Method for suppressing illegal node communication performance based on a-ris
Patent Information
- Application Number
- CN202311151718.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-07
- Publication Date
- 2026-10-30
- Estimated Expiration
- 2043-09-07
AI Technical Summary
[0003]然而，在现实网络中除了存在合法用户还存在潜在的非法节点，目前并没有一种抑制非法节点通信性能的方法
[0035]上述基于A-RIS的非法节点通信性能抑制方法，本申请通过非法目的节点根据接收到的导频信号进行计算，得到非法目的节点的接收信噪比；根据非法源节点和非法目的节点与A-RIS的信道系数以及非法源节点所采用的波束成形向量设置A-RIS反射系数优化模型的约束条件，将最小化非法目的节点的接收信噪比的反射系数设置为A-RIS反射系数优化模型的目标函数，然后利用目标函数和约束条件构建A-RIS反射系数优化模型，通过引入辅助变量将A-RIS反射系数优化模型的求解问题转换为凸优化问题，根据计算机工具包对凸优化问题进行求解，得到优化后的A-RIS反射系数，最后可以利用优化后的A-RIS反射系数调整A-RIS自身的参数来最小化非法目的节点处接收信噪比，抑制了非法目的节点的通信性能，提高了无线通信系统安全。
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Figure CN117155439B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wireless communication technology, and in particular to a method for suppressing the communication performance of illegal nodes based on A-RIS. Background Technology
[0002] In recent years, the explosive growth of mobile applications and the rapid development of wireless communication technology have greatly improved the production and lifestyles of human society, bringing a new wave of innovation to agriculture, industry, education, healthcare, and transportation. However, unlike wired networks, the openness of wireless communication media and the broadcast nature of wireless signals allow all nodes within the signal coverage area to receive the signals. It should be noted, however, that with the decreasing cost of equipment manufacturing, the shrinking size of devices, and the increasing diversity of communication services, a small number of communication nodes in the network may, for their own benefit, become illegal users. Therefore, it is now necessary to employ technical means to suppress the communication performance of these illegal users to prevent cybercriminals from engaging in criminal activities that threaten public safety.
[0003] However, in real-world networks, in addition to legitimate users, there are also potential illegitimate nodes, and there is currently no method to suppress the communication performance of illegitimate nodes. Summary of the Invention
[0004] Therefore, it is necessary to provide an A-RIS-based method for suppressing illegal node communication performance, which can suppress the communication performance of illegal nodes, in order to address the above-mentioned technical problems.
[0005] A method for suppressing illegal node communication performance based on A-RIS, the method comprising:
[0006] Construct an A-RIS communication system; the A-RIS communication system includes an illegal source node configured with multiple antenna elements, an A-RIS configured with multiple reflection elements, and an illegal destination node;
[0007] In the A-RIS communication system, A-RIS estimates the channel coefficients between illegal source nodes and illegal destination nodes based on the pilot signals sent during channel estimation between illegal source nodes and illegal destination nodes. The beamforming vector of the illegal source node is obtained by performing a conjugate operation on the channel coefficients.
[0008] The illegal destination node calculates the received pilot signal to obtain the received signal-to-noise ratio of the illegal destination node;
[0009] Based on the channel coefficients of the illegal source node and illegal destination node with A-RIS and the beamforming vector adopted by the illegal source node, the constraints of the A-RIS reflection coefficient optimization model are set, and the reflection coefficient that minimizes the received signal-to-noise ratio of the illegal destination node is set as the objective function of the A-RIS reflection coefficient optimization model.
[0010] An A-RIS reflection coefficient optimization model is constructed using the objective function and constraints. By introducing auxiliary variables, the problem of solving the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem. The convex optimization problem is solved using a computer toolkit to obtain the optimized A-RIS reflection coefficient.
[0011] The load and phase shift coefficient of the A-RIS reflection unit are adjusted according to the optimized A-RIS reflection coefficient to obtain the adjusted A-RIS;
[0012] An illegal destination node receives the superposition of reflected signals from both the illegal source node and the adjusted A-RIS to suppress the communication performance of the illegal node; the communication performance includes the received signal strength.
