An artificial noise assisted de-cellularization and sensing integrated beamforming method

Abstract

The application discloses a kind of artificial noise auxiliary de-cellular perception integrated beamforming methods, comprising: multiple distributed access points AP, user, eavesdropper and a perception target are laid out, and access point AP is centrally controlled by central processing unit.First, AP sends signal and receives target reflection signal, and detection probability is calculated based on generalized likelihood ratio test;With this probability as optimization goal, combined with communication demand and eavesdropping restriction, optimization problem is constructed.By analyzing the structure of the problem, it is decomposed into two sub-problems of beamforming and artificial noise joint optimization, and power allocation.The former is transformed into a convex optimization problem using semidefinite relaxation method;The latter uses bisection method to iteratively optimize power allocation, and updates the power allocation parameter optimization interval at each iteration, thereby maximizing system performance under a given total power.The application can effectively improve the detection probability and improve the perception safety under different power constraints with lower complexity.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and particularly relates to an artificial noise-assisted decellularized inductive beamforming method. Background Technology

[0002] Cellular-to-sensor integration is considered one of the key technologies in sixth-generation networks. This technology, through the distributed deployment of a large number of ISAC access points (APs), can significantly reduce hardware costs, improve spectrum utilization efficiency, and effectively mitigate inter-cell interference in traditional cellular structures. However, this highly collaborative and broadcast architecture also brings new security risks: with widely distributed APs working collaboratively, the risks of information leakage and malicious signal manipulation increase significantly; both communication and sensing links face potential attacks, seriously threatening the system's security performance.

[0003] Therefore, researchers have begun to focus on secure beamforming optimization to improve the performance of legitimate users while suppressing the interception capabilities of eavesdroppers. The aim is to enhance the communication quality of legitimate users while inhibiting the signal reception capabilities of eavesdroppers. However, traditional secure beamforming schemes still have significant limitations in dealing with multi-eavesdropper scenarios: eavesdroppers may not only intercept confidential communication data but also infer sensitive information of the target by analyzing the echo signal, posing a dual security threat to the system. Therefore, there is an urgent need to develop a secure beamforming optimization scheme suitable for multi-eavesdropper environments that can simultaneously ensure communication security. Summary of the Invention

[0004] Purpose of the Invention: The purpose of this invention is to provide an artificial noise-assisted decellularized, integrated beamforming method. This method effectively improves detection probability and enhances sensing security under varying power constraints with relatively low complexity.

[0005] Technical solution: The present invention provides an artificial noise-assisted decellularized and integrated beamforming method, comprising the following steps:

[0006] Step 101: Deploy distributed access points (APs), communication users, illegal eavesdroppers, and a sensing target in a decellularized massive MIMO system, and control all access points (APs) by a central processing unit;

[0007] Step 102, Signal Transmission Stage: The access point (AP) sends a signal to the user. After receiving the reflected signal from the target, the AP uses a generalized likelihood ratio test to determine the detection probability of the received signal.

[0008] Step 103: Taking the detection probability of the access point (AP) as the optimization objective, and combining communication requirements and eavesdropping restrictions, an optimization problem is proposed; by analyzing the monotonicity of the optimization objective function, the optimization objective is simplified, and the optimization problem is transformed into two sub-problems: the joint optimization problem of beamforming and artificial noise, and the problem of transmitting signal power allocation;

[0009] Step 104: Solve the joint optimization problem of beamforming and artificial noise. The problem is transformed into a convex optimization problem by using a semidefinite relaxation method and introducing auxiliary variables. The solution is then obtained using a convex optimization tool.

[0010] Step 105: Solve the transmitted signal power allocation problem. Iteratively optimize the transmitted signal power allocation parameters based on the bisection method. In each iteration, select the power allocation parameter optimization interval with higher performance improvement.

[0011] Step 106: Check whether the power allocation parameter optimization range meets the tolerance. If it meets the tolerance, continue iterative optimization. If it does not meet the tolerance, output the optimization result to obtain the artificial noise power that maximizes performance improvement under a given transmit power.

