Illegal perception suppression optimization method based on ISAC system

Abstract

The invention discloses an illegal perception suppression optimization method based on an ISAC system, and relates to an illegal perception suppression optimization method. The invention provides an illegal perception suppression optimization method based on an ISAC system. The purpose of the invention is to enhance the security of legal user perception information when illegal users carry out illegal target detection. According to the invention, the radar receiving signal-to-noise ratio of illegal users is deteriorated by using artificial noise. According to the method, the non-convexity of the illegal user side radar receiving signal-to-noise ratio is relaxed through eigenvalue decomposition. According to the method, the optimization variables are decoupled by using the alternating optimization algorithm, and the beam forming vector and the artificial noise covariance matrix are iteratively solved respectively, so that illegal perception suppression under the background of illegal target detection is realized. According to the method, illegal perception can be inhibited based on the ISAC system, the detection probability of illegal users to legal users is reduced, and the method has excellent performance under the conditions of different perception performance requirements and total transmitting power. The invention belongs to the technical field of wireless network security.

Description

Technical Field

[0001] The invention relates to an illegal perception suppression optimization method, belonging to the technical field of wireless network security. Background Art

[0002] Integrated sensing and communication (ISAC) technology has higher spectrum efficiency and energy efficiency, and will provide users with various sensing services in the next generation of wireless communication networks, such as Wi-Fi sensing for motion recognition, high-precision target positioning in the industrial Internet of Things, and beam tracking in the Internet of Vehicles. However, due to the broadcast characteristics of wireless signals and the integration of communication sensing waveforms, ISAC systems will face more severe security threats. When there are illegal users in the network, the protection of user privacy information is very important. Specifically, illegal users in the network can realize illegal target perception based on multi-base radar based on the reflected waves of specific legitimate users in the network, and then conduct illegal detection of legitimate users in the network. If a specific legitimate user is very sensitive to its own perception-related information, the protection of radar perception information will provide important protection for user privacy.

[0003] At present, most of the privacy protection methods for ISAC systems focus on communication-centric security enhancements, such as physical layer security or covert communication technology, while there are relatively few studies on suppressing illegal perception to protect user radar privacy. However, due to the broadcast characteristics of electromagnetic waves and the design architecture of the integrated communication and perception waveform, the legitimate user's demand for communication rate will make their perception information more easily exposed to illegal users, because at this time, the larger communication signal-to-noise ratio (SNR) will also cause a larger radar perception SNR at the receiving end of the illegal user. Therefore, illegal perception suppression is very necessary to improve the security of the ISAC system. At the same time, there is currently a lack of rate-illegal perception performance boundary analysis based on perception performance indicators, such as the trade-off relationship between the probability of false detection when illegal targets are detected in the network and the achievable rate of legitimate users. In short, illegal perception suppression in the presence of illegal detection in the ISAC system is still an open problem. Summary of the invention

[0004] The purpose of the present invention is to enhance the security of legitimate user perception information when an illegal user performs illegal target detection, and further propose an illegal perception suppression optimization method based on an ISAC system.

[0005] The technical solution adopted by the present invention to solve the above-mentioned problem is: the steps of the present invention include:

[0006] Step 1, initialization parameters;

[0007] Step 2: mathematically model the illegal perception suppression problem based on the ISAC system in the context of illegal users performing target detection on specific legal users. The model considers both the achievable rate requirements of legal users and the false detection probability of illegal users. ISAC is a communication perception integrated system.

[0008] Step 3: Fix Solve the artificial noise covariance matrix and update Then proceed to step 4;

[0009] Step 4: Fix Under the condition of solving the ISAC base station beamforming auxiliary variables and updating Then proceed to step 5;

[0010] Step 5: Let i = i + 1. If the iteration index of the alternating optimization algorithm satisfies i ≥ N BCD , then the algorithm ends and the output is as well as Otherwise proceed to step 3.