[0013] In one embodiment, the received pilot signal is
[0014] y = f H ws+h H ΘF H ws+h H Θn r +n
[0015] =(f H +h H ΘF H )ws+h H Θn r +n.
[0016] Where f, F, and h represent the channel coefficients between the illegal source node and the illegal destination node, between the illegal source node and A-RIS, and between A-RIS and the illegal destination node, respectively; s represents the signal transmitted by the illegal source node; the superscript H denotes the Hilbert transform; Θ represents the reflection coefficient matrix of A-RIS; w represents the beamforming vector of the illegal source node; and n r denoted by , where n represents the noise at each reflection unit of A-RIS, and n represents the noise at the illegal destination node.
[0017] In one embodiment, the illegal destination node calculates the received signal-to-noise ratio (SNR) of the illegal destination node based on the received pilot signal, including:
[0018] The illegal destination node calculates the received pilot signal to obtain the received signal-to-noise ratio of the illegal destination node.
[0019]
[0020] Where f, F, and h represent the channel coefficients between the illegal source node and the illegal destination node, between the illegal source node and A-RIS, and between A-RIS and the illegal destination node, respectively; the superscript H denotes the Hilbert transform; Θ represents the reflection coefficient matrix of A-RIS; w represents the beamforming vector of the illegal source node; |·| 2 This indicates the calculation of the square of the modulus. This represents calculating the sum of squares of all elements in a vector. σ represents the noise power at each reflector unit of the A-RIS. 2 This represents the noise power of the illegal destination node.
[0021] In one embodiment, an A-RIS reflection coefficient optimization model is constructed using an objective function and constraints, including:
[0022] An A-RIS reflection coefficient optimization model is constructed using the objective function and constraints.
[0023]
[0024]
[0025]
[0026] Where F represents the channel coefficient between the illegal source node and A-RIS. Let α denote the reflection coefficient matrix of A-RIS, and Diag(·) denote the diagonal matrix. m and φ m Let M represent the amplification factor and phase shift factor of the m-th reflecting element in A-RIS, respectively, and M represent the total number of reflecting elements. This represents a vector formed by taking the elements from the diagonal of a diagonal matrix. Representing vectors The m-th element, P tot P represents the total power constraint. s α represents the transmission power of the illegal source node, |·| represents modulo, and α m,max This represents the maximum value of the amplification factor of the m-th reflective element in A-RIS.
[0027] In one embodiment, the solution problem of the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem by introducing auxiliary variables, including:
[0028] By introducing auxiliary variables, the solution problem of the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem.
[0029]
[0030]
[0031]
[0032]
[0033] Where τ represents an auxiliary variable, τ (n) This represents the value of the nth iteration of the computer operation, Tr(·) represents the trace of the matrix, and e represents the Hamadard product. The superscript H indicates the real number transformation. Represents the vector of the nth iteration operation. The value of Θ (n) Indicates taking diagonal elements A diagonal matrix of elements, H = hh H .
[0034] In one embodiment, the computer toolkit includes CVX.
[0035] The aforementioned method for suppressing the communication performance of illegal nodes based on A-RIS involves calculating the received signal-to-noise ratio (SNR) of the illegal destination node based on the received pilot signal. Constraints are set for the A-RIS reflection coefficient optimization model based on the channel coefficients between the illegal source node, the illegal destination node, and A-RIS, as well as the beamforming vector used by the illegal source node. The reflection coefficient that minimizes the SNR of the illegal destination node is set as the objective function of the A-RIS reflection coefficient optimization model. The model is then constructed using the objective function and constraints. By introducing auxiliary variables, the problem of solving the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem. The convex optimization problem is solved using a computer toolkit to obtain the optimized A-RIS reflection coefficient. Finally, the optimized A-RIS reflection coefficient can be used to adjust the parameters of A-RIS itself to minimize the SNR at the illegal destination node, thus suppressing the communication performance of the illegal destination node and improving the security of the wireless communication system. Attached Figure Description
[0036] Figure 1 This is a flowchart illustrating an A-RIS-based method for suppressing illegal node communication performance in one embodiment.