[0012] Furthermore, step 101 specifically involves: deploying a cellular-free massive MIMO system. One distributed access point AP, individual communication users An illegal eavesdropper and a sensing target; information is transmitted between the access point (AP) and the central processing unit via a fronthaul link; the channel between the access point (AP) and the communication user is called a direct link, and the channel between the access point (AP) and the sensing target and back to the access point (AP) is called an indirect link; the illegal eavesdropper receives the signal transmitted by the access point (AP).

[0013] Furthermore, step 102 specifically involves: obtaining the detection probability of the received signal through a generalized likelihood ratio test, using the detection probability as the optimization objective, and proposing an optimization problem in conjunction with communication requirements and eavesdropping restrictions; The signal transmitted by an access point (AP) can be represented as:

[0014]

[0015] in Indicates the sensing signal as well as One communication signal, and meets the expectation. , Indicates the first The first transmission access point AP to the first Beamforming vector of a signal, Represents the artificial noise vector, and , representing a vector It follows a mean of 0 and a variance of The artificial noise vector with a complex Gaussian distribution;

[0016] No. The signal received by each access point (AP) after being reflected by the sensed target It can be represented as:

[0017]

[0018] Receiver noise This indicates that the sample follows a mean of 0 and a variance of 0. Additive noise at the receiver with a complex Gaussian distribution. This represents the conjugate transpose of a matrix. Indicates the first The signal transmitted by each access point (AP), M t Indicates the number of access points (APs). Access Point (AP) To the access point (AP) An indirect link for the transmission of sensing signals between two points is represented as:

[0019]

[0020] in The representative follows a mean of 0 and a variance of 0. The radar cross section with a complex Gaussian distribution. Represents the antenna array response vector. The angle between the signal and the access point (AP);

[0021] No. Signal received by a communication user Represented as:

[0022]

[0023] in This indicates that the sample follows a mean of 0 and a variance of 0. Additive noise at the receiver with a complex Gaussian distribution. For the first The access point AP to the first Direct links between individual communication users;

[0024] Introducing auxiliary variables ,definition ,Will The signals received by each access point (AP) are represented as follows: , ,in , ;

[0025] Therefore, based on whether the perceived target exists, two hypotheses are proposed:

[0026]

[0027] in This represents the signal received by the access point (AP) in the hypothesis; based on the above assumptions, the probability of target detection at the receiving point is given through a generalized likelihood ratio test. As an optimization objective:

[0028]

[0029] in Indicates received signal The generalized likelihood ratio test expression, This represents taking the logarithm to the base of a natural number. This represents the probability of A given condition B. Represents a non-central chi-square distribution The right-tailed distribution, its non-central parameter Optimization variables The function, This represents the threshold for a given generalized likelihood ratio test.

[0030] Furthermore, step 103 specifically involves: taking the detection probability of the access point (AP) as the optimization objective, and proposing an optimization problem in conjunction with communication requirements and eavesdropping constraints; simplifying the optimization objective and problem by analyzing the monotonicity of the objective function; and proposing a joint optimization problem of beamforming vector and artificial noise vector with the objective of maximizing the detection probability of the perceived target, based on the lower limit of the communication interference-to-noise ratio (DNR) for communication users, the eavesdropping probability of eavesdroppers, the upper limit of the signal-to-noise ratio (SNR), and the upper limit of the transmitted signal power.

[0031]

[0032] in , , , These represent the sets of ISAC transmitting AP, receiving AP, UE, and EVA, respectively. Indicates the signal-to-noise ratio of communication users Lower limit, Indicates the signal-to-noise ratio of the eavesdropper upper limit Indicates the probability of eavesdropping by the eavesdropper. Upper limit For maximum transmission power; the detection probability expression is non-smooth, and it can be observed that: with the non-center parameter As the value increases, the corresponding non-central chi-square distribution will shift to the right; this rightward shift leads to a shift in the distribution at a fixed threshold. Down, right tail probability This increases the probability of detection; therefore, maximizing the detection probability can be equivalently transformed into maximizing the non-centrality parameter.

