Intelligent metasurface auxiliary safety sensing integrated system and beam forming design method

Abstract

The invention belongs to the technical field of wireless communication physical layer secure transmission, and relates to an intelligent metasurface assisted secure communication integrated system and a beam forming design method, and the system comprises a radar communication base station, an intelligent metasurface RIS, a plurality of communication users, and a mobile eavesdropper. The radar communication base station sends a signal to the communication user, and the signal is used for sensing the eavesdropper by the radar; the intelligent metasurface RIS is used for assisting secure communication; an existing RIS-assisted security ISAC system usually only considers a static scene, but the system is challenging to obtain channel state information of a mobile eavesdropper. Meanwhile, the mobile eavesdropper has greater security threat to the deployment of the communication and sensing integrated system. The architecture provided by the invention can well guarantee the safety communication performance between the base station and the user when the mobile eavesdropper exists.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication physical layer security transmission, and relates to an intelligent metasurface assisted security interaceptive integrated system and a beamforming design method. Background Art

[0002] With the evolution of mobile communications, emerging applications such as intelligent vehicle networking and industrial Internet require high-quality wireless connections and high-precision and robust perception capabilities. As one of the key technologies of 6G networks, the integrated synaesthesia system integrates communication and perception functions, pursuing a trade-off between the two effects and mutual performance gains on the one hand, and greatly improving spectrum efficiency and energy efficiency under the condition of reducing hardware and signal costs on the other hand. Considering the spectrum sharing mechanism of the integrated synaesthesia system, including the information to be sent to legitimate users in the radar detection signal makes the communication process more vulnerable to eavesdropping by illegal users, who can use the integrated waveform signal of the information carried to detect confidential information for legitimate users. Therefore, the issue of secure integrated synaesthesia system has received a lot of attention. However, when the wireless propagation environment is blocked, the traditional secure integrated synaesthesia system will face the risk and challenge of a sharp decline in system performance.

[0003] The intelligent metasurface (RIS) technology proposed in recent years is a two-dimensional artificial electromagnetic surface composed of a large number of electromagnetic units. These units can dynamically adjust their electromagnetic characteristics through external control signals to achieve reflection, refraction and scattering of electromagnetic waves. RIS can reconstruct the wireless propagation environment of the transceiver and can effectively alleviate the challenges faced by the above-mentioned integrated security interawareness system. Specifically, RIS changes the traditional wireless propagation environment from passive adaptation to active control by intelligently regulating the incident electromagnetic waves. It can create a virtual line-of-sight link by controlling the reflected beam, thereby effectively solving the problem of reduced performance due to wireless propagation environment obstruction in the integrated security interawareness system. In addition, RIS has the advantages of low power consumption, low cost, low complexity and easy deployment, and can achieve lower-cost system design with a simpler structure. Therefore, RIS has great potential for solving the challenges in the integrated security interawareness system.

[0004] Existing research on integrated security interawareness systems has introduced different types of RIS to enhance performance. Passive RIS is used to perform beamforming design in conjunction with base stations. Active RIS is used to enhance signal power and mitigate the impact of multiplicative fading. Synchronous transmission and reflection RIS enhances the communication perception performance of the base station by designing the transmission waveform signal, transmission coefficient, and reflection coefficient of RIS. However, existing RIS-assisted integrated security interawareness systems mainly focus on static scenarios.

[0005] Therefore, a RIS-assisted security synaesthesia integrated system or method for mobile eavesdroppers is needed to solve the above technical problems. Summary of the invention

[0006] The technical solution adopted by the present invention to solve the technical problem is: an intelligent metasurface assisted security telepathy integrated system, including: a radar communication base station, an intelligent metasurface RIS, multiple communication users, and a mobile eavesdropper; the radar communication base station sends a signal to the communication user, and the signal is also used for radar perception of the eavesdropper; the intelligent metasurface RIS is used to assist secure communication.

[0007] Preferably, the radar communication base station is configured with a linear antenna array, which is provided with N antennas; the intelligent metasurface RIS is configured as a planar array, and the planar array of the intelligent metasurface RIS is provided with M reflection units; the communication user and the mobile eavesdropper are each configured with at least one antenna.

