Near-field ISAC-NOMA system communication security optimization method and system

Abstract

The invention provides a communication security optimization method and system for a near-field ISAC-NOMA system. The near-field ISAC-NOMA system comprises a base station, a sensing target, a plurality of legal users and an eavesdropper. The method comprises the following steps: S1, acquiring the confidentiality rate of a legal user in the near-field ISAC-NOMA system, wherein the confidentiality rate of the legal user comprises a covariance matrix of a base station transmitting signal; s2, constructing an objective function based on the maximization of the secrecy rate, and adding CRB constraints in the objective function; and S3, solving the target function to optimize the covariance matrix of the signal transmitted by the base station. According to the scheme, the secrecy rate is maximized by optimizing a transmission covariance matrix at a BS and limiting a CRB boundary of a sensing target by utilizing the characteristic that a near field can be combined with an angle and a distance, and particularly, signal enhancement at a legal user and an inhibition effect at an eavesdropper are realized in the aspect of beam forming, so that the communication security is enhanced.

Description

Technical Field

[0001] This invention relates to the field of communication security technology, and in particular to a method and system for optimizing communication security in a near-field ISAC-NOMA system. Background Technology

[0002] In recent years, various regions across China have actively developed the low-altitude economy. Constructing a digital low-altitude infrastructure network integrating low-altitude communication and sensing is a crucial prerequisite for developing the low-altitude economy and creating operational airspace. As a key technology in the field of sixth-generation wireless networks, integrated sensing and communication are considered a feasible and efficient solution to enhance the functions of the low-altitude economy. Beyond the low-altitude economy, sensing capabilities are increasingly in demand across a wider range of application areas. In the future, with the continuous development of artificial intelligence and sensing technologies, integrated sensing and communication (ISAC) technology will play an even more important role in intelligent systems and smart city construction. Integrated sensing and communication technology is considered one of the important candidate technologies for sixth-generation mobile communication, capable of fully sharing the time, space, and other multi-dimensional resources of wireless communication and radar sensing, achieving coexistence, mutual assistance, and shared benefits between the two technologies.

[0003] While integrated sensing systems offer potential benefits, their emergence also introduces new data security threats to wireless networks. Due to the openness of wireless sensing media, sensing services present potential security risks and privacy issues. Specifically, eavesdroppers may inadvertently hear the target's channel information status and infer information about the target's location movement. Consequently, malicious entities may be able to compromise the privacy of legitimate users or contaminate legitimate reception. Meanwhile, current research on Non-Orthogonal Multiple Access (NOMA) primarily focuses on the power sector. Compared to Orthogonal Multiple Access (NOMA), NOMA's core advantage lies in allowing multiple legitimate users to communicate on the same resource block. This feature is achieved by introducing controlled multiple access interference among legitimate users. At the receiving end, this interference is gradually eliminated through serial interference cancellation techniques, thus successfully decoding the legitimate user's signal. However, when multiple legitimate users share the same resource block, information security may be compromised. In this context, physical layer security technologies become an effective complement to traditional encryption methods to ensure the confidentiality of communications without relying on complex key management systems. Summary of the Invention

[0004] Based on the problems existing in the prior art, the present invention aims to solve the technical problem that when NOMA technology is used in a sensor-integrated system, the security of information is threatened when multiple legitimate users share the same resource block.

[0005] This invention provides a method for optimizing communication security in a near-field ISAC-NOMA system, which includes a base station, a sensing target, several legitimate users, and an eavesdropper; characterized by comprising the following steps:

[0006] S1: Obtain the confidentiality rate at the legitimate user location in the near-field ISAC-NOMA system. The confidentiality rate at the legitimate user location includes the covariance matrix of the signal transmitted by the base station.

[0007] S2: Construct an objective function based on maximizing the confidentiality rate at the legitimate user level, and add CRB constraints to the objective function;

[0008] S3: Solve the objective function to optimize the covariance matrix of the signal transmitted by the base station.