[0033]

[0034] in Let be a binary variable, representing whether the perceived target exists. Indicates the radar cross-section. Represents the transpose of a matrix. This represents the inverse of the matrix; therefore, maximizing the detection probability corresponds to minimizing the denominator term. Introducing binary matrices The power constraint of the transmitted signal in the optimization problem is rewritten as the power constraint of the beamforming vector and the artificial noise vector:

[0035]

[0036] in Represents the trace of a matrix. The power allocation coefficient represents the transmit power. The original optimization problem is expressed as two subproblems: the power allocation optimization problem of the beamforming vector and the artificial noise vector, and the joint optimization problem with the goal of maximizing the detection probability.

[0037] Furthermore, step 104 specifically involves: the joint optimization problem of beamforming and artificial noise is transformed into a convex optimization problem by using a semidefinite relaxation method and introducing auxiliary variables, and then solved using convex optimization tools;

[0038] The signal-to-interference-plus-noise ratio (SIR) constraints for signals received by communication users and eavesdroppers are rewritten as follows:

[0039]

[0040] in Indicates the access point (AP) and communication users The channel between, This indicates the access point (AP) and the eavesdropper. direct links between them This indicates the access point (AP) and the eavesdropper. The reflection link between them Beamforming vector covariance, , They represent communication users respectively With eavesdroppers The additive noise variance at a given location, constrained by the eavesdropper's eavesdropping probability, introduces two hypotheses based on the presence or absence of the perceived target, and introduces... and Indicates the second case in two situations The eavesdropper's received signal:

[0041]

[0042] The transmitter is equipped with root antenna, therefore the first The probability of an eavesdropper eavesdropping Represented as a form Functions:

[0043]

[0044] because In the interval Monotonically increasing, therefore the first The eavesdropping probability constraint for each eavesdropper is rewritten as:

[0045]

[0046] Based on the above steps, the non-convexity of the joint optimization problem is addressed using the CVX toolbox in Matlab software.

[0047] Furthermore, step 105 specifically involves: solving the transmit signal power allocation problem by iteratively optimizing the transmit signal power allocation parameters using a bisection method, with each iteration selecting an optimization range for power allocation parameters that offer the greatest performance improvement; the algorithm implementation is as follows: setting a tolerance threshold and initial boundary conditions as the transmit power allocation parameters. Solve the joint optimization problem of beamforming and artificial noise to obtain the corresponding detection probability value; then use binary search for iteration: when the interval width exceeds the tolerance, calculate the midpoint and the corresponding detection probability by solving the problem, compare the optimization effect of the two optimization intervals, and select the interval with higher performance improvement as the update interval; after each update, recalculate the detection probability on the new boundary.

[0048] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of the present invention.

[0049] The present invention also discloses a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the method of the present invention.

[0050] The present invention also discloses a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method of the present invention.

[0051] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0052] 1. This invention differs from traditional beamforming by adding an artificial noise vector to the transmitted signal. By jointly optimizing the covariance matrix of the artificial noise and the beamforming vector, the artificial noise is primarily directed towards the eavesdropper, suppressing its signal detection capability. At the same time, it ensures that the effective signal is accurately aligned with the legitimate communication user and the sensing target, minimizing the interference of noise on the useful signal. This enhances the system's sensing security while increasing the probability of target detection.

[0053] 2. This invention proposes an iterative optimization algorithm based on the bisection method. This algorithm dynamically adjusts the weighting coefficient of artificial noise in the total transmit power to avoid signal quality loss caused by blindly introducing noise. Using the detection probability as an optimization guide, the algorithm continuously narrows the interval within the power constraint range, adaptively converging to the optimal power allocation ratio, thereby maximizing the system's sensing performance under a given transmit power. This strategy has good adaptability and can achieve a balance between safety and efficiency for different power budgets and environmental conditions.

