IRS-aided fd-isac system secrecy rate maximization method

Abstract

The present application relates to a kind of IRS auxiliary FD-ISAC system secrecy rate maximization method, belong to wireless communication technical field.The method includes: the multi-user, multi-target uplink and downlink MIMO system transmission model of IRS auxiliary FD-ISAC system is established;Transmission optimization model is constructed based on the transmission model;Wherein, the optimization target of the transmission optimization model is under the condition that the perceived signal-to-interference-and-noise ratio, base station transmit power, uplink user transmit power and IRS constant module constraint are satisfied, jointly optimizing base station receiving beamforming and base station transmit beamforming, uplink user transmit power and IRS phase shift matrix;Based on the transmission optimization model, a secrecy rate maximization optimization method is designed to maximize the secrecy rate of the FD-ISAC system;Aim to satisfy the perceived signal-to-interference-and-noise ratio, base station transmit power, uplink user transmit power and IRS constant module constraint, maximize the secrecy rate of the system.

Description

Technical Field

[0001] This invention relates to a method for maximizing the security of an IRS-assisted FD-ISAC system, belonging to the field of wireless communication technology. Background Technology

[0002] To meet the demands of increasingly diverse business applications and higher performance requirements in the future, and to alleviate the strain on spectrum resources and the growing complexity of systems, Integrated Sensing and Communication (ISAC) technology is an effective solution. ISAC combines sensing and communication by sharing the same spectrum and hardware resources to achieve integrated or coordinated gain, improving resource utilization efficiency and saving spectrum resources and hardware costs. Full-duplex (FD) technology enables simultaneous transmission and reception of signals on the same frequency, achieving nearly twice the spectrum rate. Therefore, incorporating FD into ISAC systems helps to further improve the utilization rate of spectrum resources.

[0003] However, due to the openness of wireless channels, wireless communication is vulnerable to attacks and eavesdropping. Furthermore, in complex environments, obstructed direct transmission links can negatively impact both communication and sensing performance. To address the issues of communication security and obstructed direct transmission links in complex environments, a smart reflector (IRS) composed of numerous passive electromagnetic components is combined with the FD-ISAC system. This allows for the reconfiguration of the wireless propagation environment with extremely low power, thereby improving ISAC performance. Adaptively adjusting the phase shift and amplitude of the IRS components can redirect the input signal, creating beamforming in the desired direction. This enhances the signal strength of the ISAC, improving the signal-to-interference-plus-noise ratio (SINR) for both communication and sensing. Simultaneously, the sensing function detects the location information of eavesdroppers in the environment, suppressing eavesdropping. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for maximizing the security of an IRS-assisted full-duplex-inductive integrated (FD-ISAC) system. The method aims to maximize the security of the system by alternately optimizing the base station transmit / receive beamforming, the uplink user transmit power, and the IRS phase shift matrix under the conditions of sensing signal-to-interference-plus-noise ratio, base station transmit power, uplink user transmit power, and IRS constant mode constraints.

[0005] The technical solution of this invention is: a method for maximizing the confidentiality rate of an IRS-assisted FD-ISAC system, comprising the following steps: Step 1: Establish a multi-user, multi-target uplink and downlink MIMO system transmission model for the IRS-assisted FD-ISAC system; Step 2: Construct a transmission optimization model based on the transmission model; wherein, the optimization objective of the transmission optimization model is to jointly optimize the base station receiving beamforming and base station transmitting beamforming, uplink user transmitting power and IRS phase shift matrix under the conditions of satisfying the perceived signal-to-interference-plus-noise ratio, base station transmit power, uplink user transmit power and IRS constant mode constraints; Step 3: Based on the aforementioned transmission optimization model, design a method to maximize the security rate of the FD-ISAC system; Specifically, the method for maximizing the confidentiality rate is as follows: First, the optimal solution for receiver beamforming is found through variable dimensionality reduction, thereby reducing the variable dimensionality of the original planning problem. Then, the original planning problem is decomposed into two sub-problems using an alternating optimization algorithm. The base station receiver beamforming and base station transmit beamforming, uplink user transmit power, and IRS phase shift matrix are alternately optimized to obtain the optimal solution. The optimal solution includes the optimal base station receiver beamforming, the optimal base station transmit beamforming, the optimal uplink user transmit power, and the optimal IRS phase shift matrix. Based on the optimal solution, the security rate of the FD-ISAC system is maximized. Specifically, the optimal solution for the alternating optimization of base station receive beamforming is obtained through the generalized Rayleigh quotient; the base station transmit beamforming and uplink user transmit power are jointly optimized by introducing slack variables and combining them with Taylor expansion to obtain a solvable convex problem, and the optimal base station transmit beamforming and uplink user transmit power are obtained by using the SCA method; the IRS phase shift matrix is ​​solved by introducing quadratic transformation, quadratic constraints and Lagrange dual transformation methods.

