RIS-assisted mobile antenna safety ISAC system and joint scheduling optimization method

Abstract

The invention belongs to the technical field of wireless communication physical layer secure transmission, and relates to an RIS-assisted mobile antenna security ISAC system and a joint scheduling optimization method, the RIS-assisted mobile antenna security ISAC system comprises a base station, an RIS, an eavesdropper and a plurality of legal users, the base station is used for sending signals to the legal users, a radar senses the eavesdropper, and the RIS is used for assisting secure communication; the base station is a dual-function base station MA-BS; the base station is provided with a transmitting array and a receiving array of movable antennas, and the legal user and the eavesdropper are provided with single antennas; no line-of-sight link exists between the base station and the legal user and between the eavesdroppers, the base station points the beam to the RIS through beam forming, and the RIS carries out phase modulation on the beam and then points the beam to the legal user and the eavesdroppers respectively; the beam pointing to the eavesdropper is reflected from the eavesdropper, is subjected to RIS phase modulation, and is returned to the base station for sensing the position of the eavesdropper; according to the system disclosed by the invention, the safety communication performance between the base station and the user in different scenes is ensured by utilizing the high spatial freedom degree of the movable antenna.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication physical layer security transmission, and particularly relates to an RIS-aided movable antenna security ISAC system and a joint scheduling optimization method. BACKGROUND

[0002] With the continuous evolution of communication technology, 6G technology has gradually become the research focus of academia and industry. 6G not only has ultra-high speed, ultra-low latency and ultra-high reliability, but also emphasizes the fusion capability of communication and perception, i.e., communication and perception integration. The dual-functional radar and communication (DFRC, Dual-Functional Radar and Communication) system as an important carrier of communication and perception integration technology can realize communication service and radar perception function on the same hardware platform, and improve spectrum utilization through efficient resource multiplexing to meet the demand of emerging applications for communication and perception integration. However, when the DFRC system provides communication services for legitimate users, the transmitted signals are easily intercepted by potential eavesdroppers, leading to information leakage, which poses a serious challenge to secure communication. At the same time, when the direct communication link between the DFRC base station and the user is blocked by obstacles, the signal transmission quality will be significantly reduced, and the perception accuracy will also be seriously affected, which greatly limits the practical application scenarios of the system.

[0003] The emergence of RIS (Reconfigurable Intelligent Surface, intelligent super surface) provides a new technical path to solve the above problems. RIS is composed of a large number of controllable reflection elements, which can actively build or optimize the communication link by adjusting the reflection phase shift, establish an indirect transmission path when the direct link is blocked, and effectively expand the coverage of the DFRC system. In addition, RIS can flexibly control the propagation direction and intensity of signals, combined with the movable antenna technology, can further improve the directivity and anti-interference ability of signals, and the two work together to enhance the received signal strength of legitimate users and suppress the signal reception of eavesdroppers, providing support for secure communication and accurate perception.

[0004] Although existing research has introduced RIS into the DFRC system, it mainly focuses on parameter optimization in static scenarios, and does not consider the balance between security rate and perception performance in dynamic channel environment.

[0005] Therefore, there is a need for a system or method that can combine real-time channel state information and perception feedback, match the needs of dynamic scenarios, and balance communication rate and perception accuracy to solve the above technical problems. SUMMARY

[0006] The technical scheme adopted by the present application to solve the technical problem is: an RIS-assisted movable antenna secure ISAC system, comprising: a base station, an intelligent metasurface RIS, an eavesdropper, and a plurality of legitimate users, the base station being configured to send signals to the legitimate users, and the base station also being configured to perform radar sensing on the eavesdropper, and the intelligent metasurface RIS being configured to assist secure communication; and the base station being a dual-function base station MA-BS.

[0007] The base station is configured with a transmitting array and a receiving array of movable antennas, and the legitimate users and the eavesdropper are each configured with a single antenna.

[0008] There is no line-of-sight link between the base station and the legitimate users or between the base station and the eavesdropper, the base station directs a beam to the intelligent metasurface RIS through beamforming, the intelligent metasurface RIS directs the beam to the legitimate users and the eavesdropper respectively after phase modulation, and the beam directed to the eavesdropper is reflected from the eavesdropper, passes through the intelligent metasurface RIS after phase modulation, and returns to the base station for sensing the position of the eavesdropper.

