Optimization method and system of RIS-assisted multi-user multi-target security ISAC system and related device

Abstract

The invention provides an optimization method and system of an RIS-assisted multi-user multi-target safety ISAC system and a related device, the system comprises an ISAC base station, a communication user, a radar target and an RIS, the ISAC base station is used for carrying out beam forming emission on data symbols of the communication user and transmitting signals after beam forming to the communication user; the radar target is used for stealing a signal transmitted to a communication user by the ISAC base station; the RIS is used for combining a phase shift matrix of the RIS with a beam former of the ISAC base station so as to jointly adjust secure communication of the ISAC system; comprising the following steps: step 1, obtaining an ISAC system optimization target; step 2, obtaining constraint conditions of an optimization target; step 3, constructing an optimization problem of the ISAC system according to the optimization target and the constraint condition; 4, solving the optimization problem to obtain the optimal parameters of the ISAC system; according to the method, the complex non-convex problem in the system is effectively solved, the design scheme of the optimal parameters of the system is provided, and the calculation complexity is low.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication physical layer security transmission, and relates to an optimization method, system and related devices of a RIS-assisted multi-user multi-target security ISAC system. Background Art

[0002] In recent years, with the rapid development of wireless communication and perception technologies, new applications that deeply integrate perception and communication functions have emerged. The traditional concept of designing communication and perception functions independently has gradually been broken. Both in academia and industry, people are increasingly aware of the potential advantages of integrating perception and communication functions into a single platform. As a result, the ISAC system with integrated perception and communication functions was born, which significantly improves spectrum utilization efficiency through resource sharing and enhances system flexibility. The original intention of the ISAC system was to share software and hardware resources, including antenna arrays, signal processing units, and power supply systems, to achieve efficient detection of radar targets and high-quality services to communication users. By integrating perception and communication functions, the system has shown broad application prospects in many fields, such as autonomous driving, smart cities, industrial automation, and telemedicine.

[0003] However, the application of ISAC system also faces unique security challenges. On the one hand, due to the broadcast characteristics of wireless media, there is a potential risk that the communication content will be received by illegal targets; on the other hand, in perception applications, the radar system needs to illuminate the radar target with higher power to obtain the required perception information, which further increases the possibility of confidential information being leaked to the radar target. If these radar targets are illegal eavesdroppers, the system will face serious eavesdropping threats. Therefore, how to design the ISAC system so that it can achieve secure communication while taking into account radar detection performance has become an important and challenging issue.

[0004] In recent years, physical layer security (PLS) technology has been widely used to combat malicious eavesdropping in wireless communications. In traditional single-function wireless communication systems, it can significantly improve system security performance. However, in ISAC systems, due to the need to take into account perception requirements, the improvement of system security performance is largely limited by the quality of the wireless propagation environment.

[0005] At the same time, the reconfigurable smart surface (RIS) can flexibly adjust the amplitude and phase of the reflected electromagnetic wave with its unique ability, thereby realizing intelligent control of the wireless propagation environment. This feature provides a new solution for secure communications in the ISAC system. However, how to effectively combine the active beamforming of the base station and the passive beam steering of the RIS in the design to further improve the communication security performance of the ISAC system is still a key issue that needs to be solved. Summary of the invention

[0006] The object of the present invention is to provide an optimization method, system and related devices for a RIS-assisted multi-user multi-target secure ISAC system, which solves the defect of low security performance of the existing ISAC system.

[0007] To achieve the above object, the technical solution adopted by the present invention is:

[0008] In the first aspect, an optimization method for a RIS-assisted multi-user multi-target secure ISAC system provided by the present invention is based on a RIS-assisted multi-user multi-target secure ISAC system, which includes an ISAC base station, communication users, radar targets and a RIS. Among them, the ISAC base station is used to perform beamforming transmission on the data symbols of the communication users and transmit the beamformed signal to the communication users; the radar targets are used to steal the signals transmitted by the ISAC base station to the communication users; the RIS is used to combine its phase shift matrix with the beamformer of the ISAC base station to jointly adjust the secure communication of the ISAC system.

[0009] The method includes the following steps:

[0010] Step 1, obtain the optimization objective of the ISAC system;

[0011] Step 2, obtain the constraint conditions of the optimization objective;

[0012] Step 3, construct an optimization problem of the ISAC system according to the optimization objective and the constraint conditions;

[0013] Step 4, solve the optimization problem to obtain the optimal parameters of the ISAC system.

[0014] Preferably, in step 1, to obtain the optimization objective of the ISAC system, the specific method is:

[0015] S1, calculate the received signals of the RIS, communication users and radar targets respectively;

[0016] S2, calculate the achievable information rates of the communication users and radar targets respectively;

[0017] S3, assuming that the radar targets can independently decode the signals, define the sum of the secrecy rates of the communication users as the optimization objective, where the secrecy rate of the communication user is the difference between the achievable rate of the communication user and the maximum achievable rate in the radar target group.