[0006] Optionally, Step 1 specifically includes: The transmission model is a single system equipped with [equipment / devices / etc.]. A full-duplex, dual-function radar communication base station with a transceiver linear array (ULA) of one antenna, assisted by one IRS, connects with... Single-antenna uplink users and Each single-antenna downlink user communicates simultaneously. There are 10 eavesdroppers, of which the IRS has 100,000. One reflective element; Transmission signal at the base station ,in, For downlink users Associated beamforming vector, , For base station to send to downlink users The data symbol for unit power, and , This represents the expectation operation. Let a dedicated sensing signal have the following covariance matrix: The superscript H indicates the conjugate transpose of a matrix or vector; definition Let the phase shift matrix of the IRS be denoted as , where This represents the diagonal elements used to extract or construct a matrix. , Indicates the IRS's The phase of each element, , Represents natural numbers, , For the IRS The phase corresponding to each element; Therefore, considering the interference between users in the system, the downlink users are obtained. Received signal The expression is:

[0007] in, Indicates the connection between the base station and the downlink user. The channel, Indicates IRS to downlink users The channel, This indicates the channel between the base station and the IRS. Indicates uplink user With downlink users The channel between, Indicates uplink user The signal sent to the base station, , For users Prescription difference is Additive white Gaussian noise (AWGN) and Indicates except the first The first downlink user Beamforming vectors and data symbols for each downlink user; The signals received at the base station's receiving antenna ULA include uplink communication signals, sensed echo signals, and self-interference signals. The first... The eavesdropper is located at an angle of... If the location is such that the reflected signal of the perceived target received by the base station is... , ,in, For the first The repeated amplitude of the eavesdropper; Get the signal received by the base station The expression is:

[0008] in, This represents the channel from the i-th uplink user to the base station. Indicates the first Channel between an uplink user and the IRS Indicates the base station and the first Channels between eavesdroppers Indicates IRS and the Channels between eavesdroppers Indicates the first The azimuth angle of the eavesdropper relative to the base station. This indicates the remaining self-interference SI channels at the full-duplex base station. Indicates the AWGN at the base station; No. The signal received by the eavesdropper The expression is:

[0009] in, For the first The first uplink user and the first Channels between eavesdroppers AWGN for the eavesdropper.

[0010] Optionally, Step 2 specifically includes: Applying a receive beamformer at the base station signal reception point To capture the perceived echo signal from the eavesdropper, then the first The expression for the perceived signal-to-dryness ratio (SINR) for each eavesdropper is:

[0011] in, , , Let be the covariance matrix of the downlink signal. , respectively , ,but In the formula, Indicates the first Average transmit power of each uplink user Budget for the maximum transmit power of this communication user. This represents the variance of the noise introduced by the base station when receiving callback signals. express An identity matrix of order 1; Downlink users The corresponding SINR expression is:

[0012] in, , Indicates except the first The first downlink user Covariance matrix of each downstream user; Another set of receive beamformers is applied at the base station signal reception point. If the uplink user's data signal is restored, then the uplink user The corresponding receive SINR expression is:

[0013] in, and Indicates except the first The first in addition to the uplink users Transmit power and channel of each uplink user Indicates that the base station receives the first The variance of noise introduced by the signal of each uplink user; No. The SINR expression for each eavesdropper is:

[0014] in, , Indicates the first The variance of noise introduced by an eavesdropper when receiving signals; Confidentiality The expression is:

[0015] in, , , These are, respectively, uplink communication and rate, downlink communication and rate, and eavesdropping and rate; Under the constraints of perceived signal-to-interference-plus-noise ratio, base station transmit power, uplink user transmit power, and IRS constant mode, this paper proposes a method to jointly optimize base station receive beamforming and base station transmit beamforming, uplink user transmit power, and IRS phase shift matrix to maximize system security. The modeling process is as follows:

[0016] Where (1b) represents the sensing SINR constraint, (1c) represents the minimum sensing SINR threshold; (1c) represents the base station transmit power constraint. This indicates finding the trace of a matrix. (1d) represents the maximum transmit power of the base station; (1e) represents the uplink user transmit power constraint; and (1d) represents the constant mode constraint of the IRS.

