Intelligent auxiliary safety communication method, system and device

Abstract

The invention provides an intelligent auxiliary security communication method, system and device, and relates to the technical field of communication. The method is applied to a wireless communication system, and a hybrid reconfigurable intelligent surface is deployed in the wireless communication system. Based on structure information and a communication scene of the hybrid reconfigurable intelligent surface, a joint optimization problem of the wireless communication system is constructed by taking maximization of a system secrecy rate of the wireless communication system as a target, a base station beam forming vector and a hybrid reconfigurable intelligent surface coefficient matrix are coupled, and a target function and a unit mode length constraint are non-convex. The communication scene comprises a user, an eavesdropping object and an interference source. An alternative optimization framework is adopted, a joint optimization problem is decoupled into two sub-problems for iterative solution, and a hybrid reconfigurable intelligent surface coefficient capable of dynamically adjusting a signal propagation path and a base station beam forming vector corresponding to a wireless communication system are obtained; the two sub-problems include a base station beam forming sub-problem and a hybrid reconfigurable intelligent surface coefficient sub-problem. According to the invention, the communication security can be ensured.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to an intelligent assisted secure communication method, system and device. Background Technology

[0002] The rapid development of wireless communication technology has revolutionized the way information is transmitted and received, but it has also increased public concern about the security and reliability of wireless networks. In wireless communication, the broadcast nature of the wireless medium makes it vulnerable to various security threats, with eavesdropping being a major one, jeopardizing the confidentiality and integrity of wireless communications. To address this challenge, Reconfigurable Intelligent Surface (RIS) has been proposed as a potential technology to enhance Physical Layer Security (PLS). It can reconfigure electromagnetic responses without complex hardware changes and can manipulate the propagation environment to improve signal quality and security.

[0003] In related technologies, active beamforming design in wireless communication is mainly achieved through passive RIS-assisted beamforming. Based on the designed beamforming vector, the power and direction of the base station's transmitted signal are adjusted to suppress eavesdropping and achieve secure communication. However, this secure communication method still suffers from poor communication security. Summary of the Invention

[0004] This application provides an intelligent assisted secure communication method, system, and device to ensure communication security.

[0005] In a first aspect, this application provides an intelligent assisted secure communication method applied to a wireless communication system. The wireless communication system is equipped with a hybrid reconfigurable smart surface. The components in the hybrid reconfigurable smart surface include active components and passive components. The active components are used to adjust the signal amplitude and signal phase, and the passive components are used to adjust the signal phase.

[0006] This intelligent assisted secure communication method includes:

[0007] Based on the structural information and communication scenario of the hybrid reconfigurable smart surface, a joint optimization problem of the wireless communication system is constructed with the goal of maximizing the system security rate of the wireless communication system. In the joint optimization problem, the optimization variables are coupled, and the objective function and the unit modulus constraint of the passive components are non-convex. The optimization variables include the base station beamforming vector and the coefficient matrix of the hybrid reconfigurable smart surface. The communication scenario includes users, eavesdropping targets and interference sources, with the interference source and the eavesdropping target cooperating.

[0008] By adopting an alternating optimization framework, the joint optimization problem is decoupled into two sub-problems that are solved iteratively to obtain the hybrid reconfigurable smart surface coefficients and base station beamforming vectors that can dynamically adjust the signal propagation path for the wireless communication system. The two sub-problems include the base station beamforming sub-problem and the hybrid reconfigurable smart surface coefficient sub-problem.

[0009] In one possible implementation, based on the structural information and communication scenario of the hybrid reconfigurable smart surface, a joint optimization problem for the wireless communication system is constructed with the objective of maximizing the system security rate of the wireless communication system, including:

[0010] Based on the structural information of the hybrid reconfigurable smart surface, a hybrid reconfigurable smart surface coefficient matrix is ​​constructed.

[0011] Based on structural information and communication scenarios, base station beamforming vectors are constructed.

[0012] Based on the hybrid reconfigurable smart surface coefficient matrix and the base station beamforming vector, a user-end signal model and an eavesdropping target signal model are constructed. The user-end signal model includes base station signal, interference from interference sources, and noise, while the eavesdropping target signal model only includes base station signal and noise.

[0013] Based on the user-side signal model and the eavesdropping target-side signal model, a joint optimization problem for the wireless communication system is constructed with the goal of maximizing the system security rate of the wireless communication system.