[0018] Preferably, in step 2, to obtain the constraint conditions of the optimization objective, the constraint conditions include the transmission power constraint of the ISAC base station, the unit modulus constraint of the diagonal elements of the RIS phase shift matrix and the sensing constraint condition. Among them, the sensing constraint condition is that to successfully detect each radar target, it is required that the detection probability of each radar target signal is not lower than a pre-set threshold.

[0019] Preferably, in step 3, an optimization problem of the ISAC system is constructed according to the optimization objective and the constraint conditions. The specific method is as follows:

[0020] By jointly adjusting the beamforming matrix of the ISAC base station and the phase shift matrix Q parameter of the RIS to maximize the secrecy rate of the ISAC system, an optimization problem is obtained, and its expression is as follows:

[0021]

[0022] where, C s is the secrecy rate of the ISAC system; C U,j is the achievable rate of the communication user; C T,k,j is the maximum achievable rate in the radar target group; Tr(·) is the matrix trace operation; represents the beamforming matrix sent by the ISAC base station to user j; P D,k represents the detection probability of the kth radar target, and γ k represents the preset radar detection threshold; Q i,i represents the i-th diagonal element of the phase shift matrix Q of the RIS, and |·| is the modulus operation.

[0023] Preferably, in step 4, the optimization problem is solved to obtain the optimal parameters of the ISAC system. The specific method is as follows:

[0024] Set two auxiliary variables, which are respectively the capacity of the eavesdropping channel of a single radar target on the communication user and the upper bound of the eavesdropping signal-to-noise ratio of the radar target on the communication user;

[0025] Use the block coordinate descent method to block the optimization variables of the optimization problem to obtain the base station beamforming matrix and the RIS phase shift matrix Q;

[0026] Use the alternating optimization method to iteratively optimize the two optimization variables in combination with the two auxiliary variables, and respectively obtain the optimal solution of the ISAC base station beamforming matrix and the optimal solution of the RIS phase shift matrix Q.

[0027] In the second aspect, an optimization system for a RIS-assisted multi-user multi-target secure ISAC system provided by the present invention includes:

[0028] An optimization objective acquisition unit for acquiring the optimization objective of the ISAC system;

[0029] A constraint condition acquisition unit for acquiring the constraint conditions of the optimization objective;

[0030] An optimization problem construction unit for constructing an optimization problem of the ISAC system according to an optimization objective and constraint conditions;

[0031] A parameter acquisition unit for solving the optimization problem to obtain the optimal parameters of the ISAC system.

[0032] In a third aspect, an electronic device provided by the present invention includes a processor and a memory, and a computer instruction is stored on the memory. When the computer instruction is executed by the processor, the electronic device executes the method described above.

[0033] In a fourth aspect, a computing device cluster provided by the present invention includes at least one computing device, and each computing device includes a processor and a memory;

[0034] The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device, so that the computing device cluster executes the method described above.

[0035] In a fifth aspect, a computer program product provided by the present invention includes computer-executable instructions, and the computer-executable instructions implement the method described above when executed.

[0036] In a sixth aspect, a computer-readable storage medium provided by the present invention stores computer-executable instructions, and the computer-executable instructions implement the method described above when executed by a processor.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] A RIS-assisted multi-user multi-objective secure ISAC system provided by the present invention realizes intelligent reconstruction of the wireless channel by jointly optimizing the beamforming of the ISAC base station and the phase shift matrix of the RIS, concentrates the signal energy on legitimate communication users, and at the same time forms deep nulls or interference in the direction of eavesdroppers (radar targets), effectively alleviating the limitation of the wireless channel conditions on the improvement of the system security performance. Utilizing this advantage of the RIS, the system provides an additional anti-eavesdropping degree of freedom for downlink secure communication, and significantly improves the system secrecy rate while ensuring the radar detection performance.

[0039] The optimization method of the ISAC system takes the secrecy rate of communication users as the optimization objective, directly improves the information difference between legitimate users and eavesdroppers, and ensures that even if there are multiple eavesdropping targets (radar targets), communication data is still difficult to be intercepted; constructs an optimization problem with the ISAC base station transmission power constraint, the unit modulus constraint of the diagonal elements of the RIS phase shift matrix, and the sensing constraint conditions, ensuring that secure communication does not sacrifice sensing capabilities, truly realizing the dual-functional coordination of ISAC. At the same time, on the premise of meeting security and sensing requirements, it significantly reduces the total system energy consumption and improves the energy efficiency ratio.