[0017] Optionally, Step 3 specifically includes: Step 3.1: For optimizing base station receive beamforming, find the optimal solution for receive beamforming using the generalized Rayleigh quotient. and They are respectively:

[0018]

[0019] Substituting the above two equations into the expressions for radar signal-to-noise ratio and uplink user signal-to-noise ratio respectively, we obtain the variable elimination. and The radar signal-to-noise ratio after Uplink user signal-to-noise ratio The expression is:

[0020]

[0021] in, , ; Thus, the optimization problem after dimensionality reduction of the variables is obtained. expression for:

[0022] in, Let represent the objective function after dimensionality reduction of the variables, and , ; Step 3.2: Use the alternating optimization algorithm to solve the problem. The problem can be broken down into two sub-problems. The first sub-problem is the joint optimization of base station transmit beamforming. and uplink user transmit power :

[0023] The second subproblem is optimizing the IRS phase shift matrix. :

[0024] Step 3.3: An alternating optimization algorithm is adopted, and the base station transmit beamforming and uplink user transmit power are jointly optimized, specifically as follows: By introducing slack variables The problem Non-convex terms of the objective function Optimize into a solvable equivalent convex optimization form The expression is:

[0025] This also generates a new constraint, expressed as:

[0026] The new constraints generated by the transformation are restated using Taylor expansion as follows:

[0027] in, , , , , , and They are , , , and In the algorithm The result of the iteration This represents the number of algorithm iterations. Regarding the question By combining Taylor expansion, the downlink user communication rate in the objective function is... Equivalent to:

[0028] in, , and For the first The result of the next iteration and The solution, ; Regarding the question In the objective function The equivalent is:

[0029] in, , ; The final objective function expression is:

[0030] Optimization problem The non-convex constraint (2b) in the equation is a convexity constraint. It is addressed by reducing its boundary value using a first-order Taylor expansion on the left side. Combined with the derivative of the complex-valued matrix, the optimization problem is obtained. The equivalent convex constraint of the non-convex constraint (2b) is expressed as:

[0031] in, ; Obtaining subproblems For the following convex optimization problem, the expression is:

[0032] Step 3.4: The process of optimizing the IRS phase shift matrix using the alternating optimization algorithm is as follows: Using a quadratic transformation, the problem The channel expression is transformed into a phase shift matrix. The display format is expressed as:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] in, , , , , , ; Introducing auxiliary variables and Furthermore, Taylor expansion, quadratic constraints, and Lagrange duality transformation are employed to transform the original nonconvex problem. Transform it into a convex problem, the expression is:

[0039] in, The default penalty parameters are... As dual variables, , , Is the algorithm in the first... During the next iteration The value; , ,in, In the algorithm's... During the next iteration The solution, , , , , ; Step 3.5: The process of optimizing base station receive beamforming, base station transmit beamforming, uplink user transmit power, and IRS phase shift matrix using an alternating optimization algorithm is as follows: Step 3.5.1: Through variable dimensionality reduction, Taylor expansion, quadratic transformation, and Lagrange dual transformation, the nonconvex optimization problem is transformed. Transformed into a problem with two convex subs. and ; Step 3.5.2: Fix Joint optimization and By solving the problem Obtaining a local optimum and ; Step 3.5.3: Fix and optimization By solving the problem Obtaining a local optimum ; Step 3.5.4: Until The algorithm converges and the iteration ends. and They represent In the algorithm and The value at the next iteration For threshold; Step 3.5.5: Calculate the optimal solution , and Substitute and The optimal solution expression yields a local optimum. and .

