Near-field active monitoring method and system based on intelligent metasurface

Abstract

The invention discloses a near-field active monitoring method and system based on an intelligent metasurface, and belongs to the field of wireless communication. The monitoring method comprises the following steps: constructing an RIS array model; calculating communication distances from a base station, a suspicious user and a legal listener to a reflection unit of the RIS array in the space coordinate system; constructing a near-field channel model among the base station, the suspicious user, the legal listener and the RIS array by adopting a uniform spherical wave model in combination with the communication distance; signals received by the suspicious user and the legal listener are obtained through a near-field channel model and by considering suspicious information, and then the receiving signal-to-noise ratio of the suspicious user and the legal listener is obtained; considering that the signal-to-noise ratio of the legal listener is not less than the receiving signal-to-noise ratio of the suspicious user and maximizing the receiving signal-to-noise ratio of the suspicious user, and constructing an optimization problem; introducing a Lagrange multiplier, and converting the optimization problem into a constant modulus constraint problem; and solving the constant modulus constraint problem through a minimization and maximization algorithm.

Description

Technical Field

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

[0002] Wireless security has always been an important issue faced by wireless communication. The broadcast nature of the wireless channel and the openness of the wireless air interface 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 the supervision ability of the communication network.

[0003] The reconfigurable intelligent surface (RIS) significantly improves the flexibility and controllability of the wireless channel by dynamically adjusting the phase and amplitude of electromagnetic waves, and has important application value in enhancing physical layer secure communication. Currently, RIS has been widely applied to wireless anti-eavesdropping communication systems. Such research usually regards eavesdropping as an illegal attack, and creates a signal propagation environment that is favorable to legitimate users and unfavorable to eavesdroppers through RIS, thereby effectively improving the secure communication rate of legitimate users.

[0004] However, most of the existing technologies focus on using RIS to prevent eavesdropping, and there are relatively few studies on using RIS to achieve active monitoring. In addition, the existing technologies mainly rely on the far-field plane wave channel transmission model, which is applicable to 1G to 5G communication systems mainly using the spectrum below 6 GHz, because the range of wireless near-field communication is usually limited to several meters or even several centimeters and can be ignored. However, the future 6G usage band will expand to high-frequency bands (such as millimeter waves and terahertz). In order to resist the severe path loss in high-frequency bands, it is often necessary to configure a very large-scale antenna array, which significantly expands the near-field range, and the traditional far-field plane wave assumption will no longer be applicable.

[0005] In summary, there are at least the following problems to be overcome in the existing technologies: 1) The propagation characteristics of near-field spherical waves are not considered. In the future 6G network using high-frequency bands and very large-scale arrays, the near-field distance may reach hundreds of meters. Therefore, it is necessary to re-design according to the near-field characteristics; 2) It mainly focuses on preventing eavesdropping. However, this means cannot resist the harm caused by some illegal users transmitting illegal information by themselves. Therefore, it is necessary to design a scheme that can actively monitor suspicious communications. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technologies, the present invention proposes a near-field active monitoring method and system based on intelligent metasurface.

[0007] The object of the present invention can be achieved by the following technical solutions:

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

[0009] Construct a RIS array model;

[0010] Calculate the communication distances from the base station, the suspicious user, and the legitimate listener to the reflection unit of the RIS array in the space coordinate system;

[0011] Adopt a uniform spherical wave model and combine with the communication distances to construct a near-field channel model among the base station, the suspicious user, the legitimate listener, and the RIS array;

[0012] Obtain the signals received by the suspicious user and the legitimate listener through the near-field channel model and considering the suspicious information, and then obtain the received signal-to-noise ratios of the suspicious user and the legitimate listener;

[0013] Considering that the signal-to-noise ratio of the legitimate listener 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, construct an optimization problem;

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

[0015] Solve the constant modulus constraint problem by the minimax algorithm.