[0040] Furthermore, the optimization strategy based on the BCD algorithm adopted in the present invention can not only achieve efficient solution but also has a low computational complexity, successfully solving the complex non-convex optimization problem of maximizing the secrecy rate of the ISAC system. Brief Description of the Drawings

[0041] Figure 1 It is the RIS-assisted secure ISAC system model involved in the present invention;

[0042] Figure 2 It is the convergence and performance comparison diagram of the RIS-assisted ISAC secure communication joint optimization algorithm proposed in the present invention and the RIS random phase shift setting scenario;

[0043] Figure 3 It is the influence diagram of the allowable error sensing probability 1 - γ on the security performance;

[0044] Figure 4 It is the influence diagram of the number of RIS reflection elements and the transmission power on the secrecy rate;

[0045] Figure 5 It is the influence diagram of different numbers of transmitting and receiving antennas and the transmission power on the system secrecy rate. Detailed Description of the Embodiment

[0046] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0047] Embodiment 1

[0048] Figure 1 Shown is a RIS-assisted multi-user multi-target secure ISAC system provided in this embodiment, which includes an ISAC base station, a communication user group, a radar target group, and a reconfigurable intelligent surface RIS, where:

[0049] The ISAC base station is configured with an N t -element planar antenna array; the number of communication users is J, and each communication user is configured with an N r -element planar antenna array. Denote the communication user group as The number of radar targets is K, and each radar target is configured with a single antenna. Denote the radar target group as The reconfigurable intelligent surface (RIS) is configured with M reflecting elements.

[0050] The ISAC base station sends signals to communication users, and these signals are also used to detect radar targets; since the positions of radar targets are unknown, a scanning sensing strategy is adopted. In each time slot, the ISAC base station transmits a sensing beam in a selected direction and moves the beam to another direction in the next time slot; the radar targets are eavesdroppers that will eavesdrop on the content sent by the ISAC base station to communication users; joint adjustment of the beamforming matrix of the ISAC base station and the phase shift matrix of the RIS reflecting elements is used to assist secure communication.

[0051] The ISAC base station is used to beamform and transmit the data symbols of communication users and transmit the beamformed signals to communication users; the radar targets are used to steal the signals transmitted by the ISAC base station to communication users; the RIS is used to combine its phase shift matrix with the beamformer of the ISAC base station to jointly adjust the secure communication of the ISAC system.

[0052] This embodiment proposes an optimization method for a RIS-assisted multi-user multi-target secure ISAC system, including the following steps:

[0053] Step 1: Obtain the expression of the secrecy rate C s of the ISAC system. Specifically:

[0054] S11: Calculate the RIS received signal y I according to the following formula:

[0055]

[0056] where represents the channel between the ISAC base station and the RIS; represents the beamforming matrix between the ISAC base station and communication user j; represents the communication symbol stream of communication user j, represents the expectation function; N represents the length of each communication user symbol stream; I N,N is an identity matrix of dimension N; (·) H represents the conjugate transpose operation on the matrix.

[0057] S12. Calculate the signals received by the communication user and the radar target respectively according to the following formula:

[0058]

[0059] In the formula, y U,j represents the signal received by communication user j, and y T,k represents the signal received by radar target k. H BR is the channel between the ISAC base station and the RIS; H RU,j is the channel between the RIS and communication user j; is the conjugate transpose of h RT,k ; h RT,k is the channel between the RIS and radar target k. is the phase shift matrix of the RIS, where θ i , i ∈ Μ = {1,..., M} is the phase shift coefficient of reflection element i; is the channel between the RIS and radar target k; λ is the wavelength of the signal, and d RT,k is the distance between the RIS and radar target k; represents the steering vector from the RIS to radar target k, where is the departure angle of the link from the RIS to radar target k, where θ and φ are the azimuth angle and elevation angle respectively; for the convenience of representation, denote and are the Gaussian noises at communication user j and radar target k respectively, and are the Gaussian noise powers at communication user j and radar target k respectively.

[0060] S13. Based on the received signals, the achievable information rates of communication user j and radar target k are respectively expressed as C U,j and C T,k,j , specifically:

[0061]

[0062] S14. The radar target is an eavesdropper. Assuming that each radar target can independently decode the signal, define the secrecy rate C s,j of communication user j as the difference between the achievable rate C U,j of the communication user and the maximum achievable rate in the radar target group. Thus, the secrecy rate of each communication user is obtained, and the secrecy rate C s of the ISAC system is the sum of the secrecy rates of all communication users, which can be expressed as:

[0063]

[0064] Step 2: Obtain the expression of the constraints for the optimization problem, specifically:

[0065] The radar echo signal received by the ISAC base station is:

[0066]

[0067] where represents the ISAC base station receiving noise, g(x,y) represents the radar reflection power coefficient of the radar target at coordinates (x,y), and S k is the reflection surface area. represents the receiving steering vector of the RIS, represents the angle of arrival between the RIS and radar target k. For simplicity of notation, in the following text, and are used to represent the transmitting and receiving steering vectors.

