Multicast and unicast communication perception integrated security beam forming method based on penalty term

Abstract

The invention discloses a penalty term-based multicast and unicast communication sensing integrated secure beam forming method, which comprises the following steps of: sending a multicast and unicast mixed signal to a user by a base station, decoding a multicast signal by a communication user by using NOMA (Non-Orthogonal Multiple Access), eliminating the interference of the multicast signal on a unicast signal, acquiring the reachable rate of multicast and unicast communication at the communication user, and transmitting the unicast and unicast communication to the base station; obtaining a multicast communication reachable rate at the radar user; the base station perceives the radar user and the target and obtains beam gains of the radar user and the target; a target eavesdropping unicast signal serving as an eavesdropper in the sensing process is obtained, and the unicast signal eavesdropping rate at the target position is obtained; establishing a hybrid multicast and unicast secure communication beam optimization problem, converting a relation between a trace of a positive semi-definite matrix in the problem and a maximum characteristic value into a penalty term, multiplying the penalty term by a penalty factor, putting the multiplied penalty term into a target function for constraint, and obtaining a convex optimization problem; solving the convex optimization problem through iteration to obtain multicast and unicast beams; the system has the advantages of good robustness and good performance.

Description

Technical Field

[0001] The present invention relates to a beamforming technology of an ISAC (Integrated Sensing and Communication) system, and in particular to a penalty-item-based multicast and unicast communication perception integrated security beamforming method. Background Art

[0002] With the rapid development of wireless communication technology and the explosive growth of IoT devices, the preciousness of spectrum resources has become increasingly prominent and has become a bottleneck restricting the further development of the wireless communication field. At the same time, the vigorous development of cutting-edge applications such as intelligent industry 4.0, smart city construction, the popularization of telemedicine services, and the Internet of Vehicles has put forward unprecedented high requirements for the comprehensive capabilities of wireless communication systems: not only to achieve high-speed and stable data transmission to ensure seamless flow of information, but also to have accurate real-time perception capabilities to support complex and changing scenario requirements. However, traditional communication and perception systems are usually designed and operated independently, which not only leads to the waste of spectrum resources, but also increases hardware costs. In this context, the integrated communication and perception (ISAC) technology has emerged as an innovative solution, aiming to break the boundaries between communication and perception and achieve deep integration and collaborative work between the two. By designing an intelligent system that can perform communication and perception tasks simultaneously, ISAC technology not only significantly improves the utilization efficiency of spectrum resources and reduces spectrum conflicts and waste, but also reduces the hardware cost of the ISAC system by sharing hardware platforms and signal processing algorithms. More importantly, ISAC technology can integrate communication data and perception information in real time, providing more comprehensive and accurate data support for intelligent decision-making, thereby promoting the development of wireless communication technology in a more efficient, smarter and more flexible direction, and providing strong technical support for various application scenarios in the intelligent era.

[0003] With the widespread application of ISAC technology, the security risks it faces have gradually surfaced. The openness of wireless communication channels makes signals vulnerable to various forms of interference and attacks during transmission, such as eavesdropping, interference and deception, which will undoubtedly have a serious impact on the communication quality and perception performance of the ISAC system. In the ISAC system, the physical layer is the cornerstone of the wireless communication system, and its security plays a decisive role in the stability and reliability of the entire system. However, the current research on the physical layer security of communication perception integration is mostly limited to independent communication or perception objects, and fails to truly reflect the core idea of ​​communication perception integration. Therefore, it is necessary to design security beams in communication perception integration to ensure the secure transmission of communication information in the ISAC system.

[0004] As an advanced wireless multiple access technology, non-orthogonal multiple access (NOMA) significantly improves the system's spectrum efficiency and user access capabilities by allowing multiple users to transmit data non-orthogonally at the same time, frequency, or code domain. Current research has combined NOMA and ISAC to further leverage the advantages of both and improve the overall performance of the system.

[0005] The existing design method of security beam in the integrated communication and perception system processes the matrix rank-one constraint of the established security beam optimization problem. The common processing method is to use the semi-definite relaxation (SDR) technology to first ignore the rank-one constraint of the matrix, and then solve the security beam optimization problem after the rank-one constraint is relaxed, and then use Gaussian randomization to restore the rank-one solution of the original optimization problem to the solved semi-definite matrix. However, the use of Gaussian randomization to restore the rank-one solution may bring a certain degree of performance loss. For example, the obtained rank-one solution may no longer meet the constraints of the original optimization problem. As for NOMA technology, although it can effectively improve communication performance, in actual applications, it is difficult to truly achieve perfect NOMA decoding due to technical or equipment limitations, and it is necessary to consider the robustness of NOMA decoding in the case of imperfect NOMA decoding. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a multicast and unicast communication perception integrated security beamforming method based on penalty terms, which takes into account the impact of imperfect NOMA decoding on the communication perception integrated system and ensures the robustness of the system. It converts the relationship between the trace of a semi-positive definite matrix and the maximum eigenvalue of the semi-positive definite matrix into a penalty term, multiplies it by the penalty factor and puts it into an objective function for constraint without causing performance loss.