[0014] In one possible implementation, during the iterative solution process: after fixing the hybrid reconfigurable smart surface coefficients, the base station beamforming subproblem is transformed into a convex problem using semidefinite relaxation and continuous convex approximation techniques to obtain a suboptimal base station beamforming vector; after fixing the base station beamforming vector, the hybrid reconfigurable smart surface coefficient subproblem is solved using continuous convex approximation techniques, in which the continuous convex approximation technique approximates the objective function as a linear objective function in each inner loop and relaxes the unit modulus constraint.

[0015] In one possible implementation, a semi-definite relaxation and continuous convex approximation technique is used to transform the base station beamforming subproblem into a convex problem to obtain a suboptimal base station beamforming vector, including:

[0016] A semi-definite relaxation technique is used to transform the base station beamforming problem into a relaxation problem;

[0017] The Chauns-Cooper transform technique is used to transform the objective function into an equivalent non-fractional form.

[0018] The penalty method is adopted to transform the unit modulus constraint into an inequality, and based on the inequality, the equivalent non-fractional form of the objective function is transformed into an objective function containing a penalty term;

[0019] The continuous convex approximation technique is adopted to perform a first-order Taylor expansion on the objective function containing the penalty term. Based on the objective function after the first-order Taylor expansion, the base station beamforming subproblem is transformed into a convex problem to obtain the suboptimal base station beam matrix.

[0020] The suboptimal base station beam matrix is ​​transformed into a suboptimal base station beamforming vector using eigenvalue decomposition technology.

[0021] In one possible implementation, the hybrid reconfigurable smart surface coefficient subproblem is solved using a continuous convex approximation technique, including:

[0022] Based on the fixed base station beamforming vector, the objective function is transformed into a power-related objective function;

[0023] The coefficient variables of the hybrid reconfigurable smart surface to be optimized are reconstructed into real vectors, which contain amplitude and phase.

[0024] The power-related objective function is equivalently denoted as a real vector objective function, and the partial derivatives of the magnitude and phase corresponding to the real vector objective function are solved;

[0025] Based on the partial derivatives, the first-order Taylor expansion of the objective function of the real variables is performed using the continuous convex approximation technique to transform the objective function of the real variables into a convex function;

[0026] Relax the unit module constraint and transform it into a convex constraint;

[0027] Based on convex functions and convex constraints, a convex optimization solver is used to solve the subproblem of hybrid reconfigurable smart surfaces.

[0028] In one possible implementation, an alternating optimization framework is employed to decouple the joint optimization problem into two sub-problems for iterative solving, obtaining the hybrid reconfigurable smart surface coefficients and base station beamforming vectors for dynamically adjustable signal propagation paths corresponding to the wireless communication system, including:

[0029] During the iterative solution process, if the base station beamforming subproblem satisfies the first iterative convergence condition, the base station beamforming vector obtained from solving the base station beamforming subproblem is fixed, and the hybrid reconfigurable intelligent surface coefficient subproblem is solved. The first iterative convergence condition is the internal iterative convergence condition of the base station beamforming subproblem. And, if the hybrid reconfigurable intelligent surface coefficient subproblem satisfies the second iterative convergence condition, the outer loop is triggered. The outer loop is an alternating optimization process. The second iterative convergence condition is the internal iterative convergence condition or the iteration number condition of the hybrid reconfigurable intelligent surface coefficient subproblem.

[0030] If the base station beamforming subproblem does not meet the first iteration convergence condition, the iterative solution of the base station beamforming subproblem is triggered; and / or, if the hybrid reconfigurable smart surface coefficient subproblem does not meet the second iteration convergence condition, the iterative solution of the hybrid reconfigurable smart surface coefficient subproblem is triggered.

[0031] When the objective function converges or the number of alternating optimizations exceeds a preset threshold, the output is a hybrid reconfigurable smart surface coefficient and a base station beamforming vector that can dynamically adjust the signal propagation path.

[0032] Secondly, this application provides an intelligent auxiliary safety communication device applied to a wireless communication system. The wireless communication system is equipped with a hybrid reconfigurable smart surface. The components in the hybrid reconfigurable smart surface include active components and passive components. The active components are used to adjust the signal amplitude and signal phase, and the passive components are used to adjust the signal phase.

[0033] The intelligent assisted safety communication device includes:

[0034] The module is used to construct a joint optimization problem for a wireless communication system based on the structural information and communication scenario of a hybrid reconfigurable smart surface, with the goal of maximizing the system security rate of the wireless communication system. In the joint optimization problem, the optimization variables are coupled, and the objective function and the unit modulus constraints of the passive components are non-convex. The optimization variables include the base station beamforming vector and the coefficient matrix of the hybrid reconfigurable smart surface. The communication scenario includes users, eavesdropping targets, and interference sources, with the interference source and the eavesdropping target cooperating.