[0068] In each time slot, the radar receiver of the ISAC base station uses N sample points to detect the presence of radar target k, and uses the zero-forcing algorithm for beamforming to eliminate the interference of the remaining radar targets. Then, the detection signal regarding radar target k can be expressed as:

[0069]

[0070] In the formula

[0071] Therefore, the detection signal energy ε k of radar target k is:

[0072]

[0073] Model the detection of radar target k as a binary hypothesis test. Since the radar receiver of the ISAC base station does not fully know the echo signal parameters of radar target k, the generalized likelihood ratio test (GLRT) is used to determine the presence of radar target k. According to the detection theory, the detection probability of radar target k is expressed as:

[0074]

[0075] where P FA is the probability of false alarm, and Q(x) is the complementary function of the cumulative distribution function of the standard Gaussian distribution.

[0076] From the values of the parameters in Step 1, the constraints that the optimization problem needs to satisfy are obtained: the transmit power constraint of the ISAC base station, and the unit modulus constraint of the diagonal elements of the RIS phase shift matrix, which are respectively expressed as,

[0077] Furthermore, to successfully detect each radar target, it is required that the detection probability of each radar target signal is not lower than a preset threshold, and thus the sensing constraint condition is obtained, expressed as

[0078] Step 3, in this embodiment, by jointly adjusting the beamforming matrix of the ISAC base station and the phase shift matrix Q parameter of the RIS, the system secrecy rate C s is maximized, and the optimization problem is established as:

[0079]

[0080] where C s is the system secrecy rate; C U,j and C T,k,j represent the achievable rates of communication user j and radar target k respectively; P max represents the maximum transmit power of the ISAC base station, represents the beamforming matrix sent by the ISAC base station to user j, represents the set of base station beamforming matrices, Tr(·) is the matrix trace operation; P D,k represents the detection probability of the k-th radar target, γ k represents the preset radar detection threshold; Q i,i represents the i-th diagonal element of the phase shift matrix Q of the RIS, |·| is the modulus operation.

[0081] Step 4, solve the optimization problem (10) to obtain the optimal parameters of the system. Specifically:

[0082] The optimization objective in the optimization problem (10) involves a non-convex and non-differentiable complex logarithmic determinant function. In addition, the probability function in constraint (10c) is also non-differentiable, which together with the non-convex unit modulus constraint (10d) of the RIS reflection elements leads to the non-convexity of the problem and is difficult to solve. Therefore, we propose the following optimization method:

[0083] It is observed that the max function in (10a) makes the objective function non-convex and non-differentiable. Therefore, auxiliary variables η j and μ j are introduced, whose physical meanings are the capacity of the eavesdropping channel of a single radar target on communication user j and the upper bound of the signal-to-interference-plus-noise ratio (SINR) of the radar target on communication user j respectively. The auxiliary variables are combined into a vector form, expressed as η = [η1 η2 … η J T , μ = [μ1 μ2 … μ J T , then problem (10) is equivalent to problem (11):

[0084] ​​

[0085] (10b)-(10d)(11d)

[0086] Since in the objective function (11a), the beamforming matrix of the ISAC base station, which is the optimization variable, and the phase shift matrix Q of the RIS are coupled in the logarithmic determinant and present a non-convex form. Therefore, through the weighted minimum mean square error (WMMSE) algorithm, problem (11) can be equivalently transformed into problem (12):

[0087]

[0088] where \(W = [W_1\ W_2 \cdots W_{ J \) is the set of auxiliary variables of the WMMSE weight matrix, \(U = [U_1\ U_2 \cdots U_{ J \) is the set of receiving matrices at the receiving ends of communication users, and \(E_{ j}\) is the MSE matrix between the received signal of communication user \(j\) and the original signal transmitted by the ISAC base station. According to the optimality condition, we have:

[0089]

[0090]

[0091] Substituting equations (13)-(15) back into problem (12), problem (12) can be rearranged into the form of problem (16):

[0092]

[0093] In problem (16), except for the power constraint (10b), the rest are all non-convex constraints, and there is a coupling phenomenon of optimization variables in both the objective function (16a) and the constraint (10d), making it difficult to solve directly. Therefore, this paper adopts the idea of alternating optimization, that is, first fix the RIS phase shift coefficient matrix Q and solve the base station beamforming sub-problem to optimize and its corresponding auxiliary variables; then fix the base station beamforming matrix and solve the RIS phase shift matrix optimization sub-problem to optimize Q and the auxiliary variables; the two sub-problems are iterated continuously until convergence.

[0094] S41. When Q is given, problem (16) can be simplified to problem (17):

[0095]

[0096] s.t. (10b), (10c), (11b), (11c) (17b)

[0097] Although the objective function (17a) is a convex function with respect to the optimization variables, the non-convex forms presented by the constraints (10c), (11b), and (11c) pose challenges to solving the problem. The following is the treatment of each constraint separately.