[0007] The technical solution adopted by the present invention to solve the above technical problems is: a multicast and unicast communication perception integrated security beamforming method based on penalty items, characterized by comprising the following steps:

[0008] Step 1: For the integrated communication and perception system, an ISAC multicast and unicast communication model is constructed. The model includes a dual-function base station, two users, and a radar target. The dual-function base station is configured with a uniform linear array. The dual-function base station is used to perform downlink communication and perception tasks simultaneously. The two users are a communication user and a radar user. The communication user only needs to communicate with the dual-function base station. The radar user needs to communicate with the dual-function base station and be perceived by the dual-function base station. The communication intensity between the communication user and the dual-function base station is higher than the communication intensity between the radar user and the dual-function base station. The radar target will also act as a potential eavesdropper to eavesdrop on the private communication between the dual-function base station and the user;

[0009] Step 2: The dual-function base station simultaneously sends a multicast-unicast mixed signal to the communication user and the radar user. The communication user and the radar user each receive the multicast-unicast mixed signal sent by the dual-function base station. The power of the multicast signal in the multicast-unicast mixed signal is greater than the power of the unicast signal. The multicast-unicast mixed signal x sent by the dual-function base station is expressed as x=w m s m +w u s u , the signal y received by the communication user C Expressed as The signal y received by the radar user R Expressed as Under the premise that the communication user needs both multicast and unicast signals, and the power of the multicast signal is greater than that of the unicast signal, the communication user regards the multicast signal as a strong signal and uses NOMA to decode the multicast signal first. The signal-to-interference-noise ratio of the multicast signal received at the communication user is expressed as And according to the signal-to-interference-to-noise ratio of the multicast signal received at the communication user, the achievable rate of multicast communication at the communication user is calculated Then, the interference cancellation technology of NOMA is used to obtain C The interference caused by the multicast signal to the unicast signal is eliminated, and the signal-to-interference-to-noise ratio of the unicast signal received at the communication user is expressed as And according to the signal-to-interference-to-noise ratio of the unicast signal received at the communication user, the unicast communication reachable rate at the communication user is calculated Under the premise that the radar user only needs the multicast signal, the received signal-to-interference-noise ratio of the multicast signal at the radar user is expressed as And according to the received signal-to-interference-to-noise ratio of the multicast signal at the radar user, the achievable rate of multicast communication at the radar user is calculated. Among them, w m s m represents the multicast signal in x, s m represents the symbol carrying multicast information, w m Indicates m The beamforming vector, w u s u represents a unicast signal in x, s u represents the unicast information bearing symbol, w u Indicates u The beamforming vector, s m and u All have unit power and are independent of each other, w m and w u The dimension is N×1, where N represents the number of antennas in the uniform linear array configured for the dual-function base station, and hbc represents the channel from the dual-function base station to the communication user, () H is the conjugate transpose operation, n c represents the additive Gaussian white noise received by the communication user, n c It has a mean of 0 and a variance of The complex Gaussian distribution, h br represents the line-of-sight channel from the dual-function base station to the radar user, n r represents the additive white Gaussian noise received by the radar user, n r It has a mean of 0 and a variance of The complex Gaussian distribution of || is the modulo operator, ψ represents the factor of imperfect decoding of the multicast signal, ψ∈[0,1), ψ=0 means that the communication user decodes the multicast signal perfectly, ψ>0 means that the communication user decodes the multicast signal imperfectly;