[0009] According to an embodiment of the present invention, obtaining the confidentiality rate at the legitimate user location in the near-field ISAC-NOMA system in step S1 includes the following steps:

[0010] S11: Demodulate the communication signal received by the legitimate user and the signal eavesdropped by the eavesdropper respectively to obtain the signal interference plus noise ratio at the legitimate user and the signal interference plus noise ratio when the eavesdropper eavesdrops on the legitimate user;

[0011] S12: The safe rate of the legitimate user is obtained based on the signal interference plus noise ratio at the legitimate user's location, and the eavesdropping rate of the eavesdropper is obtained based on the signal interference plus noise ratio at the eavesdropper's location.

[0012] S13: Obtain the confidentiality rate at the legitimate user's location based on the definition of confidentiality rate.

[0013] According to an embodiment of the present invention, the communication signal received by the legitimate user in step S11 is:

[0014]

[0015] Where k represents the order of legitimate users in the near-field ISAC-NOMA system, k = 1, 2, 3...; ω represents the near-field communication channel vector between base station BS and legitimate user k or eavesdropper; k This represents the beamforming vector that transmits information signals to legitimate user k. s k (t) indicates that the base station uses transmit beamforming to send information signals to the legitimate user k. Let represent the near-field communication channel vector between the base station and the legitimate user k or the eavesdropper; s(t) represent the dedicated sensing signal used to achieve full sensing degrees of freedom; z(t) represent the additive white Gaussian noise at the legitimate user's receiver. Represents noise power; K = {1,...,k};

[0016] The signal-to-interference-to-noise ratio at point k for legitimate user is:

[0017]

[0018] Where, ω i This represents the beamforming vector at other legitimate users besides legitimate user k. This indicates interference signals from other legitimate users. R represents the noise power at legitimate user k. s The covariance matrix representing the dedicated sensing signal;

[0019] The signal that the eavesdropper is listening to is:

[0020]

[0021] Among them, z e (t) represents the additive white Gaussian noise at the eavesdropper's receiver. Indicates the noise power at the location of the eavesdropper;

[0022] The signal-to-interference-plus-noise ratio when an eavesdropper is eavesdropping on a legitimate user k is:

[0023]

[0024] in, This indicates the noise power at the location of the eavesdropper.

[0025] According to an embodiment of the present invention, the confidentiality rate at the legitimate user k in step S13 is:

[0026] R sec (ω k R s )=(log2(1+γ k )-log2(1+γ e )) + .

[0027] According to an embodiment of the present invention, the objective function constructed in step S2 is:

[0028]

[0029] Among them, R min,k This is the threshold value for the safe rate at point k for legitimate users.

[0030] According to an embodiment of the present invention, the CRB constraint in step S2 is obtained by the following steps:

[0031] S21: Vectorize the echo signal received at the base station;

[0032] S22: Define parameter ξ based on the position of the sensed target, and use it to estimate the Fisher information matrix of ξ;

[0033] S23: The CRB constraint is obtained by inverse calculation of the Fisher information matrix of the parameter ξ.

[0034] According to an embodiment of the present invention, the echo signal received at the base station in step S21 is:

[0035] y s (t)=Gx(t)+z s (t)

[0036] Where G = β s α(r s ,θ s )α T (r s ,θ s ), which is the near-field round-trip channel matrix of the sensing target. α(r s ,θ s ) represents the near-field array response vector, r s and θ s Let x(t) and x(t) represent the distance and angle between the sensing target and the center of the uniform linear array, respectively; This represents the cumulative transmitted signal and received echo signal over T time slots. Z s This refers to the background noise (including clutter or interference) at the base station receiver.

[0037] The echo signal is vectorized and converted into:

[0038]

[0039] in,

[0040] According to an embodiment of the present invention, in step S22, the variable ξ is defined as:

[0041] ξ=[θ s ,r s ,Re(β s ),lm(β s )).

[0042] According to an embodiment of the present invention, in step S23, the Fisher information matrix of the parameter ξ is:

[0043]

[0044] The CRB constraint is expressed as follows:

[0045]

[0046] Where, A=α(r) s ,θ s )α T (r s ,θ s ).

[0047] This invention also provides a near-field ISAC-NOMA system communication security optimization system, used to implement the aforementioned near-field ISAC-NOMA system communication security optimization, comprising:

[0048] The confidentiality rate acquisition module is used to obtain the confidentiality rate of legitimate users.