[0008] Preferably, there is no line-of-sight link between the radar communication base station, the communication user, and the mobile eavesdropper. The radar communication base station directs the beam to the smart metasurface RIS through beamforming design. The beam is phase-adjusted by the smart metasurface RIS and then directed to the communication user and the mobile eavesdropper respectively; the beam directed to the eavesdropper is reflected from the eavesdropper and then phase-adjusted by the smart metasurface RIS and then returns to the radar communication base station for sensing the position of the eavesdropper.

[0009] More preferably, artificial noise is introduced at the radar communication base station to improve the overall safe communication rate of the system.

[0010] The present invention also discloses a beamforming design method for an intelligent metasurface-assisted safety synaesthesia integrated system. The beamforming design method is used for the above-mentioned safety synaesthesia integrated system. The beamforming design method comprises the following steps:

[0011] Step S1: Obtain an expression for the overall secure communication rate of the system;

[0012] Step S2: setting constraints;

[0013] Step S3: construct an optimization problem with the goal of maximizing the overall secure communication rate of the system;

[0014] Step S4: Solve the optimization problem to obtain the optimal design of the system.

[0015] Preferably, the specific steps in step S1 include:

[0016] The base station-RIS channel, RIS-user channel and RIS-eavesdropper channel are all modeled as Rice channels. Due to the movement of the eavesdropper, the RIS-eavesdropper channel will change with the movement of the eavesdropper's position, so the RIS-eavesdropper channel is modeled for each time slot.

[0017] Calculate the received signals of the communication user and the mobile eavesdropper respectively;

[0018] Calculate the communication rate of the communication user and the eavesdropping rate of the mobile eavesdropper respectively according to the received signals of the communication user and the mobile eavesdropper;

[0019] The overall secure communication rate of the system is calculated based on the obtained communication rate of the communication users and the eavesdropping rate of the mobile eavesdropper.

[0020] Preferably, the specific steps in step S3 include:

[0021] Due to the movement of the eavesdropper, the RIS-eavesdropper channel will change in different time slots. Therefore, an optimization problem with the goal of maximizing the overall secure communication rate of the system is constructed for one time slot. The overall secure communication rate of the system is maximized by optimizing four variables, among which the four variables are the beamforming design w of the base station in the nth time slot. k [n], Artificial noise signal z[n] and phase shift matrices Θ[n] and Ψ[n] of RIS on downlink synesthesia signal and uplink echo signal;

[0022] The optimization problem (P0) is formulated as follows:

[0023]

[0024] st Tr(M[n])≤Γ max (19a)

[0025]

[0026] |[Θ[n]] m,m |=1 (19c)

[0027] |[Ψ[n]] m,m |=1 (19d)

[0028] Among them, w k [n] represents the beamforming vector of the kth communication user, z[n] represents the artificial noise signal, Θ[n] represents the phase shift matrix of RIS in the nth time slot during downlink signal transmission, Ψ[n] represents the phase shift matrix of RIS in the nth time slot during uplink signal transmission, D k [n] represents the secure communication rate that can be achieved between the base station and the kth communication user, M[n] represents the updated mean square error matrix, Γ max represents the maximum acceptable threshold of the updated mean square error matrix, P maxrepresents the maximum transmission power of the base station, 19a is the constraint on the updated mean square error matrix error, 19b is the constraint on the base station transmission power, and constraints 19c and 19d represent the constraints of RIS on the reflection coefficients of the downlink synesthesia signal and the uplink echo signal.

[0029] Preferably, the specific steps in step S4 include:

[0030] Step S4-1, split the optimization problem into three sub-problems, and transform the sub-problems into convex optimization problems through fractional programming, semi-positive definite relaxation and convex difference programming;

[0031] Step S4-2, given the uplink echo signal reflection coefficient and the downlink synaesthesia signal reflection coefficient of the RIS in the nth time slot, optimize the downlink beamforming matrix, artificial noise matrix and auxiliary variables in the nth time slot;

[0032] Step S4-3, substituting the downlink beamforming matrix and artificial noise matrix in the nth time slot obtained above, and given the uplink echo signal reflection coefficient of the RIS, optimizing the downlink synaesthesia signal reflection coefficient and auxiliary variables of the RIS in the nth time slot;

[0033] Step S4-4, substituting the downlink beamforming matrix, artificial noise matrix and reflection coefficient of the RIS downlink synaesthesia signal in the nth time slot obtained above, to find the optimal solution of the reflection coefficient of the RIS uplink echo signal in the nth time slot;

[0034] Step S4-5: Use all the obtained variable values ​​to determine whether the original problem converges. If it converges, the optimal solution to the optimization problem is obtained. If it does not converge, continue iterating.