[0054] 3. The non-convex optimization problem is decomposed into two sub-problems: joint optimization of beamforming and artificial noise, and power allocation. To address the non-convex constraints in the joint optimization, a semi-definite relaxation technique combined with auxiliary variables is used to transform it into a convex optimization problem, ensuring the solvability and effectiveness of the solution. For the power allocation problem, iterative optimization is employed, effectively reducing computational complexity and improving the algorithm's deployability in practical systems. Attached Figure Description

[0055] Figure 1 The beamforming diagram of the transmit power in unoptimized artificial noise, based on the joint optimization method proposed in this invention;

[0056] Figure 2 The beamforming diagram for the number of eavesdroppers in unoptimized artificial noise, as proposed in this invention, represents the joint optimization method.

[0057] Figure 3 This is a flowchart of an artificial noise-assisted decellularized and integrated beamforming method according to the present invention;

[0058] Figure 4 This is a schematic diagram of the network architecture of an artificial noise-assisted decellularized sensing integrated system according to the present invention. Detailed Implementation

[0059] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0060] 1. Artificial noise-assisted decellularized and inductively integrated beamforming method:

[0061] This invention provides an artificial noise-assisted decellularized, inductively integrated beamforming method, such as... Figure 3As shown, the method includes the following steps:

[0062] Step 101: Deployment of Cellular-Free Massive MIMO System One distributed access point AP, individual communication users One illegal eavesdropper and one sensing target, with all access points (APs) controlled by a central processing unit;

[0063] Step 102, Signal Transmission Stage: The access point (AP) sends a signal to the user. After receiving the reflected signal from the target, the AP uses a generalized likelihood ratio test to determine the detection probability of the received signal.

[0064] Step 103: Using the detection probability of the access point (AP) as the optimization objective, and combining communication requirements and eavesdropping restrictions, an optimization problem is proposed. By analyzing the monotonicity of the objective function, the optimization objective is simplified, and the optimization problem is transformed into two sub-problems: a joint optimization problem of beamforming and artificial noise, and a transmit signal power allocation problem.

[0065] Step 104: The joint optimization problem of beamforming and artificial noise can be transformed into a convex optimization problem by using a semidefinite relaxation method and introducing auxiliary variables, and then solved using convex optimization tools.

[0066] Step 105: The problem of transmitting signal power allocation can be solved by iteratively optimizing the power allocation parameters of the transmitting signal based on the binary search method. In each iteration, the power allocation parameter optimization range with higher performance improvement is selected.

[0067] Step 106: Check whether the power allocation parameter optimization range meets the tolerance. If it does, continue iterative optimization. If it does not, output the optimization result to obtain the artificial noise power that maximizes performance improvement under a given transmit power.

[0068] 2. Artificial noise-assisted decellularized and integrated sensory system network architecture:

[0069] The artificial noise-assisted decellularized and sensory integrated system network architecture of the present invention is as follows: Figure 4 As shown, a decellularized massive MIMO system includes One distributed access point AP, individual communication users The system consists of one illegal eavesdropper and one sensing target. Modules 201, 202, 203, 204, 205, and 206 are used. Module 201 is the access point (AP), primarily responsible for sending and receiving data; Module 202 is the user equipment, primarily responsible for receiving communication signals transmitted by the AP; Module 203 is the eavesdropper, primarily responsible for eavesdropping on information sent by the AP and signals reflected by the sensing target; Module 204 is the sensing target, primarily responsible for reflecting sensing signals sent by the AP; Module 205 is the central processing unit, primarily responsible for optimizing transmitted signals and processing received signals; Module 206 is the transmission link, primarily responsible for data transmission between the AP and the communication user and the sensing target; Module 207 is the reflection link, primarily responsible for data transmission between the sensing target and the AP.