[0010] The dual-function base station multiplexes the multicast and unicast mixed signals to perceive radar users and radar targets. The beam gain is used as a measure of the perception performance of the communication perception integrated system. The beam gain B(θ r ) is expressed as B(θ r )=a H (θ r )R x a(θ r ), the beam gain B(θ t ) is expressed as B(θ t )=a H (θ t )R x a(θ t ); In the process of the dual-function base station sensing the radar target, the radar target as a potential eavesdropper eavesdrops on the unicast signal, and the signal y T Expressed as Under the premise that the radar target as a potential eavesdropper does not know the decoding order of the communication user using NOMA decoding, the unicast signal received signal to noise ratio at the radar target as a potential eavesdropper is expressed as And according to the unicast signal receiving signal to noise ratio at the radar target as a potential eavesdropper, the unicast signal eavesdropping rate at the radar target as a potential eavesdropper is calculated. Among them, θ r represents the azimuth from the dual-function base station to the radar user, a(θ r ) represents the uniform linear array of dual-function base station configuration towards θ r The steering vector at e is a natural constant, j is an imaginary unit, d is the antenna spacing of the uniform linear array of the dual-function base station configuration, λ is the carrier wavelength, () T is the transpose operation, R x represents the covariance matrix of x, θ t represents the azimuth from the dual-function base station to the radar target as a potential eavesdropper, a(θ t ) represents the uniform linear array of dual-function base station configuration towards θ t The steering vector at h bt represents the line-of-sight channel from the dual-function base station to the radar target as a potential eavesdropper, n t represents the additive white Gaussian noise received at the radar target as a potential eavesdropper, n t It has a mean of 0 and a variance of The complex Gaussian distribution of

[0011] Step 3: Based on step 2, construct the hybrid multicast unicast secure communication beam optimization problem P1, which is described as:

[0012]

[0013] Among them, Tr() means finding the trace of the matrix, Indicates the unicast communication rate threshold. Indicates the multicast communication rate threshold. represents the unicast signal eavesdropping rate threshold, ρ t represents the radar target beam gain threshold, ρ r Indicates the radar user beam gain threshold;

[0014] Step 4: For the hybrid multicast-unicast secure communication beam optimization problem P1 constructed in step 3, define in, and are all positive semi-definite matrices and satisfy the conditions: rank() means to find the rank of the matrix. for and The rank-one constraint of

[0015] Step 5: Target and The rank-one constraint is used to convert the relationship between the trace of the semi-positive definite matrix and the maximum eigenvalue of the semi-positive definite matrix into a penalty term and put it into the objective function of the hybrid multicast-unicast secure communication beam optimization problem P1 constructed in step 3. The objective function is rewritten as: Then, using the approximate solution of the maximum eigenvalue of the semi-positive definite matrix, the objective function is rewritten as: Among them, λ max () represents the maximum eigenvalue of the matrix, η represents the penalty factor of the penalty term, for The approximate solution of n Indicates the nth iteration The eigenvector corresponding to the maximum eigenvalue of , n is the number of iterations, n = 1, 2, ...;

[0016] Step 6: Based on step 5, the final optimization problem P2 is described as: The final optimization problem P2 is a convex optimization problem. Then, the initial value of η is set, and the convex optimization problem is solved by iteration using a convex optimization solver. The value of η is updated during each iteration. When the convergence condition is met, the iteration ends and w is obtained. u and w m The best solution for each.

[0017] In step 2, h bc Modeled as the Rice channel model, Where k is the Rice factor, represents the line-of-sight channel component from the dual-function base station to the communication user, represents the non-line-of-sight channel component from the dual-function base station to the communication user, It follows a complex Gaussian distribution with mean 0 and variance 1.

[0018] In step 2, h br and h bt Both are modeled as line-of-sight links. Among them, φ r represents the path loss from the dual-function base station to the radar user, φ r =Ld r -2.4 , L represents the path loss per unit distance, d r represents the distance between the dual-function base station and the radar user, φ t represents the path loss from the dual-function base station to the radar target as a potential eavesdropper, φ t =Ld t -2.4 , d t Indicates the distance between the dual-function base station and the radar target.

[0019] In step 6, iteratively solve w u and w m The specific process of each optimal solution is:

[0020] Step 6.1: Let n represent the number of iterations, and the initial value of n is 1; set the initial value of η;

[0021] Step 6.2: Use the convex optimization solver to solve the convex optimization problem and obtain w at the nth iteration. u and w m The respective solutions are denoted by w u,n and w m,n ;

[0022] Step 6.3: Determine the convergence conditions Is it satisfied? If it is satisfied, the iteration ends and w is obtained. u and w m The optimal solution for each, w u The optimal solution is w u,n , w m The optimal solution is w m,n ; If not, update the value of η, multiply the value of η at the nth iteration by the update step size to get the value of η at the n+1th iteration, then set n=n+1, and return to step 6.2 to continue execution; where, ∈ is the preset convergence threshold, and the “=” in n=n+1 is the assignment symbol.