[0049] The objective function construction module is used to construct an objective function based on maximizing the confidentiality rate at the legitimate user level.

[0050] The objective function solving module is used to solve the objective function to optimize the covariance matrix of the signal transmitted by the base station.

[0051] The beneficial effects of this invention are:

[0052] This invention provides a near-field ISAC-NOMA system communication security optimization method and system. By utilizing the near-field characteristic that angle and distance can be combined, the confidentiality rate is maximized by optimizing the transmit covariance matrix at the BS and limiting the CRB limit of the sensed target. Specifically, in terms of beamforming, it achieves signal enhancement at legitimate users and suppression at eavesdroppers, thereby enhancing communication security. Attached Figure Description

[0053] In order to more clearly illustrate the technical solutions in the embodiments or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is a schematic diagram of the near-field ISAC-NOMA system model in an embodiment of the present invention;

[0055] Figure 2 This is a flowchart illustrating a near-field ISAC-NOMA system communication security optimization method provided in an embodiment of the present invention;

[0056] Figure 3This is a schematic diagram illustrating the relationship between the system security rate and the distance from the base station to the legitimate user under four different communication strategies;

[0057] Figure 4 This is a schematic diagram showing the relationship between the system security rate and the maximum transmission power of the base station for four different communication strategies;

[0058] Figure 5 This is a schematic diagram of beamforming directions for two legitimate users and one eavesdropper in a near-field ISAC-NOMA system according to an embodiment of the present invention, when the base station has different transmission power.

[0059] Figure 6 This is a schematic diagram of the beamforming direction of two legitimate users and one eavesdropper in a near-field ISAC-NOMA system according to an embodiment of the present invention, when the antenna arrays of the base station are of different sizes. Detailed Implementation

[0060] The following descriptions of the embodiments refer to the accompanying drawings to illustrate specific embodiments in which the present invention may be implemented.

[0061] This invention provides a method for optimizing communication security in a near-field ISAC-NOMA system, wherein the model of the near-field ISAC-NOMA system is as follows: Figure 1 The diagram illustrates a near-field multi-function integrated sensor communication (ISAC) system, comprising a base station (BS), a sensing target (Target), several legitimate users (User1...Userk..., where Userk represents the kth legitimate user), and an eavesdropper (EVE). The base station (BS) is equipped with a near-field multi-function integrated sensor communication (ISAC) system using a uniform linear array (ULA) with an antenna spacing of d. In this system, the base station (BS) sends confidential messages to several legitimate users (User) while simultaneously wirelessly sensing the target (Target). It is assumed that the legitimate users, the sensing target, and the eavesdropper are located in the near-field region of the base station (BS), meaning their distance from the base station (BS) is less than the Rayleigh distance 2D. 2 λ. In this scenario, the base station (BS) sends a confidential message to the legitimate user (User) and locates the sensed target (Target).

[0062] The flowchart of the near-field ISAC-NOMA system communication security optimization method is as follows: Figure 2 As shown, it includes the following steps:

[0063] S1: Obtain the confidentiality rate at the legitimate user location in the near-field ISAC-NOMA system. The confidentiality rate at the legitimate user location includes the covariance matrix of the signal transmitted by the base station.

[0064] S2: Construct an objective function based on maximizing the confidentiality rate at the legitimate user level, and add CRB constraints to the objective function;

[0065] S3: Solve the objective function to optimize the covariance matrix of the signal transmitted by the base station.

[0066] The following is in conjunction with the appendix Figure 1-6 The technical solution of the present invention will be further described below.

[0067] like Figure 1 As shown, a Cartesian coordinate system is established in the model, with the origin of the coordinate system placed at the center of the ULA at the base station BS. Therefore, the coordinates of the nth element of the ULA are: The coordinates of a valid user k are determined by r k = [rcosθ,rsinθ] is given, where k represents the order of legitimate users in the near-field ISAC-NOMA system, k = 1, 2, 3...; r and θ represent the distance and angle between the kth legitimate user and the array center, respectively.