[0035] The beneficial effects of the present invention are:

[0036] 1. Existing RIS-assisted secure ISAC systems often only consider static scenarios, but it is challenging for the system to obtain channel state information for mobile eavesdroppers. At the same time, mobile eavesdroppers pose a greater security threat to the deployment of the synaesthesia integrated system. The architecture proposed in the present invention can well guarantee the secure communication performance between the base station and the user when a mobile eavesdropper exists.

[0037] 2. Due to the obstruction between the base station and the mobile eavesdropper, the integrated signal needs to be reflected back to the base station through a four-hop channel, which makes multiple channels highly coupled. The design scheme proposed in the present invention can well solve the challenges brought by channel coupling to algorithm design.

[0038] 3. RIS can dynamically adjust its electromagnetic characteristics through external control signals to achieve reflection, refraction and scattering of electromagnetic waves, thereby reconstructing the wireless propagation environment of the transceiver, and changing the traditional wireless propagation environment from passive adaptation to active controllability. In addition, by controlling the reflected beam to create a virtual line-of-sight link, the problem of reduced performance due to wireless propagation environment obstruction in the safety interawareness integrated system can be effectively solved. In the present invention, by introducing RIS into the safety interawareness integrated system, the system structure is simpler, the design cost is reduced, and the safety interawareness performance and perception accuracy of the system are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a system model diagram of the intelligent metasurface assisted safety synaesthesia integrated system and beamforming design method of the present invention;

[0040] Figure 2 It is a flow chart of the beamforming design method of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the relevant technologies in the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] refer to Figures 1 to 2 , the intelligent metasurface (RIS) assisted safety synaesthesia integrated system and beamforming design method provided in this embodiment, such as Figure 1 As shown, it includes a multi-antenna synaesthesia integrated base station, an intelligent metasurface RIS, multiple communication users, a mobile eavesdropper, and a system configuration (including: the synaesthesia integrated signal beam design transmitted by the base station, the artificial noise design introduced at the base station, the RIS uplink and downlink reflection coefficient design, and the power control scheme of the base station).

[0043] In the intelligent metasurface-assisted secure interaception integrated system, considering that the channels between the base station and the user and the eavesdropper are blocked, the base station must provide secure communication services to the user and sense the mobile eavesdropper through the intelligent metasurface RIS, so the system channels are divided into three parts: base station-RIS channel, RIS-user channel and RIS-eavesdropper channel. At the same time, considering the line-of-sight and non-line-of-sight factors, all channels in the model are modeled as Rice fading channels.

[0044] The base station-RIS channel can be expressed as The channel from RIS to the kth user can be expressed as The channel from RIS to the eavesdropper in the nth time slot can be expressed as The base station-RIS channel is expressed as:

[0045]

[0046] Among them C 0 Represents the reference distance D 0 =1m path loss, ο represents the channel loss index, d dr =||q r -q 0 || represents the distance between the base station and RIS, κ represents the Ricean factor of the channel, The channel from RIS to the kth user is expressed as:

[0047]

[0048] Where ρ represents the loss index of the channel, d rk =||q r -q k || represents the distance between RIS and the kth communication user, θ represents the Ricean factor of the channel, The channel from RIS to the eavesdropper in the nth time slot is expressed as:

[0049]

[0050] Where ι represents the loss index of the channel, d re [n]=||q r -q e [n]|| represents the distance between RIS and the eavesdropper in the nth time slot, η represents the Ricean factor of the channel, The elements of RIS are arranged in UPA mode. When the downlink signal is transmitted, the phase shift matrix of RIS in the nth time slot is expressed as:

[0051]

[0052] in They represent the phase and amplitude of the mth unit of RIS respectively. When the uplink signal is transmitted, the phase shift matrix of RIS in the nth time slot is expressed as:

[0053]

[0054] in They represent the phase and amplitude of the mth unit of RIS respectively.