[0037] Figure 2 This is a block diagram of the A-RIS communication system in one embodiment. Detailed Implementation
[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0039] In one embodiment, such as Figure 1 As shown, an A-RIS-based method for suppressing illegal node communication performance is provided, including the following steps:
[0040] Step 102: Construct the A-RIS communication system; the A-RIS communication system includes an illegal source node configured with multiple antenna elements, an A-RIS configured with multiple reflection elements, and an illegal destination node.
[0041] A-RIS stands for Active Intelligent Metasurface.
[0042] Step 104: In the A-RIS communication system, A-RIS estimates the channel coefficients between the illegal source node and the illegal destination node based on the pilot signals sent during channel estimation between the illegal source node and the illegal destination node. The beamforming vector of the illegal source node is obtained by performing a conjugate operation on the channel coefficients.
[0043] A-RIS estimates the channel coefficients between illegal communication nodes based on the signals transmitted during channel estimation between illegal source nodes and illegal destination nodes. The conjugate operation of these channel coefficients yields the beamforming vector of the illegal source node, at which point the antenna gain from the illegal source node to the illegal destination node is maximized.
[0044] Step 106: The illegal destination node calculates the received pilot signal to obtain the received signal-to-noise ratio of the illegal destination node; based on the channel coefficients of the illegal source node, the illegal destination node and A-RIS, and the beamforming vector adopted by the illegal source node, the constraints of the A-RIS reflection coefficient optimization model are set, and the reflection coefficient that minimizes the received signal-to-noise ratio of the illegal destination node is set as the objective function of the A-RIS reflection coefficient optimization model.
[0045] Step 108: Construct an A-RIS reflection coefficient optimization model using the objective function and constraints. By introducing auxiliary variables, the problem of solving the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem. Solve the convex optimization problem using a computer toolkit to obtain the optimized A-RIS reflection coefficient.
[0046] Step 110: Adjust the load and phase shift coefficient of the reflection unit of A-RIS according to the optimized A-RIS reflection coefficient to obtain the adjusted A-RIS; the illegal destination node simultaneously receives the superposition of the reflected signals from the illegal source node and the adjusted A-RIS to suppress the communication performance of the illegal node; the communication performance includes the received signal strength.
[0047] A-RIS adjusts the load and phase shift coefficient of its own reflection unit according to the optimized A-RIS reflection coefficient, thereby controlling the amplitude and phase parameters of the reflected signal. The illegal destination node simultaneously receives the superposition of the reflected signals from the illegal source node and the adjusted A-RIS, resulting in a reduction in the received signal strength.
[0048] In the aforementioned method for suppressing the communication performance of illegal nodes based on A-RIS, this application calculates the received signal-to-noise ratio (SNR) of the illegal destination node based on the received pilot signal. Constraints are set for the A-RIS reflection coefficient optimization model based on the channel coefficients between the illegal source node, the illegal destination node, and A-RIS, as well as the beamforming vector used by the illegal source node. The reflection coefficient that minimizes the SNR of the illegal destination node is set as the objective function of the A-RIS reflection coefficient optimization model. Then, the A-RIS reflection coefficient optimization model is constructed using the objective function and constraints. By introducing auxiliary variables, the solution problem of the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem. The convex optimization problem is solved using a computer toolkit to obtain the optimized A-RIS reflection coefficient. Finally, the optimized A-RIS reflection coefficient can be used to adjust the parameters of A-RIS itself to minimize the SNR at the illegal destination node, thus suppressing the communication performance of the illegal destination node and improving the security of the wireless communication system.
[0049] In one embodiment, the received pilot signal is
[0050] y = f H ws+h H ΘF H ws+h H Θn r +n
[0051] =(f H +h H ΘF H )ws+h H Θn r +n.
[0052] Where f, F, and h represent the channel coefficients between the illegal source node and the illegal destination node, between the illegal source node and A-RIS, and between A-RIS and the illegal destination node, respectively, and s represents the signal transmitted by the illegal source node. H Let Θ denote the Hilbert transform, Θ denote the reflection coefficient matrix of A-RIS, w denote the beamforming vector of the illegal source node, and n r denoted by , where n represents the noise at each reflection unit of A-RIS, and n represents the noise at the illegal destination node.