[0023] Compared with the prior art, the advantages of the present invention are:

[0024] 1) In the method of the present invention, the dual-function base station needs to perform mixed multicast and unicast communications with communication users and radar users, and simultaneously sense radar users and radar targets. Compared with the previous sensing objects that only need to be sensed without communicating with them, the method of the present invention involves two types of sensing objects, namely radar users and radar targets. Radar users need to be sensed by the dual-function base station and communicate with the dual-function base station, namely, radar users integrate communication and perception. Radar targets are ordinary targets that only need to be sensed by the dual-function base station and do not need to communicate with it. These two types of sensing objects have both independent perception and communication and fusion of perception and communication, which fully considers the diversity of ISAC multicast and unicast communication models.

[0025] 2) The method of the present invention takes into account the impact of imperfect NOMA decoding caused by technical or equipment limitations on the communication perception integrated system, ensuring the robustness of the system.

[0026] 3) The method of the present invention does not use the commonly used semi-positive definite relaxation technology to relax the rank-one constraint and add a Gaussian randomization method for processing the rank-one constraint of the semi-positive definite matrix in the multicast and unicast beam optimization problems. Instead, the relationship between the trace of the semi-positive definite matrix and the maximum eigenvalue of the semi-positive definite matrix is ​​converted into a penalty term, which is multiplied by the penalty factor and put into the objective function for constraint, thereby ensuring that the obtained semi-positive definite matrix satisfies the rank-one constraint, that is, it is a feasible solution to the original problem, and no performance loss is caused. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flowchart of the overall implementation process of the method of the present invention;

[0028] Figure 2 A simple schematic diagram of the ISAC multicast and unicast communication model constructed by the method of the present invention;

[0029] Figure 3 The present invention is a curve comparison diagram showing changes in the security rate of the communication perception integrated system using the method of the present invention and the existing MUC method and OMA method as a function of the beam gain threshold. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below with reference to the accompanying drawings.

[0031] The present invention proposes a multicast and unicast communication perception integrated security beamforming method based on penalty terms, and its overall implementation flow chart is as follows: Figure 1 As shown, it includes the following steps:

[0032] Step 1: Build an ISAC multicast and unicast communication model for the communication awareness integrated system, such as Figure 2 As shown in the figure, the model includes a dual-function base station (BS), two users, and a radar target. The dual-function base station is configured with a uniform linear array (ULA). The dual-function base station is used to perform downlink communication and perception tasks at the same time. The two users are the communication user and the radar user. The communication user only needs to communicate with the dual-function base station. The dual-function base station performs multicast communication and unicast communication with the communication user. The radar user needs to communicate with the dual-function base station and be perceived by the dual-function base station. The dual-function base station performs multicast communication with the radar user. The communication intensity between the communication user and the dual-function base station is higher than the communication intensity between the radar user and the dual-function base station. The radar target will also act as a potential eavesdropper to eavesdrop on the private communication between the dual-function base station and the user. Since the dual-function base station multiplexes the communication signal to perform the perception task on the radar target, the radar target may also act as a potential eavesdropper to eavesdrop on the more private unicast signal; among them, the radar user and the radar target are both flying objects.

[0033] Step 2: The dual-function base station simultaneously sends a multicast-unicast mixed signal to the communication user and the radar user. The communication user and the radar user each receive the multicast-unicast mixed signal sent by the dual-function base station. The power of the multicast signal in the multicast-unicast mixed signal is greater than the power of the unicast signal. The multicast-unicast mixed signal x sent by the dual-function base station is expressed as x=w m s m +w u s u , the signal y received by the communication user C Expressed as The signal y received by the radar user R Expressed as Under the premise that the communication user needs both multicast and unicast signals and the power of the multicast signal is greater than that of the unicast signal, the communication user regards the multicast signal as a strong signal and uses NOMA to decode the multicast signal first. The received signal-to-interference-and-noise ratio (SINR) of the multicast signal at the communication user is expressed as And according to the signal-to-interference-to-noise ratio of the multicast signal received at the communication user, the achievable rate of multicast communication at the communication user is calculated Then, the interference cancellation technology of NOMA is used to obtain C The interference caused by the multicast signal to the unicast signal is eliminated in the process. However, due to the limitation of technology and equipment, it is impossible to achieve perfect serial interference cancellation (SIC), that is, the multicast signal decoded first still has some residual signals that interfere with the decoding of the unicast signal. Therefore, the received signal-to-interference-and-noise ratio (SINR) of the unicast signal at the communication user is expressed as And according to the signal-to-interference-to-noise ratio of the unicast signal received at the communication user, the unicast communication reachable rate at the communication user is calculated Under the premise that the radar user only needs the multicast signal, the received signal-to-interference-noise ratio of the multicast signal at the radar user is expressed as And according to the received signal-to-interference-to-noise ratio of the multicast signal at the radar user, the achievable rate of multicast communication at the radar user is calculated. Among them, w m s m represents the multicast signal in x, s m represents the symbol carrying multicast information, w m Indicates m The beamforming vector, w u s u represents a unicast signal in x, s u represents the unicast information bearing symbol, w u Indicates u The beamforming vector, s m and u All have unit power and are independent of each other, w m and w u The dimension is N×1, where N represents the number of antennas in the uniform linear array configured for the dual-function base station. In the simulation, N is set to 15. bc represents the channel from the dual-function base station to the communication user, () H is the conjugate transpose operation, n c represents the additive white Gaussian noise (AWGN) received by the communication user, n c It has a mean of 0 and a variance of The complex Gaussian distribution of In this embodiment Set to -30dBm, h br represents the line-of-sight channel from the dual-function base station to the radar user, n r represents the additive white Gaussian noise (AWGN) received by the radar user, n r It has a mean of 0 and a variance of The complex Gaussian distribution of In this embodiment It is set to -30dBm, || is the modulo operator, ψ represents the factor of imperfect decoding of the multicast signal, ψ∈[0,1), ψ=0 indicates that the communication user decodes the multicast signal perfectly, ψ>0 indicates that the communication user decodes the multicast signal imperfectly, and ψ is set to 0.01 in the simulation.