[0040] The beneficial effects of this invention are as follows: Compared with existing technologies, this invention, targeting IRS-assisted FD-ISAC systems, effectively solves the problem of maximizing system security while satisfying the constraints of perceived signal-to-interference-plus-noise ratio, base station transmit power, uplink user transmit power, and IRS constant mode. The proposed method for maximizing system security constructs a complete and feasible optimization process by jointly optimizing base station transmit / receive beamforming, uplink user transmit power, and IRS phase shift matrix, which can significantly improve the secure transmission performance of the system under limited power conditions. Compared with traditional half-duplex mode and partially simplified system models in existing research, this invention not only ensures the perceived performance of the system but also further improves the quality and security of communication services, possessing high practical application value. Attached Figure Description

[0041] Figure 1 This is a flowchart of the steps of the present invention; Figure 2 This is a diagram of the secure transmission model of the IRS-assisted FD-ISAC system of this invention; Figure 3 This is a graph showing the relationship between the number of IRS components and the system security rate in an embodiment of the present invention; Figure 4 This is a graph showing the relationship between radar SINR and system security rate in an embodiment of the present invention. Detailed Implementation

[0042] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0043] Example 1: As Figure 1 As shown, an IRS-assisted method for maximizing the confidentiality of an FD-ISAC system includes the following steps: Step 1: Establish a multi-user, multi-target uplink and downlink multiple-input multiple-output (MIMO) system transmission model for the IRS-assisted FD-ISAC system; Optionally, such as Figure 2 As shown, the transmission model is a single unit equipped with... A full-duplex, dual-function radar communication base station with a transceiver linear array (ULA) of one antenna, assisted by one IRS, connects with... Single-antenna uplink users and Each single-antenna downlink user communicates simultaneously. There are several eavesdroppers, among which, the full-duplex dual-function radar communication base station transmits a combined sensing signal through beamforming, realizing communication with downlink users and sensing eavesdroppers, while simultaneously receiving uplink user communication signals and sensing echoes through a receiving beamformer. The IRS has a total of A reflective element is used to enhance signal strength and overcome environmental interference.

[0044] Transmission signal at the base station ,in, For downlink users Associated beamforming vector, , For base station to send to downlink users The data symbol for unit power, and , This represents the expectation operation. Let a dedicated sensing signal have the following covariance matrix: The superscript H indicates the conjugate transpose of a matrix or vector; definition Let the phase shift matrix of the IRS be denoted as , where This represents the diagonal elements used to extract or construct a matrix. , Indicates the IRS's The phase of each element, , Represents natural numbers, , For the IRS The phase corresponding to each element; Get downstream users Received signal The expression is:

[0045] in, Indicates the connection between the base station and the downlink user. The channel, Indicates IRS to downlink users The channel, This indicates the channel between the base station and the IRS. Indicates uplink user With downlink users The channel between, Indicates uplink user The signal sent to the base station, , For users Prescription difference is Additive white Gaussian noise (AWGN) and Indicates except the first The first downlink user Beamforming vectors and data symbols for each downlink user; The signals received at the base station's receiving antenna ULA include uplink communication signals, sensed echo signals, and self-interference signals. The first... The eavesdropper is located at an angle of... If the location is such that the reflected signal of the perceived target received by the base station is... , ,in, For the first The repeated amplitude of the eavesdropper; Get the signal received by the base station The expression is:

[0046] in, This represents the channel from the i-th uplink user to the base station. Indicates the first Channel between an uplink user and the IRS Indicates the base station and the first Channels between eavesdroppers Indicates IRS and the Channels between eavesdroppers Indicates the first The azimuth angle of the eavesdropper relative to the base station. This indicates the remaining self-interference SI channels at the full-duplex base station. Indicates the AWGN at the base station; No. The signal received by the eavesdropper The expression is:

[0047] in, For the first The first uplink user and the first Channels between eavesdroppers AWGN for the eavesdropper.

