A ris-assisted common-sensing dual-security enhancement method

Abstract

This invention discloses a RIS-assisted dual-security enhancement method for sensing, relating to the field of communication security technology. By introducing RIS to regulate the wireless propagation environment and combining the channel state information of legitimate users with the coarse-grained distance, angle, and other positional information of Eve, the method jointly designs the base station transmit beamforming matrix, the dedicated sensing signal covariance matrix, and the RIS phase shift. This reduces Eve's eavesdropping and sensing capabilities while ensuring legitimate communication and sensing performance, thereby improving the overall security performance of the system with lower hardware overhead.

Description

Technical Field

[0001] This invention relates to the field of communication security technology, and in particular to a RIS-assisted inductive dual security enhancement method. Background Technology

[0002] Due to the inherent broadcast characteristics of wireless transmission, both communication and sensing signals in an integrated sensing and communication (ISAC) system are easily intercepted. This not only poses a security threat to the system's communication and sensing functions but also means that simultaneously ensuring both types of security often involves high hardware costs and resource consumption. Therefore, how to balance communication security and sensing security within limited cost constraints has become a key issue in the design of sensing and communication systems.

[0003] In existing technologies, to improve the security performance of wireless systems, physical layer security (PLS) solutions are typically adopted. These solutions utilize the spatial freedom provided by multi-antenna architecture or multi-node collaboration, such as transmitter beamforming, artificial noise, cooperative interference, and multi-antenna precoding, to improve the reception quality of communication and sensing signals for legitimate users, while suppressing the interception capabilities of unauthorized nodes.

[0004] For multi-node solutions, system security is primarily enhanced through collaboration between multiple access points and the central processing unit. In this type of solution, multiple access points, under the coordination of the central processing unit, perform joint beam design and coordinated transmission. By leveraging the spatial diversity gain brought about by multi-node collaboration, legitimate communication and sensing performance are improved simultaneously, while suppressing eavesdroppers' (Eve) information decoding and target perception capabilities, thereby achieving coordinated protection of communication security and sensing security.

[0005] For multi-antenna solutions, the spatial freedom provided by the multi-antenna configuration at the transmitter is mainly used to improve system security performance. In this type of solution, the transmitter performs joint beamforming, precoding, and artificial noise based on a multi-antenna architecture. By leveraging the spatial resolution and directional control capabilities provided by multiple antennas, the system effectively suppresses Eve's perceptual capabilities, thereby achieving coordinated protection of communication security and perception security.

[0006] However, in practical deployments, such solutions often require a large number of active antennas, RF links, or auxiliary nodes, thus placing a high demand on system hardware. When the number of available antennas or cooperating nodes is limited, the system typically struggles to effectively suppress Eve's ability to intercept communication signals and sensing information while ensuring legitimate communication and sensing performance, making it difficult to achieve a coordinated improvement in communication and sensing security. Furthermore, multi-antenna and multi-node configurations introduce additional hardware costs, power consumption, system complexity, and latency, further limiting their application in real-world scenarios.

[0007] Reconfigurable Smart Surfaces (RIS), as a low-cost, low-power, and easily deployed environmental reconfiguration technology, can provide additional spatial degrees of freedom to the system by intelligently regulating the wireless propagation environment. This reduces the system's reliance on complex active multi-antenna and multi-node architectures at a lower hardware cost, offering a new approach to the collaborative optimization of sensing security. In terms of communication security enhancement, RIS can adjust the phase of the reflecting unit to change the signal propagation path and energy distribution, thereby improving the quality of legitimate links while weakening the reception capability of eavesdropping links. However, most existing solutions still primarily focus on enhancing communication security, with relatively insufficient consideration given to the collaborative protection of communication security and sensing security. Summary of the Invention

[0008] This invention addresses the problems of existing ISAC PLS schemes, which typically require large-scale multi-antenna configurations and multi-auxiliary node collaboration, resulting in high hardware overhead and difficulty in simultaneously ensuring communication and sensing security. It proposes a RIS-assisted dual-security enhancement method for both communication and sensing. By introducing RIS to regulate the wireless propagation environment and combining legitimate user channel state information with Eve's coarse-grained distance, angle, and other positional information, the method jointly designs the base station transmit beamforming matrix, the dedicated sensing signal covariance matrix, and the RIS phase shift. This approach ensures legitimate communication and sensing performance while reducing Eve's eavesdropping and sensing capabilities, thereby improving the overall system security performance with lower hardware overhead.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] This invention provides a RIS-assisted inductive dual security enhancement method, comprising the following steps:

[0011] S1. System initialization: The base station establishes synchronization with RIS through the control channel, establishes RRC connection between the base station and the user, allocates communication channel resources, and performs initial search and coarse-grained estimation of the user and Eve target within a given range based on beam scanning to obtain the initial distance and angle parameters of the user target, as well as the coarse-grained distance and angle parameters of Eve, for subsequent secure transmission and waveform optimization.