[0098] For the sensing constraint (10c), without loss of generality, setting each radar target to have a normalized radar cross-sectional area, the radar echo signal received by the ISAC base station can be expressed as:

[0099]

[0100] where n B represents the additive white Gaussian noise at the receiving end of the ISAC base station. Then, the power of the radar echo signal reflected by radar target k received by the ISAC base station is:

[0101]

[0102] where

[0103]

[0104] Constraint (10c) can be equivalently rewritten in the form of Equation (21), that is

[0105]

[0106] where is the equivalent radar detection power threshold for radar target k. Let represent the receiving noise power of the ISAC base station, and define the function a is an arbitrary constant, and N s is the total number of sampling points of the received signal. That is, there is

[0107] Therefore, constraint (10c) can be transformed into the form of constraint (22), that is

[0108]

[0109] Since constraint (22) is a non-convex constraint, the left side of the inequality is approximated by the first-order Taylor expansion. Constraint (22) can be approximated as:

[0110]

[0111] where F j(0) represents the initial value of the beamforming matrix of the ISAC base station for communication user j in the iteration.

[0112] The rate upper bound constraint (11b) is a non-convex constraint. By performing a first-order Taylor expansion on its right side, we have

[0113]

[0114] Among them, represents the initial value of the auxiliary variable in the iteration.

[0115] Since the upper bound constraint (11c) of SINR is non-convex and the right side of the inequality appears in fractional form, it is difficult to solve directly. The present invention uses fractional programming transformation for its right side, then the constraint (11c) can be transformed into the form of equation (25):

[0116]

[0117] Among them, is the auxiliary variable, and when it takes the following values, the constraints (25) and (11b) are equivalent:

[0118]

[0119] For the right side of (25), the first-order Taylor expansion is as follows:

[0120]

[0121] In summary, the optimization sub-problem of the ISAC base station beamforming matrix can be expressed as:

[0122]

[0123] This problem is a convex problem and can be solved by CVX.

[0124] S42, fix Problem (16) can be simplified to problem (29):

[0125]

[0126] s.t. (10c), (10d), (11b), (11c) (29b) Substitute into (29a), this problem can be sorted out as:

[0127]

[0128] Among them, q = diag(Q),

[0129] For the sensing constraint (10c), according to equation (20), expanding and sorting can be equivalently transformed into the form of constraint (31):

[0130]

[0131] Among them,

[0132] Since the constraint (31) is in a non - convex form, performing a first - order Taylor expansion of the left - hand side of the inequality with respect to q, we have:

[0133]

[0134] where, vec(·) represents the vectorization transformation of the matrix, and q0 is the initial value of the iteration of q.

[0135] Therefore, the sensing constraint (10c) can be transformed into the form of constraint (33), that is

[0136]

[0137] For the upper - bound constraint of SINR (11c), substituting we get:

[0138]

[0139] Both the numerator and denominator on the right - hand side of the inequality can be written in the form of quadratic forms with respect to q. And due to the unit - modulus constraint brought by the RIS, the denominator noise term can be incorporated into the quadratic - form according to this special property, that is, the constraint (34) can be equivalently transformed into:

[0140]

[0141] where,

[0142]

[0143]

[0144] Since the target variable in equation (35) is in a fractional form and is difficult to solve directly, using fractional programming for the right - hand side of the constraint (35) and performing a first - order Taylor expansion of the quadratic term, then the constraint (35) can be transformed into the constraint (38):

[0145]

[0146] where, q0 is the initial value of the iteration of q, and α k,j is the auxiliary variable introduced by the fractional programming, and its optimal value has a closed - form solution, that is

[0147] In summary, the optimization sub - problem of the RIS phase - shift matrix Q can be expressed as:

[0148]

[0149] Let u H =[q H ,1] H , V = uuH , then problem (39) can be transformed into

[0150]

[0151] (24),(40e)

[0152] rank(V) = 1. (40f)

[0153] wherein,

[0154] It can be observed that in problem (40), except that constraint (40f) is non-convex, the rest are in the form of semidefinite programming.

[0155] Therefore, in this embodiment, semidefinite relaxation (SDR) is used to process the non-convex rank-one constraint (40f), and its basic idea is: first, ignore constraint (40f) and solve the semidefinite programming problem (41), and then use the Gaussian random sampling method to recover the rank-one solution.

[0156]

[0157] To sum up, the execution process of the algorithm proposed in this embodiment is shown in Table 1:

[0158] Table 1 Pseudo-code of the joint active and passive beamforming control algorithm for the ISAC secure communication system

[0159]

[0160] wherein, is the value of the t-th iteration of the ISAC base station beamforming matrix set, Q (t) is the value of the t-th iteration of the RIS phase shift matrix, t is the loop counting variable, U j and W j are the WMMSE auxiliary variables, β k,j is the SINR constraint auxiliary variable, α k,j is the denominator noise auxiliary variable, and V is the extended RIS phase shift coefficient.

