A near-field active monitoring method and system based on intelligent metasurface

Abstract

The present invention discloses a near-field active monitoring method and system based on an intelligent metasurface, belonging to the field of wireless communications. The monitoring method comprises the following steps: constructing a RIS array model; calculating the communication distances from a base station, a suspicious user, and a legal monitor to a reflective unit of the RIS array in a spatial coordinate system; using a uniform spherical wave model and combining the communication distances to construct a near-field channel model between the base station, the suspicious user, the legal monitor, and the RIS array; using the near-field channel model and taking into account suspicious information, obtaining the signals received by the suspicious user and the legal monitor, and then obtaining the received signal-to-noise ratio of the suspicious user to the legal monitor; constructing an optimization problem by considering that the legal monitor's signal-to-noise ratio is not less than that of the suspicious user's, and maximizing the suspicious user's received signal-to-noise ratio; introducing Lagrange multipliers to transform the optimization problem into a constant modulus constraint problem; and solving the constant modulus constraint problem using a minimax algorithm.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to a near-field active monitoring method and system based on an intelligent metasurface. Background Art

[0002] Wireless security has always been a major issue facing wireless communications. The broadcast nature of wireless channels and the openness of wireless air interfaces increase the risk of information being maliciously eavesdropped and tampered with. Therefore, it is necessary for relevant government departments to actively monitor potential suspicious communications and intervene in suspicious wireless communications, thereby enhancing their regulatory capabilities over communication networks.

[0003] Reconfigurable Intelligent Surfaces (RIS) significantly enhance the flexibility and controllability of wireless channels by dynamically manipulating the phase and amplitude of electromagnetic waves, offering significant application value in enhancing physical-layer secure communications. RIS is currently widely used in wireless anti-eavesdropping communication systems. Such research typically considers eavesdropping as an illegal attack. RIS creates a signal propagation environment that favors legitimate users and disadvantages eavesdroppers, effectively increasing the secure communication rate for legitimate users.

[0004] However, most existing technologies focus on using RIS to prevent eavesdropping, while relatively little research has been conducted on using RIS for active monitoring. Furthermore, existing technologies are primarily based on the far-field plane wave channel transmission model, which is applicable in 1G to 5G communication systems that primarily utilize spectrum below 6 GHz, as the range of wireless near-field communication is typically limited to a few meters or even centimeters, which can be ignored. However, in the future, 6G will expand its frequency bands to higher frequencies (such as millimeter waves and terahertz). To mitigate the severe path loss in these high-frequency bands, ultra-large-scale antenna arrays will often be required. This significantly expands the near-field range, making the traditional far-field plane wave assumption no longer applicable.

[0005] In summary, the existing technology has at least the following problems to be overcome: 1) It does not take into account the propagation characteristics of near-field spherical waves. The future 6G network uses high-frequency bands and ultra-large-scale arrays, and its near-field distance may reach hundreds of meters. Therefore, it needs to be redesigned based on the near-field characteristics; 2) It mainly focuses on preventing eavesdropping, but this method cannot resist the harm caused by some illegal users transmitting illegal information themselves. Therefore, it is necessary to design a solution that can actively monitor suspicious communications. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention proposes a near-field active monitoring method and system based on intelligent metasurface.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A first aspect of the present invention relates to a near-field active monitoring method based on a smart metasurface, comprising the following steps:

[0009] Construct RIS array model;

[0010] Calculate the communication distances from the base station, suspicious users, legal eavesdroppers to the reflective units of the RIS array in the spatial coordinate system;

[0011] The uniform spherical wave model is used in combination with the communication distance to construct the near-field channel model between the base station, suspicious users, legal eavesdroppers, and the RIS array.

[0012] By using the near-field channel model and considering suspicious information, the signals received by the suspicious user and the legal eavesdropper are obtained, and then the received signal-to-noise ratio of the suspicious user and the legal eavesdropper is obtained.