[0055] Let x[n] = ∑ k∈K w k [n]s k [n]+z[n] represents the synaesthesia integrated signal after introducing artificial noise transmitted by the base station in the nth time slot, where represents the beamforming vector of the kth communication user, s k [n] represents the information-bearing symbol corresponding to the kth communication user, where it is assumed Artificial noise signal Therefore, the received signal of the kth legal user in the nth time slot can be expressed as:

[0056]

[0057] in represents the Gaussian white noise introduced at the receiving antenna of the kth communication user. At this time, the signal received by the eavesdropper is:

[0058]

[0059] in represents the Gaussian white noise introduced by the eavesdropper. At this time, the echo received by the base station from the eavesdropper can be expressed as:

[0060]

[0061] in Represents the Gaussian white noise introduced at the base station antenna.

[0062] The achievable communication rate between the base station and the kth communication user in the nth time slot is expressed as:

[0063]

[0064] Assuming that the eavesdropper can eliminate the interference from other users before decoding the information of a specific communication user, the eavesdropping rate when the eavesdropper eavesdrops on the communication between the base station and the kth communication user is expressed as:

[0065]

[0066] Therefore, under the premise of ensuring secure communication, the communication rate that can be achieved between the base station and the kth communication user is:

[0067]

[0068] Based on the state information of the eavesdropper perceived in the n-1th time slot, the state information of the eavesdropper in the nth time slot is predicted. The specific implementation is realized by the extended Kalman filter. Assuming that the movement rate of the eavesdropper is constant, the state transition model can be expressed as:

[0069] ξ[n+1]=S cv ξ[n]+z t [n] (10)

[0070] in represents the position and speed of the eavesdropper in the nth time slot (assuming that the eavesdropper moves in a two-dimensional plane, which can be extended to three-dimensional space), represents the state transfer matrix, I 3 and 0 3 Represent the 3×3 identity matrix and zero matrix respectively, z t [n] is the noise term with covariance matrix, whose covariance matrix is:

[0071]

[0072] The base station analyzes the eavesdropper's echo in the nth time slot through the equipped matching filter, and can obtain some state parameters of the eavesdropper at this time, including the round-trip delay. Doppler shift The vertical angle θ between the eavesdropper and RIS e [n] and the horizontal angle φ between the eavesdropper and the RIS e [n]. Therefore, the connection between the observed parameter and the state ξ[n] of the eavesdropper in the nth time slot can be established as:

[0073]

[0074]

[0075] where f c and c represent the carrier frequency and the speed of light, w τ[n] , w μ[n] , w sinθ[n] , w cosθ[n] and w sinφ[n] represents Gaussian measurement noise with zero mean and variances and

[0076] Since the measurement noise is inversely proportional to the SNR of the received echo, we can write and where a 1 , a 2 , a 3 and a 4 The system structure is related to the specific signal processing algorithm. and When I was very young, I roughly thought and These Gaussian measurement noises are actually the manifestation of robustness in the eavesdropper motion model. The SNR can be expressed as:

[0077]

[0078] make Therefore, the observation parameters and the state information of the eavesdropper in the nth time slot can be written as:

[0079] Ω[n]=g n (ξ[n])+w[n] (13)

[0080] where w[n] = [w τ[n] ,w μ[n] ,w sinθ[n] ,w cosθ[n] ,w sinφ[n] ] T represents a zero-mean Gaussian noise vector, and the covariance matrix is ​​expressed as:

[0081]

[0082] Since the measurement model is nonlinear, the extended Kalman filter (EKF) is used to linearize the measurement model and convert the Jacobian matrix Writing G n , so the detailed prediction process can be expressed as:

[0083] Prediction section

[0084]

[0085] The predicted mean square error matrix

[0086]

[0087] Kalman gain matrix

[0088]

[0089] Correction

[0090]

[0091] Updated mean square error matrix

[0092] M[n]=(IK[n]G n )M[n|n-1] (18)

[0093] In this embodiment, the downlink beamforming vector w in the nth time slot is jointly optimized. k [n], artificial noise signal z[n], and the phase shift matrices Θ[n] and Ψ[n] of the RIS for the downlink synesthesia signal and the uplink echo signal to maximize the overall safe communication rate of the system. The joint optimization problem is constructed as:

[0094]

[0095] st Tr(M[n])≤Γ max (19a)

[0096]

[0097] |[Θ[n]] m,m |=1 (19c)

[0098] |[Ψ[n]] m,m |=1 (19d)

[0099] Among them, 19a is the constraint on the updated mean square error matrix error, 19b is the constraint on the base station transmission power, 19c and 19d are the reflection coefficient constraints of RIS on the downlink synesthesia signal and the uplink echo signal respectively.