[0034] The dual-function base station multiplexes the multicast and unicast mixed signals to perceive radar users and radar targets. The beam gain is used as a measure of the perception performance of the communication perception integrated system. The beam gain B(θ r ) is expressed as B(θ r )=a H (θ r )R x a(θ r ), the beam gain B(θ t ) is expressed as B(θ t )=a H (θ t )R x a(θ t ); In the process of the dual-function base station sensing the radar target, the radar target as a potential eavesdropper eavesdrops on the unicast signal, and the signal y T Expressed as Under the premise that the radar target as a potential eavesdropper does not know the decoding order of the communication user using NOMA decoding, it is impossible to eliminate the interference of the multicast signal on the unicast signal. The received signal-to-interference-to-noise ratio (SINR) of the unicast signal at the radar target as a potential eavesdropper is expressed as And according to the unicast signal receiving signal to noise ratio at the radar target as a potential eavesdropper, the unicast signal eavesdropping rate at the radar target as a potential eavesdropper is calculated. Among them, θ r represents the azimuth from the dual-function base station to the radar user. In the simulation, θ r Set to -30°, a(θ r ) represents the uniform linear array of dual-function base station configuration towards θ r The steering vector at e is a natural constant, e = 2.71…, j is an imaginary unit, d represents the antenna spacing of the uniform linear array of the dual-function base station configuration, λ represents the carrier wavelength, () T is the transpose operation, R x represents the covariance matrix of x, θ t represents the azimuth from the dual-function base station to the radar target as a potential eavesdropper. In the simulation, θ t Set to 30°, a(θ t ) represents the uniform linear array of dual-function base station configuration towards θ t The steering vector at h bt represents the line-of-sight channel from the dual-function base station to the radar target as a potential eavesdropper, n t represents the additive white Gaussian noise (AWGN) received at the radar target as a potential eavesdropper, n t It has a mean of 0 and a variance of The complex Gaussian distribution of In this embodiment Set to -30dBm.

[0035] In this embodiment, h bc The model is the Rice channel model, that is, the line-of-sight channel component plus the non-line-of-sight channel component. Where k is the Rice factor, k=2, represents the line-of-sight channel component from the dual-function base station to the communication user, represents the non-line-of-sight channel component from the dual-function base station to the communication user, It obeys a complex Gaussian distribution with mean 0 and variance 1, that is,

[0036] In this embodiment, h br and h bt Both are modeled as line-of-sight links. Among them, φ r represents the path loss from the dual-function base station to the radar user, φ r =Ld r -2.4 , L represents the path loss per unit distance (1m), in the simulation L = 30dB, d r represents the distance between the dual-function base station and the radar user. In the simulation, d r =400m,φ t represents the path loss from the dual-function base station to the radar target as a potential eavesdropper, φ t =Ld t -2.4 , d t Indicates the distance between the dual-function base station and the radar target. In the simulation, dt =400m.

[0037] Step 3: Based on step 2, construct the hybrid multicast unicast secure communication beam optimization problem P1, which is described as:

[0038]

[0039] Among them, problem P1 is a minimization problem, min is the minimum function, st means "subject to the constraints...", Tr() means finding the trace of the matrix, Indicates the unicast communication rate threshold. Indicates the multicast communication rate threshold. represents the unicast signal eavesdropping rate threshold, ρ t represents the radar target beam gain threshold, ρ r represents the radar user beam gain threshold, constraint C1 is used to ensure the unicast signal communication rate, constraints C2 and C3 are used to ensure the multicast signal communication rate, constraint C4 is used to suppress the unicast signal eavesdropping rate of the radar target as a potential eavesdropper, and constraints C5 and C6 are used to ensure the perception performance of the radar target and radar user, respectively.