[0012] S2. Channel estimation: The base station or user uses pilot signals to estimate channel state information;

[0013] S3. Joint optimization of beam matrix and RIS phase shift: Based on the channel state information estimated by the user and the sensed coarse-grained location information of Eve, the base station transmit beamforming matrix, the dedicated sensing signal covariance matrix and the RIS phase matrix are designed as joint optimization variables, and the corresponding parameter configurations are sent to the base station and RIS control terminal for execution.

[0014] S4. Signal Transmission: Based on the base station transmit beamforming matrix and the dedicated sensing signal covariance matrix, the base station generates a transmit signal to complete the transmission of communication and sensing signals. The obtained RIS reflection parameters are sent to the RIS controller through the control link, thereby realizing the collaborative beam control between the base station and the RIS.

[0015] S5. Communication and Sensing: The user receives communication information, and the base station estimates the target state parameters using the echo signal according to the predetermined sensing task requirements; Eve is not used as a subsequent sensing target.

[0016] S6. Parameter Update: When the target location or propagation environment changes, the base station executes the aforementioned steps S2-S5 again to re-estimate the user channel state information and track and update Eve's coarse-grained location information.

[0017] Furthermore, in step S1, the initial search and coarse-grained estimation employ multiple parameter estimation strategies, specifically including the following cases:

[0018] Case 1: When estimating target distance parameters

[0019] The base station performs matched filtering, cross-correlation peak detection, generalized cross-correlation processing, or maximum likelihood delay estimation on the received echo and reference signal to obtain the target propagation delay parameters, and obtains the target initial distance parameters based on the propagation delay parameters.

[0020] Case 2: When estimating target angle parameters

[0021] The base station uses the Capon beamforming algorithm to process the spatial spectrum and obtains the initial angle parameters of the target by searching for the peak of the spatial spectrum; when the range cell is known, the peak position represents the target's orientation;

[0022] Case 3: When performing joint estimation of target distance and angle parameters

[0023] The base station constructs a joint distance-angle parameter model and uses one or more of the following methods: compressed sensing reconstruction, orthogonal matching pursuit, sparse Bayesian learning, or atomic norm minimization, to jointly estimate the initial distance and initial angle parameters of the target.

[0024] Case 4: When in a multi-object scenario

[0025] The base station first uses a generalized likelihood ratio test to determine the existence and number of targets, and removes false peaks or interference peaks. On this basis, it separates, distinguishes or correlates multiple target echoes. For the estimation of the distance parameters, angle parameters or joint distance-angle parameters of each target, the corresponding methods in Case 1 to Case 3 are used respectively.

[0026] Furthermore, in step S2, the process of obtaining legitimate user channel state information includes the following steps:

[0027] S11: The base station sends pilot signals to all legitimate communication users during the downlink pilot training phase;

[0028] S12: Each legitimate communication user estimates the direct link channel from the base station to the legitimate communication user based on the received pilot signal using the least squares method or the minimum mean square error method;

[0029] S13: Each legitimate communication user feeds back the direct link channel information to the base station through the uplink, so that the base station can obtain the direct link channel status information from the base station to each legitimate communication user;

[0030] S14: The RIS control unit switches different reflection coefficient configurations in different training time slots and, in conjunction with phased pilot training, obtains relevant channel information of the RIS auxiliary link;

[0031] S15: The direct link channel from the base station to the legitimate communication user, the channel from the base station to the RIS, and the reflected link channel from the RIS to the legitimate communication user all follow Rician fading; based on the direct link channel, the reflected link channel, and the RIS reflection matrix, the equivalent concatenated channel of the legitimate communication user is obtained.

[0032] Furthermore, in step S2, the process of obtaining the communication eavesdropping channel state information includes the following steps:

[0033] S21: The base station uses the sensing module to detect the environment and obtain the distance and angle estimation information of Eve; the multipath fading component is regarded as the channel uncertainty that deviates from the strong line-of-sight component, and each element in the multipath fading component is constrained by a known upper bound;

[0034] S22: Based on the distance and angle parameters, obtain the LoS components from the base station to Eve and from RIS to Eve;

[0035] S23: Determine the non-line-of-sight component of the eavesdropping channel based on the distance and angle parameters;

[0036] S24: Both the base station to Eve channel and the RIS to Eve channel follow Rician fading. A communication eavesdropping channel is obtained based on the base station to Eve channel and the RIS to Eve channel.

[0037] Furthermore, in step S2, the legitimate sensing channel information acquisition process includes: after obtaining Eve's coarse-grained location information during the initialization phase, the subsequent sensing process is only carried out for legitimate users. The signal transmitted by the base station can reach the target via the direct link and the RIS reflection link, and then be reflected back to the base station through these two links to form the target echo.

[0038] Furthermore, in step S2, the process of modeling the eavesdropping channel includes: describing the base station's transmitted signal via the direct link and the RIS reflection link using a bounded uncertainty model.