[0053] In one embodiment, the illegal destination node calculates the received signal-to-noise ratio (SNR) of the illegal destination node based on the received pilot signal, including:
[0054] The illegal destination node calculates the received pilot signal to obtain the received signal-to-noise ratio of the illegal destination node.
[0055]
[0056] Where f, F, and h represent the channel coefficients between the illegal source node and the illegal destination node, between the illegal source node and A-RIS, and between A-RIS and the illegal destination node, respectively. H Let Hilbert represent the Hilbert transform, Θ represent the reflection coefficient matrix of A-RIS, w represent the beamforming vector of the illegal source node, and |·| 2 This indicates the calculation of the square of the modulus. This represents calculating the sum of squares of all elements in a vector. σ represents the noise power at each reflector unit of the A-RIS. 2 This represents the noise power of the illegal destination node.
[0057] In one embodiment, an A-RIS reflection coefficient optimization model is constructed using an objective function and constraints, including:
[0058] An A-RIS reflection coefficient optimization model is constructed using the objective function and constraints.
[0059]
[0060]
[0061]
[0062] Where F represents the channel coefficient between the illegal source node and A-RIS. Let α denote the reflection coefficient matrix of A-RIS, and Diag(·) denote the diagonal matrix. m and φ m Let M represent the amplification factor and phase shift factor of the m-th reflecting element in A-RIS, respectively, and M represent the total number of reflecting elements. This represents a vector formed by taking the elements from the diagonal of a diagonal matrix. Representing vectors The m-th element, P tot P represents the total power constraint. s α represents the transmission power of the illegal source node, |·| represents modulo, and α m,max This represents the maximum value of the amplification factor of the m-th reflective element in A-RIS.
[0063] In one embodiment, the solution problem of the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem by introducing auxiliary variables, including:
[0064] By introducing auxiliary variables, the solution problem of the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem.
[0065]
[0066]
[0067]
[0068]
[0069] Where τ represents an auxiliary variable, τ (n) This represents the value of the nth iteration of the computer operation, Tr(·) represents the trace of the matrix, and e represents the Hamadard product. The superscript H indicates the real number transformation. Represents the operation vector for the nth iteration. The value of Θ (n) Indicates taking diagonal elements A diagonal matrix of elements, H = hh H .
[0070] In a specific embodiment, the objective function of the optimization problem is in fractional form, which is difficult for a computer to solve directly. Therefore, this invention transforms the above optimization problem into an auxiliary variable.
[0071]
[0072]
[0073]
[0074]
[0075] Here, τ represents the auxiliary variable introduced during the calculation. Furthermore, through simple algebraic operations, the above optimization problem can be equivalently expressed as:
[0076]
[0077]
[0078]
[0079]
[0080] Due to the constraints in the above formula Since it is non-convex, it is difficult for computers to solve directly. This invention addresses the equation in this constraint. Approximated by Taylor expansion:
[0081]
[0082] Where, τ (n) This represents the value of the computer's nth iteration. Furthermore, for the expression in this constraint...
[0083] This invention reduces it to a more easily processed calculation formula as follows:
[0084]
[0085] Where Tr(·) denotes the trace of the matrix, and e denotes the Hamadard product. To represent real numbers, superscript H Represents the Hilbert transform. Represents the operation vector for the nth iteration. The value of Θ (n) Indicates taking diagonal elements A diagonal matrix of elements, H = hh H Therefore, the aforementioned optimization problem can be further expressed as a convex problem that can be directly computed by a computer, i.e.
[0086]
[0087]
[0088]
[0089]
[0090] By inputting the optimization problem into a computer, the optimal reflection coefficient of A-RIS can be directly solved using toolkits (such as CVX).
[0091] In one embodiment, the computer toolkit includes CVX.