[0040] Step 4: For the hybrid multicast-unicast secure communication beam optimization problem P1 constructed in step 3, define

[0041] in, and are all positive semi-definite matrices and satisfy the conditions: rank() means to find the rank of the matrix.

[0042] for and The rank-one constraint.

[0043] Step 5: Since is a non-convex constraint that is difficult to handle. Rank

[0044] When is 1, there is Established, similarly, when When the rank of is 1, we have

[0045] Established, therefore and The rank-one constraint is used to convert the relationship between the trace of the semi-positive definite matrix and the maximum eigenvalue of the semi-positive definite matrix into a penalty term and put it into the objective function of the hybrid multicast-unicast secure communication beam optimization problem P1 constructed in step 3. The objective function is rewritten as: Since the maximum eigenvalue of a semi-positive definite matrix is ​​not differentiable, the objective function is rewritten again using the approximate solution of the maximum eigenvalue of a semi-positive definite matrix as follows: Among them, λ max () represents the maximum eigenvalue of the matrix, η represents the penalty factor of the penalty term, The approximate solution of n Indicates the nth iteration The eigenvector corresponding to the maximum eigenvalue of , n is the number of iterations, n=1,2,…

[0046] Step 6: Based on step 5, the final optimization problem P2 is described as: The final optimization problem P2 is a convex optimization problem. Then, the initial value of η is set, and the convex optimization problem is solved by iteration using a convex optimization solver. The value of η is updated during each iteration. When the convergence condition is met, the iteration ends and w is obtained. u and w m The best solution for each.

[0047] In this embodiment, w is iteratively solved u and w m The specific process of each optimal solution is:

[0048] Step 6.1: Let n represent the number of iterations, and the initial value of n is 1; set the initial value of η, and set the initial value of η to 10 in the simulation.

[0049] Step 6.2: Use a convex optimization solver such as CVX to solve the convex optimization problem and obtain w at the nth iteration. u and w m The respective solutions are denoted by w u,n and w m,n .

[0050] Step 6.3: Determine the convergence conditions Is it satisfied? If it is satisfied, the iteration ends and w is obtained. u and w m The optimal solution for each, w u The optimal solution is w u,n , w m The optimal solution is w m,n ; If not, update the value of η, multiply the value of η at the nth iteration by the update step size to get the value of η at the n+1th iteration, then set n=n+1, and return to step 6.2 to continue execution; where, ∈ is the preset convergence threshold, which is set to 10 in the simulation. -5 In the simulation, the update step size is set to 2, and the "=" in n=n+1 is an assignment symbol.

[0051] The feasibility and effectiveness of the method of the present invention are further illustrated by the following simulation.

[0052] In the actual simulation, the azimuth angle from the dual-function base station to the communication user is set to 0°, and the azimuth angle from the dual-function base station to the radar user is θ r Set to -30°, the azimuth angle θ from the dual-function base station to the radar target as a potential eavesdropper t Set to 30°; n c Variance n r Variance n t Variance are all set to -30dbm; the path loss per unit distance (1m) is set to L = 30dB, and the distance between the dual-function base station and the radar user is d r and the distance d between the dual-function base station and the radar target t Both are set to 400m; unicast communication rate threshold Set to 5 bit / s / Hz, the multicast communication rate threshold Set to 1 bit / s / Hz, unicast signal eavesdropping rate threshold Set to 0.1 bit / s / Hz, set the radar target beam gain threshold ρ t and radar user beam gain threshold ρ r Consistent.

[0053] In the simulation, the methods used for comparison are the MUC method and the OMA method. Among them, the MUC (Multicast and Unicast Communications) method refers to the method that does not consider system security, that is, does not add constraints to suppress unicast eavesdropping; the OMA method refers to the method formed by replacing the NOMA technology in the method of the present invention with the OMA (Orthogonal Multiple Access) technology.