[0039] Furthermore, in step S3, the constraints for the joint optimization of the beam matrix and RIS phase shift include: communication service quality constraints, communication security constraints, sensing service quality constraints, sensing security constraints, total transmit power constraints, positive semidefinite constraints of the covariance matrix, rank constraints of the beamforming matrix, and constant mode constraints of the RIS reflection unit.

[0040] Furthermore, the optimization problem in step S3 is modeled under a unified constraint framework, and the optimization objective can be divided into the following three cases:

[0041] Case 1: Prioritizing Communication Performance

[0042] With the optimization objective of maximizing legitimate users and communication rate, the transmit beamforming matrix, dedicated sensing signal covariance matrix, and RIS phase shift are used as joint optimization variables.

[0043] Case 2: Prioritizing perceived performance

[0044] The optimization objective is to maximize the legally perceived signal-to-noise ratio.

[0045] Case 3: Synesthesia Coordination Optimization

[0046] The optimization objective is the weighted sum of the normalized sum of legitimate users' communication rate and legitimate perceived SNR.

[0047] Based on the above optimization model, an alternating optimization framework is used to solve the problem, and the block coordinate descent method is used to alternately update the base station transmit beamforming matrix, the dedicated sensing signal covariance matrix, and the RIS phase shift.

[0048] Further, step S4 specifically includes: decomposing the rank-1 matrix to obtain the corresponding beamforming vector, and generating a dedicated sensing signal that satisfies the covariance constraint based on the optimized dedicated sensing signal covariance matrix.

[0049] Furthermore, step S5 specifically includes:

[0050] 1) Legitimate users

[0051] During the information transmission phase, legitimate communication users receive signals sent by the base station. Based on the received signals, the communication rate is used to measure the communication performance of legitimate communication users.

[0052] 2) Base station

[0053] During the sensing phase, the base station receives the echo signal formed by the reflection of the target. Based on the echo signal, the sensing SNR is used to measure the sensing performance of the base station, which is used to characterize the quality of the base station receiving the target echo signal.

[0054] 3) Eve

[0055] During the communication eavesdropping phase, Eve receives signals used to eavesdrop on legitimate users' communication information. Based on the received communication eavesdropping signals, assuming that the eavesdropper knows the communication channel state information and the sequence of dedicated radar sensing signals, and can eliminate the interference caused by the dedicated sensing signals, Eve's ability to eavesdrop on legitimate users is represented by the eavesdropping rate. During the sensing eavesdropping phase, Eve receives the echo signals formed by the reflection of the target. Based on the echo signals, Eve's sensing eavesdropping ability is represented by its sensing signal-to-interference-plus-noise ratio.

[0056] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0057] Existing ISAC physical layer security schemes typically rely on large-scale multi-antenna configurations and multi-auxiliary node collaboration, resulting in significant hardware overhead and difficulty in simultaneously ensuring communication and sensing security. To address these issues, this invention proposes a RIS-assisted dual-security enhancement method for both communication and sensing. By introducing RIS assistance into the ISAC system, the wireless propagation environment is controlled, thereby achieving dual protection for both communication and sensing security. Simultaneously, the impact of parameter estimation errors on system performance is considered, and relevant parameters are re-acquired and adjusted when the target location or propagation environment changes, thus reducing performance degradation caused by parameter errors and environmental changes and improving the robustness of the scheme. This invention supports designs that emphasize either communication or sensing performance, as well as joint designs that consider both, enabling flexible configuration and improved overall system sensing security performance at a lower hardware cost for different application scenarios. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0059] Figure 1 This is a system model of the RIS-assisted inductive dual security enhancement method provided in the embodiments of the present invention. Detailed Implementation

[0060] To better understand this technical solution, the method of the present invention will be described in detail below with reference to the accompanying drawings.

[0061] To address the risks of communication eavesdropping and perception privacy leakage in the ISAC system, this invention proposes a RIS-assisted sensory dual security enhancement method, such as... Figure 1 As shown, consider a Multiple-Input Multiple-Output (MIMO) ISAC system model. This system consists of a... An ISAC base station with one antenna, containing RIS of a single reflection unit A single-antenna communication user (CU) that simultaneously serves as a sensing target, and a matching The system consists of dual-function Eve antennas. The RIS (Radio Router Array) intelligently regulates the wireless propagation environment to assist the base station in target detection and achieve synergistic enhancement of communication and perception security. Eve not only intercepts communication information destined for various users but also attempts to perceive surrounding targets and the environment. All antennas in the system are modeled as uniform linear arrays (ULAs).

[0062] This invention can be summarized in the following steps:

[0063] (1) System initialization

[0064] 1) The base station establishes synchronization with the RIS via the control channel;

[0065] 2) An RRC connection is established between the base station and the user, and communication channel resources are allocated;

[0066] 3) Based on beam scanning, the base station performs an initial search and coarse-grained estimation of targets (including users and Eve) within a given range to obtain the initial distance and angle parameters of user targets, as well as the coarse-grained distance and angle parameters of Eve, which are used for subsequent secure transmission and waveform optimization; among them, the sensing targets in the subsequent sensing tasks are only communication users, and Eve is not used as a fine sensing target.