[0011] Furthermore, the initialization parameters in step 1 include the number of transmitting antennas N of the ISAC base station t , the number of ISAC base station receiving antennas N r , the number of illegal user receiving antennas Number of users N B , base station-legal user channel Communication noise variance ISAC base station radar perception noise variance Illegal user radar perception noise variance Illegal user reflection factor α, number of iterations of the alternating optimization algorithm N BCD , the azimuth angle of the illegal user relative to the ISAC base station η, the azimuth angle of Bob relative to the ISAC base station θ, the azimuth angle of the illegal user relative to Bob φ, the maximum transmission power P t , legal perception Cramer-Laurence threshold Γ0, minimum communication rate threshold R0, symbol length L, base station beamforming auxiliary variables Initial value of artificial noise covariance matrix Alternating optimization algorithm iteration index i=0;

[0012] Bob is a specific legitimate user.

[0013] Furthermore, in step 3 is the ISAC base station beamforming auxiliary variable in the i-th alternating optimization algorithm iteration, is the artificial noise covariance matrix in the i+1th alternating optimization algorithm iteration.

[0014] Furthermore, in step 4 is the ISAC base station beamforming auxiliary variable in the i+1th alternating optimization algorithm iteration, is the artificial noise covariance matrix in the i+1th alternating optimization algorithm iteration.

[0015] Furthermore, in step 2, a mathematical modeling method for the illegal perception suppression problem based on the ISAC system in the target detection background is provided; the specific process is as follows:

[0016] The transmission signal X of the ISAC base station is:

[0017] X=WS+S n (1)

[0018] In formula (1), represents the beamforming matrix, S = [s[1], s[2], ..., s[L]] represents the transmitted data stream, and S satisfies S n represents an artificial noise signal, whose covariance matrix is ​​R0, S n Used to degrade the perceived performance of illegal users;

[0019] Connect the ISAC base station to the k th The Rician fading signal between legitimate users is denoted as For the legitimate user Bob, it is assumed that the channel is line-of-sight fading, so k th The received signal of the legitimate user is expressed as:

[0020]

[0021] In formula (2), n B [l] is the variance is additive Gaussian white noise, k th The achievable rate for legitimate users is:

[0022]

[0023] For the ISAC base station working in the single-base radar mode, the received echo signal Y A for:

[0024] Y A =αb(η)a(η) H X+N A (4)

[0025] In formula (4), b(η) represents the receiving steering vector of the ISAC base station, the azimuth angle of the illegal user relative to the ISAC base station is η, and N A Each element in represents the variance The additive Gaussian white noise is α, which represents the reflection parameter. The Cramer-Laurent bound of the illegal user azimuth estimation is used as the legitimate perception evaluation index. The Cramer-Laurent bound can be expressed as:

[0026]

[0027] In formula (5),

[0028] The received echo signal Y W It is expressed as:

[0029] Y W =βc(φ)a(θ) H (WS+S n )+N W (6)

[0030] In formula (6), β represents the reflection parameter, N W The covariance matrix is The additive Gaussian white noise, and the receiving steering vector of the illegal user is c(φ);

[0031] Assume that an illegal user knows all the information of S, but has no knowledge of S n Unknown, illegal users after matching filtering based on S, Y W Translates to:

[0032]

[0033] Let B(φ)=c(φ)a(θ) H , The vectorized form of is:

[0034]

[0035] The receive beamforming vector is denoted as The signal after receiving beamforming is expressed as:

[0036]

[0037] From the perspective of illegal users, in order to detect Bob, the following hypothesis testing problem needs to be solved:

[0038]

[0039] In formula (10), H1 indicates the existence of a target, and H0 indicates the absence of a target;

[0040] y W The probability distributions under H0 and H1 are and The variance terms ξ0 and ξ1 are expressed as:

[0041]

[0042] Illegal users use the Neyman-Pearson detection method to fix the false alarm probability P FA The test statistic for target detection on Bob is:

[0043]

[0044] The probability of missing an illegal user is P MD It is expressed as:

[0045]

[0046] in,

[0047] By reducing the SNR r Ways to improve P MD ;

[0048] From the perspective of illegal users, SNR is achieved by optimizing the receiving beamforming r r According to Rayleigh entropy, SNR r The maximum value of is expressed as:

[0049]

[0050] in,

[0051]

[0052] For the ISAC base station, by optimizing the transmit beamforming vector w k And the artificial noise covariance matrix R0 is used to suppress illegal perception in target detection scenarios.

[0053] Furthermore, the specific process of step 3 is fixed Solve the artificial noise covariance matrix and update The specific process is:

[0054] Given The optimization problem about {R0} is:

[0055]

[0056] In formula (22), for The maximum eigenvalue of .

[0057] Furthermore, in step 4, Under the condition of solving the ISAC base station beamforming auxiliary variables and updating The specific process is:

[0058] In a given About {W k The optimization problem of} is expressed as:

[0059]

[0060] In formula (23), G (i) About function.

[0061] The beneficial effects of the present invention are as follows: the present invention proposes an optimization method for suppressing illegal perception based on the ISAC system under the background of illegal users performing illegal detection on specific legal users. First, the present invention proposes a modeling method for suppressing illegal perception in combination with artificial noise under illegal target detection. This modeling uses artificial noise to deteriorate the radar receiving signal-to-noise ratio of illegal users. Secondly, the present invention uses eigenvalue decomposition to relax the non-convexity of the radar receiving signal-to-noise ratio of the illegal user end. Finally, the present invention uses an alternating optimization algorithm to decouple the optimization variables, and iteratively solves the beamforming vector and the artificial noise covariance matrix respectively, thereby realizing illegal perception suppression under the background of illegal target detection. The present invention can enhance the security of legal user perception information based on the ISAC system, and has excellent performance under a variety of communication and perception requirements. The significance of the present invention is to suppress illegal target detection based on the ISAC system and reduce hardware overhead under the same security index conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the system model;

[0063] Figure 2 It is a schematic diagram of algorithm convergence. DETAILED DESCRIPTION

[0064] Specific implementation method 1: Figure 1 As shown, an optimization method for suppressing illegal perception based on an ISAC system includes the following specific steps:

[0065] Step 1: Initialize parameters; the initialization parameters include the number of transmitting antennas N of the ISAC base station t , the number of ISAC base station receiving antennas N r , the number of illegal user receiving antennas Number of users N B , base station-legal user channel Communication noise variance ISAC base station radar perception noise variance Illegal user radar perception noise variance Illegal user reflection factor α, number of iterations of the alternating optimization algorithm N BCD , the azimuth angle of the illegal user relative to the ISAC base station η, the azimuth angle of Bob relative to the ISAC base station θ, the azimuth angle of the illegal user relative to Bob φ, the maximum transmission power P t , legal perception Cramer-Laurence threshold Γ0, minimum communication rate threshold R0, symbol length L, base station beamforming auxiliary variables Initial value of artificial noise covariance matrix Alternating optimization algorithm iteration index i=0;

[0066] Bob is a specific legitimate user;

[0067] Step 2: Under the background of illegal users detecting specific legitimate users, mathematical modeling is performed on the illegal perception suppression problem based on the ISAC system. The model considers both the achievable rate requirements of legitimate users and the false detection probability of illegal users. ISAC is a communication perception integrated system. The mathematical modeling method of the illegal perception suppression problem based on the ISAC system under the background of target detection is as follows:

[0068] The transmission signal X of the ISAC base station is:

[0069] X=WS+S n (1)

[0070] In formula (1), W = [w1, w2, ..., w NB ] represents the beamforming matrix, S = [s[1], s[2], ..., s[L]] represents the transmitted data stream, and S satisfies S n represents an artificial noise signal, whose covariance matrix is ​​R0, S n Used to degrade the perceived performance of illegal users;