[0068] Then, the distance from the nth antenna element to the legitimate user can be calculated using the following formula:

[0069]

[0070] Let α(r,θ) denote the near-field array response vector, and the nth element of α(r,θ) It is given by the following formula:

[0071]

[0072] Where λ is the signal wavelength and j is the imaginary unit.

[0073] make c represents wavelength, and f represents frequency. Let r k ,θ k and β k Let represent the distance, angle, and complex channel gain of the legitimate user k, respectively. This represents the near-field communication channel vector between the base station BS and the legitimate user k or the eavesdropper. It can be modeled as: h k =β k α(r k ,θ k ).

[0074] Unlike communication, target sensing relies on the echo signal received at the BS. Therefore, the round-trip path needs to be considered. Let r s ,θ s and β s Represent the range, angle, and complex channel gain of the target, respectively, and the near-field round-trip channel matrix of the target. It is given by the following formula:

[0075] G = β s α(rs ,θ s )α T (r s ,θ s ).

[0076] 1. Transmission signal model

[0077] The base station (BS) employs beam focusing to simultaneously transmit information signals used for communication and radar sensing tasks, as well as radar signals. Let x(t) represent the baseband signal at the base station (BS), where t = 1, 2, ..., T is a discrete-time index. The combined communication and sensing signals transmitted by the base station BS at time t are represented as follows:

[0078]

[0079] Specifically, BS uses transmit beamforming to send information signals to legitimate user k. Let s(t) represent the beamforming vector that transmits the information signal to the legitimate user k, and let s(t) represent the dedicated sensing signal used to achieve full sensing degrees of freedom, and K = {1,...,k}. Assume the information signal is independently distributed and has unit power, i.e., if k = i, then... otherwise set up Let the covariance matrix of the dedicated sensing signal be given, and then the covariance matrix of the signal transmitted by the base station (BS) is given by the following equation:

[0080]

[0081] Where, x H (t) denotes the conjugate transpose of x(t). Represents ω k The conjugate transpose of .

[0082] 2. Communication model

[0083] The communication signal received at legitimate user k is given by the following formula:

[0084]

[0085] Where z(t) represents the additive white Gaussian noise (AWGN) at the receiver of legitimate user k.

[0086] The signal-to-interference-plus-noise ratio (SINR) received at legitimate user k is:

[0087]

[0088] Where, ω iThis represents the beamforming vector at other legitimate users besides legitimate user k. This indicates interference signals from other legitimate users. R represents the noise power at legitimate user k. s This represents the covariance matrix of the dedicated sensing signal.

[0089] The signal received by the eavesdropper is represented as:

[0090]

[0091] in, This represents the additive white Gaussian noise (AWGN) at the receiver of the eavesdropper.

[0092] The interference-to-noise ratio (SINR) of the signal received by the eavesdropper for the k-th legitimate user is:

[0093]

[0094] in, This indicates the noise power at the location of the eavesdropper.

[0095] We assume h e Given that the BS is fully known, in order to promote a secure ISAC design, the achievable confidentiality rate at legitimate user k is given by the following formula: R sec (ω k R s )=(log2(1+γ k )-log2(1+γ e )) + .

[0096] 3. Sensing Model

[0097] The received echo signal at the BS used for target sensing is given by the following formula:

[0098] y s (t)=Gx(t)+z s (t)

[0099] Where G = β s α(r s ,θ s )α T (r s ,θ s ), which is the near-field round-trip channel matrix of the sensing target. α(r s ,θ s ) represents the near-field array response vector, r s and θ sLet x(t) and x(t) represent the distance and angle between the sensing target and the center of the uniform linear array, respectively; This represents the cumulative transmitted and received echo signals over T time slots. Z s This indicates the background noise (including clutter or interference) at the BS receiver. Each entry (referring to a single complex unit constituting the Zs(t) matrix) is the variance. The zero-mean CSCG random variable.

[0100] We will s The (t) matrix is ​​vectorized as follows:

[0101]

[0102] in, The purpose of vectorization is to convert a matrix or array into a column vector, which makes it easier to perform mathematical operations and programming.