[0009] Preferably, the transmitting array has M discrete antenna candidate positions, and N t transmitting MA units; the receiving array has M discrete antenna candidate positions, and N r receiving MA units; the transmitting and receiving arrays are configured as planar arrays; the intelligent metasurface RIS is configured as a uniform planar array, and the intelligent metasurface RIS is equipped with T reflecting units.

[0010] More preferably, artificial noise is injected at the base station to improve the overall secure communication rate of the system.

[0011] The present application also discloses a joint scheduling optimization method for an RIS-assisted movable antenna secure ISAC system, which is used to optimize the above-mentioned RIS-assisted movable antenna secure ISAC system, and the joint scheduling optimization method comprises beamforming and joint optimization of antenna positions, and comprises the following steps:

[0012] Step S1: obtaining an expression of the overall secure communication rate of the system;

[0013] Step S2: setting a constraint condition;

[0014] Step S3: constructing an optimization problem with the maximum overall secure communication rate of the system as the target;

[0015] Step S4: solving the optimization problem to obtain the optimal design of the system.

[0016] Preferably, the specific steps in step S1 include:

[0017] The base station-intelligent metasurface RIS channel, the intelligent metasurface RIS-legitimate user channel, and the intelligent metasurface RIS-eavesdropper channel are all modeled as Ricean channels.

[0018] Calculate the received signals for legitimate users and eavesdroppers separately;

[0019] Calculate the communication rate of the legitimate user and the eavesdropping rate of the eavesdropper based on the received signals from the legitimate user and the eavesdropper, respectively.

[0020] The overall secure communication rate of the system is calculated based on the communication rates of legitimate users and the eavesdropping rates of eavesdroppers.

[0021] Preferably, the specific steps in step S3 include: constructing an optimization problem with the goal of maximizing the overall secure communication rate of the system; and maximizing the overall secure communication rate of the system through six variables in the optimization problem, the six variables being the beamforming design w between the base station and the k-th user. k Artificial noise signal z, movable antenna transmit array selection matrix B t Movable antenna receiver array selection matrix B r And the phase shift matrices Θ and Ψ of the intelligent metasurface RIS for the downlink syn-sensing signal and the uplink echo signal.

[0022] More preferably, the optimization problem is formulated as follows:

[0023]

[0024]

[0025] |[Θ] i，j |=1 (18h)

[0026] |[Ψ] i，i |=1 (18i)

[0027]

[0028] Among them, D k P represents the secure communication rate between the base station and the kth legitimate user. max Indicates the maximum transmission power of the base station. Indicates the nth t A binary position selection vector for each transmit movable antenna element. Indicates the nth t A binary position selection vector for receiving movable antenna elements, where L is the elevation domain. Discrete L directions, Q represents the azimuth domain Discretized into Q directions, D mindenotes the minimum distance between any two movable antenna elements, D(θ l ,φ q ) denotes the ideal beam pattern, ε is the maximum acceptable threshold of mean square error, ρ0 represents a scaling factor, R x represents the covariance matrix of the transmitted signal, represents the field response vector matrix, is the field response vector of the nth r movable antenna element at all M candidate discrete positions, which can be expressed as:

[0029]

[0030] wherein θ e represents the included angle of the RIS to the vertical direction of the eavesdropper, and φ e represents the included angle of the RIS to the horizontal direction of the eavesdropper.

[0031] Preferably, the specific steps in the step S4 include:

[0032] Step S4-1, the optimization problem is split into four sub-problems by using the block coordinate descent technique, and the sub-problems are converted into convex optimization problems by the methods of discrete binary particle swarm optimization, semi-definite relaxation, and differential convex programming;

[0033] Step S4-2, given the beamforming matrix, the artificial noise matrix, the downlink signal phase shift matrix of the RIS, and the uplink echo signal phase shift matrix of the RIS, the selection matrix of the movable antenna transmitting array and the receiving array is optimized;

[0034] Step S4-3, the selection matrix is substituted, the uplink echo signal phase shift matrix and the downlink signal phase shift matrix of the RIS are given, and the downlink beamforming matrix and the artificial noise matrix are optimized;

[0035] Step S4-4, the selection matrix, the downlink beamforming matrix, and the artificial noise matrix are substituted, the uplink echo signal phase shift matrix of the RIS is given, and the downlink signal phase shift matrix of the RIS is optimized;

[0036] Step S4-5, the selection matrix, the downlink beamforming matrix, the artificial noise matrix, and the downlink signal phase shift matrix of the RIS are substituted, and the optimal solution of the uplink echo signal phase shift matrix of the RIS is found;

[0037] Step S4-6, whether the original problem converges is judged by using the values of all the variables found, if the original problem converges, the optimal solution of the optimization problem is successfully found; if the original problem does not converge, iteration is continued.