[0016] Optionally, the calculation method of 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 coordinates, and the reflection unit in the nth row and mth column is represented by s n,m 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 , respectively. Then the position coordinate vector of the RIS reflection unit s n,m can be expressed as:

[0018]

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

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

[0021]

[0022] Let \(d\) E represent the distance from Eve to the center of the RIS array, and \(\theta\) E and be the elevation angle and azimuth angle of Eve relative to the center of the RIS array, respectively. Then, the position coordinate vector of Eve is expressed as:

[0023]

[0024] Let \(d\) B represent the distance from Bob to the center of the RIS array, and \(\theta\) B and be the elevation angle and azimuth angle of Bob relative to the center of the RIS array, respectively. Then, the position coordinate vector of Bob is expressed as:

[0025]

[0026] Let \(r\) AR , \(r\) RE , \(r\) RB represent the distances from Alan, Eve, and Bob to the RIS reflecting element \(s\) n,m respectively. Since the size of each RIS reflecting element is on the order of the 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 obtain:

[0027]

[0028]

[0029]

[0030] where \(\|\cdot\|\) represents the Euclidean norm.

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

[0032] Let the channel gain where \(\lambda\) represents the wavelength. The USW model of the near-field channel between Alan and the RIS reflecting element \(s\) n,m is given by the following formula:

[0033]

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

[0035]

[0036] The RIS reflection unit s 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] Define s A ~CN(0,1) as the suspicious information transmitted from Alan to Bob, where CN(0,1) represents a complex Gaussian distribution with a mean of 0 and a variance of 1. Then the signals received by Bob and Eve are respectively:

[0040]

[0041]

[0042] Where is the channel vector from Alan to the RIS, is the channel vector from the RIS to Eve, is the channel vector from the RIS to Bob; P A is the transmission power of Alan, and are the additive Gaussian white noises of Bob and Eve respectively;

[0043] By calculating the ratio of the target signal power to the noise, the received signal - to - noise ratios of Bob and Eve are obtained:

[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 the equivalent cascaded channel, is the vector composed of the phase shifts of the RIS units.

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

[0048] Considering that the signal-to-noise ratio of the eavesdropper 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, the following optimization problem is constructed:

[0049] P1:

[0050] s.t.C1:|v i | = 1,

[0051] C2:SNR E ≥SNR B

[0052] In P1, C1 is the constant modulus constraint of the RIS reflection unit, and v i represents the i-th element of the vector v. C2 is the constraint condition to ensure that Eve can successfully eavesdrop on the suspicious communication.

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

[0054] Introduce Lagrange multipliers into the optimization problem to obtain a problem that only contains the constant modulus constraint:

[0055] P 2:

[0056] s.t.C1

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

[0058] Optionally, the solution of the constant modulus constraint problem by the minimax algorithm specifically includes the following steps:

[0059] Construct the objective function in the constant modulus constraint problem as g(v), and the value of v at the t-th iteration is Construct a lower bound function equal to the function value of the objective function g(v) at the point, denoted as Use this lower bound as the alternative objective function; secondly, use the v corresponding to the maximum value of this alternative function as the value of v at the next iteration, that is

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

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

[0062] S20: Initialize the internal iteration count \(t = 1\) and initialize the parameters

[0063] S30: Determine the solution expression of \(w\) according to the value range of \(\mu\), and calculate \(w\) (t) ; (t) ;

[0064] S40: Update the value of \(v_0\) according to ; (t+1) ;

[0065] S50: If \(\left\lVert v_0\right.\) (t+1) \(- v_0\) (t) \(\right\rVert^2\leq\varepsilon\) holds or \(t = T\) max holds, it is considered that the internal iteration converges, terminate the internal iteration, and calculate the SNR B and SNR E at this time; otherwise, \(t\leftarrow t + 1\) and go to step S30;

[0066] S60: If SNR B \(\leq\) SNR E it is considered that the external iteration converges, terminate the external iteration, or \(\mu\geq\mu\) max , it is considered that there is no solution and terminate the external iteration, otherwise \(\mu\leftarrow\mu+\mu\) d , and go to step S20.