[0054] Figure 3 The safety rate of the communication perception integrated system using the method of the present invention and the existing MUC method and OMA method is given as the beam gain threshold (ρ t or r ) changes. Figure 3 As can be seen from the figure, with the beam gain threshold (ρ t or r), the eavesdropping rate of the comparison method will increase, so the security rate of the communication perception integrated system will decrease. However, the method of the present invention takes the security performance of the communication perception integrated system into consideration, ensuring that the eavesdropping rate is less than or equal to the unicast signal eavesdropping rate threshold of 0.1 bit / s / Hz. Therefore, the security performance of the communication perception integrated system will not decrease with the increase of the beam gain threshold (ρ t or r ) increased but decreased.

[0055] The method of the present invention takes the security of the integrated communication and perception system into consideration and designs a robust security beam. Therefore, although some power loss may be caused, the method of the present invention can achieve a relatively high security rate, ensure the secure communication of the integrated communication and perception system, and is superior to other comparative methods.

Claims

1. A penalty-based multicast and unicast communication perception integrated security beamforming method, characterized in that The following steps are involved: Step 1: For the integrated communication and perception system, an ISAC multicast and unicast communication model is constructed. The model includes a dual-function base station, two users, and a radar target. The dual-function base station is configured with a uniform linear array. The dual-function base station is used to perform downlink communication and perception tasks at the same time. The two users are a communication user and a radar user. The communication user only needs to communicate with the dual-function base station. The radar user needs to communicate with the dual-function base station and be perceived by the dual-function base station. The communication intensity between the communication user and the dual-function base station is higher than the communication intensity between the radar user and the dual-function base station. The radar target will also act as a potential eavesdropper to eavesdrop on the private communication between the dual-function base station and the user; Step 2: The dual-function base station simultaneously sends a multicast-unicast mixed signal to the communication user and the radar user. The communication user and the radar user each receive the multicast-unicast mixed signal sent by the dual-function base station. The power of the multicast signal in the multicast-unicast mixed signal is greater than the power of the unicast signal. The multicast-unicast mixed signal x sent by the dual-function base station is expressed as x=w m s m +w u s u , the signal y received by the communication user C Expressed as The signal y received by the radar user R Expressed as Under the premise that the communication user needs both multicast and unicast signals, and the power of the multicast signal is greater than that of the unicast signal, the communication user regards the multicast signal as a strong signal and uses NOMA to decode the multicast signal first. The signal-to-interference-noise ratio of the multicast signal received at the communication user is expressed as And according to the signal-to-interference-to-noise ratio of the multicast signal received at the communication user, the achievable rate of multicast communication at the communication user is calculated Then, the interference cancellation technology of NOMA is used to obtain C The interference caused by the multicast signal to the unicast signal is eliminated, and the signal-to-interference-to-noise ratio of the unicast signal received at the communication user is expressed as And according to the signal-to-interference-to-noise ratio of the unicast signal received at the communication user, the unicast communication reachable rate at the communication user is calculated Under the premise that the radar user only needs the multicast signal, the received signal-to-interference-noise ratio of the multicast signal at the radar user is expressed as And according to the received signal-to-interference-to-noise ratio of the multicast signal at the radar user, the achievable rate of multicast communication at the radar user is calculated. Among them, w m s m represents the multicast signal in x, s m represents the symbol carrying multicast information, w m Indicates m The beamforming vector, w u s u represents a unicast signal in x, s u represents the unicast information bearing symbol, w u Indicates u The beamforming vector, s m and u All have unit power and are independent of each other, w m and w u The dimension is N×1, where N represents the number of antennas in the uniform linear array configured for the dual-function base station, and h bc represents the channel from the dual-function base station to the communication user, () H is the conjugate transpose operation, n c represents the additive Gaussian white noise received by the communication user, n c It has a mean of 0 and a variance of The complex Gaussian distribution, h br represents the line-of-sight channel from the dual-function base station to the radar user, n r represents the additive white Gaussian noise received by the radar user, n r It has a mean of 0 and a variance of The complex Gaussian distribution, | | is the modulus operator, ψ represents the factor of imperfect decoding of the multicast signal, ψ∈[0,1), ψ=0 means that the communication user decodes the multicast signal perfectly, ψ>0 means that the communication user decodes the multicast signal imperfectly; The dual-function base station multiplexes the multicast and unicast mixed signals to perceive radar users and radar targets. The beam gain is used as a measure of the perception performance of the communication perception integrated system. The beam gain B(θ r ) is expressed as B(θ r )=a H (θ r )R x a(θ r ), the beam gain B(θ t ) is expressed as B(θ t )=a H (θ t )R x a(θ t ); In the process of the dual-function base station sensing the radar target, the radar target as a potential eavesdropper eavesdrops on the unicast signal, and the signal y T Expressed as Under the premise that the radar target as a potential eavesdropper does not know the decoding order of the communication user using NOMA decoding, the unicast signal received signal to noise ratio at the radar target as a potential eavesdropper is expressed as And according to the unicast signal receiving signal to noise ratio at the radar target as a potential eavesdropper, the unicast signal eavesdropping rate at the radar target as a potential eavesdropper is calculated. Among them, θ r represents the azimuth from the dual-function base station to the radar user, a(θ r ) represents the uniform linear array of dual-function base station configuration towards θ r The steering vector at e is a natural constant, j is an imaginary unit, d is the antenna spacing of the uniform linear array of the dual-function base station configuration, λ is the carrier wavelength, () T is the transpose operation, R x represents the covariance matrix of x, θ t represents the azimuth from the dual-function base station to the radar target as a potential eavesdropper, a(θ t ) represents the uniform linear array of dual-function base station configuration towards θ t The steering vector at h bt represents the line-of-sight channel from the dual-function base station to the radar target as a potential eavesdropper, n t represents the additive white Gaussian noise received at the radar target as a potential eavesdropper, n t It has a mean of 0 and a variance of The complex Gaussian distribution of Step 3: Based on step 2, construct the hybrid multicast unicast secure communication beam optimization problem P1, which is described as: C5:B(θ t ) ≥ρ t C6:B(θ r )≥ρ r Among them, Tr() means finding the trace of the matrix, Indicates the unicast communication rate threshold. Indicates the multicast communication rate threshold. represents the unicast signal eavesdropping rate threshold, ρ t represents the radar target beam gain threshold, ρ r Indicates the radar user beam gain threshold; Step 4: For the hybrid multicast-unicast secure communication beam optimization problem P1 constructed in step 3, define in, and are all positive semi-definite matrices and satisfy the conditions: rank() means to find the rank of the matrix. for and The rank-one constraint of Step 5: Target and The rank-one constraint is used to convert the relationship between the trace of the semi-positive definite matrix and the maximum eigenvalue of the semi-positive definite matrix into a penalty term and put it into the objective function of the hybrid multicast-unicast secure communication beam optimization problem P1 constructed in step 3. The objective function is rewritten as: Then, using the approximate solution of the maximum eigenvalue of the semi-positive definite matrix, the objective function is rewritten as: Among them, λ max () represents the maximum eigenvalue of the matrix, η represents the penalty factor of the penalty term, for The approximate solution of n Indicates the nth iteration The eigenvector corresponding to the maximum eigenvalue of , n is the number of iterations, n = 1, 2, ...; Step 6: Based on step 5, the final optimization problem P2 is described as: The final optimization problem P2 is a convex optimization problem. Then, the initial value of η is set, and the convex optimization problem is solved by iteration using a convex optimization solver. The value of η is updated during each iteration. When the convergence condition is met, the iteration ends and w is obtained. u and w m The best solution for each.