[0161] Embodiment 2

[0162] The simulation experiment of this embodiment verifies the feasibility and effectiveness of this application. In this embodiment, considering the ISAC scenario, it is assumed that the number of communication users J = 2 and the number of radar targets K = 2. Without loss of generality, it is assumed that both the communication users and the radar targets are on the same side of the ISAC base station, and the ISAC base station, the radar targets, and the communication users are on the same straight line. The coordinates of the ISAC base station are: (0m, 0m, 5m), the coordinates of the RIS are: (5m, 0m, 10m), the coordinates of the radar targets are: (15m, 0m, 1.5m), (20m, 0m, 1.5m) respectively, and the coordinates of the communication users are: (90m, 0m, 1.5m), (100m, 0m, 1.5m) respectively. For the channel setting, since the RIS is deployed at a relatively high altitude and there is less occlusion, the channels between the ISAC base station - RIS and RIS - communication users follow the Rice distribution, and its small - scale fading expression is shown in Equation (38), that is:

[0163]

[0164] where the Rice coefficient κ = 3, respectively represent the line - of - sight (LoS) component and the non - line - of - sight (nLoS) component of the corresponding channel. The large - scale attenuation of each channel is defined as χd -ε , where the path loss χ = - 20dB at a reference distance of 1m, d is the link communication distance, and ε is the attenuation exponent. Since there is less occlusion in the air - to - ground channel in the present invention, ε = 2.2 is set. Without other instructions, the node noise power in the present invention is all σ 2 = - 74dBm, the total number of sampling points N s = 5×10 4 , the number of signal transmission streams N = 2, the antenna scale N t = 4, N t = 2, the lower limit values of the detection probabilities of each radar target are equal, that is

[0165] The carrier frequency is f c = 10GHz, and the transmission power of the ISAC base station is P = 20dBm. The radar target scattering area is the unit scattering surface area, and the reflection radiation density is g T (x, y)=1. The sensing time slot is T0 = 0.05s, and the sampling frequency of the radar receiver of the ISAC base station is f s = 1MHz. In order to reduce the error caused by channel randomness, the following simulation results are the average values of 100 different channel coefficient conditions randomly experimented.

[0166] Channel where λ is the wavelength of the signal transmitted by the ISAC base station, is the distance between the RIS and the radar target k, Denote the reflection steering vector of the RIS. is the departure angle from the RIS to the radar target k, where θ and φ are the azimuth angle and elevation angle respectively.

[0167] In the formula, [a AE n denotes the n-th element of a AE , where n = 1, 2, …, N; [a IE m denotes the m-th element of a IE , where m = 1, 2, …, M; d K denotes the spacing between adjacent reflecting elements of the RIS; M x and M y respectively denote the number of elements of the RIS along the x-axis and y-axis; θ ∈ [0, 2π) and φ ∈ [-π / 2, π / 2) respectively denote the angle of departure (AOD) in the horizontal and vertical directions; λ denotes the wavelength; denotes the largest integer not greater than x.

[0168] Figure 2 Shows the convergence and performance comparison between the RIS-assisted ISAC secure communication joint optimization algorithm proposed in the present invention and the scenario of random phase shift setting of the RIS. It can be seen from Figure 2 that the algorithm proposed in the present invention has good convergence. Compared with the scenario of random RIS phase setting, through joint active and passive beamforming, the system secrecy rate is increased by 49.49%, verifying the great advantage of introducing the RIS into ISAC secure communication.

[0169] Figure 3 Shows the influence of the allowable error perception probability 1 - γ on the security performance. It can be seen from the figure that as the allowable error perception probability increases, the secrecy rate of the ISAC system increases accordingly, and the growth rate gradually decreases. First, a qualitative analysis is carried out. When the allowable error perception probability increases, the power threshold of the echo signal required to detect the radar target becomes lower, so that the ISAC base station and the RIS use less power and lower sidelobe gain for sensing during resource allocation, reducing the SINR of the radar target and increasing the system secrecy rate. There is a trade-off relationship between the sensing quality and communication security. Second, a quantitative analysis is carried out. The calculation method of the echo signal power threshold is defined after formula (21) in the patent. It can be theoretically verified that the slope of the decrease in the power threshold gradually becomes flat as the allowable error perception probability increases. That is, when the allowable error perception probability is very small, the change in its magnitude has a more significant effect on the decrease in the power threshold, and the significant degree decreases continuously as the allowable error perception probability increases, resulting in the curve trend as in Figure 3 , and the theory is consistent with the actual simulation.

