

[0013] x[l]=W c s c [l]+W r s r [l]=Ws[l]

[0014] in Contains the communication symbols of K users, satisfying Contains M independent radar waveforms, and has Assuming that the communication and perception symbols do not interfere with each other, we have and denote the beamforming matrices for communication and radar, respectively. In particular, to facilitate the representation of the transmit signal matrix, we define a joint signal vector and a joint beamforming matrix W = [W c W r ], where w i is the i-th column of the joint beamforming matrix W.

[0015] We assume that the channel gain G,H of the communication link d,i ,h i,e ,h d,k ,h r,k for All are subject to the Rice decline, such as h i,e Expressed as in and κ represent the path loss and Ricean factor respectively, h (i,e)LoS is the LoS fading component that depends on the problem geometry, h (i,e)NLoS represents the NLOS fading component with zero mean and unit variance. In particular, where ζ0 and Represent the path loss coefficient and path loss exponent respectively. where θ IE represents the arrival angle between IRIS and Eve, and the steering vector For this communication system, we assume that the BS uses the existing advanced channel estimation method, except h i,e Except for h, all other channels are known. The radar sensing channels are considered to be line-of-sight links. d,t =α dt a M (θ1),h r,t =α rt a N (θ2), where θ1 and θ2 are the arrival angles of the sensing target relative to the BS and RIS respectively. Under this model, the safety rate of the system can be expressed as follows:

[0016]

[0017] where h k (φ)=h d,k +G T Φ T h r,k represents the channel from the base station to the user, the phase shift matrix Φ = diag(φ) reflected by RIS, where φ = [φ1, φ2, ..., φ N ] T ,and Likewise, is the channel gain between IRIS and Eve, is the channel gain between BS and IRIS, the phase shift matrix Φ reflected by IRIS e =diag(φe ),in and Scalar n k [l] and n e [l] are the additive white Gaussian noise generated during communication and eavesdropping, respectively.

[0018] The BS analyzes the received target reflected echo signal and can obtain the target detection probability P under the ISAC system. D Relationship with each variable:

[0019]

[0020] in is the additive white Gaussian noise generated by the perception process, represents the receive beamforming matrix of the BS.

[0021] Furthermore, the expression of the problem of maximizing the system safety rate established from a legal perspective in step 2 of the present invention is as follows: P1:

[0022] st C2:

[0023] C3:

[0024] Among them, the objective function represents the safe rate when traversing the possible locations of Eve; constraint C1 represents the constraint on the target detection probability; constraint C2 represents the unit constant modulus constraint on the RIS phase shift; constraint C3 represents the constraint on the total BS transmission power.

[0025] Furthermore, the optimization problem of maximizing the eavesdropping rate established in step 2 of the present invention from the perspective of eavesdropping is as follows:

[0026] P2:

[0027] stC1:

[0028] The constraint C1 represents the unit constant modulus constraint of the IRIS phase shift.

[0029] Furthermore, the process of deriving the conversion problem P1 in step 3 of the present invention is as follows:

[0030] The approximation of the traversal security rate is obtained, and CFFP is used to convert the problem P1 into a solved form. Since only the eavesdropping rate part is traversed, according to

[0031] Proposition 1: Assume and is a non-negative random variable x i and i The sum of , then we have the following approximation

[0032]

[0033] The accuracy of this approximation increases with K x and K y increases with the increase of . So there is

[0034]

[0035] When Eve is uniformly distributed in the plane area When , we can get the following probability distribution:

[0036]

[0037] Similarly, the approximate ergodic eavesdropping rate under other probability distribution conditions can be obtained.

[0038] The CFFP transformation is used to transform the objective function into a form that is easy to solve:

[0039] where γ k and k They represent two auxiliary variables introduced in the CFFP conversion process, and const(1) and const(2) are constant terms independent of the variables. Similarly, the CFFP conversion of problem P2 can be obtained.

[0040] The eavesdropping rate can be calculated by introducing two exponential auxiliary variables C e =[C e,1 ,C e,2 ,...,C e,K ] T and D e =[D e,1 ,D e,2 ,...,D e,K ] T To complete the transformation of the eavesdropping rate function from non-convex to convex, we first define:

[0041]

[0042] So problem P1 can be transformed into

[0043]

[0044] stC1: C2: C3: C4:

[0045] C5:

[0046] Furthermore, the optimization process of problem P1 in step 4 of the present invention is as follows. We use an alternating optimization algorithm to optimize the converted problem P1:

[0047] Update the auxiliary variables γ and y: We only need to take partial derivatives to solve.