[0048] Step 2: Construct a transmission optimization model based on the transmission model; wherein, the optimization objective of the transmission optimization model is to jointly optimize the base station receiving beamforming and base station transmitting beamforming, uplink user transmitting power and IRS phase shift matrix under the conditions of satisfying the perceived signal-to-interference-plus-noise ratio, base station transmit power, uplink user transmit power and IRS constant mode constraints; Optionally, a receive beamformer can be applied at the base station receiving signal. To capture the perceived echo signal from the eavesdropper, then the first The expression for the perceived signal-to-dryness ratio (SINR) for each eavesdropper is:

[0049] in, , , Let be the covariance matrix of the downlink signal. , respectively , ,but In the formula, Indicates the first Average transmit power of each uplink user Budget for the maximum transmit power of this communication user. This represents the variance of the noise introduced by the base station when receiving callback signals. express An identity matrix of order 1; Downlink users The corresponding SINR expression is:

[0050] in, , Indicates except the first The first downlink user Covariance matrix of each downstream user; Another set of receive beamformers is applied at the base station signal reception point. If the uplink user's data signal is restored, then the uplink user The corresponding receive SINR expression is:

[0051] in, and Indicates except the first The first in addition to the uplink users Transmit power and channel of each uplink user Indicates that the base station receives the first The variance of noise introduced by the signal of each uplink user; No. The SINR expression for each eavesdropper is:

[0052] in, , Indicates the first The variance of noise introduced by an eavesdropper when receiving signals; Confidentiality The expression is:

[0053] in, , , These are, respectively, uplink communication and rate, downlink communication and rate, and eavesdropping and rate; Under the constraints of perceived signal-to-interference-plus-noise ratio, base station transmit power, uplink user transmit power, and IRS constant mode, this paper proposes a method to jointly optimize base station receive beamforming and base station transmit beamforming, uplink user transmit power, and IRS phase shift matrix to maximize system security. The modeling process is as follows:

[0054] Where (1b) represents the sensing SINR constraint, (1c) represents the minimum sensing SINR threshold; (1c) represents the base station transmit power constraint. This indicates finding the trace of a matrix. (1d) represents the maximum transmit power of the base station; (1e) represents the uplink user transmit power constraint; and (1d) represents the constant mode constraint of the IRS.

[0055] Step 3: Based on the aforementioned transmission optimization model, design a method to maximize the security rate of the FD-ISAC system; Specifically, the method for maximizing the confidentiality rate is as follows: First, the optimal solution for receiver beamforming is found through variable dimensionality reduction, thereby reducing the variable dimensionality of the original planning problem. Then, the original planning problem is decomposed into two sub-problems using an alternating optimization algorithm. The base station receiver beamforming and base station transmit beamforming, uplink user transmit power, and IRS phase shift matrix are alternately optimized to obtain the optimal solution. The optimal solution includes the optimal base station receiver beamforming, the optimal base station transmit beamforming, the optimal uplink user transmit power, and the optimal IRS phase shift matrix. Based on the optimal solution, the security rate of the FD-ISAC system is maximized. Specifically, the optimal solution for the alternating optimization of base station receive beamforming is obtained through the generalized Rayleigh quotient; the base station transmit beamforming and uplink user transmit power are jointly optimized by introducing slack variables and combining them with Taylor expansion to obtain a solvable convex problem, and the optimal base station transmit beamforming and uplink user transmit power are obtained by using the successive convex approximation (SCA) method; the IRS phase shift matrix is ​​solved by introducing quadratic transformation, quadratic constraints and Lagrange dual transformation methods.

[0056] Step 3.1: For optimizing base station receive beamforming, find the optimal solution for receive beamforming using the generalized Rayleigh quotient. and They are respectively:

[0057]

[0058] Eliminate variables and The radar signal-to-noise ratio after Uplink user signal-to-noise ratio The expression is:

[0059]

[0060] in, , ; Thus, the optimization problem after dimensionality reduction of the variables is obtained. expression for:

[0061] in, Let represent the objective function after dimensionality reduction of the variables, and , ; Step 3.2: Use the alternating optimization algorithm to solve the problem. The problem can be broken down into two sub-problems. The first sub-problem is the joint optimization of base station transmit beamforming. and uplink user transmit power :

[0062] The second subproblem is optimizing the IRS phase shift matrix. :

[0063] Step 3.3: An alternating optimization algorithm is adopted, and the base station transmit beamforming and uplink user transmit power are jointly optimized, specifically as follows: By introducing slack variables The problem Non-convex terms of the objective function Optimize into a solvable equivalent convex optimization form The expression is:

[0064] This also generates a new constraint, expressed as:

[0065] The new constraints generated by the transformation are restated using Taylor expansion as follows:

[0066] in, , , , , , and They are , , , and In the algorithm The result of the iteration This represents the number of algorithm iterations. Regarding the question By combining Taylor expansion, the downlink user communication rate in the objective function is... Equivalent to:

[0067] in, , and For the first The result of the next iteration and The solution, ; Regarding the question In the objective function The equivalent is:

[0068] in, , ; The final objective function expression is:

[0069] The optimization problem is obtained through first-order Taylor expansion and complex-valued matrix derivatives. The equivalent convex constraint of the non-convex constraint (2b) is expressed as:

[0070] in, ; Obtaining subproblems For the following convex optimization problem, the expression is:

[0071] Step 3.4: The process of optimizing the IRS phase shift matrix using the alternating optimization algorithm is as follows: Using a quadratic transformation, the problem The channel expression is transformed into a phase shift matrix. The display format is expressed as:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] in, , , , , , ; Introducing auxiliary variables and Furthermore, Taylor expansion, quadratic constraints, and Lagrange duality transformation are employed to transform the original nonconvex problem. Transform it into a convex problem, the expression is:

[0078] in, The default penalty parameters are... As dual variables, , , Is the algorithm in the first... During the next iteration The value; , ,in, In the algorithm's... During the next iteration The solution, , , , , ; Step 3.5: The process of optimizing base station receive beamforming, base station transmit beamforming, uplink user transmit power, and IRS phase shift matrix using an alternating optimization algorithm is as follows: Step 3.5.1: Through variable dimensionality reduction, Taylor expansion, quadratic transformation, and Lagrange dual transformation, the nonconvex optimization problem is transformed. Transformed into a problem with two convex subs. and ; Step 3.5.2: Fix Joint optimization and By solving the problem Obtaining a local optimum and ; Step 3.5.3: Fix and optimization By solving the problem Obtaining a local optimum ; Step 3.5.4: Until The algorithm converges and the iteration ends. and They represent In the algorithm and The value at the next iteration For threshold; Step 3.5.5: Calculate the optimal solution , and Substitute and The optimal solution expression yields a local optimum. and .

[0079] Based on the specific implementation details, the effectiveness of the technical solution of the present invention will be demonstrated through experiments.

[0080] Specifically, in this experiment, the base station and IRS were located at (0m, 0m, 0m) and (50m, 0m, 0m) respectively. Two uplink users, two downlink users, and two eavesdroppers were randomly distributed on a semicircle with a radius of 50m, centered on the base station, with an angle range of (-90°, 90°). The number of transmit / receive antennas of the base station and the number of components of the IRS were set as follows. and The AWGN at the user, base station, and eavesdropper are all set to -80dBm, with the maximum transmit power at the base station. The maximum transmit power at the uplink user is , The radar signal-to-noise ratio threshold is 15dB. Based on the above parameter settings, MATLAB is used to perform simulation.

[0081] Specifically, Figure 3 The relationship between the number of IRS components and the system security rate is shown. The system security rate maximization algorithm proposed in this invention increases with the increase of the number of IRS components, indicating that the larger the number of IRS components, the better the system security performance.

[0082] Furthermore, Figure 4 The relationship between radar SINR and system security rate is shown. It can be noted that the security rate decreases as radar performance requirements increase. When the radar SINR is low enough, the performance of this invention is close to that of communication only.