[0071] Connect the ISAC base station to the k th The Rician fading signal between legitimate users is denoted as For the legitimate user Bob, it is assumed that the channel is line-of-sight fading, so k th The received signal of the legitimate user is expressed as:

[0072]

[0073] In formula (2), n B [l] is the variance is additive Gaussian white noise, k th The achievable rate for legitimate users is:

[0074]

[0075] For the ISAC base station working in the single-base radar mode, the received echo signal Y A for:

[0076] Y A =αb(η)a(η) H X+N A (4)

[0077] In formula (4), b(η) represents the receiving steering vector of the ISAC base station, the azimuth angle of the illegal user relative to the ISAC base station is η, and N A Each element in represents the variance The additive Gaussian white noise is α, which represents the reflection parameter. The Cramer-Laurent bound of the illegal user azimuth estimation is used as the legitimate perception evaluation index. The Cramer-Laurent bound can be expressed as:

[0078]

[0079] In formula (5),

[0080] The received echo signal Y W It is expressed as:

[0081] Y W =βc(φ)a(θ) H (WS+S n )+N W (6)

[0082] In formula (6), β represents the reflection parameter, N W The covariance matrix is The additive Gaussian white noise, and the receiving steering vector of the illegal user is c(φ);

[0083] Assume that an illegal user knows all the information of S, but has no knowledge of S n Unknown, illegal users after matching filtering based on S, Y W Translates to:

[0084]

[0085] Let B(φ)=c(φ)a(θ) H , The vectorized form of is:

[0086]

[0087] The receive beamforming vector is denoted as The signal after receiving beamforming is expressed as:

[0088]

[0089] From the perspective of illegal users, in order to detect Bob, the following hypothesis testing problem needs to be solved:

[0090]

[0091] In formula (10), H1 indicates the existence of a target, and H0 indicates the absence of a target;

[0092] The probability distributions under H0 and H1 are and The variance terms ξ0 and ξ1 are expressed as:

[0093]

[0094] Illegal users use the Neyman-Pearson detection method to fix the false alarm probability P FA The test statistic for target detection on Bob is:

[0095]

[0096] The probability of missing an illegal user is P MD It is expressed as:

[0097]

[0098] in,

[0099] By reducing the SNR r Ways to improve P MD ;

[0100] From the perspective of illegal users, SNR is achieved by optimizing the receiving beamforming r r According to Rayleigh entropy, SNR r The maximum value of is expressed as:

[0101]

[0102] in,

[0103]

[0104] For the ISAC base station, by optimizing the transmit beamforming vector w k and artificial noise covariance matrix R0 to suppress illegal perception in target detection scenarios;

[0105] Step 3: Fix Solve the artificial noise covariance matrix and update Then proceed to step 4; is the ISAC base station beamforming auxiliary variable in the i-th alternating optimization algorithm iteration, is the artificial noise covariance matrix in the i+1th alternating optimization algorithm iteration; the specific process is:

[0106] Given The optimization problem about {R0} is:

[0107]

[0108] In formula (22), for The maximum eigenvalue of

[0109] Step 4: Fix Under the condition of solving the ISAC base station beamforming auxiliary variables and updating Then proceed to step 5; is the ISAC base station beamforming auxiliary variable in the i+1th alternating optimization algorithm iteration, is the artificial noise covariance matrix in the i+1th alternating optimization algorithm iteration; the specific process is:

[0110] In a given About {W k The optimization problem of} is expressed as:

[0111]

[0112] In formula (23), G (i) About Function of

[0113] Step 5: Let i = i + 1. If the iteration index of the alternating optimization algorithm satisfies i ≥ N BCD , then the algorithm ends and the output is as well as Otherwise proceed to step 3.