[0103] Our goal is to estimate r s and θ s To locate the target, r s and θ s Let ξ = [θ] represent the distance and angle of the sensing target relative to the center of the ULA, respectively. s ,r s ,Re(β s ),lm(β s [)] represents the unknown parameter to be estimated, used to estimate the Fisher information matrix (FIM) of ξ:

[0104]

[0105] use For any l, p∈{r s θ s We have:

[0106]

[0107] thereby,

[0108]

[0109] and then,

[0110]

[0111]

[0112] Where, A=α(r) s ,θ s )αT (r s ,θ s ),

[0113] We adopt The Rao bound (CRB), serving as the performance matrix for target perception, provides a lower bound on the mean squared error (MSE) and has a closed-form expression. The CRB matrix can be computed by inverting the Fisher information matrix (FIM) for the unknown parameter ξ. The CRB matrix is ​​given by:

[0114]

[0115] 4. Problem Description

[0116] Our goal is to optimize the transmit information covariance matrix to maximize the confidentiality rate of legitimate users, while ensuring the sensing performance (CRB) of the joint distance and angle estimation of the target. Based on this, we construct the objective function P1:

[0117]

[0118] Where □ represents the set threshold. R represents the power of the signal transmitted to a legitimate user. u (R s ω k R represents the reachable rate of a legitimate user k. min,k This is the threshold value for the safe rate for legitimate users.

[0119] In optimization problems, Schur decomposition can help transform the primal problem into a more easily solvable form. By converting the CRB constraint into matrix inequalities, the optimization problem becomes easier to analyze and solve. Therefore, we first use Schur components to reformulate the CRB constraint CRB(θ) ≤ □:

[0120]

[0121] Observing that the objective function is still nonconvex, we introduce a variable ζ, ζ>0, using the Charnes-Cooper transform, and define... get Therefore, the CRB constraint is equivalently restated in the following convex form:

[0122]

[0123] Therefore, the objective function can be expressed as P2:

[0124]

[0125] Specifically, in one embodiment of the present invention, suppose there are two legitimate users U1 and U2 in a near-field ISAC-NOMA system, and let s1 be the signal sent to legitimate user U1 and s2 be the signal sent to legitimate user U2. When a legitimate user receives a signal, the decoding order is as follows: U2 (the legitimate user with the weaker signal) directly demodulates the data (treating the legitimate user's signal as interference), while U1 (the legitimate user with the stronger signal) first demodulates the weaker legitimate user's data s2, then performs interference cancellation, and finally demodulates its own data. The signal strength is related to the distance between the legitimate user and the base station. In this case, legitimate user U1 can achieve a rate of R1 = min{R... 1,s1 ,R 1,s2},but:

[0126]

[0127] Based on the principle of downlink NOMA, by directly decoding and synthesizing s2, the legitimate U2 can achieve a rate of R2:

[0128]

[0129] The achievable rate at which EVE can eavesdrop on U1 and U2 is given by the following formula:

[0130]

[0131] The security rate is defined as the positive difference between the legally achievable rate and the eavesdropping achievable rate. Therefore, the achievable security rates at U1 and U2 are R1 and R2, respectively. sec,1 =[R1-R e,s1 ] + R sec,2 =[R2-R e,s2 ] + .

[0132] For two legitimate users U1 and U2, to address the non-convex unit modulus constraints of ω1 and ω2, this embodiment employs a semi-positive definite relaxation (SDR) technique to update ω1 and ω2. Specifically, a new variable W is defined such that... This article ensures A new auxiliary variable H is defined, where

[0133] The achievable rate for legitimate users can be rewritten as:

[0134]

[0135] The achievable rate at which EVE can eavesdrop on U1 and U2 is given by the following formula:

[0136]

[0137] Let variable R min =min(R) 1,s2 If R2), then the optimization function can be reformulated as P3:

[0138]

[0139] because Since it is non-convex, it can be handled using the first-order Taylor approximation through the Successive Convex Approximation Algorithm (SCA). The SCA algorithm is described in detail in Table 1.

[0140] Table 1 SCA Algorithm Process

[0141]

[0142]

[0143] The SCA algorithm uses a first-order Taylor approximation to handle this. and The derivation process is similar and can be uniformly represented as follows:

[0144]

[0145] And the problem It is convex, and suboptimal solutions can be effectively solved using convex optimization tools (such as CVX). and The derivation process is similar and can be uniformly represented as follows:

[0146]

[0147] Then, through iterative updates and An approximate solution is obtained.