[0038] This invention also discloses a beamforming design and antenna position selection device for a RIS-assisted movable antenna secure ISAC system. This beamforming design and antenna position selection device is used to implement the aforementioned joint scheduling optimization method. The beamforming design and antenna position selection device includes:

[0039] The expression acquisition unit is used to acquire the expression for the overall secure communication rate of the system.

[0040] The optimization problem acquisition unit is used to construct an optimization model with the goal of maximizing the overall secure communication rate of the system.

[0041] The optimization problem solving unit is used to solve optimization problems. It first divides the optimization problem into three sub-problems, then transforms it into a convex optimization problem, and obtains the optimal design through the block coordinate descent technique.

[0042] The beneficial effects of this invention are:

[0043] 1. This invention can combine real-time channel status information with sensing feedback to match dynamic scenario requirements, balancing communication rate and sensing accuracy.

[0044] 2. This invention can effectively solve the challenges that channel coupling poses to algorithm design.

[0045] 3. This invention introduces RIS and a movable antenna into the ISAC system, which not only simplifies the system structure and reduces design costs, but also improves the system's security sensing performance and sensing accuracy. Attached Figure Description

[0046] Figure 1 This is a system model diagram of a RIS-assisted movable antenna secure ISAC system and a joint scheduling optimization method according to the present invention.

[0047] Figure 2 This is a flowchart illustrating the joint scheduling optimization method of the present invention. Detailed Implementation

[0048] The related technologies of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0049] like Figures 1-2 As shown in the figure, the RIS-assisted movable antenna secure ISAC system and joint scheduling optimization method of this specific embodiment are as follows: Figure 1The system is shown, including MA-BS, RIS, multiple legitimate users, eavesdroppers, and system configuration (including: base station transmit array and receive array antenna position selection design, base station transmitted omnidirectional integrated signal beam design, artificial noise design introduced at the base station, RIS uplink and downlink reflection coefficient design, and base station power control scheme).

[0050] In the RIS-assisted movable antenna secure ISAC system, because the channels between the base station and users and eavesdroppers are blocked, the base station must provide secure communication services for users and effectively sense eavesdroppers through RIS. Therefore, the channels of the system 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 a Rician fading channel.

[0051] The base station-RIS channel is represented as The channel from the RIS to the kth user is represented as The channel from the RIS to the eavesdropper is represented as where the base station-RIS channel is represented as:

[0052]

[0053] where represents the channel between T RIS reflecting elements and M candidate discrete positions of the transmitting movable antenna, represents the channel matrix between T RIS reflecting elements and M candidate discrete positions of the nth transmitting movable antenna element, t represents the channel matrix between the nth transmitting movable antenna element and T RIS reflecting elements at candidate discrete position p t m represents the channel coefficient between the nth transmitting movable antenna element at candidate discrete position p t m , respectively represent the elevation angle and azimuth angle of the channel path of the tth RIS reflecting element, then can be represented as

[0054]

[0055] where α t is the channel fading factor between the DFRC BS and the tth RIS reflecting element. In addition, as the selection matrix of the transmitting movable antenna, is specifically represented as

[0056] ​​​​​

[0057] Similarly, the receiving movable antenna selection matrix B for receiving the echo r is denoted as

[0058]

[0059] The channel from the RIS to the kth legitimate user and the channel from the RIS to the eavesdropper are denoted as:

[0060]

[0061] where C0represents the path loss at the reference distance D0= 1 m, and ι represents the loss exponent of the channel, d rk = ||q r -q k || represents the distance between the RIS and the kth legitimate user, re = ||q r -q e || represents the distance between the RIS and the eavesdropper, and η represent the Rician factor of the channel. The RIS units are arranged in the form of a UPA, and the phase adjustment is performed on the downlink signal transmitted by the base station and the received echo signal, respectively. The phase shift matrix of the RIS is denoted as:

[0062]