[0170] Figure 4 ​​shows the impact of the number of RIS reflection elements and the transmission power on the secrecy rate. It can be seen from Figure 4 that as the transmission power continues to increase, the system secrecy rate of the proposed scheme in each scenario is improved. It can be judged that by increasing the number of RIS reflection elements and carrying out reasonable optimization, the security performance can be significantly improved. It can be seen from Figure 4 that in this simulation scenario, for every additional 10 RIS reflection elements, the performance can be improved by about 8%.

[0171] Figure 5 shows the impact of different numbers of transmitting and receiving antennas and the transmission power on the system secrecy rate. It can be obtained from Figure 5 that first, increasing the transmission power improves the system secrecy rate in all scenarios. Second, since the increase in the antenna scale will introduce higher degrees of freedom, the performance is significantly improved.

[0172] Embodiment 3

[0173] An optimization system of a RIS-assisted multi-user multi-objective secure ISAC system provided in this embodiment includes:

[0174] An optimization objective acquisition unit for acquiring the optimization objective of the ISAC system;

[0175] A constraint condition acquisition unit for acquiring the constraint conditions of the optimization objective;

[0176] An optimization problem construction unit for constructing the optimization problem of the ISAC system according to the optimization objective and the constraint conditions;

[0177] A parameter acquisition unit for solving the optimization problem to obtain the optimal parameters of the ISAC system.

[0178] Embodiment 4

[0179] This embodiment also provides a computing device. The computing device includes: a bus, a processor, a memory, and a communication interface. The processor, the memory, and the communication interface communicate with each other through the bus. The computing device can be a server or a terminal device. It should be understood that the number of processors and memories in the computing device of the present application is not limited.

[0180] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of easy representation, the bus can include a path for transmitting information between various components of the computing device (for example, the memory, the processor, the communication interface).

[0181] The processor may include any one or more of processors such as a central processing unit (CPU), a graphics processing unit (GPU), a Tensor Processing Unit (TPU), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a micro processor (MP), or a digital signal processor (DSP).

[0182] The memory may include volatile memory, such as random access memory (RAM). The processor may also include non-volatile memory, such as read-only memory (ROM), flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0183] The memory stores executable program code, and the processor executes the executable program code to implement the functions of the foregoing first generation module, second generation module, and adjustment module respectively, so as to implement, for example, *methods, etc. That is, instructions for the methods and functions of the computing device involved in any of the foregoing embodiments may be stored on the memory.

[0184] The communication interface uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computing device and other devices or a communication network.

[0185] Embodiment 5

[0186] This embodiment also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device may be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device may also be a terminal device such as a desktop computer, a laptop computer, or a smart phone.

[0187] The computing device cluster includes at least one computing device. Instructions for performing the methods and functions of the computing device involved in any of the above embodiments may be stored in the memories of one or more computing devices in the computing device cluster.

[0188] In some possible implementation manners, partial instructions for performing the methods and functions of the computing device involved in any of the above embodiments may also be separately stored in the memories of one or more computing devices in the computing device cluster. In other words, a combination of one or more computing devices may jointly execute the instructions for performing the methods and functions of the computing device.

[0189] It should be noted that the memories in different computing devices in the computing device cluster may store different instructions, which are respectively used to perform partial functions of the device.

[0190] In some possible implementation manners, one or more computing devices in the computing device cluster may be connected through a network. Wherein, the network may be a wide area network, a local area network, etc. Two computing devices are connected through the network. Specifically, they are connected to the network through the communication interfaces in each computing device.

[0191] Embodiments of the present disclosure also provide a computer program product including instructions, which, when running on a computer, cause the computer to execute the methods and functions of the computing device involved in any of the above embodiments.

[0192] Embodiment 6

[0193] This embodiment also provides a computer-readable storage medium, on which computer instructions are stored, and when a processor runs the instructions, the processor is caused to execute the methods and functions of the computing device involved in any of the above embodiments.

[0194] Generally, various embodiments of the present disclosure may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be executed by a controller, a microprocessor, or other computing devices. Although aspects of the embodiments of the present disclosure are shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that the blocks, devices, systems, technologies, or methods described herein may be implemented as, by way of non-limiting example, hardware, software, firmware, dedicated circuits or logic, general hardware or controllers or other computing devices, or some combination thereof.

[0195] Embodiment 7

[0196] This embodiment provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which are executed in a device on a target real or virtual processor to perform the processes / methods as referenced above with respect to the accompanying drawings. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided as needed among the program modules. The machine-executable instructions for the program modules can be executed within local or distributed devices. In a distributed device, the program modules can be located in local and remote storage media.

[0197] The computer program code for implementing the methods of the present disclosure can be written in one or more programming languages. This computer program code can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the computer or other programmable data processing device, it causes the functions / operations specified in the flowchart and / or block diagram to be implemented. The program code can be executed entirely on the computer, partially on the computer, as a stand-alone software package, partially on the computer and partially on a remote computer, or entirely on a remote computer or server.