[0067] The second aspect of the present invention relates to a near - field active listening system based on intelligent metasurface, including:

[0068] RIS array model module;

[0069] Communication distance calculation module, used to calculate the communication distances from the base station, the suspicious user, and the legitimate listener to the reflection unit of the RIS array in the space coordinate system;

[0070] Near - field channel model construction module, adopting the uniform spherical wave model, combined with the communication distance, constructs the near - field channel model between the base station, the suspicious user, the legitimate listener, and the RIS array;

[0071] Communication model construction module, through the near - field channel model and considering the suspicious information, obtains the signals received by the suspicious user and the legitimate listener, and further obtains the received signal - to - noise ratios of the suspicious user and the legitimate listener;

[0072] Optimization problem construction module, considering that the signal - to - noise ratio of the legitimate listener 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, constructs 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] The 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 the traditional monitoring system relying on multiple antenna arrays, the monitoring system based on RIS proposed in the present invention has lower deployment cost. The passive characteristics of the RIS array reduce the demand for radio frequency links, and reduce the complexity and cost of the system.

[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 the traditional solution based on the far-field channel model. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0082] Figure 2 A schematic diagram showing a comparison of monitoring rates between the method of the present application and a method based on a far-field channel model;

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

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0085] In some embodiments of the present invention, a near-field active eavesdropping method based on intelligent metasurface is provided, as follows Figure 3 shown, which specifically includes the following steps:

[0086] Step 1: Establish a system model

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

[0088] (1) RIS array model

[0089] The system deploys a passive RIS array composed of N R reflecting elements, where N R reflecting elements are regularly arranged at equal intervals in N rows and M columns, and N×M = N R .

[0090] Each reflecting element can independently adjust the phase of the incident signal. is the phase shift matrix of the RIS, and α i ∈[0,2π), i = 1,…,N R , where diag(x) represents a diagonal matrix, and the diagonal elements are all the elements in the vector x, that is, the reflection coefficient, and the non-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 coordinates, and the reflecting element in the nth row and mth column is represented by s n,m , and the distances between the centers of two adjacent reflecting elements along the x-axis and the z-axis are d x and d z , respectively. Then the position coordinate vector of the RIS reflecting element s n,m can be expressed as:

[0093]

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

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

[0096]

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

[0098]

[0099] Assume d B denotes the distance from Bob to the center of the RIS array, and θ B and are the elevation angle and azimuth angle of Bob relative to the center of the RIS array respectively. Then, the position coordinate vector of Bob is expressed as:

[0100]

[0101] Let r AR , r RE , r RB represent the distances from Alan, Eve, and Bob to the RIS reflecting element s n,m respectively. Since the size of each RIS reflecting element is on the order of the 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. It can be calculated that:

[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 terms.

[0106] (3) Alan-RIS-Eve / Bob Near-Field Channel Model

[0107] Considering that the RIS array is within the near-field range of the base station Alan, and Eve and Bob are also within the near-field range of the RIS-reflected signals, the near-field channel is modeled using the Uniform Spherical Wave (USW) model.

[0108] In the USW model, when the propagation distance r is greater than a certain threshold, i.e., the uniform power distance, it can be considered that the received power at the receiver can remain relatively stable and will not cause excessive power fluctuations due to small changes in distance. Therefore, the channel gain can be assumed where β 0,0 is mainly determined by the free-space path loss, and its expression is where λ represents the wavelength. The USW model of the near-field channel between Alan and the RIS reflection unit s n,m is given by the following formula:

[0109]

[0110] Similarly, the near-field channel from the RIS reflection unit s n,m to Eve is:

[0111]

[0112] The near-field channel from the RIS reflection unit s n,m to Bob is:

[0113]

[0114] (4) Communication Model

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

[0116]

[0117]

[0118] where is the channel vector from Alan to the RIS, is the channel vector from the RIS to Eve, is the channel vector from the RIS to Bob. P A is the transmit power of Alan, and The additive white Gaussian noises of Bob and Eve respectively.

[0119] By calculating the ratio of the target signal power to the noise, the received signal-to-noise ratios of Bob and Eve are obtained:

[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 the equivalent cascaded channel, is the vector composed of the phase shifts of the RIS units, and j is the imaginary unit.

[0123] Step 2: Modeling the active eavesdropping optimization problem

[0124] To improve the active eavesdropping performance, an optimization problem model is established for the system shown in Figure 1 . The following two principles need to be followed: (1) Ensure that Eve can effectively eavesdrop on the communication from Alan to Bob; (2) Maximize the eavesdropping rate. The first principle requires that SNR E ≥ SNR B to ensure that Eve can receive and decode the suspicious information at the physical layer. The second principle requires that SNR E ≥ SNR B while making SNR B as high as possible.