[0092] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
[0093] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0094] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
Claims
1. A method for suppressing illegal node communication performance based on A-RIS, characterized in that, The method includes: Construct an A-RIS communication system; the A-RIS communication system includes an illegal source node configured with multiple antenna elements, an A-RIS configured with multiple reflection elements, and an illegal destination node; In the A-RIS communication system, A-RIS estimates the channel coefficients between the illegal source node and the illegal destination node based on the pilot signals sent during channel estimation between the illegal source node and the illegal destination node. The beamforming vector of the illegal source node is obtained by performing a conjugate operation on the channel coefficients. The illegal destination node calculates the received pilot signal to obtain the received signal-to-noise ratio of the illegal destination node; Based on the channel coefficients of the illegal source node and illegal destination node with A-RIS and the beamforming vector adopted by the illegal source node, the constraints of the A-RIS reflection coefficient optimization model are set, and the reflection coefficient that minimizes the received signal-to-noise ratio of the illegal destination node is set as the objective function of the A-RIS reflection coefficient optimization model. An A-RIS reflection coefficient optimization model is constructed using the objective function and constraints. By introducing auxiliary variables, the problem of solving the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem. The convex optimization problem is solved using a computer toolkit to obtain the optimized A-RIS reflection coefficient. The load and phase shift coefficient of the A-RIS reflection unit are adjusted according to the optimized A-RIS reflection coefficient to obtain the adjusted A-RIS; An illegal destination node receives the superposition of reflected signals from both the illegal source node and the adjusted A-RIS to suppress the communication performance of the illegal node; communication performance includes received signal strength. The illegal destination node calculates the received signal-to-noise ratio (SNR) of the illegal destination node based on the received pilot signal, including: The illegal destination node calculates the received pilot signal to obtain the received signal-to-noise ratio of the illegal destination node. in, , , These represent the channel coefficients between the illegal source node and the illegal destination node, between the illegal source node and A-RIS, and between A-RIS and the illegal destination node, respectively. (Superscript) Represents the Hilbert transform. This represents the reflection coefficient matrix of A-RIS. Represents the beamforming vector of an illegal source node. This indicates the calculation of the square of the modulus. This represents calculating the sum of squares of all elements in a vector. This represents the noise power at each reflector unit of the A-RIS. Indicates the noise power of the illegal destination node; An A-RIS reflection coefficient optimization model is constructed using the objective function and constraints, including: An A-RIS reflection coefficient optimization model is constructed using the objective function and constraints. in, This represents the channel coefficient between the illegal source node and A-RIS. This represents the reflection coefficient matrix of A-RIS. Represents a diagonal matrix. and They represent the A-RIS number 1, 2, 3, 4, 5, 6, 7, 8, The amplification factor and phase shift factor of each reflecting unit. M Indicates the total number of reflecting units. This represents a vector formed by taking the elements from the diagonal of a diagonal matrix. Representing vectors The One element, Indicates total power constraint. Indicates the sending power of the illegal source node. Indicates modulo, Indicates A-RIS number The maximum value of the amplification factor of each reflective element.
2. The method according to claim 1, characterized in that, The received pilot signal is in, , , These represent the channel coefficients between the illegal source node and the illegal destination node, between the illegal source node and A-RIS, and between A-RIS and the illegal destination node, respectively. Indicates the signal sent by the illegal source node, superscript Represents the Hilbert transform. This represents the reflection coefficient matrix of A-RIS. Represents the beamforming vector of an illegal source node. This represents the noise at each reflector unit of the A-RIS. Noise representing an illegal destination node.
3. The method according to claim 1, characterized in that, The solution problem of the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem by introducing auxiliary variables, including: By introducing auxiliary variables, the solution problem of the A-RIS reflection coefficient optimization model is transformed into a convex optimization problem. in, Represents auxiliary variables. Indicates the computer's first The value of the next iteration. Represents the trace of a matrix. Represents the Hamadard product. To represent real numbers, superscript Represents the Hilbert transform. Indicates the first The next iteration operation vector The value of , Indicates taking diagonal elements A diagonal matrix of elements .
4. The method according to claim 1, characterized in that, The computer toolkit includes CVX.