[0013] Considering that the signal-to-noise ratio of the legitimate monitor is not less than the received signal-to-noise ratio of the suspicious user, and maximizing the received signal-to-noise ratio of the suspicious user, an optimization problem is constructed;

[0014] Lagrange multipliers are introduced to transform the optimization problem into a constant modulus constraint problem;

[0015] The constant modulus constraint problem is solved by a minimax algorithm.

[0016] Optionally, the method for calculating the communication distance includes the following steps:

[0017] The RIS array is deployed in the xoz plane of the coordinate system. The center of the array is located at the origin of the coordinate system. The reflection unit in the nth row and mth column is denoted by s. n,m Indicates that the distances between the centers of two adjacent reflection units along the x-axis and along the z-axis are d x and d z , then the RIS reflection unit s n,m The position coordinate vector can be expressed as:

[0018]

[0019] Where n∈[-(N-1) / 2,(N-1) / 2], m∈[-(M-1) / 2,(M-1) / 2];

[0020] Assume d A represents the distance from Alan to the center of the RIS array, θ A and are the elevation angle and azimuth angle of Alan relative to the center of the RIS array, respectively. The position coordinate vector of Alan is expressed as:

[0021]

[0022] Assume d E represents the distance from Eve to the center of the RIS array, θ E and are the elevation angle and azimuth angle of Eve relative to the center of the RIS array, respectively. The position coordinate vector of Eve is expressed as:

[0023]

[0024] Assume d B represents the distance from Bob to the center of the RIS array, θ B and are the elevation and azimuth angles of Bob relative to the center of the RIS array, respectively. The coordinate vector of Bob's position is expressed as:

[0025]

[0026] r AR , r RE , r RB Represents Alan, Eve, Bob to RIS reflection unit s n,m Since the size of each RIS reflector unit is on the order of wavelength, d x / d A <<1,d z / d A <<1,d x / d E <<1,d z / d E <<1,d x / d B <<1,d z / d B <<1, we get:

[0027]

[0028]

[0029]

[0030] where ||·|| represents the Euclidean norm.

[0031] Optionally, the method for constructing the near-field channel model includes the following steps:

[0032] Set channel gain in λ represents wavelength; Alan to RIS reflection unit s n,m The USW model of the near-field channel between is given by the following formula:

[0033]

[0034] Similarly, RIS reflection units n,m The near-field channel to Eve is:

[0035]

[0036] RIS reflection units n,m The near-field channel to Bob is:

[0037]

[0038] Optionally, the method for calculating the received signal-to-noise ratio of the user and the listener includes the following steps:

[0039] Definitions A ~CN(0,1) is the suspicious information transmitted from Alan to Bob, where CN(0,1) represents a complex Gaussian distribution with mean 0 and variance 1. The signals received by Bob and Eve are:

[0040]

[0041]

[0042] in is the channel vector from Alan to RIS, is the channel vector from RIS to Eve, is the channel vector from RIS to Bob; P A is Alan’s transmit power, and are the additive white Gaussian noise of Bob and Eve respectively;

[0043] By calculating the ratio of the target signal power to the noise, we can get the received signal-to-noise ratio of Bob and Eve:

[0044]

[0045]

[0046] where h A-B =h RIS-Bob diag(h Alan-RIS ), h A-E =h RIS-Eve diag(h Alan-RIS ) is an equivalent cascade channel, is the vector formed by the phase shift of the RIS unit.

[0047] Optionally, the construction optimization problem specifically includes the following steps:

[0048] Considering that the listener's signal-to-noise ratio is not less than the suspicious user's received signal-to-noise ratio, and maximizing the suspicious user's received signal-to-noise ratio, the following optimization problem is constructed:

[0049] P1:

[0050] stC1:|v i |=1,

[0051] C2:SNR E ≥SNR B

[0052] In P1, C1 is the constant modulus constraint of the RIS reflection unit, v i represents the i-th element of vector v, and C2 is the constraint that ensures Eve can successfully monitor suspicious communications.