[0100] Problem (P0) is a non-convex optimization problem, mainly due to the following two reasons: First, there is a high degree of coupling between the four sets of optimization variables, so the objective function is non-convex. In addition, the four constraints are all non-convex constraints. Therefore, problem (P0) is a non-convex optimization problem, and it is not easy to solve it directly. Therefore, it is necessary to design an efficient algorithm to solve the RIS-assisted secure ISAC system based on eavesdropper mobility.

[0101] The joint optimization scheme for maximizing the overall security communication rate of the intelligent metasurface-assisted security interawareness integrated system proposed in this embodiment can be controlled by the base station and the intelligent controller of RIS. The base station needs to predict the state information of the eavesdropper in the current time slot based on the state information of the eavesdropper obtained in the previous time slot, and the base station and the intelligent controller perform the overall configuration of the system based on the predicted state information.

[0102] Solution to the problem of maximizing the overall safety communication rate of the intelligent metasurface-assisted safety interawareness integrated system. This implementation uses an alternating optimization framework to decouple the problem into three sub-problems and solve them separately. The three sub-problems are then optimized alternately until the entire problem converges. First, the existence of the fractional structure makes the objective function of the problem (P0) non-convex. In order to solve the problem (P0), we first relax the objective function to a convex function. Introduce the auxiliary variable b k ,y k , The objective function of the problem (P0) is changed from

[0103]

[0104] in Converts to:

[0105]

[0106] The auxiliary variable y k and b k They can be expressed as:

[0107]

[0108] Therefore, problem (P0) can be transformed into problem (P1):

[0109]

[0110] st Tr(M[n])≤Γ max (20a)

[0111]

[0112] |[Θ[n]] m,m |=1 (20c)

[0113] |[Ψ[n]] m,m |=1 (20d)

[0114]

[0115] Z[n]≥0 (20g)

[0116]

[0117] Constraints 20a, 20c, 20d and 20e in problem (P1) are non-convex constraints.

[0118] In this embodiment, the uplink echo signal reflection coefficient Ψ[n] and the downlink synaesthesia signal reflection coefficient Θ[n] of the RIS in the nth time slot are first given, and the downlink beamforming matrix in the nth time slot is jointly optimized. Artificial noise matrix Z[n] and auxiliary variables y k . Problem (P1) can be transformed into problem (P2), expressed as:

[0119]

[0120] Tr(M[n])≤Γ max (21b)

[0121]

[0122] Z[n]≥0 (21f)

[0123] It can be seen that problem (P2) is a non-convex optimization problem. The present invention adopts semidefinite programming to obtain an approximate optimal solution to subproblem (P2).

[0124] Specifically, the inverse matrix of M[n] is derived as:

[0125]

[0126] Where M[n|n-1] is the predicted mean square error matrix. Rewrite as in is a constraint matrix that is only related to the observed parameters and By introducing the auxiliary variable v i ≥0, and set the constraint Tr(M[n])≤Γ max Rewritten as:

[0127]

[0128] v i ≥0,i∈{1,...,5} (23c)

[0129] where e i isI 5 For the non-convex constraint (21e), in order to solve this problem, the semi-positive definite relaxation method is used to remove the constraint from the problem statement, which is specifically expressed as:

[0130]

[0131]

[0132] v i ≥0,i∈{1,...,5} (24d)

[0133]

[0134] Z[n]≥0 (24g)

[0135] It can be concluded that this optimization problem is a standard SDP problem, and a high-quality solution can be obtained by applying the CVX toolbox. If the optimal solution of the SDP problem satisfies (21e), then the optimal solution of the original problem can be obtained by rank-one decomposition. If the optimal solution of the SDP problem does not satisfy (21e), additional steps such as Gaussian randomization can be applied to extract a suboptimal solution to the original problem.

[0136] In this embodiment, the downlink beamforming matrix W in the nth time slot is given as k [n], the artificial noise matrix Z[n] and the phase shift matrix Ψ[n] of the uplink echo signal of RIS, and the phase shift matrix Θ[n] and auxiliary variables of the downlink synaesthesia signal of RIS are jointly optimized make but rank(O[n])=1. Problem (P1) can be transformed into problem (P3), expressed as:

[0137]

[0138]

[0139] Tr(M[n])≤Γ max (25b)

[0140] O m，m [n] = 1 (25c)

[0141] rank(O[n])=1 (25d)

[0142] O[n]≥0 (25e)

[0143] in:

[0144]

[0145] Similarly, problem (P3) is transformed into problem (P3.1) by semidefinite programming and convex difference programming, which can be expressed as:

[0146]

[0147] v i ≥0,i∈{1,...,5} (26d)

[0148] O m,m [n] = 1 (26e)

[0149] O[n]≥0 (26f)

[0150] in:

[0151]

[0152] It can be seen that this optimization problem is a standard SDP problem, and the CVX toolbox can be used to obtain a high-quality solution.