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

[0126] P1:

[0127] s.t.C1:|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 the vector v, and C2 is the constraint condition to ensure that Eve can successfully eavesdrop on the suspicious communication.

[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 transformed into a problem that only contains the constant modulus constraint:

[0131] P2:

[0132] s.t.C1

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

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

[0135] The main difficulty in solving P2 is the unit modulus constraint C1. In this embodiment, the Minorize-Maximization (MM) algorithm is used to solve this problem. Its main idea is: assume that the objective function is g(v), and the value of v at the t-th iteration is First, construct a lower bound function that is equal to the function value of the objective function g(v) at the point, denoted as Use this lower bound as the surrogate objective function; second, take the v corresponding to the maximum value of this surrogate function as the value of v at the next iteration, that is This can ensure that the value of g(v) is monotonically increasing from one iteration to the next, that is The key to applying the MM method lies in constructing a lower bound function that is easy to find the maximum value

[0136]

[0137] According to the principle of the MM method, first re-express the objective function of problem P2 as:

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

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

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

[0141] ≥ f(v|v0) + [g(v0) - f(v0|v0)]

[0142] where \(f(v|v_0)\) is a lower bound of the original objective function \(g(v)\). Next, we will discuss it in two cases:

[0143] Case 1: \(0 \lt \mu \leq 1\)

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

[0145]

[0146]

[0147] where \(\text{Re}(\cdot)\) is the operation of taking the real part, then we have:

[0148]

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

[0150]

[0151] Case 2: \(\mu \gt 1\)

[0152] For Hermitian matrices \(M\) and \(L\), if \(M \pm L\) is satisfied, then at the point \(x_0\), we have:

[0153]

[0154] holds. Take \(L = H\) A-B , \(M=\lambda\) max (H A-B )I, where \(\lambda\) max (\cdot)\) represents the maximum eigenvalue of the matrix, and \(I\) represents the identity matrix. We can get:

[0155]

[0156] Using the above inequalities and the first-order Taylor expansion to scale the objective function of problem P2, we can get:

[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 objective function of P2, the solution of problem P2 at the \((t + 1)\)-th iteration can be obtained as:

[0160]

[0161] where, when \(0 \lt \mu \lt 1\), when \(\mu \gt 1\), To make \(\text{Re}((w(t) ) H v) reaches its maximum value as long as v0 (t+1) and w (t) have equal phase angles for the elements at corresponding positions, that is:

[0162]

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

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

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

[0166] S20: Initialize the internal iteration count t = 1 and initialize the parameter ;

[0167] S30: Determine the solution expression for w (t) based on the value range of μ, and calculate w (t) ;

[0168] S40: Update the value of v0 according to (t+1) ;

[0169] S50: If ||v0 (t+1) - v0 (t) ||2 ≤ ε holds or t = T max holds, then consider the internal iteration to have converged, terminate the internal iteration, and calculate the SNR B and SNR E values at this time. Otherwise, t ← t + 1 and go to step S30;

[0170] S60: If SNR B ≤ SNR E is considered that the external iteration has converged, terminate the external iteration, or if μ ≥ μ max , then consider there to be no solution and terminate the external iteration. Otherwise, μ ← μ + μ d , and go to step S20.

[0171] To further prove the effectiveness of the present invention, a simulation experiment is carried out using the Matlab platform to evaluate the monitoring performance of the system.

[0172] As shown Figure 2 In this embodiment, the proposed scheme of the present invention is compared with the scheme based on the far-field channel model in the prior art. The wavelength is set to 0.02 m, the number of RIS reflection units is set to 25×25, the distance between the legitimate eavesdropper (Eve) and the center of the RIS array is set to 2 m, 3 m, 4 m, 5 m, 6 m, 7 m, and the distance between the suspicious user (Bob) and the center of the RIS array is set to 4 m. It is assumed that Eve and Bob are in the same direction angle relative to the RIS. The numerical results show that the scheme based on the near-field channel model can achieve a better eavesdropping rate through precise RIS phase regulation. The scheme based on the far-field channel model can only achieve a positive eavesdropping 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 scheme cannot achieve effective eavesdropping. 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, while the proposed scheme of the present invention can achieve effective eavesdropping in the near-field communication scenario.