[0053] Optionally, converting the optimization problem into a constant modulus constraint problem specifically includes the following steps:

[0054] Introducing Lagrange multipliers into the optimization problem, we obtain a problem that only contains constant modulus constraints:

[0055] P 2:

[0056] stC1

[0057] Where μ>0 is the Lagrange multiplier.

[0058] Optionally, the method of solving the constant modulus constraint problem by using a minimax algorithm specifically includes the following steps:

[0059] The objective function in the constant modulus constraint problem is constructed as g(v), and the value of v at the tth iteration is Construct a function that is consistent with the objective function g(v) The lower bound function of the point function value is equal, denoted as This lower bound is used as the alternative objective function; secondly, the alternative function The v corresponding to the maximum value is used as the value of v in the next iteration, that is,

[0060] Optionally, the execution of the optimization iteration comprises the following steps:

[0061] S10: Initialize μ = 0, set the step size μ for each iteration d , and the maximum value μ max , set the maximum number of internal iterations T max and convergence threshold ε;

[0062] S20: Initialize the internal iteration number t=1, initialize the parameters

[0063] S30: Determine w according to the value range of μ (t) Solve the expression and calculate w (t) ;

[0064] S40: Based on Update v0 (t+1) The value of

[0065] S50: If || v0 (t+1) -v0 (t) ||2≤ε holds or t=T max If it holds, the internal iteration is considered to have converged, the internal iteration is terminated, and the SNR at this time is calculated B and SNR E Otherwise, t←t+1 and go to step S30;

[0066] S60: If SNR B ≤SNR E The external iteration is considered to have converged, and the external iteration is terminated, or μ ≥ μ max , then it is considered that there is no solution and the external iteration is terminated, otherwise μ←μ+μ d , and go to step S20.

[0067] A second aspect of the present invention relates to a near-field active monitoring system based on an intelligent metasurface, comprising:

[0068] RIS array model module;

[0069] The communication distance calculation module is used to calculate the communication distance between the base station, the suspicious user, the legal eavesdropper and the reflective unit of the RIS array in the spatial coordinate system;

[0070] The near-field channel model construction module uses the uniform spherical wave model and combines the communication distance to construct the near-field channel model between the base station, suspicious users, legal eavesdroppers, and the RIS array.

[0071] The communication model construction module uses the near-field channel model and takes into account suspicious information to obtain the received signals of the suspicious user and the legal eavesdropper, and then obtains the received signal-to-noise ratio of the suspicious user and the legal eavesdropper;

[0072] An optimization problem construction module is constructed, which considers that the signal-to-noise ratio of the legitimate eavesdropper is not less than the received signal-to-noise ratio of the suspicious user, and maximizes the received signal-to-noise ratio of the suspicious user to construct an optimization problem; introduces Lagrange multipliers to transform the optimization problem into a constant modulus constraint problem;

[0073] And, an optimization problem solving module solves the constant modulus constraint problem through a mini-max algorithm.

[0074] A third aspect of the present invention relates to a computer-readable storage medium storing instructions, which, when executed, implement the above-mentioned near-field active monitoring method based on smart metasurface.

[0075] A fourth aspect of the present invention relates to a communication device, comprising the above-mentioned near-field active monitoring system based on smart metasurface or the above-mentioned computer-readable storage medium.

[0076] Beneficial effects of the present invention:

[0077] (1) Compared with traditional monitoring systems that rely on multiple antenna arrays, the monitoring system based on RIS proposed in this invention has lower deployment costs. The passive nature of the RIS array reduces the need for radio frequency links, reducing system complexity and cost.

[0078] (2) Compared with the anti-eavesdropping scheme based on RIS, the active monitoring method proposed in the present invention can effectively resist the harm caused by some illegal users transmitting illegal information themselves.

[0079] (3) The RIS's ability to regulate the near-field propagation environment is fully utilized. By rationally optimizing the RIS phase shift matrix, the system's ability to monitor suspicious communications in near-field scenarios is significantly improved compared to traditional solutions based on far-field channel models. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The present invention will be further described below with reference to the accompanying drawings.