[0153] In this embodiment, the downlink beamforming matrix W in the nth time slot is given k [n], the artificial noise matrix Z[n] and the phase shift matrix Θ[n] of the downlink synaesthesia signal of RIS, find the optimal solution of the phase shift matrix Ψ[n] of the uplink echo signal of RIS. but rank(P[n]) = 1. Problem (P1) can be transformed into problem (P4), expressed as:

[0154] (P4) find P[n] (27)

[0155] st Tr(M[n])≤Γ max (27a)

[0156] P m,m [n]=1 (27b)

[0157] rank(P[n])=1 (27c)

[0158] P[n]≥0 (27d)

[0159] It can be seen that problem (P4) is non-convex, and the present invention can obtain an approximate optimal solution to sub-problem (P5) by using semidefinite programming and convex difference programming.

[0160] According to the convex difference algorithm, the optimization problem (P4) is rewritten as problem (P4.1), which is expressed as:

[0161] (P4.1)

[0162]

[0163]

[0164] v i ≥0,i∈{1,...,5} (28c)

[0165] P m,m [n] = 1 (28d)

[0166] P[n]≥0 (28e)

[0167] in:

[0168]

[0169] The optimization problem (P4.1) is a convex optimization problem that can be solved using the CVX toolkit. By iterating the optimization problem (P4.1) until the optimal value is 0, we can obtain a rank-one solution. In numerical simulations, we usually set a cutoff criterion Tr(P[n])-||P[n]||≤ε, where ε is a sufficiently small constant, so that the convergence of the problem can be guaranteed.

[0170] By alternately optimizing subproblems (P2.2), subproblems (P3.1) and subproblems (P4.1) until the whole problem converges, the present invention obtains the downlink beamforming matrix W in the nth time slot that maximizes the overall security rate of the system in the nth time slot for the RIS-assisted secure ISAC system based on eavesdropper mobility k[n], artificial noise matrix Z[n], phase shift matrix Θ[n] of the downlink synaesthesia signal of RIS, and phase shift matrix Ψ[n] of the uplink echo signal of RIS.

[0171] In summary, the architecture proposed in the present invention can well guarantee the secure communication performance between the base station and the user when a mobile eavesdropper exists.

[0172] It should be emphasized that the above are only preferred embodiments of the present invention and do not limit the present invention in any form. Any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. Intelligent super surface assisted safety synaesthesia integrated system, characterized by: include: Radar communication base station, intelligent metasurface RIS, multiple communication users, and a mobile eavesdropper; The radar communication base station sends a signal to the communication user, and the signal is also used for radar perception of eavesdroppers; the intelligent metasurface RIS is used to assist secure communication.

2. The intelligent metasurface assisted safety synaesthesia integrated system according to claim 1 is characterized in that: The radar communication base station is configured with a linear antenna array, which is provided with N antennas; the smart metasurface RIS is configured as a planar array, and the planar array of the smart metasurface RIS is provided with M reflection units; the communication user and the mobile eavesdropper are each configured with at least one antenna.

3. The intelligent metasurface assisted safety synaesthesia integrated system according to claim 1 is characterized in that: There is no line-of-sight link between the radar communication base station, the communication user, and the mobile eavesdropper. The radar communication base station directs the beam to the smart metasurface RIS through beamforming design. The beam is phase-adjusted by the smart metasurface RIS and is directed to the communication user and the mobile eavesdropper respectively. The beam directed toward the eavesdropper is reflected from the eavesdropper, phase-modulated by the intelligent metasurface RIS, and then returns to the radar communication base station to sense the eavesdropper's location.

4. The intelligent metasurface assisted safety synaesthesia integrated system according to claim 3 is characterized in that: Artificial noise is introduced at the radar communication base station to improve the overall safe communication rate of the system.