[0148] 5. Simulation Verification

[0149] Simulations provide numerical results to validate the effectiveness of the proposed near-field ISAC physical layer security framework. We assume the base station BS is equipped with a uniform linear array (ULA) with N = 65 antennas operating at a frequency of 28 GHz (λ = 1.07 cm). The antenna aperture is set to D = 0.5 m, resulting in a Rayleigh distance of 2D. 2 / λ = 46.73m. There are two legitimate communication users, one sensing target, and one eavesdropper located in the near-field region of the base station (BS). The positions of the two legitimate users are set to (8m, 45°) and (20m, 15°), respectively; the target's position is set to (20m, 45°); and the eavesdropper's position is set to (10m, 20°). The CRB thresholds for angle and distance are set to □ = 0.1. The maximum transmit power and noise power at the base station (BS) are set to 40dBm and -60dBm, respectively.

[0150] For comparison, this embodiment simulates beamforming patterns under different antenna sizes and base station transmit powers in the near field, comparing the beamforming of two legitimate users at different locations. For security analysis, the communication security rates in the near and far fields are simulated under NOMA and OMA conditions, respectively. Through simulation and comparison, we discuss how the system security rate varies with the distance *d* between the BS and the legitimate user, and how the system security rate varies with the maximum BS transmit power *Pmax*. The results confirm that considering the near-field model introduces additional distance dimension information, leading to a higher system security rate compared to traditional far-field communication schemes.

[0151] Specifically, Figure 3 The diagram illustrates the relationship between the system security rate and the distance from the base station to the legitimate user for four different communication strategies. As the distance from the base station to the legitimate user increases, the system security rate of all four strategies decreases. This is because the channel gain from the base station to the legitimate user decreases with increasing distance, leading to increased signal loss for the legitimate user and consequently a decrease in the system security rate. Compared to the far-field model, the near-field model provides additional distance-dimensional information, which can be used to further enhance the security of the communication link. With increasing distance, signal path loss and attenuation intensify, posing challenges to all wireless communication systems. Among all communication modes, NF-NOMA exhibits the highest security rate. While the security rate gradually decreases with increasing distance between the legitimate user and the base station, it remains at a high level. However, with increasing distance, the system gradually transitions to the far-field region, weakening the beam-focusing advantage and reducing the difference in security rate between the near-field and far-field regions. Overall, these characteristics of near-field communication make it more effective at maintaining communication security over shorter distances, but this advantage gradually diminishes with increasing distance.

[0152] Figure 4The diagram illustrates the relationship between the system security rate and the maximum transmit power of the base station for four different communication strategies. As the maximum transmit power of the base station increases, the security rate of all four communication modes shows an upward trend, indicating that increasing transmit power can enhance the security performance of the communication system. At this point, the minimum data rate between two legitimate users is Rmin = 1 bps / Hz. Increased transmit power strengthens signal propagation and overcomes signal attenuation, while higher power improves the signal-to-noise ratio, enhancing the system's ability to identify signals and thus improving security. Near-field communication exhibits a more significant advantage over far-field communication at all power levels, maximizing the security rate through fine-tuning power allocation, demonstrating superior performance, particularly in terms of power and space resource utilization. However, compared to OMA technology, NOMA technology shows a more pronounced advantage in secure communication with increasing transmit power. Therefore, NF-NOMA is a worthwhile option to consider when designing a highly secure communication system in a sensor-integrated system.