[0083] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for maximizing the security of an IRS-assisted FD-ISAC system, characterized in that, The method includes the following steps: Step 1: Establish a multi-user, multi-target uplink and downlink MIMO system transmission model for the IRS-assisted FD-ISAC system; Step 2: Construct a transmission optimization model based on the transmission model; wherein, the optimization objective of the transmission optimization model is to jointly optimize the base station receiving beamforming and base station transmitting beamforming, uplink user transmitting power and IRS phase shift matrix under the conditions of satisfying the perceived signal-to-interference-plus-noise ratio, base station transmit power, uplink user transmit power and IRS constant mode constraints; Step 3: Based on the aforementioned transmission optimization model, design a method to maximize the security rate of the FD-ISAC system; Specifically, the method for maximizing the confidentiality rate is as follows: First, the optimal solution for receiver beamforming is found through variable dimensionality reduction, thereby reducing the variable dimensionality of the original planning problem. Then, the original planning problem is decomposed into two sub-problems using an alternating optimization algorithm. The base station receiver beamforming and base station transmit beamforming, uplink user transmit power, and IRS phase shift matrix are alternately optimized to obtain the optimal solution. The optimal solution includes the optimal base station receiver beamforming, the optimal base station transmit beamforming, the optimal uplink user transmit power, and the optimal IRS phase shift matrix. Based on the optimal solution, the security rate of the FD-ISAC system is maximized. Specifically, the optimal solution for the alternating optimization of base station receive beamforming is obtained through the generalized Rayleigh quotient; the base station transmit beamforming and uplink user transmit power are jointly optimized by introducing slack variables and combining them with Taylor expansion to obtain a solvable convex problem, and the optimal base station transmit beamforming and uplink user transmit power are obtained by using the SCA method; the IRS phase shift matrix is ​​solved by introducing quadratic transformation, quadratic constraints and Lagrange dual transformation methods.

2. The method for maximizing the confidentiality rate of an IRS-assisted FD-ISAC system according to claim 1, characterized in that, Step 1 specifically refers to: The transmission model is a single system equipped with [equipment / devices / etc.]. A full-duplex, dual-function radar communication base station with a transceiver linear array (ULA) of one antenna, assisted by one IRS, connects with... Single-antenna uplink users and Each single-antenna downlink user communicates simultaneously. There are 10 eavesdroppers, of which the IRS has 100,000. One reflective element; Transmission signal at the base station ,in, For downlink users Associated beamforming vector, , For base station to send to downlink users The data symbol for unit power, and , This represents the expectation operation. Let a dedicated sensing signal have the following covariance matrix: The superscript H indicates the conjugate transpose of a matrix or vector; definition Let the phase shift matrix of the IRS be denoted as , where This represents the diagonal elements used to extract or construct a matrix. , Indicates the IRS's The phase of each element, , Represents natural numbers, , For the IRS The phase corresponding to each element; Get downstream users Received signal The expression is: ； in, Indicates the connection between the base station and the downlink user. The channel, Indicates IRS to downlink users The channel, This indicates the channel between the base station and the IRS. Indicates uplink user With downlink users The channel between, Indicates uplink user The signal sent to the base station, , For users Prescription difference is Additive white Gaussian noise (AWGN) and Indicates except the first The first downlink user Beamforming vectors and data symbols for each downlink user; The signals received at the base station's receiving antenna ULA include uplink communication signals, sensed echo signals, and self-interference signals. The first... The eavesdropper is located at an angle of... If the location is such that the reflected signal of the perceived target received by the base station is... , ,in, For the first The repeated amplitude of the eavesdropper; Get the signal received by the base station The expression is: ； in, This represents the channel from the i-th uplink user to the base station. Indicates the first Channel between an uplink user and the IRS Indicates the base station and the first Channels between eavesdroppers Indicates IRS and the Channels between eavesdroppers Indicates the first The azimuth angle of the eavesdropper relative to the base station. This indicates the remaining self-interference SI channels at the full-duplex base station. Indicates the AWGN at the base station; No. The signal received by the eavesdropper The expression is: ； in, For the first The first uplink user and the first Channels between eavesdroppers AWGN for the eavesdropper.