[0198] In the context of the present disclosure, the computer program code or related data can be carried by any suitable carrier such that a device, apparatus, or processor can perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals can include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, etc.

[0199] A computer-readable medium can be any tangible medium that contains or stores a program for or related to an instruction execution system, apparatus, or device, or a data storage device such as a data center that contains one or more available media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More specific examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0200] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. An optimization method for a RIS-assisted multi-user multi-objective secure ISAC system, characterized in that, Based on a RIS-assisted multi-user multi-target secure ISAC system, the system includes an ISAC base station, communication users, radar targets, and RIS. Among them, the ISAC base station is used to beamform and transmit the data symbols of the communication users, and transmit the beamformed signal to the communication users; the radar targets are used to steal the signals transmitted by the ISAC base station to the communication users; the RIS is used to combine its phase shift matrix with the beamformer of the ISAC base station to jointly adjust the secure communication of the ISAC system. The method includes the following steps: Step 1, obtain the optimization objective of the ISAC system; Step 2, obtain the constraint conditions of the optimization objective; Step 3, construct an optimization problem of the ISAC system according to the optimization objective and the constraint conditions; Step 4, solve the optimization problem to obtain the optimal parameters of the ISAC system.

2. The optimization method of a RIS-assisted multi-user multi-objective secure ISAC system according to claim 1, characterized in that, In Step 1, to obtain the optimization objective of the ISAC system, the specific method is: S1, calculate the received signals of the RIS, communication users, and radar targets respectively; S2, calculate the achievable information rates of the communication users and radar targets respectively; S3, assuming that the radar target can independently decode the signal, define the sum of the secrecy rates of the communication users as the optimization objective, where the secrecy rate of the communication user is the difference between the achievable rate of the communication user and the maximum achievable rate in the radar target group.

3. The optimization method of a RIS-assisted multi-user multi-objective secure ISAC system according to claim 1, wherein In Step 2, to obtain the constraint conditions of the optimization objective, the constraint conditions include the transmit power constraint of the ISAC base station, the unit modulus value constraint of the diagonal elements of the RIS phase shift matrix, and the sensing constraint condition. Among them, the sensing constraint condition is that to successfully detect each radar target, the detection probability of each radar target signal is required to be not lower than a preset threshold.

4. The optimization method of a RIS-assisted multi-user multi-objective secure ISAC system according to claim 1, wherein, In Step 3, to construct an optimization problem of the ISAC system according to the optimization objective and the constraint conditions, the specific method is: By jointly adjusting the beamforming matrix of the ISAC base station and the phase shift matrix Q parameter of the RIS to maximize the secrecy rate of the ISAC system, an optimization problem is obtained, and its expression is as follows: Among them, C s is the secrecy rate of the ISAC system; C U,j is the achievable rate of the communication user; C T,k,j is the maximum achievable rate in the radar target group; Tr(·) is the matrix trace operation; represents the beamforming matrix sent by the ISAC base station to user j; P D,k represents the detection probability of the k-th radar target, γ k represents the preset radar detection threshold; Q i,i represents the i-th diagonal element of the phase shift matrix Q of the RIS, and |·| is the modulus operation.

5. The optimization method of a RIS-assisted multi-user multi-objective secure ISAC system according to claim 1, characterized in that, In Step 4, to solve the optimization problem to obtain the optimal parameters of the ISAC system, the specific method is: Set two auxiliary variables, which are respectively the capacity of the eavesdropping channel of a single radar target to the communication user and the upper bound of the eavesdropping signal-to-noise ratio of the radar target to the communication user; The optimization variables of the optimization problem are partitioned using the block coordinate descent method to obtain the base station beamforming matrix and the RIS phase shift matrix Q; Using the alternating optimization method, two optimization variables are iteratively optimized by combining two auxiliary variables to obtain the optimal solution of the beamforming matrix of the ISAC base station and the optimal solution of the RIS phase shift matrix Q, respectively. ​ 6. An optimization system for a RIS-assisted multi-user multi-objective secure ISAC system, characterized in that, Include: An optimization objective acquisition unit, used to obtain the optimization objective of the ISAC system; A constraint condition acquisition unit, used to obtain the constraint conditions of the optimization objective; An optimization problem construction unit, used to construct an optimization problem of the ISAC system according to the optimization objective and the constraint conditions; A parameter acquisition unit, used to solve the optimization problem to obtain the optimal parameters of the ISAC system.

7. An electronic device, characterized in that, It includes a processor and a memory, and computer instructions are stored on the memory. When the computer instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 5.

8. A cluster of computing devices, characterized in that, It includes at least one computing device, and each computing device includes a processor and a memory; The processor of the at least one computing device is used to execute the instructions stored in the memory of the at least one computing device, so that the computing device cluster executes the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product contains computer-executable instructions, and when the computer-executable instructions are executed, the method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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