[0067] (2) Channel estimation

[0068] The base station or user estimates channel state information (CSI) using pilot signals. If the user estimates the CSI, the CSI can be sent to the base station through a dedicated feedback channel.

[0069] (3) Joint optimization of beam matrix and RIS phase shift

[0070] Based on the aforementioned estimated user CSI information and the sensed coarse-grained location information of Eve, while ensuring communication security and sensing security performance, the base station transmit beamforming matrix, dedicated sensing signal covariance matrix, and RIS phase matrix are designed, and the corresponding parameter configurations are sent to the base station and RIS control terminals for execution. Various optimization schemes can be designed to meet different practical application requirements; specific algorithms are described later.

[0071] (4) Signal transmission

[0072] The designed transmission parameters are used by the base station to complete the transmission of communication and sensing signals. The obtained RIS reflection parameters are sent to the RIS controller through the control link, thereby realizing the collaborative beam control between the base station and the RIS.

[0073] (5) Communication and perception

[0074] The user receives communication information, and the base station estimates the target state parameters using the echo signal according to the predetermined sensing task requirements; however, Eve is not used as a subsequent sensing target.

[0075] (6) Parameter update

[0076] When the target location and propagation environment change, the base station executes the aforementioned steps (2)-(5) again to re-estimate the user's CSI and track and update Eve's coarse-grained location information.

[0077] The specific process of this invention is as follows:

[0078] (1) System initialization

[0079] Initial Target Detection and Parameter Estimation

[0080] The base station performs an initial search and coarse-grained estimation of targets within a given range based on beam scanning. These targets include the communication user Eve. The base station employs different parameter estimation strategies depending on different system conditions and estimation requirements, specifically including the following cases:

[0081] Case 1: When estimating target distance parameters

[0082] The base station performs matched filtering, cross-correlation peak detection, generalized cross-correlation processing, or maximum likelihood delay estimation on the received echo and the reference signal to obtain the target propagation delay parameters, and obtains the target initial distance parameters based on the propagation delay parameters.

[0083] Case 2: When estimating target angle parameters

[0084] The base station uses the Capon beamforming algorithm to process the spatial spectrum and obtains the initial angle parameters of the target by searching for peaks in the spatial spectrum; when the range cell is known, the peak position represents the target's azimuth. The Capon method has high resolution and is suitable for target angle parameter estimation.

[0085] Case 3: When performing joint estimation of target distance and angle parameters

[0086] The base station constructs a joint distance-angle parameter model and uses one or more methods, such as compressed sensing reconstruction, orthogonal matching pursuit, sparse Bayesian learning, or atomic norm minimization, to jointly estimate the initial distance and initial angle parameters of the target.

[0087] Case 4: When in a multi-object scenario

[0088] The base station first uses the generalized likelihood ratio test (GLRT) to determine the existence and number of targets, and eliminates spurious or interfering peaks. Based on this, multiple target echoes are separated, differentiated, or correlated. Furthermore, APES, CAPES, or CAML can be used to characterize the target echo response or complex amplitude to assist in multi-target differentiation and effective peak extraction. For the estimation of range parameters, angle parameters, or joint range-angle parameters for each target, the corresponding methods in Cases 1 to 3 are used, respectively.

[0089] (2) Channel estimation

[0090] Specifically, the channel acquisition process in this invention includes the following steps:

[0091] 1) Acquisition of legitimate user channel information

[0092] S11: The base station sends pilot signals to all legitimate communication users during the downlink pilot training phase;

[0093] S12: Each legitimate communication user estimates the direct link channel from the base station to the legitimate communication user based on the received pilot signal using the least squares (LS) method or the minimum mean square error (MMSE) method;

[0094] S13: Each legitimate communication user feeds back its direct link channel information to the base station via the uplink, enabling the base station to obtain the direct link channel status information from the base station to each legitimate communication user. Legitimate communication user The corresponding direct link channel is denoted as Obeying the Rician decay;

[0095] S14: The RIS control unit switches between different reflection coefficient configurations in different training time slots and, in conjunction with phased pilot training, obtains relevant channel information for the RIS auxiliary link. This includes information on the RIS to legitimate communication users. The reflection link channel is denoted as The channel from the base station to the RIS is denoted as All follow Rician fading; the reflection matrix of RIS is denoted as ,in Let be the reflection coefficient vector, and ;

[0096] S15: The direct link channel from the base station to the legitimate communication user, the channel from the base station to the RIS, and the reflected link channel from the RIS to the legitimate communication user all conform to Rician fading. Based on the direct link channel, the reflected link channel, and the RIS reflection matrix, the legitimate communication user is obtained. The equivalent concatenated channel is denoted as:

[0097] (1)

[0098] 2) Acquisition of communication eavesdropping channel information

[0099] S21: The base station uses its sensing module to detect the environment and obtain distance and angle estimates for Eve. The distance from the base station to Eve and the angle of departure (AoD) are expressed as follows: and Considering Eve is in a passive listening state, the base station has difficulty accurately obtaining her precise CSI. Therefore, the sensed distance and angle information is modeled as bounded uncertain parameters. Simultaneously, since the base station has difficulty knowing the multipath fading components in the eavesdropping channel... This component is treated as a channel uncertainty deviating from the strong line-of-sight (LoS) component, and each element in the multipath fading component is constrained by a known upper bound.