[0081] Figure 1 A schematic diagram of the system model of this application;

[0082] Figure 2 A schematic diagram comparing the monitoring rates of the method of this application and the method based on the far-field channel model;

[0083] Figure 3 This is a flow chart of the near-field active monitoring method based on smart metasurface of this application. DETAILED DESCRIPTION

[0084] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0085] In some embodiments of the present invention, a near-field active monitoring method based on a smart metasurface is provided, such as Figure 3 As shown, the specific steps include:

[0086] Step 1: Build a system model

[0087] like Figure 1 As shown in Figure 1, a near-field active eavesdropping system based on a smart metasurface consists of a base station named Alan, a legitimate eavesdropper (Eve), and a suspicious user (Bob). The legitimate eavesdropper, Eve, needs to actively monitor the suspicious communication link from Alan to Bob. Alan, Bob, and Eve are each equipped with a single antenna and operate in half-duplex mode. Considering the urban environment, the direct link between Alan and Bob / Eve is blocked by obstacles. Therefore, a RIS is deployed between Alan and Bob / Eve to provide a reflection link.

[0088] (1) RIS array model

[0089] The system deploys a R The passive RIS array consists of reflection units, where N R The reflection units are arranged regularly with equal spacing into N rows and M columns, N×M=N R .

[0090] Each reflection unit can autonomously adjust the phase of the incident signal. is the phase shift matrix of RIS, α i ∈[0,2π), i=1,…,N R , where diag(x) represents a diagonal matrix. The diagonal elements are all elements in the vector x, that is, the reflection coefficients, and the off-diagonal elements are all 0.

[0091] (2) Communication distance

[0092] For convenience, it is assumed that the RIS array is deployed in the xoz plane of the coordinate system, the center of the array is located at the origin of the coordinate system, and the reflection unit in the nth row and mth column is denoted by s n,m Indicates that the distances between the centers of two adjacent reflection units along the x-axis and along the z-axis are d x and d z , then the RIS reflection unit s n,m The position coordinate vector can be expressed as:

[0093]

[0094] Where n∈[-(N-1) / 2,(N-1) / 2], m∈[-(M-1) / 2,(M-1) / 2];

[0095] Assume dA represents the distance from Alan to the center of the RIS array, θ A and are the elevation angle and azimuth angle of Alan relative to the center of the RIS array, respectively. The position coordinate vector of Alan is expressed as:

[0096]

[0097] Assume d E represents the distance from Eve to the center of the RIS array, θ E and are the elevation angle and azimuth angle of Eve relative to the center of the RIS array, respectively. The position coordinate vector of Eve is expressed as

[0098]

[0099] Assume d B represents the distance from Bob to the center of the RIS array, θ B and are the elevation and azimuth angles of Bob relative to the center of the RIS array, respectively. The coordinate vector of Bob's position is expressed as:

[0100]

[0101] Let r AR , r RE , r RB Represents Alan, Eve, Bob to RIS reflection unit s n,m Since the size of each RIS reflector unit is on the order of wavelength, d x / d A <<1,d z / d A <<1,d x / d E <<1,d z / d E <<1,d x / d B <<1,d z / d B <<1, we can calculate:

[0102]

[0103]

[0104]

[0105] where ||·|| represents the Euclidean norm. The last step of the above three equations is obtained by using the Fresnel approximation and omitting the square term.

[0106] (3) Alan-RIS-Eve / Bob near-field channel model

[0107] Considering that the RIS array is located in the near field of base station Alan, and Eve and Bob are also located in the near field of the RIS reflected signal, the uniform spherical wave (USW) model is used to model the near field channel.