[0070] 3. Detection probability determined:

[0071] No. The signal transmitted by an access point (AP) can be represented as:

[0072]

[0073] in Indicates the sensing signal as well as One communication signal, and meets the expectation. , Indicates the first The first transmission access point AP to the first Beamforming vector of a signal, Represents the artificial noise vector, and , representing a vector It follows a mean of 0 and a variance of The artificial noise vector with a complex Gaussian distribution;

[0074] No. The signal received by each access point (AP) after being reflected by the sensed target It can be represented as:

[0075]

[0076] Receiver noise This indicates that the sample follows a mean of 0 and a variance of 0. Additive noise at the receiver with a complex Gaussian distribution. This represents the conjugate transpose of a matrix. Indicates the first The signal transmitted by each access point (AP), M t Indicates the number of access points (APs). Access Point (AP) To the access point (AP) An indirect link for the transmission of sensing signals between two points is represented as:

[0077]

[0078] in The representative follows a mean of 0 and a variance of 0. The radar cross section with a complex Gaussian distribution. Represents the antenna array response vector. The angle between the signal and the access point (AP);

[0079] No. Signal received by a communication user Represented as:

[0080]

[0081] in This indicates that the sample follows a mean of 0 and a variance of 0. Additive noise at the receiver with a complex Gaussian distribution. For the first The access point AP to the first Direct links between individual communication users;

[0082] Introducing auxiliary variables ,definition ,Will The signals received by each access point (AP) are represented as follows: , ,in , ;

[0083] Therefore, based on whether the perceived target exists, two hypotheses are proposed:

[0084]

[0085] in This represents the signal received by the access point (AP) in the hypothesis; based on the above assumptions, the probability of target detection at the receiving point is given through a generalized likelihood ratio test. As an optimization objective:

[0086]

[0087] in Indicates received signal The generalized likelihood ratio test expression, This represents taking the logarithm to the base of a natural number. This represents the probability of A given condition B. Represents a non-central chi-square distribution The right-tailed distribution, its non-central parameter Optimization variables The function, This represents the threshold for a given generalized likelihood ratio test.

[0088] 4. Optimize target conversion:

[0089] Based on the lower limit of the communication interference-to-noise ratio (DNR) for communication users, the eavesdropping probability of eavesdroppers, the upper limit of the signal-to-noise ratio (SNR), and the upper limit of the transmitted signal power, a joint optimization problem of beamforming vector and artificial noise vector is proposed, with the goal of maximizing the detection probability of the sensed target.

[0090]

[0091] in , , , These represent the sets of ISAC transmitting AP, receiving AP, UE, and EVA, respectively. Indicates the signal-to-noise ratio of communication users Lower limit, Indicates the signal-to-noise ratio of the eavesdropper Upper limit Indicates the probability of eavesdropping by the eavesdropper. Upper limit For maximum transmission power; the detection probability expression is non-smooth, and it can be observed that: with the non-center parameter As the value increases, the corresponding non-central chi-square distribution will shift to the right; this rightward shift leads to a shift in the distribution at a fixed threshold. Down, right tail probability This increases the probability of detection; therefore, maximizing the detection probability can be equivalently transformed into maximizing the non-centrality parameter.

[0092]

[0093] in Let be a binary variable, representing whether the perceived target exists. Indicates the radar cross-section. Represents the transpose of a matrix. This represents the inverse of the matrix; therefore, maximizing the detection probability corresponds to minimizing the denominator term. Introducing binary matrices The power constraint of the transmitted signal in the optimization problem is rewritten as the power constraint of the beamforming vector and the artificial noise vector:

[0094]

[0095] in Represents the trace of a matrix. The power allocation coefficient represents the transmit power. The original optimization problem is expressed as two subproblems: the power allocation optimization problem of the beamforming vector and the artificial noise vector, and the joint optimization problem with the goal of maximizing the detection probability.