[0100] In addition, the Eve channel parameters are configured at the RIS end. and Estimating the Eve channel parameters also presents significant challenges, primarily because the RIS itself is a passive device and lacks data processing capabilities. Therefore, the Eve channel parameters can be estimated by utilizing acquired sensing information and based on the fixed and known position and angle of the RIS relative to the base station. However, uncertainties involved in the base station's estimation of the Eve parameters may affect the accuracy of its estimation of parameters related to the RIS. Similarly, the RIS-Eve channel must be described using a bounded uncertainty model.

[0101] The distance between the base station or RIS and Eve can be expressed as:

[0102] (2)

[0103] in, This is the initial estimate of Eve's distance. It's the distance error. Assume this distance error is bounded by a known upper bound. Restricted.

[0104] Similarly, the uncertainty of angle can be expressed as:

[0105] (3)

[0106] in, This represents the initial estimate of Eve's angle. This represents the angular error, assuming that the angular error is bounded by a known upper bound. Restricted.

[0107] The uncertainty of multipath fading can be expressed as:

[0108] (4)

[0109] in, This represents the upper bound of the multipath fading components, which is determined by the system configuration and the surrounding scattering environment.

[0110] In summary, the errors in distance, angle, and multipath components mentioned above can be considered as uncertainties in the communication link. For simplicity, the relevant set of uncertain parameters is... Recorded as:

[0111] (5)

[0112] S22: Based on the distance and angle parameters, obtain the LosS components from the base station to Eve and from RIS to Eve: , and The transmit and receive vectors are represented as follows:

[0113] (6)

[0114] in, This represents the normalized spacing between adjacent antennas. This indicates the LoS angle direction between the base station (RIS) and Eve, which is also used as the AoD and Angle of Arrival (AoA) from the base station and RIS to Eve.

[0115] S23: Determine the non-line-of-sight (NLoS) component of the eavesdropping channel based on the parameters, denoted as: ,in, For simplicity, use Alternative , .

[0116] S24: Both the base station to Eve channel and the RIS to Eve channel follow Rician fading, as shown below:

[0117] (7)

[0118] in Wavelength Related constants, This indicates the distance from the base station (RIS) to Eve. It is the Rician factor.

[0119] S25: Therefore, the communication eavesdropping channel can be represented as:

[0120] (8)

[0121] 3) Acquisition of legitimate sensing channel information

[0122] After obtaining Eve's coarse-grained location information during the initialization phase, subsequent sensing processes are only conducted for legitimate users. Signals transmitted by the base station can reach the target via a direct link and a RIS reflection link, and then be reflected back to the base station through these two links, forming a target echo. The target reflection path can be equivalently represented as the sensing channel between the base station and the target, expressed as:

[0123] (9)

[0124] in, Represents the RCS coefficient. , and These represent the base station and RIS to the user, respectively. distance, Indicates the corresponding Loss channel, and ,in, From base station or RIS to user AoD.

[0125] 4) Modeling of the eavesdropping channel

[0126] As an unauthorized sensing receiver, Eve can receive the echo signal reflected from the target. Specifically, the signal transmitted by the base station propagates to the target via the direct link and the RIS reflection link, is scattered at the target, and is received by Eve. Therefore, the sensing eavesdropping channel can be represented as:

[0127] (10)

[0128] in, Indicates Eve to the user distance, Indicates user The Loss Channel between Eve and ,in, From the user To RIS's AoA.

[0129] In particular, given that legitimate users are also perceived targets, Eve's position estimation error will also lead to uncertainty in the distance and angle from the user to Eve. Therefore, the relevant links are described using a bounded uncertainty model.

[0130] Similarly, the distance and angle uncertainties in the sensing eavesdropping link are modeled as follows: and .in, and It is an estimate of the initial distance and angle between the user and Eve. and These are distance and angle errors. Assume these distance and angle errors are bounded by known upper limits. and The limitations. For simplicity, the relevant set of uncertain parameters is represented. Recorded as:

[0131] (11)

[0132] Through the above methods, channel modeling of the communication link and sensing link and the characterization of related uncertainties were completed, providing a foundation for subsequent received signal modeling and security performance optimization.