[0153] Figure 5 The diagram illustrates the beamforming patterns of two legitimate users and one eavesdropper in a near-field ISAC-NOMA system according to an embodiment of the present invention, under different base station transmission powers. The three curves represent the beamforming patterns of legitimate user 1 and legitimate user 2 at the base station's maximum transmission power of 0dBm, 10dBm, and 20dBm, respectively. From these curves, it can be observed that the gain at the legitimate user increases with increasing transmission power, because higher power makes the signal stronger in the target direction. Specifically, the gain is maximized at 20dBm, indicating that higher transmission power effectively enhances the received signal strength of the target legitimate user. The gain at the eavesdropper decreases with increasing transmission power, indicating that beamforming successfully suppresses signal transmission in this direction. For higher transmission power values, we see the minimum signal gain at the eavesdropper, suggesting that by increasing transmission power, the system more effectively prevents signal leakage to the potential eavesdropper's location. Simulation results illustrate the impact of transmission power on beamforming, particularly in enhancing the signal of the target legitimate user and suppressing the signal of the non-target legitimate user. As transmission power increases, beamforming technology can more effectively provide gain in a predetermined direction while reducing gain in other directions, thereby improving communication quality and enhancing system security.

[0154] Figure 6The diagram illustrates the beamforming patterns of two legitimate users and one eavesdropper in a near-field ISAC-NOMA system according to an embodiment of the present invention, with different antenna array sizes at the base station. The three curves represent the beamforming patterns at the locations of legitimate user 1, legitimate user 2, and the eavesdropper, respectively, for different antenna array sizes (M=4, M=12, M=20). In this diagram, distinct peaks and troughs are visible on the horizontal axis, representing the enhancement and suppression effects of beamforming. The signal gain at the locations of legitimate user 1 and legitimate user 2 is enhanced, allowing the base station to more accurately direct signals to the target legitimate user, thereby improving signal reception quality and enhancing security. The diagram shows that the more antennas there are, the narrower the gain peaks become, indicating a more concentrated beam. At the eavesdropper (Eve) on the horizontal axis, a trough is observed, indicating that the signal gain at Eve's location is suppressed at that angle. In our system, Eve is a potential eavesdropper; therefore, reducing signal gain at her location is a key strategy for improving communication security. As can be seen from the figure, the suppression effect becomes more significant as the number of antennas increases, indicating that a larger antenna array can more effectively reduce signal leakage to the location of potential eavesdroppers.

[0155] This invention also provides a near-field ISAC-NOMA system communication security optimization system for implementing the aforementioned near-field ISAC-NOMA system communication security optimization. The system includes a security rate acquisition module, an objective function construction module, and an objective function solving module. The security rate acquisition module acquires the security rate at legitimate users; the objective function construction module constructs an objective function based on maximizing the security rate at legitimate users; and the objective function solving module solves the objective function to optimize the covariance matrix of the signal transmitted by the base station.

[0156] In summary, the present invention provides a near-field ISAC-NOMA system communication security optimization method and system. By utilizing the characteristic that the near field can combine angle and distance, the system maximizes the confidentiality rate by optimizing the transmit covariance matrix at the BS and limiting the CRB limit of the sensing target. Specifically, in terms of beamforming, it achieves signal enhancement at legitimate users and suppression at eavesdroppers, thereby enhancing communication security.

[0157] It should be noted that although the present invention is disclosed above with specific embodiments, the above embodiments are not intended to limit the present invention. Ordinary technicians in this field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope defined by the claims.

Claims

1. A method for optimizing communication security in a near-field ISAC-NOMA system, the near-field ISAC-NOMA system comprising a base station, a sensing target, several legitimate users, and an eavesdropper; characterized in that, Includes the following steps: S1: Obtain the confidentiality rate at the legitimate user location in the near-field ISAC-NOMA system. The confidentiality rate at the legitimate user location includes the covariance matrix of the signal transmitted by the base station. S2: Construct an objective function based on maximizing the confidentiality rate at the legitimate user level, and add CRB constraints to the objective function; S3: Solve the objective function to optimize the covariance matrix of the signal transmitted by the base station.

2. The near-field ISAC-NOMA system communication security optimization method according to claim 1, characterized in that, Step S1, obtaining the confidentiality rate at the legitimate user location in the near-field ISAC-NOMA system, includes the following steps: S11: Demodulate the communication signal received by the legitimate user and the signal eavesdropped by the eavesdropper respectively to obtain the signal interference plus noise ratio at the legitimate user and the signal interference plus noise ratio when the eavesdropper eavesdrops on the legitimate user; S12: The safe rate of the legitimate user is obtained based on the signal interference plus noise ratio at the legitimate user's location, and the eavesdropping rate of the eavesdropper is obtained based on the signal interference plus noise ratio at the eavesdropper's location. S13: Obtain the confidentiality rate at the legitimate user's location based on the definition of confidentiality rate.