[0096] 5. Solving Subproblem 1:

[0097] The signal-to-interference-plus-noise ratio (SIR) constraints for signals received by communication users and eavesdroppers are rewritten as follows:

[0098]

[0099] in Indicates the access point (AP) and communication users The channel between, This indicates the access point (AP) and the eavesdropper. direct links between them This indicates the access point (AP) and the eavesdropper. The reflection link between them Beamforming vector covariance, , They represent communication users respectively With eavesdroppers The additive noise variance at a given location, constrained by the eavesdropper's eavesdropping probability, introduces two hypotheses based on the presence or absence of the perceived target, and introduces... and Indicates the second case in two situations The eavesdropper's received signal:

[0100]

[0101] The transmitter is equipped with root antenna, therefore the first The probability of an eavesdropper eavesdropping Represented as a form Functions:

[0102]

[0103] because In the interval Monotonically increasing, therefore the first The eavesdropping probability constraint for each eavesdropper is rewritten as:

[0104]

[0105] Based on the above steps, the non-convexity of the joint optimization problem is addressed using the CVX toolbox in Matlab software.

[0106] 6. Solving Subproblem Two:

[0107] To address the problem of transmitted signal power allocation, a binary search-based iterative optimization method is used to optimize the transmitted signal power allocation parameters. The algorithm implementation is as follows:

[0108] Set the tolerance threshold and initial boundary conditions as transmit power allocation parameters. Solve the joint optimization problem of beamforming and artificial noise to obtain the corresponding detection probability value; then use binary search for iteration: when the interval width exceeds the tolerance, calculate the midpoint and the corresponding detection probability by solving the problem, compare the optimization effect of the two optimization intervals, and select the interval with higher performance improvement as the update interval; after each update, recalculate the detection probability on the new boundary;

[0109] 7. Iteration condition check:

[0110] The algorithm checks whether the optimization range of the power allocation parameters meets the tolerance. If it does, iterative optimization continues; otherwise, the optimization result is output, thus obtaining the artificial noise power that maximizes performance improvement for a given transmit power. This algorithm optimizes the power allocation of the transmitted signal, thereby solving the target problem.

[0111] In summary, this invention can effectively improve the detection probability, limit the eavesdropping performance of eavesdroppers in the system, and at the same time ensure the communication performance of communication users, thereby improving the security of the system.

[0112] Example:

[0113]

[0114] The simulation data of the artificial noise-assisted decellularized inductive integrated beamforming method in this embodiment are shown in Table 1. The simulation was performed using the parameters shown in Table 1. Figure 1 and Figure 2 The curves shown encompass beamforming using the proposed joint optimization method, unoptimized artificial noise beamforming, and beamforming without artificial noise. Figure 1 The curve shown illustrates the detection probability. With transmission power The increasing trend verifies the superiority of the proposed artificial noise-assisted beamforming method under different transmit powers. Figure 2 The curve shown illustrates a given transmit power Detection probability With the number of eavesdroppers The proposed artificial noise-assisted beamforming method shows a relatively low dependence on the number of eavesdroppers and exhibits good robustness. It can be seen that the artificial noise-assisted decellularized and integrated transducer beamforming method disclosed in this invention can provide a high detection probability with lower complexity, significantly outperforming beamforming without optimized artificial noise or without artificial noise.

[0115] The above embodiments are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make several improvements and equivalent substitutions without departing from the principle of the present invention. All such improvements and equivalent substitutions to the claims of the present invention fall within the protection scope of the present invention.