[0063] where α t ∈ [0, 2π), β t ∈ [0, 2π), denote the phase and amplitude of the tth unit of the RIS. The candidate discrete positions of the movable antenna are also arranged in the form of a UPA, and the M possible discrete positions are collected into a position matrix P = [p1, …, pm], where pm= [xm, ym] represents the mth candidate discrete position. In addition, the binary position selection vector of the nth transmitting movable antenna element is defined as M m m m t For all m, and

[0064] Let denote the integrated sensing and communication signal after introducing artificial noise by the base station, denote the beamforming vector of the kth legitimate user, s k be the information-bearing symbol corresponding to the kth legitimate user, and the artificial noise signal​​​​​ and Thus, the received signal of the kth legitimate user can be expressed as:

[0065]

[0066] where represents the AWGN introduced at the kth legitimate user's receive antenna. The received signal at the eavesdropper is

[0067]

[0068] where represents the Gaussian white noise introduced at the eavesdropper. The echo received at the base station from the eavesdropper can be expressed as

[0069]

[0070] where, represents the Gaussian white noise introduced at the base station antenna.

[0071] The achievable communication rate between the base station and the kth legitimate user is expressed as:

[0072]

[0073] Assuming that the eavesdropper is able to cancel the interference from other users before decoding the information of a particular legitimate user, the eavesdropping rate when the eavesdropper eavesdrops the communication between the base station and the kth legitimate user is expressed as:

[0074]

[0075] Thus, under the premise of ensuring secure communication, the achievable communication rate between the base station and the kth legitimate user is:

[0076]

[0077] To ensure high-quality perception for the eavesdropper, the target position is irradiated by a beam with energy focusing and low sidelobe leakage. For this purpose, the elevation domain is discretized into L directions, and the azimuth domain is discretized into Q directions, and the ideal beam pattern is designated as

[0078]

[0079] where 2Δ and 2δ are the beam widths of each target in the elevation and azimuth angles, respectively, E represents the number of eavesdroppers, θ e represents the angle between the RIS and the vertical direction of the eavesdropper, φ erepresents the included angle of the RIS to the eavesdropper level direction. Based on the ideal beam pattern, the mean square error between the ideal beam pattern and the actual beam pattern is used as the perception performance index to quantify the beam pattern matching accuracy, and its expression is:

[0080]

[0081] wherein ρ0 represents a scaling factor, represents the covariance matrix of the transmitted signal, and here is the field response vector of the nth r movable antenna element at all M candidate discrete positions, which can be expressed as:

[0082]

[0083] The embodiment jointly optimizes the downlink beamforming vector w k , the artificial noise signal z, the phase shift matrix Θ and Ψ of the RIS to the downlink through-sensing signal and the uplink echo signal, and the selection matrix B t and the selection matrix B r of the movable antenna transmitting array and the movable antenna receiving array to maximize the overall secure communication rate of the system. The joint optimization problem is constructed as:

[0084]

[0085] |[Θ] i，i |=1 (18h)

[0086] |[Ψ] i，i |=1 (18i)

[0087]

[0088] wherein 18a is the base station transmit power constraint, 18b-18e are the movable antenna position selection constraints, 18f and 18g are the minimum distance constraints of the movable antenna, 18h and 18i represent the reflection coefficient constraints of the RIS to the downlink through-sensing signal and the uplink echo signal respectively, and the constraint 18j is the perception constraint for quantifying the mean square error between the beam patterns.

[0089] Problem (P0) is a non-convex optimization problem, mainly due to the following two reasons: first, there is a high coupling between the six groups of optimization variables, so the objective function is non-convex. In addition, 18b-18j are all non-convex constraints. Therefore, problem (P0) is a non-convex optimization problem, which is challenging to solve directly. Therefore, it is necessary to design an efficient algorithm for the RIS-aided movable antenna secure ISAC system.

[0090] A solution scheme for the total safe communication rate maximization problem of RIS-aided mobile antenna safe ISAC systems. The present embodiment adopts a block coordinate descent framework to decouple the problem into four sub-problems, which are then solved separately, and then block coordinate descent is performed on each sub-problem until the entire problem converges. First, the linear combination of concave functions makes the objective function of problem (P0) non-convex. In order to solve problem (P0), first transform the objective function of problem (P0) from

[0091]

[0092] where Let Transformed into:

[0093]

[0094] Therefore, problem (P0) can be transformed into problem (P1):

[0095]

[0096] |[Θ] i，i |=1 (19h)

[0097] |[Ψ] i，i |=1 (19i)

[0098]

[0099] Z≥0 (19m)

[0100]

[0101] The constraints 19b-19n in problem (P1) are all non-convex constraints.