[0133] (3) Joint optimization of beam matrix and RIS phase shift

[0134] The optimization problem is modeled under a unified constraint framework, including communication service quality constraint C1, communication security constraint C2, sensing service quality constraint C3, sensing security constraint C4, total transmit power constraint C5, positive semi-definite covariance matrix constraint C6, beamforming matrix rank constraint C7, and RIS reflector constant mode constraint C8. Simultaneously, RIS phase shift, base station transmit beamforming matrix, and dedicated sensing signal covariance matrix are introduced as joint optimization variables. Depending on the system focus, the optimization objective can be divided into the following three cases:

[0135] Case 1: Prioritizing Communication Performance

[0136] With the optimization objective of maximizing legitimate users and communication rates, the transmit beamforming matrix is ​​used. Covariance matrix of dedicated sensing signals RIS phase shift As joint optimization variables, the following optimization problem model is formed:

[0137] (12)

[0138] Constraints C1 and C3 ensure that the achievable transmission rate and legitimate perceived SNR for each legitimate user exceed preset thresholds, thereby guaranteeing communication and perception quality. To ensure communication security, the eavesdropping rate must be lower than the upper limit of the threshold specified in constraint C2. The non-convex constraint C4 controls Eve's perceived eavesdropping capability. (The constraints C2 and C4 are incomplete and require further context.) and The nonlinear fractional form of the problem, combined with continuous channel uncertainties, transforms it into a semi-infinite programming problem with infinite constraints, significantly increasing the complexity of robust design. Constraint C5 limits the transmit power. Constraint C6 ensures that S is a covariance matrix. Constraint C7 ensures... satisfy Constraint C8 ensures that the reflection coefficient of each RIS reflection unit is constant modulus.

[0139] Case 2: Prioritizing perceived performance

[0140] With maximizing the legally perceived signal-to-noise ratio (SNR) as the optimization objective, the following optimization problem model is formed:

[0141] (13)

[0142] Case 3: Synesthesia Coordination Optimization

[0143] The optimization problem model is formed by taking the normalized sum of legitimate user communication rates and legitimate perceived SNR as the optimization objective:

[0144] (14)

[0145] in, and This represents the upper bound of the legitimate user's communication rate and the legitimate perceived SNR. To obtain the upper bound, constraints C3 and C4 can be removed from the optimization problem in Case 1, i.e., the perception-related constraints are ignored, and the resulting optimal value is taken as the upper bound of the communication rate; constraints C1 and C2 can be removed from the optimization problem in Case 2, i.e., the communication-related constraints are ignored, and the resulting optimal value is taken as the upper bound of the perceived SNR.

[0146] Based on the above optimization model, the Alternating Optimization (AO) framework can be used to solve the problem, and the Block Coordinate Descent (BCD) method can be used to alternately update the base station transmit beamforming matrix, the dedicated sensing signal covariance matrix, and the RIS phase shift. For non-convex targets and non-convex constraints, the SCA / FOTE approximation, SDR, rank penalty, S-procedure, Schur complement, and matrix inequality transformation methods are combined for processing.

[0147] (4) Signal transmission

[0148] After obtaining the optimized base station transmit beamforming matrix and dedicated sensing signal covariance matrix The base station then generates the transmission signal based on this. Specifically, the rank-1 matrix is ​​decomposed to obtain the corresponding beamforming vector, which satisfies... Based on the optimized covariance matrix of the dedicated sensing signal... Generate a dedicated sensing signal that satisfies the covariance constraint. .

[0149] The base station transmits ISAC signals that contain both information signals and dedicated sensing signals:

[0150] (15)

[0151] (5) Communication and perception

[0152] Furthermore, the received signals of legitimate users, base stations, and Eve during the communication and sensing phases are determined respectively, and the corresponding performance is measured based on the received signals.

[0153] 1) Legitimate users

[0154] During the information transmission phase, legitimate communication users The received signal is represented as follows:

[0155] (16)

[0156] in Indicates legitimate communication user The equivalent white Gaussian noise (AWGN).

[0157] Based on the received signals, the communication rate is used to identify legitimate communication users. The communication performance is measured, and the communication rate is expressed as:

[0158] (17)

[0159] The numerator represents the data sent by the base station to legitimate users. The useful communication signal power is given by the denominator, which represents the sum of multi-user interference, dedicated sensing signal interference, and noise power from other legitimate users.

[0160] 2) Base station

[0161] During the sensing phase, the base station receives the echo signal formed by the reflection from the target, and the received signal is represented as follows:

[0162] (18)

[0163] in This represents the equivalent AWGN at the base station.

[0164] Based on the echo signal, the sensing performance of the base station is measured using the sensing SNR to characterize the quality of the base station receiving the target echo signal, as detailed below:

[0165] (19)

[0166] 3) Eve

[0167] During the communication eavesdropping phase, Eve receives signals used to eavesdrop on legitimate user communication information, and the received signals are represented as follows:

[0168] (20)

[0169] in This represents the equivalent AWGN in the context of communication eavesdropping.

[0170] Based on the aforementioned communication eavesdropping signal receiving model, and assuming that the eavesdropper is aware of the communication channel state information and the dedicated radar sensing signal sequence, and can eliminate interference caused by the dedicated sensing signal using advanced signal processing techniques, then, in the case of legitimate users... When Eve eavesdrops on communication signals, the interference originates from multi-user interference from other legitimate users and received noise. Therefore, Eve can intercept legitimate users' communications. The eavesdropping capability can be expressed by the eavesdropping rate as:

[0171] (twenty one)

[0172] During the eavesdropping detection phase, Eve receives the echo signal reflected from the target, and the received signal is represented as follows:

[0173] (twenty two)

[0174] in This represents the equivalent AWGN when eavesdropping is detected.