[0048]

[0049] Update the beamforming matrix W: Since we have obtained the explicit objective function of the beamforming matrix W in the above CFFP transformation, we only need to make the constraint term convex. We need to first replace the right side of constraint C1 with A first-order Taylor expansion is performed at is the optimal value at the last iteration and is converted into a second-order cone constraint.

[0050]

[0051] in Then constrain the left side of C2 at w i Perform a first-order Taylor expansion at

[0052] At this point, the problem of solving W can be transformed into a convex problem, which can be solved by standard convex optimization techniques.

[0053] Update the BS receiving beamforming matrix u: Solving this matrix is ​​an unconstrained optimization problem. In order to accelerate the convergence of the algorithm, we solve the following unconstrained problem to update u:

[0054]

[0055] It can be found that this is a typical Rayleigh quotient problem, and its optimal solution is

[0056]

[0057] Update RIS phase shift φ: Since RIS phase shift φ is not included in the eavesdropping rate of the objective function, and constraints C1, C2, and C5 are all independent of variable φ. In addition, the explicit objective function about phase shift φ has been obtained in the above CFFP conversion, so we only need to convert the constraint term to convex. We first use the MM algorithm to find an approximate convex constraint for radar perception constraint C3, and then use the ADMM algorithm to construct the Lagrangian function to solve the unit constant modulus constraint C4.

[0058] Specifically, the radar perception constraints are first converted:

[0059]

[0060] in Then use the MM algorithm to solve the non-convex term u on the left side of the inequality H Lvec(φφ H ) Find a linear lower bound. Let L = (u H L) T , and Again Performing a first-order Taylor expansion at point φ gives the following relationship:

[0061]

[0062] where λ is a matrix The maximum eigenvalue of N JI N ] H At this point, the original radar constraint can be written as:

[0063]

[0064] in

[0065] Next, the Lagrangian function is constructed through the ADMM algorithm to solve the unit constant modulus constraint C4, and the planning problem is solved as follows:

[0066] stC1:

[0067] C2:

[0068] C3:

[0069] C4: Obviously, this is a convex problem.

[0070] Furthermore, the above step 5 of the present invention can obtain better security performance by combining the transmit / receive beamforming matrix of the BS and the RIS phase shift design according to the proposed algorithm, and also reveals the key role of the perceptual beamforming matrix design in alleviating the impact of IRIS eavesdropping.

[0071] Beneficial effects:

[0072] 1. The present invention makes full use of the characteristics of the ISAC system, allowing the sensing signal to complete the sensing task while interfering with the potential equipment assisted by IRIS, and at the same time improves the security performance of the system by deploying legal RIS.

[0073] 2. The present invention finds a suitable solution for the communication and sensing beamforming matrix. In order to simulate the real IRIS reflection phase shift, the present invention also completes the IRIS phase shift design from the perspective of eavesdropping to maximize the eavesdropping rate. After simulation verification, the present invention can effectively improve the security performance of the system in scenarios with limited resources such as power. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A diagram of a secure ISAC system model in an illegal RIS scenario provided by the present invention.

[0075] Figure 2 The present invention provides an overall beam diagram of a system under normal operation.

[0076] Figure 3 A convergence diagram of the safe rate that the system can achieve under various condition parameters provided by the present invention.

[0077] Figure 4 The present invention provides a safe rate diagram that can be obtained by each optimization scheme under different numbers of IRIS reflection units. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0079] like Figure 1 As shown, the present invention establishes an ISAC BS consisting of an M-transmitting and receiving antenna, a single N-element RIS, a single N e The ISAC system consists of an IRIS array element, a point-like sensing target, a single-antenna Eve, and K single-antenna users. The ISACBS simultaneously completes the tasks of communicating with the user and sensing a point-like target. To avoid being monitored, Eve hides herself behind an obstacle and only completes the eavesdropping activity through the cascade link BS-IRIS-Eve constructed by IRIS.