[0108] In the USW model, when the propagation distance r is greater than a certain threshold, that is, the uniform power distance, it can be assumed that the receiving end power can remain relatively stable and will not fluctuate too much due to a small change in distance. Therefore, it can be assumed that the channel gain where β 0,0 It is mainly determined by the free space path loss, which is expressed as Where λ is the wavelength. Alan to RIS reflection unit s n,m The USW model of the near-field channel between is given by the following formula:

[0109]

[0110] Similarly, RIS reflection units n,m The near-field channel to Eve is:

[0111]

[0112] RIS reflection units n,m The near-field channel to Bob is:

[0113]

[0114] (4) Communication model

[0115] Definitions A ~CN(0,1) is the suspicious information transmitted from Alan to Bob, where CN(0,1) represents a complex Gaussian distribution with mean 0 and variance 1. The signals received by Bob and Eve are:

[0116]

[0117]

[0118] in is the channel vector from Alan to RIS, is the channel vector from RIS to Eve, is the channel vector from RIS to Bob. A is Alan’s transmit power, and are the additive white Gaussian noise of Bob and Eve respectively.

[0119] By calculating the ratio of the target signal power to the noise, we can get the received signal-to-noise ratio of Bob and Eve:

[0120]

[0121]

[0122] where h A-B =h RIS-Bob diag(h Alan-RIS ), h A-E =h RIS-Eve diag(h Alan-RIS ) is an equivalent cascade channel, is the vector formed by the phase shift of the RIS unit, and j is the imaginary unit.

[0123] Step 2: Active Monitoring Optimization Problem Modeling

[0124] To improve active monitoring performance, Figure 1 The system shown in the figure establishes an optimization problem model, which must follow the following two principles: (1) ensuring that Eve can effectively monitor the communication from Alan to Bob; (2) maximizing the monitoring rate. The first principle requires that the SNR E ≥SNR B , ensuring that Eve can receive and decode suspicious information at the physical layer. The second principle requires that E ≥SNR B Under the premise of making SNR B As high as possible.

[0125] Based on the above two principles, the following optimization problem is established:

[0126] P1:

[0127] stC1:|v i |=1,

[0128] C2:SNR E ≥SNR B

[0129] In P1, C1 is the constant modulus constraint of the RIS reflection unit, v i represents the i-th element of vector v, and C2 is the constraint that ensures Eve can successfully monitor suspicious communications.

[0130] Since both C1 and C2 are non-convex constraints, this problem is a non-convex problem. To make the optimization problem easier to handle, it is converted into a problem containing only constant modulus constraints:

[0131] P2:

[0132] stC1

[0133] where μ>0 is the Lagrange multiplier.

[0134] Step 3: Design a solution algorithm for the active monitoring optimization problem

[0135] The main difficulty in solving P2 is the unit modulus constraint C1. In this embodiment, the Minimization-Maximization (MM) algorithm is used to solve this problem. The main idea is: assuming that the objective function is g(v), the value of v at the tth iteration is First, construct a function that is consistent with the objective function g(v) The lower bound function of the point function value is equal, denoted as This lower bound is used as the alternative objective function; secondly, the alternative function The v corresponding to the maximum value is used as the value of v in the next iteration, that is, This ensures that the value of g(v) increases monotonically from one iteration to the next, that is, The key to using the MM method is to construct a lower bound function that is easy to find the maximum value.

[0136]

[0137] According to the principle of MM method, the objective function of problem P2 is first reformulated as:

[0138] g(v)=v H H A-B v+μ(v H H A-E vv H H A-B v)

[0139] in The above formula can be further written as:

[0140] g(v)=v H H A-B v+μ(v H H A-E vv H H A-B v)

[0141] ≥f(v|v0)+[g(v0)-f(v0|v0)]

[0142] Where f(v|v0) is the lower bound of the original objective function g(v). Next, we will discuss two cases:

[0143] Case 1: 0<μ≤1

[0144] From the first-order Taylor expansion we can get:

[0145]

[0146]

[0147] Where Re(·) is the real part operation, then:

[0148]

[0149] Then the lower bound of the objective function g(v) can be written as:

[0150]

[0151] Case 2: μ>1

[0152] For Hermitian matrices M and L, if M±L is satisfied, then at point x0 we have:

[0153]

[0154] Established. Take L=H A-B , M=λ max (H A-B )I, where λ max (·) represents the maximum eigenvalue of the matrix, I represents the unit matrix, and we can get:

[0155]