[0175] Based on the echo signal, and considering that Eve's reconstruction error of the communication signal is relatively large and the reconstructed signal quality is insufficient to support sensing processing, it is difficult to use the communication signal for target sensing; correspondingly, the communication signal can be regarded as interference at the eavesdropping end. Therefore, Eve's sensing eavesdropping capability can be expressed by its sensing signal-to-interference-plus-noise ratio (SINR):

[0176] (twenty three)

[0177] It should be understood that the method of the present invention can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, it can be considered a sequenced list of executable instructions for implementing logical functions, which can be stored in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as computer-based systems, including processor-based systems, and other systems that can fetch and execute instructions from, or in conjunction with, such instruction execution systems, apparatus, or devices). For the purposes of this description, a computer-readable medium can be any means that contains, stores, communicates, propagates, or transmits programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0178] Specifically, more specific examples of computer-readable media (a non-exhaustive list) include, but are not limited to: electrical connections (electronic devices) having one or more wirings, portable computer disks (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable read-only memory (CDROM).

[0179] It should be understood that the various functional units of the system of the present invention can be implemented in hardware, software, firmware, or a combination thereof. For example, if implemented in hardware, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), or field-programmable gate arrays (FPGAs), etc. Furthermore, the various functional units of the system of the present invention can be integrated into a single module, or each functional unit can exist physically separately, or two or more functional units can be integrated into a single module.

[0180] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. However, these modifications or substitutions 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 invention.

Claims

1. A RIS-assisted dual-security enhanced method of common sense, characterized in that, Includes the following steps: S1. System initialization: The base station establishes synchronization with RIS through the control channel, establishes RRC connection between the base station and the user, allocates communication channel resources, and performs initial search and coarse-grained estimation of the user and Eve target within a given range based on beam scanning to obtain the initial distance and angle parameters of the user target, as well as the coarse-grained distance and angle parameters of Eve, for subsequent secure transmission and waveform optimization. S2. Channel estimation: The base station or user uses pilot signals to estimate channel state information; S3. Joint optimization of beam matrix and RIS phase shift: Based on the channel state information estimated by the user and the sensed coarse-grained location information of Eve, the base station transmit beamforming matrix, the dedicated sensing signal covariance matrix and the RIS phase matrix are designed as joint optimization variables, and the corresponding parameter configurations are sent to the base station and RIS control terminal for execution. S4. Signal Transmission: Based on the base station transmit beamforming matrix and the dedicated sensing signal covariance matrix, the base station generates a transmit signal to complete the transmission of communication and sensing signals. The obtained RIS reflection parameters are sent to the RIS controller through the control link, thereby realizing the collaborative beam control between the base station and the RIS. S5. Communication and Sensing: The user receives communication information, and the base station estimates the target state parameters using the echo signal according to the predetermined sensing task requirements; Eve is not used as a subsequent sensing target. S6. Parameter Update: When the target location or propagation environment changes, the base station executes the aforementioned steps S2-S5 again to re-estimate the user channel state information and track and update Eve's coarse-grained location information.

2. The RIS-assisted COMINT-INTIMEL double security enhancement method of claim 1, wherein, In step S1, the initial search and coarse-grained estimation employ multiple parameter estimation strategies, specifically including the following cases: Case 1: When estimating target distance parameters The base station performs matched filtering, cross-correlation peak detection, generalized cross-correlation processing, or maximum likelihood delay estimation on the received echo and reference signal to obtain the target propagation delay parameters, and obtains the target initial distance parameters based on the propagation delay parameters. Case 2: When estimating target angle parameters The base station uses the Capon beamforming algorithm to process the spatial spectrum and obtains the initial angle parameters of the target by searching for the peak of the spatial spectrum; When the distance unit is known, the peak position represents the target orientation; Case 3: When performing joint estimation of target distance and angle parameters The base station constructs a joint distance-angle parameter model and uses one or more of the following methods: compressed sensing reconstruction, orthogonal matching pursuit, sparse Bayesian learning, or atomic norm minimization, to jointly estimate the initial distance and initial angle parameters of the target. Case 4: When in a multi-object scenario The base station first uses a generalized likelihood ratio test to determine the existence and number of targets, and removes false peaks or interference peaks. On this basis, it separates, distinguishes or correlates multiple target echoes. For the estimation of the distance parameters, angle parameters or joint distance-angle parameters of each target, the corresponding methods in Case 1 to Case 3 are used respectively.