3. The method for maximizing the confidentiality rate of an IRS-assisted FD-ISAC system according to claim 2, characterized in that, Step 2 specifically includes: Applying a receive beamformer at the base station signal reception point To capture the perceived echo signal from the eavesdropper, then the first The expression for the perceived signal-to-dryness ratio (SINR) for each eavesdropper is: ； in, , , Let be the covariance matrix of the downlink signal. , respectively , ,but In the formula, Indicates the first Average transmit power of each uplink user Budget for the maximum transmit power of this communication user. This represents the variance of the noise introduced by the base station when receiving callback signals. express An identity matrix of order 1; Downlink users The corresponding SINR expression is: ； in, , Indicates except the first The first downlink user Covariance matrix of each downstream user; Another set of receive beamformers is applied at the base station signal reception point. If the uplink user's data signal is restored, then the uplink user The corresponding receive SINR expression is: ； in, and Indicates except the first The first in addition to the uplink users Transmit power and channel of each uplink user Indicates that the base station receives the first The variance of noise introduced by the signal of each uplink user; No. The SINR expression for each eavesdropper is: ； in, , Indicates the first The variance of noise introduced by an eavesdropper when receiving signals; Confidentiality The expression is: ； in, , , These are, respectively, uplink communication and rate, downlink communication and rate, and eavesdropping and rate; Under the constraints of perceived signal-to-interference-plus-noise ratio, base station transmit power, uplink user transmit power, and IRS constant mode, this paper proposes a method to jointly optimize base station receive beamforming and base station transmit beamforming, uplink user transmit power, and IRS phase shift matrix to maximize system security. The modeling process is as follows: ； Where (1b) represents the sensing SINR constraint, (1c) represents the minimum sensing SINR threshold; (1c) represents the base station transmit power constraint. This indicates finding the trace of a matrix. (1d) represents the maximum transmit power of the base station; (1e) represents the uplink user transmit power constraint; and (1d) represents the constant mode constraint of the IRS.

4. The method for maximizing the confidentiality rate of an IRS-assisted FD-ISAC system according to claim 3, characterized in that, Step 3 specifically refers to: Step 3.1: For optimizing base station receive beamforming, find the optimal solution for receive beamforming using the generalized Rayleigh quotient. and They are respectively: ； ； Eliminate variables and The radar signal-to-noise ratio after Uplink user signal-to-noise ratio The expression is: ； ； in, , ; Thus, the optimization problem after dimensionality reduction of the variables is obtained. expression for: ； in, Let represent the objective function after dimensionality reduction of the variables, and , ; Step 3.2: Use the alternating optimization algorithm to solve the problem. The problem can be broken down into two sub-problems. The first sub-problem is the joint optimization of base station transmit beamforming. and uplink user transmit power : ； The second subproblem is optimizing the IRS phase shift matrix. : ； Step 3.3: An alternating optimization algorithm is adopted, and the base station transmit beamforming and uplink user transmit power are jointly optimized, specifically as follows: By introducing slack variables The problem Non-convex terms of the objective function Optimize into a solvable equivalent convex optimization form The expression is: ； The new constraints generated by the transformation are restated using Taylor expansion as follows: ； in, , , , , , and They are , , , and In the algorithm The result of the iteration This represents the number of algorithm iterations. Regarding the question By combining Taylor expansion, the downlink user communication rate in the objective function is... Equivalent to: ； in, , and For the first The result of the next iteration and The solution, ; Regarding the question In the objective function The equivalent is: ； in, , ; The final objective function expression is: ； The optimization problem is obtained through first-order Taylor expansion and complex-valued matrix derivatives. The equivalent convex constraint of the non-convex constraint (2b) is expressed as: ； in, ; Obtaining subproblems For the following convex optimization problem, the expression is: ； Step 3.4: The process of optimizing the IRS phase shift matrix using the alternating optimization algorithm is as follows: Using a quadratic transformation, the problem The channel expression is transformed into a phase shift matrix. The display format is expressed as: ； ； ； ； ； ； in, , , , , , ; Introducing auxiliary variables and Furthermore, Taylor expansion, quadratic constraints, and Lagrange duality transformation are employed to transform the original nonconvex problem. Transform it into a convex problem, the expression is: ； in, The default penalty parameters are... As dual variables, , , Is the algorithm in the first... During the next iteration The value; , ,in, In the algorithm's... During the next iteration The solution, , , , , ; Step 3.5: The process of optimizing base station receive beamforming, base station transmit beamforming, uplink user transmit power, and IRS phase shift matrix using an alternating optimization algorithm is as follows: Step 3.5.1: Through variable dimensionality reduction, Taylor expansion, quadratic transformation, and Lagrange dual transformation, the nonconvex optimization problem is transformed. Transformed into a problem with two convex subs. and ; Step 3.5.2: Fix Joint optimization and By solving the problem Obtaining a local optimum and ; Step 3.5.3: Fix and optimization By solving the problem Obtaining a local optimum ; Step 3.5.4: Until The algorithm converges and the iteration ends. and They represent In the algorithm and The value at the next iteration For threshold; Step 3.5.5: Calculate the optimal solution , and Substitute and The optimal solution expression yields a local optimum. and .

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