[0080] The dual-function signal transmitted by the BS in the system model of the present invention in the lth time slot is:

[0081] x[l]=W c s c [l]+W r s r [l]=Ws[l]

[0082] in Contains the communication symbols of K users, satisfying Contains M independent radar waveforms, and has Assuming that the communication and perception symbols do not interfere with each other, we have and denote the beamforming matrices for communication and radar, respectively. In particular, to facilitate the representation of the transmit signal matrix, we define a joint signal vector and a joint beamforming matrix W = [W c W r ], where w i is the i-th column of the joint beamforming matrix W.

[0083] We assume that the channel gain G,H of the communication link d,i ,h i,e ,h d,k ,h r,k for All are subject to the Rice decline, such as h i,e Expressed as in and κ represent the path loss and Ricean factor respectively, h (i,e)LoS is the LoS fading component that depends on the problem geometry, h (i,e)NLoS represents the NLOS fading component with zero mean and unit variance. In particular, where ζ0 and Represent the path loss coefficient and path loss exponent respectively. where θ IE represents the arrival angle between IRIS and Eve, and the steering vector For this communication system, we assume that the BS uses the existing advanced channel estimation method, except h i,e Except for h, all other channels are known. The radar sensing channels are considered to be line-of-sight links. d,t =α dt a M (θ1),h r,t =α rt a N (θ2), where θ1 and θ2 are the arrival angles of the sensed target relative to the BS and RIS, respectively.

[0084] Under this model, the safe rate of the system can be expressed as follows:

[0085]

[0086] where h k (φ)=h d,k +G T Φ T h r,k represents the channel from the base station to the user, the phase shift matrix Φ = diag(φ) reflected by RIS, where φ = [φ1, φ2, ..., φN ] T ,and Likewise, is the channel gain between IRIS and Eve, is the channel gain between BS and IRIS, the phase shift matrix Φ reflected by IRIS e =diag(φ e ),in and Scalar n k [l] and n e [l] are the additive white Gaussian noise generated during communication and eavesdropping, respectively.

[0087] The BS analyzes the received target reflected echo signal and can obtain the target detection probability P under the ISAC system. D Relationship with each variable

[0088]

[0089] in is the additive white Gaussian noise generated by the perception process, represents the receive beamforming matrix of the BS.

[0090] Step 2 The expression of the problem of maximizing the system safety rate from a legal perspective is as follows:

[0091] P1:

[0092] stC1:

[0093] C2:

[0094] C3:

[0095] Among them, the objective function represents the safe rate when traversing the possible locations of Eve; constraint C1 represents the constraint on the target detection probability; constraint C2 represents the unit constant modulus constraint on the RIS phase shift; constraint C3 represents the constraint on the total BS transmission power.

[0096] Step 2: The optimization problem of maximizing the eavesdropping rate from the eavesdropping perspective is as follows:

[0097] P2:

[0098] stC1:

[0099] The constraint C1 represents the unit constant modulus constraint of the IRIS phase shift.

[0100] Step 3 The process of deriving the transformation problem P1 is as follows:

[0101] The approximation of the traversal security rate is obtained, and CFFP is used to convert the problem P1 into a solved form. Since only the eavesdropping rate part is traversed, according to

[0102] Proposition 1: Assume and is a non-negative random variable x i and i The sum of , then we have the following approximation

[0103]

[0104] The accuracy of this approximation increases with K x and K y increases with the increase of . So there is

[0105]

[0106] When Eve is uniformly distributed in the plane area When

[0107]

[0108] Similarly, the approximate ergodic eavesdropping rate under other probability distribution conditions can be obtained.

[0109] The CFFP transformation is used to transform the objective function into a form that is easy to solve:

[0110] where γ k and k They represent two auxiliary variables introduced in the CFFP conversion process, and const(1) and const(2) are constant terms independent of the variables. Similarly, the CFFP conversion of problem P2 can be obtained.

[0111] The eavesdropping rate can be calculated by introducing two exponential auxiliary variables C e =[C e,1 ,C e,2 ,...,C e,K ] T and D e =[D e,1 ,D e,2 ,...,D e,K ] T To complete the transformation of the eavesdropping rate function from non-convex to convex, we first define:

[0112]

[0113] So problem P1 can be transformed into

[0114]

[0115] stC1:

[0116] C2:

[0117] C3:

[0118] C4:

[0119] C5:

[0120] The optimization process for problem P1 in step 4 is as follows. We use the alternating optimization algorithm to optimize the transformed problem P1: Update the auxiliary variables γ and y: We only need to take partial derivatives to solve it.