2. The penalty-based multicast and unicast communication perception integrated security beamforming method according to claim 1 is characterized in that In step 2, h bc Modeled as the Rice channel model, Where k is the Rice factor, represents the line-of-sight channel component from the dual-function base station to the communication user, represents the non-line-of-sight channel component from the dual-function base station to the communication user, It follows a complex Gaussian distribution with mean 0 and variance 1.

3. The penalty-based multicast and unicast communication perception integrated security beamforming method according to claim 1 or 2, characterized in that In step 2, h br and h bt All are modeled as line-of-sight links. Among them, φ r represents the path loss from the dual-function base station to the radar user, φ r =Ld r -2.4 , L represents the path loss per unit distance, d r represents the distance between the dual-function base station and the radar user, φ t represents the path loss from the dual-function base station to the radar target as a potential eavesdropper, φ t =Ld t -2.4 , d t Indicates the distance between the dual-function base station and the radar target.

4. The penalty-based multicast and unicast communication perception integrated security beamforming method according to claim 1 is characterized in that In step 6, iteratively solve w u and w m The specific process of each optimal solution is: Step 6.1: Let n represent the number of iterations, and the initial value of n is 1; set the initial value of η; Step 6.2: Use the convex optimization solver to solve the convex optimization problem and obtain w at the nth iteration. u and w m The respective solutions are denoted by w u,n and w m,n ; Step 6.3: Determine the convergence conditions Is it satisfied? If it is satisfied, the iteration ends and w is obtained. u and w m The optimal solution for each, w u The optimal solution is w u,n , w m The optimal solution is w m,n ; If not, update the value of η, multiply the value of η at the nth iteration by the update step size to get the value of η at the n+1th iteration, then set n=n+1, and return to step 6.2 to continue execution; where, ∈ is the preset convergence threshold, and "=" in n=n+1 is the assignment symbol.

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