[0156] Using the above inequality and the first-order Tate expansion to scale the objective function of problem P2, we can obtain:

[0157]

[0158] Then the lower bound of the objective function g(v) can be written as:

[0159] According to the analysis of the above two cases, by maximizing the lower bound of the P2 objective function, the solution of problem P2 in t+1 iterations can be obtained as:

[0160]

[0161] Among them, when 0<μ<1, When μ>1, To make Re((w(t) ) H v) reaches its maximum value, as long as v0 (t+1) and w (t) The phase angles of the elements at corresponding positions of the two vectors are equal, that is:

[0162]

[0163] where ∠(·) represents the phase angle of the complex number, and are vector v0 respectively (t+1) and w (t) The i-th element of .

[0164] Step 4: Implementation steps of the optimization algorithm

[0165] S10: Initialize μ = 0, set the step size μ for each iteration d , and the maximum value μ max , set the maximum number of internal iterations T max and convergence threshold ε;

[0166] S20: Initialize the internal iteration number t=1, initialize the parameters ;

[0167] S30: Determine w according to the value range of μ (t) Solve the expression and calculate w (t) ;

[0168] S40: Based on Update v0 (t+1) The value of

[0169] S50: If || v0 (t+1) -v0 (t) ||2≤ε holds or t=T max If it holds, the internal iteration is considered to have converged, the internal iteration is terminated, and the SNR at this time is calculated B and SNR E Otherwise, t←t+1 and go to step S30;

[0170] S60: If SNR B ≤SNR E The external iteration is considered to have converged, and the external iteration is terminated, or μ ≥ μ max , then it is considered that there is no solution and the external iteration is terminated, otherwise μ←μ+μ d , and go to step S20.

[0171] In order to further demonstrate the effect of the present invention, a simulation experiment was carried out using the Matlab platform to evaluate the monitoring performance of the system.

[0172] like Figure 2 As shown in the figure, this embodiment compares the proposed solution with a solution based on a far-field channel model in the prior art, where the wavelength is set to 0.02m, the number of RIS reflective units is set to 25×25, the distance between the legitimate eavesdropper (Eve) and the center of the RIS array is set to 2m, 3m, 4m, 5m, 6m, and 7m, and the distance between the suspicious user (Bob) and the center of the RIS array is set to 4m. It is assumed that Eve and Bob are at the same azimuth angle relative to the RIS. Numerical results show that the solution based on the near-field channel model can achieve a better monitoring rate through precise RIS phase control. The solution based on the far-field channel model can only achieve a positive monitoring rate when Eve is close to the RIS. Once the distance between Eve and the RIS is greater than the distance between Bob and the RIS, this solution cannot achieve effective monitoring. Therefore, in the near-field communication scenario, if the traditional far-field channel model based on plane waves is still used, the security communication performance will be significantly reduced. However, the solution proposed in the present invention can achieve effective monitoring in the near-field communication scenario.

[0173] In summary, this paper proposes an active monitoring system based on RIS, in which the base station, suspicious users, and monitors are all located in the near field of the RIS. A uniform spherical wave model is used to describe the RIS near-field channel, thereby establishing a signal propagation model. This model considers the impact of the size and position of the RIS array on signal propagation, and compared to traditional plane wave-based models, it can more accurately describe signal propagation characteristics in high-frequency near-field communication scenarios.

[0174] The present invention designs an optimization problem to maximize the monitoring signal-to-noise ratio of suspicious communications by optimizing the RIS phase shift matrix while ensuring that the monitor can successfully decode the suspicious information. The optimization problem design can maximize the monitoring rate while ensuring successful monitoring.

[0175] This paper proposes an algorithm based on the concept of minimax to solve the optimization problem. This algorithm first transforms the optimization problem into one consisting of a single constant modulus constraint by introducing Lagrange multipliers. Secondly, under two ranges of Lagrange multipliers, a lower bound function of the objective function is constructed at a feasible solution. Finally, by maximizing the lower bound function, the feasible solution is updated, and the optimal solution is gradually approached iteratively. By iteratively optimizing the reflection coefficient matrix of the RIS, the algorithm achieves fine-grained control of near-field signal propagation, thereby improving monitoring performance.