[0121]

[0122] Update the beamforming matrix W: Since we have obtained the explicit objective function of the beamforming matrix W in the above CFFP transformation, we only need to make the constraint term convex. We need to first replace the right side of constraint C1 with A first-order Taylor expansion is performed at is the optimal value at the last iteration and is converted into a second-order cone constraint.

[0123]

[0124] in Then constrain the left side of C2 at w i Perform a first-order Taylor expansion at

[0125] At this point, the problem of solving W can be transformed into a convex problem, and then solved using the cvx toolbox and standard convex optimization techniques.

[0126] Update the BS receiving beamforming matrix u: The solution is an unconstrained optimization problem. In order to accelerate the convergence of the algorithm, we solve the following unconstrained problem to update u:

[0127]

[0128] It can be found that this is a typical Rayleigh quotient problem, and its optimal solution is

[0129]

[0130] Update RIS phase shift φ: Since RIS phase shift φ is not included in the eavesdropping rate of the objective function, and constraints C1, C2, and C5 are all independent of variable φ. The explicit objective function about phase shift φ has been obtained in the above CFFP conversion, so we only need to convert the constraint term to convex. We first use the MM algorithm to find an approximate convex constraint for radar perception constraint C3, and then use the ADMM algorithm to construct the Lagrangian function to solve the unit constant modulus constraint C4.

[0131] Specifically, the radar perception constraints are first converted:

[0132]

[0133] in Then use the MM algorithm to solve the non-convex term u on the left side of the inequality H Lvec(φφ H ) Find a linear lower bound. Let L = (u H L) T , and Again Performing a first-order Taylor expansion at point φ gives the following relationship:

[0134]

[0135] where λ is a matrix The maximum eigenvalue of N JI N ] H At this point, the original radar constraint can be written as:

[0136]

[0137] in

[0138] Next, the Lagrangian function is constructed through the ADMM algorithm to solve the unit constant modulus constraint C4, and the planning problem is solved as follows:

[0139] stC1:

[0140] C2:

[0141] C3:

[0142] C4: Obviously, this is a convex problem.

[0143] Step 5: According to the proposed algorithm, the combined BS transmit / receive beamforming matrix and RIS phase shift design can achieve better security performance. This reveals the key role of the sensing beamforming matrix design in mitigating the impact of IRIS.

[0144] The preferred embodiments of the present invention have been specifically described above, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. An ISAC system security transmission method for illegal RIS scenarios, characterized in that: The method comprises the following steps: Step 1: Establish a system model including BS, RIS, IRIS, users and sensing targets, where the BS is equipped with M transmitting and receiving antennas, and each antenna is a uniform linear array with a half-wavelength spacing. The BS completes the communication and sensing tasks simultaneously with the help of RIS. Step 2: From the perspective of legitimate devices, a transmit / receive beamforming matrix associated with the BS and an associated optimization problem of the RIS phase shift are constructed to maximize the system security rate, while satisfying the system target detection requirements, the BS transmit power budget, and the unit constant modulus constraint of each RIS phase shift. From the perspective of eavesdropping, an optimization problem is constructed to maximize the eavesdropping rate by adjusting the IRIS reflection phase shift, while satisfying the unit constant modulus constraint of the IRIS phase shift. Step 3: Based on the possible location range of Eve, the traversal safety rate of the system is derived, and the closed-form fractional programming (CFFP) is used to transform the optimization problem of the legal angle into a form that is easy to solve. The same method can be used to deal with the optimization problem of the eavesdropping angle; Step 4: An alternating optimization method combining Taylor approximation, minimization-maximization algorithm (MM) and alternating optimization method of multipliers (ADMM) is used to solve the transmit / receive beamforming matrix of the BS and the RIS phase shift; Step 5: Based on the algorithm proposed in step 4, simulate and analyze how to design the ISAC system to obtain better security performance.

2. According to claim 1, a method for secure transmission of an ISAC system for illegal RIS scenarios, characterized in that: The dual-function signal transmitted by the system model BS in the lth time slot is: x[l]=W c s c [l]+W r s r [l]=Ws[l] in Contains the communication symbols of K users, satisfying Contains M independent radar waveforms, and has Assuming that the communication and perception symbols do not interfere with each other, we have and Denote the beamforming matrices for communication and radar respectively, and define a joint signal vector and a joint beamforming matrix W = [W c W r ], where w i is the i-th column of the joint beamforming matrix W.