5. A beamforming design method for an intelligent metasurface-assisted safety synaesthesia integrated system, characterized in that: The beamforming design method is used for the safety synaesthesia integration system according to any one of claims 1 to 4, and the beamforming design method comprises the following steps: Step S1: Obtain an expression for the overall secure communication rate of the system; Step S2: setting constraints; Step S3: construct an optimization problem with the goal of maximizing the overall secure communication rate of the system; Step S4: Solve the optimization problem to obtain the optimal design of the system.

6. The beamforming design method for the intelligent metasurface-assisted safety synaesthesia integrated system according to claim 5 is characterized in that: The specific steps in step S1 include: The base station-RIS channel, RIS-user channel and RIS-eavesdropper channel are all modeled as Rice channels; the RIS-eavesdropper channel is modeled for each time slot; Calculate the received signals of the communication user and the mobile eavesdropper respectively; Calculate the communication rate of the communication user and the eavesdropping rate of the mobile eavesdropper respectively according to the received signals of the communication user and the mobile eavesdropper; The overall secure communication rate of the system is calculated based on the obtained communication rate of the communication users and the eavesdropping rate of the mobile eavesdropper.

7. The beamforming design method for the intelligent metasurface-assisted safety synaesthesia integrated system according to claim 5 is characterized in that: The specific steps in step S3 include: For a time slot, an optimization problem is constructed with the goal of maximizing the overall safe communication rate of the system; the overall safe communication rate of the system is maximized by optimizing four variables, where the four variables are the beamforming design of the base station in the nth time slot. Artificial noise signal z[n] and phase shift matrices Θ[n] and Ψ[n] of RIS on downlink synesthesia signal and uplink echo signal; The optimization problem (P0) is formulated as follows: s.t.Tr(M[n])≤Γ max (19a) |[Θ[n]] m，m |=1 (19c) |[Ψ[n]] m，m |=1 (19d) Among them, w k [n] represents the beamforming vector of the kth communication user, z[n] represents the artificial noise signal, Θ[n] represents the phase shift matrix of RIS in the nth time slot during downlink signal transmission, Ψ[n] represents the phase shift matrix of RIS in the nth time slot during uplink signal transmission, D k [n] represents the secure communication rate that can be achieved between the base station and the kth communication user, M[n] represents the updated mean square error matrix, Γ max represents the maximum acceptable threshold of the updated mean square error matrix, P max represents the maximum transmission power of the base station, 19a is the constraint on the updated mean square error matrix error, 19b is the constraint on the base station transmission power, and constraints 19c and 19d represent the constraints of RIS on the reflection coefficients of the downlink synesthesia signal and the uplink echo signal.

8. The beamforming design method for the intelligent metasurface-assisted safety synaesthesia integrated system according to claim 5 is characterized in that: The specific steps in step S4 include: Step S4-1, split the optimization problem into three sub-problems, and transform the sub-problems into convex optimization problems through fractional programming, semi-positive definite relaxation and convex difference programming; Step S4-2, given the uplink echo signal reflection coefficient and the downlink synaesthesia signal reflection coefficient of the RIS in the nth time slot, optimize the downlink beamforming matrix, artificial noise matrix and auxiliary variables in the nth time slot; Step S4-3, substituting the downlink beamforming matrix and artificial noise matrix in the nth time slot obtained above, and given the uplink echo signal reflection coefficient of the RIS, optimizing the downlink synaesthesia signal reflection coefficient and auxiliary variables of the RIS in the nth time slot; Step S4-4, substituting the downlink beamforming matrix, artificial noise matrix and reflection coefficient of the RIS downlink synaesthesia signal in the nth time slot obtained above, to find the optimal solution of the reflection coefficient of the RIS uplink echo signal in the nth time slot; Step S4-5: Use all the obtained variable values ​​to determine whether the original problem converges. If it converges, the optimal solution to the optimization problem is obtained. If it does not converge, continue iterating.

9. A beamforming design device for an intelligent metasurface-assisted safety synaesthesia integrated system, the beamforming design device being used to implement the beamforming design method described in any one of claims 5 to 8, characterized in that: The device comprises: An expression acquisition unit, used for acquiring an expression of the overall secure communication rate of the system; An optimization problem acquisition unit, used to construct an optimization model with the goal of maximizing the overall security communication rate of the system; The optimization problem solving unit is used to solve the optimization problem, split the optimization problem into three sub-problems and transform them into convex optimization problems respectively, and obtain the optimal design through the alternating optimization algorithm.

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