[0176] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0177] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A near-field active monitoring method based on smart metasurface, characterized in that: The following steps are involved: Construct RIS array model; Calculate the communication distances from the base station, suspicious users, legal eavesdroppers to the reflective units of the RIS array in the spatial coordinate system; The uniform spherical wave model is used in combination with the communication distance to construct the near-field channel model between the base station, suspicious users, legal eavesdroppers, and the RIS array. By using the near-field channel model and considering suspicious information, the signals received by the suspicious user and the legal eavesdropper are obtained, and then the received signal-to-noise ratio of the suspicious user and the legal eavesdropper is obtained. Considering that the signal-to-noise ratio of the legitimate monitor is not less than the received signal-to-noise ratio of the suspicious user, and maximizing the received signal-to-noise ratio of the suspicious user, an optimization problem is constructed; Lagrange multipliers are introduced to transform the optimization problem into a constant modulus constraint problem; Solving the constant modulus constraint problem by minimax algorithm; The method for constructing the near-field channel model comprises the following steps: Set channel gain in λ represents wavelength; s represents the wavelength from base station to RIS reflection unit n,m The uniform spherical wave model of the near-field channel between is given by the following formula: RIS reflection units n,m The near-field channel to Eve is: RIS reflection units n,m The near-field channel to Bob is: Among them, r A 、r B 、r E They are the location coordinate vectors of the base station, suspicious user, and listener, respectively. RIS reflection units n,m The position coordinate vector of The calculation method of the receiving signal-to-noise ratio of the user and the monitor includes the following steps: Definitions A ~CN(0,1) is the suspicious information transmitted from the base station to the suspicious user, where CN(0,1) represents a complex Gaussian distribution with mean 0 and variance 1. The signals received by the suspicious user and the legitimate eavesdropper are: in is the channel vector from the base station to the RIS, is the channel vector from the RIS to the lawful interceptor, is the channel vector from RIS to the suspicious user; P A is the transmission power of the base station, and are the additive Gaussian white noise of suspicious users and legitimate monitors respectively; By calculating the ratio of the target signal power to the noise, the received signal-to-noise ratio of the suspicious user and the legal monitor is obtained: where h A-B =h RIS-Bob diag(h Alan-RIS ), h A-E =h RIS-Eve diag(h Alan-RIS ) is an equivalent cascade channel, is the vector formed by the phase shift of the RIS unit.

2. The near-field active monitoring method based on the smart metasurface according to claim 1 is characterized in that: The method for calculating the communication distance comprises the following steps: The RIS array is deployed in the xoz plane of the coordinate system. The center of the array is located at the origin of the coordinate system. The reflection unit in the nth row and mth column is denoted by s. n,m Indicates that the distances between the centers of two adjacent reflection units along the x-axis and along the z-axis are d x and d z , then the RIS reflection unit s n,m The position coordinate vector is expressed as: Where n∈[-(N-1) / 2,(N-1) / 2], m∈[-(M-1) / 2,(M-1) / 2]; Assume d A represents the distance from the base station to the center of the RIS array, θ A and are the elevation angle and azimuth angle of the base station relative to the center of the RIS array, respectively. The position coordinate vector of the base station is expressed as: Assume d E represents the distance from the legal monitor to the center of the RIS array, θ E and are the elevation angle and azimuth angle of the legal listener relative to the center of the RIS array, respectively. The position coordinate vector of the legal listener is expressed as: Assume d B represents the distance from the suspicious user to the center of the RIS array, θ B and are the elevation angle and azimuth angle of the suspicious user relative to the center of the RIS array, respectively. The position coordinate vector of the suspicious user is expressed as: r AR , r RE , r RB Represents base station, legal monitor, suspicious user to RIS reflection unit s respectively n,m The distance is: where ||·|| represents the Euclidean norm.