Claims

1. A method for integrated decellularized and synthesized beamforming with artificial noise assistance, characterized in that, Includes the following steps: Step 101: Deploy distributed access points (APs), communication users, illegal eavesdroppers, and a sensing target in a decellularized massive MIMO system, and control all access points (APs) by a central processing unit; Step 102, Signal Transmission Stage: The access point (AP) sends a signal to the user. After receiving the reflected signal from the target, the AP uses a generalized likelihood ratio test to determine the detection probability of the received signal. Step 103: Taking the detection probability of the access point (AP) as the optimization objective, and combining communication requirements and eavesdropping restrictions, an optimization problem is proposed; by analyzing the monotonicity of the optimization objective function, the optimization objective is simplified, and the optimization problem is transformed into two sub-problems: the joint optimization problem of beamforming and artificial noise, and the problem of transmitting signal power allocation; Step 104: Solve the joint optimization problem of beamforming and artificial noise. The problem is transformed into a convex optimization problem by using a semidefinite relaxation method and introducing auxiliary variables. The solution is then obtained using a convex optimization tool. Step 105: Solve the transmitted signal power allocation problem. Iteratively optimize the transmitted signal power allocation parameters based on the bisection method. In each iteration, select the power allocation parameter optimization interval with higher performance improvement. Step 106: Check whether the power allocation parameter optimization range meets the tolerance. If it meets the tolerance, continue iterative optimization. If it does not meet the tolerance, output the optimization result to obtain the artificial noise power that maximizes performance improvement under a given transmit power.

2. The artificial noise-assisted decellularized and integrated beamforming method according to claim 1, characterized in that, Step 101 specifically involves: Deploying cellular-free massive MIMO systems. One distributed access point AP, individual communication users One unauthorized eavesdropper and one target being detected; information is transmitted between the access point (AP) and the central processing unit via a fronthaul link; The channel between an access point (AP) and a communication user is called a direct link; the channel between the AP, the sensing target, and back to the AP is called an indirect link; an unauthorized eavesdropper receives the signal transmitted by the AP; each transmitting AP and receiving AP is equipped with... A uniform linear array antenna; assuming all APs are perfectly synchronized and the channel state information is perfect and known.

3. The artificial noise-assisted decellularized and integrated beamforming method according to claim 1, characterized in that, Step 102 specifically involves: obtaining the detection probability of the received signal through a generalized likelihood ratio test, using the detection probability as the optimization objective, and proposing an optimization problem based on communication requirements and eavesdropping limitations; The signals transmitted by each access point (AP) are represented as follows: （1） in Indicates the sensing signal as well as One communication signal, and meets the expectation. , Indicates the first The first transmission access point AP to the first Beamforming vector of a signal, Represents the artificial noise vector, and , representing a vector It follows a mean of 0 and a variance of The artificial noise vector with a complex Gaussian distribution; No. The signal received by each access point (AP) after being reflected by the sensed target Represented as: （2） Receiver noise This indicates that the sample follows a mean of 0 and a variance of 0. Additive noise at the receiver with a complex Gaussian distribution. This represents the conjugate transpose of a matrix. Indicates the first The signal transmitted by each access point (AP), M t Indicates the number of access points (APs). Access Point (AP) To the access point (AP) An indirect link for the transmission of sensing signals between two points is represented as: （3） in The representative follows a mean of 0 and a variance of 0. The radar cross section with a complex Gaussian distribution. Represents the antenna array response vector. The angle between the signal and the access point (AP); No. Signal received by a communication user Represented as: （4） in This indicates that the sample follows a mean of 0 and a variance of 0. Additive noise at the receiver with a complex Gaussian distribution. For the first The access point AP to the first Direct links between individual communication users; Introducing auxiliary variables ,definition ,Will The signals received by each access point (AP) are represented as follows: , ,in , ; Therefore, based on whether the perceived target exists, two hypotheses are proposed: （5） in This represents the signal received by the hypothetical access point (AP). Based on the above assumptions, the probability of target detection at the receiver is given by the generalized likelihood ratio test. As an optimization objective: （6） in Indicates received signal The generalized likelihood ratio test expression, This represents taking the logarithm to the base of a natural number. This represents the probability of A given condition B. Represents a non-central chi-square distribution The right-tailed distribution, its non-central parameter Optimization variables The function, This represents the threshold for a given generalized likelihood ratio test.