[0102] The present embodiment gives the downlink beamforming matrix W k , the artificial noise matrix Z, the RIS downlink co-sensing signal phase shift matrix Θ and the uplink echo signal phase shift matrix Ψ, and finds the optimal solution of the mobile antenna transmitting array selection matrix B t and the receiving array selection matrix B r . Taking the mobile antenna transmitting array as an example, first initialize the positions of I particles to where represents a possible distribution of N t transmitting mobile antenna elements in M candidate discrete positions. The velocity of I particles is initialized to where represents the nth tvelocity of the i-th mobile antenna element at the j-th iteration. With the BPSO algorithm, the velocity of each particle is updated as

[0103]

[0104] where is the individual best position of the i-th particle, is the global best position, is the individual best position of the I particles in the whole particle swarm, and j is the iteration number satisfying 0≤j≤J. ω is the inertia weight, which is expressed as ω = (ω max - ω min )(J - j) / J + ω min , where ω max = 1.2, ω min = 0.4. c1 and c2 are the individual and global learning factors that push each particle towards the individual and global best positions, respectively. e1 and e2 are two random vectors where each entry is a uniform random number in the range [0, 1] that are used to increase randomness to reduce the possibility of converging to an undesirable local optimal solution. In the j-th iteration, the position update of the mobile antenna can be expressed as

[0105] where maps the velocity to the interval [0, 1] as a probability, which is the probability that the particle takes the value 1 in the next step. Based on the above framework, the problem (P1) can be transformed into the following form:

[0106]

[0107] where is a function that returns the number of mobile antenna elements that violate the minimum mobile antenna distance constraint at position When two different mobile antenna elements are at the same candidate position, they push the updated position to satisfy the constraints 19b and 19f, and finally obtain a suboptimal solution. In combination with the mobile antenna receiving array, the optimization problem (P1) can be converted to (P2), which is specifically expressed as

[0108]

[0109] Z≥0 (21g)

[0110] rank(Z) = 1 (21h)

[0111] The embodiment gives the above mobile antenna transmitting array selection matrix B t , the receiving array selection matrix B r ​, the phase shift matrix Θ of the RIS downlink sensing signal and the phase shift matrix Ψ of the uplink echo signal jointly optimize the downlink beamforming matrix W k and the optimal solution of the artificial noise matrix Z. The optimization problem (P3) can be expressed as

[0112]

[0113] Z≥0 (22e)

[0114] rank(Z)=1 (22f)

[0115] Since problem (P3) is a non-convex optimization problem, the present application adopts semi-definite relaxation and difference convex programming to obtain an approximate optimal solution of sub-problem (P3).

[0116] Specifically, the objective function is transformed by Taylor expansion, and the value of W k at the nth iteration is and the value of Z is Z n , the expansion point is to obtain

[0117]

[0118] where

[0119]

[0120] Therefore, the optimization objective function is successfully transformed into a concave function:

[0121]

[0122] For non-convex constraints 22d and 22f, to solve this problem, convex difference programming is used to remove the constraint from the optimization problem, and the optimization problem is rewritten as follows:

[0123]

[0124] Z≥0 (32d)

[0125] where and is the sub-gradient of the spectral norm in the r-1th iteration, and τ1 and τ2 are penalty factors related to the rank-1 constraint. Optimization problem (P2.1) is an SDP problem, which can be effectively solved by CVX.

[0126] The present embodiment gives the downlink beamforming matrix W k , the artificial noise matrix Z, the movable antenna transmitting array selection matrix B t and the receiving array selection matrix B r, the RIS uplink echo signal phase shift matrix Ψ, find the optimal solution of the RIS downlink channel signal phase shift matrix Θ. Let Problem (P2) can be transformed into problem (P4)

[0127]

[0128] s.t.O i，i = 1 (33a)

[0129] rank(O) = 1 (33b)

[0130] O ≥ 0 (33c)

[0131] wherein:

[0132]

[0133]

[0134] Similarly, applying convex difference programming and Taylor expansion to constraint 32b, the optimization problem (P4) is transformed into (P4.1), which is specifically represented as

[0135]

[0136] s.t.O i，i = 1 (38a)

[0137] O ≥ 0 (38b)

[0138] wherein:

[0139]

[0140] So far, the non-convex constraints in (P4) have been completely eliminated, and the optimization problem (P4.1) is successfully transformed into an SDP problem that can be effectively solved using the CVX solver.