3. The near-field active monitoring method based on the smart metasurface according to claim 1 is characterized in that: The construction optimization problem specifically includes the following steps: Considering that the listener's signal-to-noise ratio is not less than the suspicious user's received signal-to-noise ratio, and maximizing the suspicious user's received signal-to-noise ratio, the following optimization problem is constructed: In P1, C1 is the constant modulus constraint of the RIS reflection unit, v i represents the i-th element of vector v, and C2 is the constraint condition that ensures that a legitimate eavesdropper can successfully eavesdrop on suspicious communications.

4. The near-field active monitoring method based on the smart metasurface according to claim 3 is characterized in that The step of converting the optimization problem into a constant modulus constraint problem specifically includes the following steps: Introducing Lagrange multipliers into the optimization problem, we obtain a problem that only contains constant modulus constraints: Where μ>0 is the Lagrange multiplier.

5. The near-field active monitoring method based on smart metasurface according to claim 1 is characterized in that: The method of solving the constant modulus constraint problem by the minimax algorithm specifically includes the following steps: The objective function in the constant modulus constraint problem is constructed as g(v), and the value of v at the tth iteration is Construct a function that is consistent with the objective function g(v) The lower bound function of the point function value is equal, denoted as This lower bound is used as the alternative objective function; secondly, the alternative function The v corresponding to the maximum value is used as the value of v in the next iteration, that is, 6. A near-field active monitoring system based on intelligent metasurface, characterized in that: include: RIS array model module; The communication distance calculation module is used to calculate the communication distance between the base station, the suspicious user, the legal eavesdropper and the reflective unit of the RIS array in the spatial coordinate system; The near-field channel model construction module uses the uniform spherical wave model and combines the communication distance to construct the near-field channel model between the base station, suspicious users, legal eavesdroppers, and the RIS array. The communication model construction module uses the near-field channel model and takes into account suspicious information to obtain the received signals of the suspicious user and the legal eavesdropper, and then obtains the received signal-to-noise ratio of the suspicious user and the legal eavesdropper; An optimization problem construction module is constructed, which considers that the signal-to-noise ratio of the legitimate eavesdropper is not less than the received signal-to-noise ratio of the suspicious user, and maximizes the received signal-to-noise ratio of the suspicious user to construct an optimization problem; introduces Lagrange multipliers to transform the optimization problem into a constant modulus constraint problem; and, an optimization problem solving module, which solves the constant modulus constraint problem by using a minimax algorithm; The method for constructing the near-field channel model comprises the following steps: Set channel gain in λ represents wavelength; s represents the wavelength from base station to RIS reflection unit n,m The uniform spherical wave model of the near-field channel between is given by the following formula: RIS reflection units n,m The near-field channel to Eve is: RIS reflection units n,m The near-field channel to Bob is: The calculation method of the receiving signal-to-noise ratio of the user and the monitor includes the following steps: Definitions A ~CN(0,1) is the suspicious information transmitted from the base station to the suspicious user, where CN(0,1) represents a complex Gaussian distribution with mean 0 and variance 1. The signals received by the suspicious user and the legitimate eavesdropper are: in is the channel vector from the base station to the RIS, is the channel vector from the RIS to the lawful interceptor, is the channel vector from RIS to the suspicious user; P A is the transmission power of the base station, and are the additive Gaussian white noise of suspicious users and legitimate monitors respectively; By calculating the ratio of the target signal power to the noise, the received signal-to-noise ratio of the suspicious user and the legal monitor is obtained: where h A-B =h RIS-Bob diag(h Alan-RIS ), h A-E =h RIS-Eve diag(h Alan-RIS ) is an equivalent cascade channel, is the vector formed by the phase shift of the RIS unit.

7. A computer-readable storage medium storing instructions, characterized in that: When the instruction is executed, the near-field active monitoring method based on the smart metasurface according to any one of claims 1 to 5 is implemented.

8. A communication device, characterized in that: Including the near-field active monitoring system based on the smart metasurface as described in claim 6 or the computer-readable storage medium as described in claim 7.

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