3. The ISAC system secure transmission method for illegal RIS scenarios according to claim 1, characterized in that: Under the system model, the safe rate of the system can be expressed as follows: where h k (φ)=h d,k +G T Φ T h r,k represents the channel from the base station to the user, the phase shift matrix Φ = diag(φ) reflected by RIS, where φ = [φ1, φ2, ..., φ N ] T ,and Likewise, is the channel gain between IRIS and Eve, is the channel gain between BS and IRIS, the phase shift matrix Φ reflected by IRIS e =diag(φ e ),in and Scalar n k [l] and n e [l] are the additive white Gaussian noise generated during communication and eavesdropping, respectively.

4. The ISAC system secure transmission method for illegal RIS scenarios according to claim 1, characterized in that: Under the system model, the target detection probability of the system is P D in is the additive Gaussian white noise generated by the perception process, represents the receive beamforming matrix of the BS.

5. The ISAC system secure transmission method for illegal RIS scenarios according to claim 1, characterized in that: In step 2, a transmit / receive beamforming matrix associated with the BS and an associated optimization problem of the RIS phase shift are constructed to maximize the system safety rate problem, while satisfying the system target detection requirements, the BS transmit power budget, and the unit constant modulus constraint of each RIS phase shift: Among them, the objective function represents the safe rate when traversing the possible locations of Eve; constraint C1 represents the constraint on the target detection probability; constraint C2 represents the unit constant modulus constraint on the RIS phase shift; constraint C3 represents the constraint on the total power transmitted by the BS; From the perspective of eavesdropping, an optimization problem is constructed to maximize the eavesdropping rate by adjusting the IRIS reflection phase shift, and satisfying the unit constant modulus constraint of the IRIS phase shift; The constraint C1 represents the unit constant modulus constraint of the IRIS phase shift.

6. The ISAC system secure transmission method for illegal RIS scenarios according to claim 1, characterized in that: In step 3, the traversal security rate is approximated, and CFFP is used to convert problem P1 into a solved form. Since only the eavesdropping rate part is traversed, according to Proposition 1: Assume and is a non-negative random variable x i and i The sum of , then we have the following approximation The accuracy of this approximation increases with K x and K y increases with the increase of , so we have: When Eve is uniformly distributed in the plane area S e ={d IE ,θ IE |d IE ∈[d min ,d max ],θ IE ∈[θ1,θ2]}, we can get the following according to the probability distribution: Similarly, the approximate ergodic eavesdropping rate under other probability distributions can be obtained; The CFFP transformation is used to transform the objective function into a form that is easy to solve: where γ k and k They represent two auxiliary variables introduced in the CFFP conversion process, const(1) and const(2) are constant terms independent of the variables. Similarly, the CFFP conversion of problem P2 can be obtained. The eavesdropping rate can be calculated by introducing two exponential auxiliary variables C e =[C e,1 ,C e,2 ,...,C e,K ] T and D e =[D e,1 ,D e,2 ,...,D e,K ] T To complete the transformation of the eavesdropping rate function from non-convex to convex, first define: Therefore, problem P1 can be transformed into:

7. The ISAC system secure transmission method for illegal RIS scenarios according to claim 1, characterized in that: The step 4 specifically includes: The alternating optimization algorithm is used to optimize the transformed problem P1; Update auxiliary variables γ and y: We only need to take partial derivatives to solve; Update the beamforming matrix W: Substitute the equation on the right side of constraint C1 into A first-order Taylor expansion is performed at is the optimal value of the last iteration, and is converted into a second-order cone constraint. Then, the left side of constraint C2 is constrained at w i By performing a first-order Taylor expansion at , the problem of solving W can be transformed into a convex problem; Update the BS receiving beamforming matrix u: The solution is an unconstrained optimization problem. In order to accelerate the convergence of the algorithm, we solve the following unconstrained problem to update u: Update RIS phase shift φ: Since the RIS phase shift φ is not included in the eavesdropping rate of the objective function, and constraints C1, C2, and C5 are independent of the variable φ, the MM algorithm is used to find an approximate convex constraint for the perception demand constraint C3, and then the ADMM algorithm is used to construct the Lagrangian function to solve the unit constant modulus constraint C4.

Images