[0141] The embodiment gives the downlink beamforming matrix W k , the artificial noise matrix Z, the movable antenna transmitting array selection matrix B t and the receiving array selection matrix B r and the downlink channel signal phase shift matrix Θ of the RIS, find the optimal solution of the RIS uplink echo signal phase shift matrix. Let Then rank(C[n]) = 1. Problem (P2) can be transformed into problem (P5), which is represented as:

[0142] (P5): find C

[0143] s.t.C i，i = 1 (45a)

[0144] rank(C)=1 (45b)

[0145] C≥0 (45c)

[0146] It can be seen that the problem (P5) is non-convex, and the present application can obtain an approximate optimal solution of the sub-problem (P5) by using semi-definite programming and convex difference programming. According to the convex difference algorithm, the optimization problem (P5) is rewritten as problem (P5.1), which is represented as:

[0147]

[0148] s.t.C i，i =1 (46a)

[0149] C≥0 (46b)

[0150] The optimization problem (P5.1) is a convex optimization problem and can be solved by the CVX tool kit. By iteratively optimizing (P5.1) until the optimal value is 0, we can obtain a rank-one solution. In numerical simulation, we usually set a cutoff criterion Tr(C)-||C||2≤θ, where θ is a small enough constant, so as to guarantee the convergence of the problem.

[0151] By alternately optimizing the sub-problem (P2.1), the sub-problem (P3.1), the sub-problem (P4.1), and the sub-problem (P5.1) until the entire problem converges, the present application obtains the movable antenna transmitting array selection matrix B t , the movable antenna receiving array selection matrix B r , the RIS downlink sensing signal phase shift matrix Θ and the uplink echo signal phase shift matrix Ψ, the downlink beamforming matrix W k , and the artificial noise matrix Z scheme.

[0152] In summary, by introducing the RIS into the ISAC system, the present application not only simplifies the system structure and reduces the design cost, but also improves the security sensing performance and sensing accuracy of the system. Therefore, the present application can match the dynamic scene demand by combining real-time channel state information and sensing feedback, and balance the communication rate and sensing accuracy.

[0153] It should be noted that the above is only a preferred embodiment of the present application, and does not limit the present application in any form. Any simple modification, equivalent change and modification of the above embodiment according to the technical essence of the present application still falls within the scope of the technical solution of the present application.

Claims

1. A RIS-assisted movable antenna secure ISAC system, characterized in that, include: The system includes a base station, a smart metasurface RIS, an eavesdropper, and multiple legitimate users. The base station is used to send signals to the legitimate users and is also used for radar detection of the eavesdropper. The smart metasurface RIS is used to assist in secure communication. The base station is a dual-function base station MA-BS; The base station is equipped with a transmitting array and a receiving array with movable antennas, and both the legitimate user and the eavesdropper are equipped with a single antenna; There is no line-of-sight link between the base station and the legitimate user, or between the base station and the eavesdropper. The base station directs the beam to the intelligent metasurface RIS through beamforming. The intelligent metasurface RIS then modulates the beam and directs it to the legitimate user and the eavesdropper, respectively. The beam directed at the eavesdropper is reflected from the eavesdropper, phased by the intelligent metasurface RIS, and then returned to the base station to sense the eavesdropper's location.

2. The RIS-assisted movable antenna secure ISAC system according to claim 1, characterized in that, The transmitting array has M discrete antenna candidate positions, N t The receiving array has M discrete antenna candidate positions, and N transmit MA units; r The transmitting and receiving arrays are both configured as planar arrays; the intelligent metasurface RIS is configured as a uniform planar array, and the intelligent metasurface RIS is equipped with T reflection units.

3. The RIS-assisted movable antenna secure ISAC system according to claim 2, characterized in that, Artificial noise is injected at the base station to improve the overall secure communication rate of the system.

4. A joint scheduling optimization method for a RIS-assisted movable antenna secure ISAC system, characterized in that, The joint scheduling optimization method is used to optimize the RIS-assisted movable antenna secure ISAC system according to any one of claims 1 to 3. The joint scheduling optimization method includes joint optimization of beamforming and antenna position, and includes the following steps: Step S1: Obtain the expression for the overall secure communication rate of the system; Step S2: Set 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.