[0114] Among them, an optimization model for suppressing illegal perception in target detection scenarios is given. In order to deteriorate the target detection performance of the illegal user end, the present invention minimizes the receiving SNR of Bob's reflected echo at the illegal user on the basis of ensuring the legal perception performance and communication rate. r Therefore, the present invention proposes the following optimization model

[0115]

[0116] The optimization model can be used to ensure that the communication rate of each legitimate user is greater than the threshold R0, the legal azimuth angle estimated by the ISAC base station is less than the threshold Γ0, and the signal power sent by the ISAC base station is less than the maximum power P t .

[0117] However, since the variable w k The coupling between and R0 causes the problem (19) to be a non-convex problem. Therefore, the present invention will use semi-positive definite relaxation to solve the above problem. Let represents auxiliary variables, where W k ±0, rank(W k )=1. Then according to Schur's complement theorem, (19) can be converted to

[0118]

[0119] However, due to the non-convexity of the objective function, problem (20) still cannot be solved directly. k ) to relax. Denotes B(φ)W k B(φ) H The maximum eigenvalue of , then tr(GW k ) can be relaxed to

[0120]

[0121] Therefore, the non-convex problem (20) can be solved using an alternating optimization framework. Specifically, the optimization variables are divided into two independent parts, namely {W k} and {R0}. Then (20) can be solved by alternately solving {W k} and {R0} are optimized.

[0122] Table 1 Symbols

[0123]

[0124]

[0125]

[0126]

[0127] Example

[0128] This example considers 3 legal users and a single illegal user. The ISAC base station is equipped with 16 transmit antennas and 20 receive antennas. The illegal user is equipped with 20 receive antennas. The fading of the reference distance is set to 46dB, the Rice factor is set to 5, the path loss exponent parameter is set to 2, the noise power is set to -90dBm, the maximum transmit power is set to 30dBm, and the symbol length is 30. This example tests the convergence of the algorithm and the probability of underreporting of the illegal user end after convergence.

[0129] like Figure 2 As shown, the algorithm framework proposed in the present invention achieves convergence after 10 iterations. After convergence, the probability of missed reports of illegal users is about 90%. At this time, illegal users can no longer detect legal users in most cases, thus achieving illegal perception suppression.

[0130] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. An ISAC system-based illegal perception suppression optimization method, characterized in that: The specific steps include: Step 1, initialization parameters; Step 2: mathematically model the illegal perception suppression problem based on the ISAC system in the context of illegal users performing target detection on specific legal users. The model considers both the achievable rate requirements of legal users and the false detection probability of illegal users. ISAC is a communication perception integrated system. Step 3: Fix Solve the artificial noise covariance matrix and update Then proceed to step 4; Step 4: Fix Under the condition of solving the ISAC base station beamforming auxiliary variables and updating Then proceed to step 5; Step 5: Let i = i + 1. If the iteration index of the alternating optimization algorithm satisfies i ≥ N BCD , then the algorithm ends and the output is as well as Otherwise proceed to step 3.

2. According to claim 1, an ISAC system-based illegal perception suppression optimization method is characterized in that: The initialization parameters in step 1 include the number of ISAC base station transmit antennas N t , the number of ISAC base station receiving antennas N r , the number of illegal user receiving antennas Number of users N B , base station-legal user channel Communication noise variance ISAC base station radar perception noise variance Illegal user radar perception noise variance Illegal user reflection factor α, number of iterations of the alternating optimization algorithm N BCD , the azimuth angle of the illegal user relative to the ISAC base station η, the azimuth angle of Bob relative to the ISAC base station θ, the azimuth angle of the illegal user relative to Bob φ, the maximum transmission power P t , legal perception Cramer-Laurence threshold Γ0, minimum communication rate threshold R0, symbol length L, base station beamforming auxiliary variables Initial value of artificial noise covariance matrix Alternating optimization algorithm iteration index i=0; Bob is a specific legitimate user.