4. The artificial noise-assisted decellularized and integrated beamforming method according to claim 3, characterized in that, Step 103 specifically involves: taking the detection probability of the access point (AP) as the optimization objective, and proposing an optimization problem in conjunction with communication requirements and eavesdropping restrictions; simplifying the optimization objective and optimization problem by analyzing the monotonicity of the optimization objective function. Based on the lower limit of the communication interference-to-noise ratio (DNR) for communication users, the eavesdropping probability of eavesdroppers, the upper limit of the signal-to-noise ratio (SNR), and the upper limit of the transmitted signal power, a joint optimization problem of beamforming vector and artificial noise vector is proposed, with the goal of maximizing the detection probability of the sensed target. （7） in , , , These represent the sets of ISAC transmitting AP, receiving AP, UE, and EVA, respectively. Indicates the signal-to-noise ratio of communication users Lower limit, Indicates the signal-to-noise ratio of the eavesdropper Upper limit Indicates the probability of eavesdropping by the eavesdropper. upper limit For maximum transmission power; the detection probability expression is non-smooth, and it is observed that: with the non-central parameter As the value increases, the corresponding non-central chi-square distribution will shift to the right; this rightward shift leads to a shift in the distribution at a fixed threshold. Down, right tail probability Increase; therefore, maximizing the detection probability is equivalent to maximizing the non-central parameters: （8） in Let be a binary variable, representing whether the perceived target exists. Indicates the radar cross-section. Represents the transpose of a matrix. This represents the inverse of the matrix; therefore, maximizing the detection probability corresponds to minimizing the denominator term. Introducing binary matrices The power constraint of the transmitted signal in the optimization problem is rewritten as the power constraint of the beamforming vector and the artificial noise vector: （9） in Represents the trace of a matrix. The power allocation coefficient represents the transmit power. The original optimization problem is expressed as two subproblems: the power allocation optimization problem of the beamforming vector and the artificial noise vector, and the joint optimization problem with the goal of maximizing the detection probability.

5. The artificial noise-assisted decellularized and integrated beamforming method according to claim 4, characterized in that, Step 104 specifically involves: the joint optimization problem of beamforming and artificial noise is transformed into a convex optimization problem by using a semidefinite relaxation method and introducing auxiliary variables, and then solved using convex optimization tools; The signal-to-interference-plus-noise ratio (SIR) constraints for signals received by communication users and eavesdroppers are rewritten as follows: （10） in Indicates the access point (AP) and communication users The channel between, This indicates the access point (AP) and the eavesdropper. direct links between them This indicates the access point (AP) and the eavesdropper. The reflection link between them Beamforming vector covariance, , They represent communication users respectively With eavesdroppers The additive noise variance at a given location, constrained by the eavesdropper's eavesdropping probability, proposes two hypotheses based on the presence or absence of the perceived target, and introduces... and Indicates the second case in two situations The eavesdropper's received signal: （11） The transmitter is equipped with root antenna, therefore the first The probability of an eavesdropper eavesdropping Represented as Functions: （12） because In the interval Monotonically increasing, therefore the first The eavesdropping probability constraint for each eavesdropper is rewritten as: （13） Based on the above steps, the non-convexity of the joint optimization problem is addressed using the CVX toolbox in Matlab software.

6. The artificial noise-assisted decellularized and integrated beamforming method according to claim 1, characterized in that, Step 105 specifically involves the transmission signal power allocation problem. The power allocation parameters of the transmission signal are iteratively optimized based on the binary search method, and the power allocation parameter optimization range with higher performance improvement is selected in each iteration. Set the tolerance threshold and initial boundary conditions as transmit power allocation parameters. Solve the joint optimization problem of beamforming and artificial noise to obtain the corresponding detection probability value; then use binary search for iteration: when the interval width exceeds the tolerance, calculate the midpoint and the corresponding detection probability by solving the problem, compare the optimization effect of the two optimization intervals, and select the interval with higher performance improvement as the update interval; after each update, recalculate the detection probability on the new boundary.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method of claim 1.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

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