5. The joint scheduling optimization method for a RIS-assisted movable antenna secure ISAC system according to claim 4, characterized in that, The specific steps in step S1 include: The base station-intelligent metasurface RIS channel, the intelligent metasurface RIS-legitimate user channel, and the intelligent metasurface RIS-eavesdropper channel are all modeled as Ricean channels. Calculate the received signals for legitimate users and eavesdroppers separately; Calculate the communication rate of the legitimate user and the eavesdropping rate of the eavesdropper based on the received signals from the legitimate user and the eavesdropper, respectively. The overall secure communication rate of the system is calculated based on the communication rates of legitimate users and the eavesdropping rates of eavesdroppers.

6. The joint scheduling optimization method for a RIS-assisted movable antenna secure ISAC system according to claim 4, characterized in that, The specific steps in step S3 include: constructing an optimization problem with the goal of maximizing the overall secure communication rate of the system; and maximizing the overall secure communication rate of the system through six variables in the optimization problem, wherein the six variables are the beamforming design w between the base station and the k-th user. k Artificial noise signal z, movable antenna transmit array selection matrix B t Movable antenna receiver array selection matrix B r And the phase shift matrices Θ and Ψ of the intelligent metasurface RIS for the downlink syn-sensing signal and the uplink echo signal.

7. The joint scheduling optimization method for a RIS-assisted movable antenna secure ISAC system according to claim 6, characterized in that, The optimization problem is described as follows: |[I] i，i |=1 (18h) |[Ψ] i，i |=1 (18i) Among them, D k P represents the secure communication rate between the base station and the kth legitimate user. max Indicates the maximum transmission power of the base station. Indicates the nth t A binary position selection vector for each transmit movable antenna element. Indicates the nth t A binary position selection vector for receiving movable antenna elements, where L is the elevation domain. Discrete L directions, Q represents the azimuth domain Discretized into Q directions, D min D(θ) represents the minimum distance between any two movable antenna elements. l ,φ q ) represents the ideal beam pattern, ε is the maximum acceptable threshold for mean square error, ρ0 represents the scaling factor, and R x The covariance matrix representing the transmitted signal. The field response vector matrix represents the field response vector matrix. represent The conjugate transpose of .

8. The joint scheduling optimization method for a RIS-assisted movable antenna secure ISAC system according to claim 4, characterized in that, The specific steps in step S4 include: Step S4-1: Use the block coordinate descent technique to break down the optimization problem into four sub-problems, and transform the sub-problems into convex optimization problems through discrete binary particle swarm optimization, semidefinite relaxation, and differential convex programming. Step S4-2: Given the beamforming matrix, artificial noise matrix, downlink sensing signal phase shift matrix of RIS, and uplink echo signal phase shift matrix, optimize the selection matrix of the movable antenna transmitting array and receiving array; Step S4-3: Substitute the selection matrix, given the uplink echo signal phase shift matrix and downlink syn-sensing signal phase shift matrix of RIS, optimize the downlink beamforming matrix and artificial noise matrix; Step S4-4: Substitute the selection matrix, downlink beamforming matrix, and artificial noise matrix, and given the uplink echo signal phase shift matrix of the RIS, optimize the downlink synesthetic signal phase shift matrix of the RIS. Steps S4-5: Substitute the selection matrix, downlink beamforming matrix, artificial noise matrix, and RIS downlink synesthetic signal phase shift matrix to find the optimal solution for the RIS uplink echo signal phase shift matrix. Step S4-6: Use all the calculated variable values ​​to determine whether the original problem has converged. If it has converged, the optimal solution to the optimization problem has been successfully obtained; if it has not converged, continue the iteration.

9. A beamforming design and antenna position selection device for a RIS-assisted movable antenna secure ISAC system, characterized in that, The beamforming design and antenna position selection device is used to implement the joint scheduling optimization method according to any one of claims 4-8, and the beamforming design and antenna position selection device includes: The expression acquisition unit is used to acquire the expression for the overall secure communication rate of the system. The optimization problem acquisition unit is used to construct an optimization model with the goal of maximizing the overall secure communication rate of the system. The optimization problem solving unit is used to solve optimization problems. It first divides the optimization problem into three sub-problems, then transforms it into a convex optimization problem, and obtains the optimal design through the block coordinate descent technique.

Images