3. According to the ISAC system-based illegal perception suppression optimization method of claim 1, it is characterized in that: Step 3 is the ISAC base station beamforming auxiliary variable in the i-th alternating optimization algorithm iteration, is the artificial noise covariance matrix in the i+1th alternating optimization algorithm iteration.

4. The illegal perception suppression optimization method based on ISAC system according to claim 1 is characterized in that: In step 4 is the ISAC base station beamforming auxiliary variable in the i+1th alternating optimization algorithm iteration, is the artificial noise covariance matrix in the i+1th alternating optimization algorithm iteration.

5. The illegal perception suppression optimization method based on ISAC system according to claim 1 is characterized in that: In step 2, a mathematical modeling method for the illegal perception suppression problem based on the ISAC system in the context of target detection is proposed; the specific process is as follows: The transmission signal X of the ISAC base station is: X=WS+S n (1), In formula (1), represents the beamforming matrix, S = [s[1], s[2], ..., s[L]] represents the transmitted data stream, and S satisfies S n represents an artificial noise signal, whose covariance matrix is ​​R0, S n Used to degrade the perceived performance of illegal users; Connect the ISAC base station to the k th The Rician fading signal between legitimate users is denoted as For the legitimate user Bob, it is assumed that the channel is line-of-sight fading, so k th The received signal of the legitimate user is expressed as: In formula (2), n B [l] is the variance is additive Gaussian white noise, k th The achievable rate for legitimate users is: For the ISAC base station working in the single-base radar mode, the received echo signal Y A for: Y A =αb(η)a(η) H X+N A (4), In formula (4), b(η) represents the receiving steering vector of the ISAC base station, the azimuth angle of the illegal user relative to the ISAC base station is η, and N A Each element in represents the variance The additive Gaussian white noise is α, which represents the reflection parameter. The Cramer-Laurent bound of the illegal user azimuth estimation is used as the legitimate perception evaluation index. The Cramer-Laurent bound can be expressed as: In formula (5), The received echo signal Y W It is expressed as: Y W =βc(φ)a(θ) H (WS+S n )+N W (6), In formula (6), β represents the reflection parameter, N W The covariance matrix is The additive Gaussian white noise, and the receiving steering vector of the illegal user is c(φ); Assume that an illegal user knows all the information of S, but has no knowledge of S n Unknown, illegal users after matching filtering based on S, Y W Translates to: Let B(φ)=c(φ)a(θ) H , The vectorized form of is: The receive beamforming vector is denoted as The signal after receiving beamforming is expressed as: From the perspective of illegal users, in order to detect Bob, the following hypothesis testing problem needs to be solved: In formula (10), H1 indicates the existence of a target, and H0 indicates the absence of a target; The probability distributions under H0 and H1 are and The variance terms ξ0 and ξ1 are expressed as: Illegal users use the Neyman-Pearson detection method to fix the false alarm probability P FA The test statistic for target detection on Bob is: The probability of missing an illegal user is P MD It is expressed as: in, By reducing the SNR r Ways to improve P MD ; From the perspective of illegal users, SNR is achieved by optimizing the receiving beamforming r r According to Rayleigh entropy, SNR r The maximum value of is expressed as: in, For the ISAC base station, by optimizing the transmit beamforming vector w k And the artificial noise covariance matrix R0 is used to suppress illegal perception in target detection scenarios.

6. The illegal perception suppression optimization method based on ISAC system according to claim 1 is characterized in that: In step 3, fix Solve the artificial noise covariance matrix and update The specific process is: Given The optimization problem about {R0} is: In formula (22), for The maximum eigenvalue of .

7. The illegal perception suppression optimization method based on ISAC system according to claim 1 is characterized in that: In step 4, fix Under the condition of solving the ISAC base station beamforming auxiliary variables and updating The specific process is: In a given About {W k The optimization problem of} is expressed as: In formula (23), G (i) About function.

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