3. The near-field ISAC-NOMA system communication security optimization method according to claim 2, characterized in that, The communication signal received by the legitimate user in step S11 is: Where k represents the order of legitimate users in the near-field ISAC-NOMA system, k = 1, 2, 3...; ω represents the near-field communication channel vector between base station BS and legitimate user k or eavesdropper; k This represents the beamforming vector that transmits information signals to legitimate user k. s k (t) indicates that the base station uses transmit beamforming to send information signals to the legitimate user k. Let represent the near-field communication channel vector between the base station and the legitimate user k or the eavesdropper; s(t) represent the dedicated sensing signal used to achieve full sensing degrees of freedom; z(t) represent the additive white Gaussian noise at the legitimate user's receiver. Represents noise power; K = {1,...,k}; The signal-to-interference-to-noise ratio at point k for legitimate user is: Where, ω i This represents the beamforming vector at other legitimate users besides legitimate user k. This indicates interference signals from other legitimate users. R represents the noise power at legitimate user k. s The covariance matrix representing the dedicated sensing signal; The signal that the eavesdropper is listening to is: Among them, z e (t) represents the additive white Gaussian noise at the eavesdropper's receiver. Indicates the noise power at the location of the eavesdropper; The signal-to-interference-plus-noise ratio when an eavesdropper is eavesdropping on a legitimate user k is: in, This indicates the noise power at the location of the eavesdropper.

4. The near-field ISAC-NOMA system communication security optimization method according to claim 3, characterized in that, The confidentiality rate at the legitimate user k in step S13 is: R sec (oh k ,R s )=(log2(1+γ k )-log2(1+γ e )) + 。 5. The near-field ISAC-NOMA system communication security optimization method according to claim 4, characterized in that, The objective function constructed in step S2 is: CRB(θ)≤m R u (R s ,w k )≥R min,k Among them, R min,k This is the threshold value for the safe rate at point k for legitimate users.

6. The near-field ISAC-NOMA system communication security optimization method according to claim 1, characterized in that, The CRB constraint mentioned in step S2 is obtained by the following steps: S21: Vectorize the echo signal received at the base station; S22: Define parameter ξ based on the position of the sensed target, and use it to estimate the Fisher information matrix of ξ; S23: The CRB constraint is obtained by inverse calculation of the Fisher information matrix of the parameter ξ.

7. The near-field ISAC-NOMA system communication security optimization method according to claim 6, characterized in that, The echo signal received at the base station in step S21 is: y s (t)=Gx(t)+z s (t) Where G = β s α(r s ,θ s )α T (r s ,θ s ), which is the near-field round-trip channel matrix of the sensing target. α(r s ,θ s ) represents the near-field array response vector, r s and θ s Let x(t) and x(t) represent the distance and angle between the sensing target and the center of the uniform linear array, respectively; This represents the cumulative transmitted signal and received echo signal over T time slots. t = {1, 2, ..., T}; Z s This refers to the background noise (including clutter or interference) at the base station receiver. The echo signal is vectorized and converted into: in, 8. The near-field ISAC-NOMA system communication security optimization method according to claim 7, characterized in that, In step S22, the variable ξ is defined as: ξ=[θ s ,r s ,Re(β s ),lm(β s )]。 9. A near-field ISAC-NOMA system communication security optimization method according to claim 8, characterized in that, In step S23, the Fisher information matrix of the parameter ξ is: The CRB constraint is expressed as follows: Where, A=α(r s ,i s )a T (r s ,i s )。 10. A near-field ISAC-NOMA system communication security optimization system, used to implement the near-field ISAC-NOMA system communication security optimization as described in any one of claims 1 to 9, characterized in that, include: The confidentiality rate acquisition module is used to obtain the confidentiality rate of legitimate users. The objective function construction module is used to construct an objective function based on maximizing the confidentiality rate at the legitimate user level. The objective function solving module is used to solve the objective function to optimize the covariance matrix of the signal transmitted by the base station.

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