3. The RIS-assisted COMINT-INTIMEL double security enhancement method of claim 1, wherein, In step S2, the process of obtaining legitimate user channel state information includes the following steps: S11: The base station sends pilot signals to all legitimate communication users during the downlink pilot training phase; S12: Each legitimate communication user estimates the direct link channel from the base station to the legitimate communication user based on the received pilot signal using the least squares method or the minimum mean square error method; S13: Each legitimate communication user feeds back the direct link channel information to the base station through the uplink, so that the base station can obtain the direct link channel status information from the base station to each legitimate communication user; S14: The RIS control unit switches different reflection coefficient configurations in different training time slots and, in conjunction with phased pilot training, obtains relevant channel information of the RIS auxiliary link; S15: The direct link channel from the base station to the legitimate communication user, the channel from the base station to the RIS, and the reflected link channel from the RIS to the legitimate communication user all follow Rician fading; based on the direct link channel, the reflected link channel, and the RIS reflection matrix, the equivalent concatenated channel of the legitimate communication user is obtained.

4. The RIS-assisted COMINT-INTIMEL double security enhancement method of claim 1, wherein, In step S2, the process of obtaining communication eavesdropping channel state information includes the following steps: S21: The base station uses the sensing module to detect the environment and obtain the distance and angle estimation information of Eve; the multipath fading component is regarded as the channel uncertainty that deviates from the strong line-of-sight component, and each element in the multipath fading component is constrained by a known upper bound; S22: Based on the distance and angle parameters, obtain the LoS components from the base station to Eve and from RIS to Eve; S23: Determine the non-line-of-sight component of the eavesdropping channel based on the distance and angle parameters; S24: Both the base station to Eve channel and the RIS to Eve channel follow Rician fading. A communication eavesdropping channel is obtained based on the base station to Eve channel and the RIS to Eve channel.

5. The RIS-assisted COMINT-FULINT dual security enhancement method of claim 1, wherein, In step S2, the legitimate sensing channel information acquisition process includes: after obtaining Eve's coarse-grained location information during the initialization phase, the subsequent sensing process is only carried out for legitimate users. The signal transmitted by the base station can reach the target via the direct link and the RIS reflection link, and then be reflected back to the base station through these two links to form the target echo.

6. The RIS-assisted COMINT-FULINT dual security enhancement method of claim 1, wherein, In step S2, the process of modeling the eavesdropping channel includes: the base station transmitting signals via the direct link and the RIS reflection link are described using a bounded uncertainty model.

7. The RIS-assisted inductive dual security enhancement method according to claim 1, characterized in that, In step S3, the constraints for the joint optimization of the beam matrix and RIS phase shift include: communication service quality constraints, communication security constraints, sensing service quality constraints, sensing security constraints, total transmit power constraints, positive semidefinite constraints of the covariance matrix, rank constraints of the beamforming matrix, and constant mode constraints of the RIS reflection unit.

8. The RIS-assisted inductive dual security enhancement method according to claim 7, characterized in that, The optimization problem in step S3 is modeled under a unified constraint framework, and the optimization objective can be divided into the following three cases: Case 1: Prioritizing Communication Performance With the optimization objective of maximizing legitimate users and communication rate, the transmit beamforming matrix, dedicated sensing signal covariance matrix, and RIS phase shift are used as joint optimization variables. Case 2: Prioritizing perceived performance The optimization objective is to maximize the legally perceived signal-to-noise ratio. Case 3: Synesthesia Coordination Optimization The optimization objective is the weighted sum of the normalized sum of legitimate users' communication rate and legitimate perceived SNR. Based on the above optimization model, an alternating optimization framework is used to solve the problem, and the block coordinate descent method is used to alternately update the base station transmit beamforming matrix, the dedicated sensing signal covariance matrix, and the RIS phase shift.

9. The RIS-assisted inductive dual security enhancement method according to claim 1, characterized in that, Step S4 specifically includes: decomposing the rank-1 matrix to obtain the corresponding beamforming vector, and generating a dedicated sensing signal that satisfies the covariance constraint based on the optimized dedicated sensing signal covariance matrix.

10. The RIS-assisted inductive dual security enhancement method according to claim 1, characterized in that, Step S5 specifically includes: 1) Legitimate users During the information transmission phase, legitimate communication users receive signals sent by the base station. Based on the received signals, the communication rate is used to measure the communication performance of legitimate communication users. 2) Base station During the sensing phase, the base station receives the echo signal formed by the reflection of the target. Based on the echo signal, the sensing SNR is used to measure the sensing performance of the base station, which is used to characterize the quality of the base station receiving the target echo signal. 3) Eve During the communication eavesdropping phase, Eve receives signals used to eavesdrop on legitimate users' communication information. Based on the received communication eavesdropping signals, assuming that the eavesdropper knows the communication channel state information and the sequence of dedicated radar sensing signals, and can eliminate the interference caused by the dedicated sensing signals, Eve's ability to eavesdrop on legitimate users is represented by the eavesdropping rate. During the sensing eavesdropping phase, Eve receives the echo signals formed by the reflection of the target. Based on the echo signals, Eve's sensing eavesdropping ability is represented by its sensing signal-to-interference-plus-noise ratio.

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