[0173] In summary, the present invention proposes an active eavesdropping system based on RIS, where the base station, the suspicious user, and the eavesdropper are all located in the near field of the RIS. A uniform spherical wave model is used to describe the near-field channel of the RIS, thereby establishing a signal propagation model. The model considers the influence of the size and position of the RIS array on signal propagation and can more accurately describe the signal propagation characteristics in the high-frequency near-field communication scenario compared with the traditional plane-wave-based model.

[0174] The present invention designs an optimization problem to maximize the eavesdropping signal-to-noise ratio of the suspicious communication by optimizing the RIS phase shift matrix, while ensuring that the eavesdropper can successfully decode the suspicious information. The designed optimization problem can improve the eavesdropping rate as much as possible on the basis of ensuring successful eavesdropping.

[0175] The present invention proposes an algorithm based on the min-max idea to solve the optimization problem. The algorithm first transforms the optimization problem into a problem containing only a single constant modulus constraint by introducing Lagrange multipliers. Secondly, under two value ranges of the Lagrange multipliers, a lower bound function of the objective function is constructed at a certain feasible solution. Finally, by maximizing the lower bound function and updating the feasible solution, the optimal solution is gradually approximated by iteration. The algorithm realizes the fine regulation of near-field signal propagation by iteratively optimizing the reflection coefficient matrix of the RIS to improve the eavesdropping performance.

[0176] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0177] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention 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 to build the near-field channel model between the base station, suspicious users, legal eavesdroppers and RIS array in combination with the communication distance. By using the near-field channel model and considering suspicious information, the signals received by the suspicious user and the legal monitor are obtained, and then the received signal-to-noise ratio of the suspicious user and the legal monitor 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; Introducing Lagrange multipliers, the optimization problem is transformed into a constant modulus constraint problem; The constant modulus constraint problem is solved by a minimax algorithm.

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 It means 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 legitimate 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 n,m The distance is: where ||·|| represents the Euclidean norm.

3. The near-field active monitoring method based on smart metasurface according to claim 1 is characterized in that: The method for constructing the near-field channel model comprises the following steps: Set channel gain in λ represents wavelength; base station to RIS reflection unit s n,m The uniform spherical wave model of the near-field channel between is given by the following formula: Similarly, RIS reflection units n,m The near-field channel to Eve is: RIS Reflection Units n,m The near-field channel to Bob is:

4. The near-field active monitoring method based on smart metasurface according to claim 1 is characterized in that: The method for calculating the receiving signal-to-noise ratio of the user and the listener 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 a mean of 0 and a variance of 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 legal 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 eavesdroppers 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: in is an equivalent cascade channel, 5. The near-field active monitoring method based on smart metasurface according to claim 1 is characterized in that: The construction optimization problem specifically includes the following steps: Considering that the signal-to-noise ratio of the listener 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, 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 the vector v, and C2 is the constraint condition that ensures that a legitimate eavesdropper can successfully eavesdrop on suspicious communications.

6. The near-field active monitoring method based on smart metasurface according to claim 5 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.

7. 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 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) in 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, 8. A near-field active monitoring system based on a smart metasurface, characterized in that: include: RIS array model module; A communication distance calculation module is used to calculate the communication distance from the base station, the suspicious user, the legal eavesdropper to the reflection unit of the RIS array in the spatial coordinate system; The near-field channel model construction module uses a uniform spherical wave model and combines the communication distance to construct a near-field channel model between the base station, suspicious users, legal eavesdroppers, and the RIS array. The communication model building module obtains the signals received by the suspicious user and the legal eavesdropper through the near-field channel model and considering the suspicious information, and then obtains the receiving signal-to-noise ratio of the suspicious user and the legal eavesdropper; An optimization problem construction module is provided, which considers that the signal-to-noise ratio of the legitimate listener 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, and constructs an optimization problem; introduces Lagrange multipliers, and transforms the optimization problem into a constant modulus constraint problem; And, an optimization problem solving module solves the constant modulus constraint problem through a mini-max algorithm.

9. 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 described in any one of claims 1 to 7 is implemented.

10. A communication device, characterized in that: It includes the near-field active monitoring system based on the smart metasurface as described in claim 8 or the computer-readable storage medium as described in claim 9.

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