[0035] The processing module is used to decouple the joint optimization problem into two sub-problems by employing an alternating optimization framework and solve them iteratively to obtain the hybrid reconfigurable smart surface coefficients and base station beamforming vectors that can dynamically adjust the signal propagation path corresponding to the wireless communication system. The two sub-problems include the base station beamforming sub-problem and the hybrid reconfigurable smart surface coefficient sub-problem.

[0036] In one possible implementation, the building module is specifically used for:

[0037] Based on the structural information of the hybrid reconfigurable smart surface, a hybrid reconfigurable smart surface coefficient matrix is ​​constructed.

[0038] Based on structural information and communication scenarios, base station beamforming vectors are constructed.

[0039] Based on the hybrid reconfigurable smart surface coefficient matrix and the base station beamforming vector, a user-end signal model and an eavesdropping target signal model are constructed. The user-end signal model includes base station signal, interference from interference sources, and noise, while the eavesdropping target signal model only includes base station signal and noise.

[0040] Based on the user-side signal model and the eavesdropping target-side signal model, a joint optimization problem for the wireless communication system is constructed with the goal of maximizing the system security rate of the wireless communication system.

[0041] In one possible implementation, during the iterative solution process: after fixing the hybrid reconfigurable smart surface coefficients, the base station beamforming subproblem is transformed into a convex problem using semidefinite relaxation and continuous convex approximation techniques to obtain a suboptimal base station beamforming vector; after fixing the base station beamforming vector, the hybrid reconfigurable smart surface coefficient subproblem is solved using continuous convex approximation techniques, in which the continuous convex approximation technique approximates the objective function as a linear objective function in each inner loop and relaxes the unit modulus constraint.

[0042] In one possible implementation, a semi-definite relaxation and continuous convex approximation technique is used to transform the base station beamforming subproblem into a convex problem to obtain a suboptimal base station beamforming vector, including:

[0043] A semi-definite relaxation technique is used to transform the base station beamforming problem into a relaxation problem;

[0044] The Chauns-Cooper transform technique is used to transform the objective function into an equivalent non-fractional form.

[0045] The penalty method is adopted to transform the unit modulus constraint into an inequality, and based on the inequality, the equivalent non-fractional form of the objective function is transformed into an objective function containing a penalty term;

[0046] The continuous convex approximation technique is adopted to perform a first-order Taylor expansion on the objective function containing the penalty term. Based on the objective function after the first-order Taylor expansion, the base station beamforming subproblem is transformed into a convex problem to obtain the suboptimal base station beam matrix.

[0047] The suboptimal base station beam matrix is ​​transformed into a suboptimal base station beamforming vector using eigenvalue decomposition technology.

[0048] In one possible implementation, the hybrid reconfigurable smart surface coefficient subproblem is solved using a continuous convex approximation technique, including:

[0049] Based on the fixed base station beamforming vector, the objective function is transformed into a power-related objective function;

[0050] The coefficient variables of the hybrid reconfigurable smart surface to be optimized are reconstructed into real vectors, which contain amplitude and phase.

[0051] The power-related objective function is equivalently denoted as a real vector objective function, and the partial derivatives of the magnitude and phase corresponding to the real vector objective function are solved;

[0052] Based on the partial derivatives, the first-order Taylor expansion of the objective function of the real variables is performed using the continuous convex approximation technique to transform the objective function of the real variables into a convex function;

[0053] Relax the unit module constraint and transform it into a convex constraint;

[0054] Based on convex functions and convex constraints, a convex optimization solver is used to solve the subproblem of hybrid reconfigurable smart surfaces.

[0055] In one possible implementation, the processing module is specifically used for:

[0056] During the iterative solution process, if the base station beamforming subproblem satisfies the first iterative convergence condition, the base station beamforming vector obtained from solving the base station beamforming subproblem is fixed, and the hybrid reconfigurable intelligent surface coefficient subproblem is solved. The first iterative convergence condition is the internal iterative convergence condition of the base station beamforming subproblem. And, if the hybrid reconfigurable intelligent surface coefficient subproblem satisfies the second iterative convergence condition, the outer loop is triggered. The outer loop is an alternating optimization process. The second iterative convergence condition is the internal iterative convergence condition or the iteration number condition of the hybrid reconfigurable intelligent surface coefficient subproblem.

[0057] If the base station beamforming subproblem does not meet the first iteration convergence condition, the iterative solution of the base station beamforming subproblem is triggered; and / or, if the hybrid reconfigurable smart surface coefficient subproblem does not meet the second iteration convergence condition, the iterative solution of the hybrid reconfigurable smart surface coefficient subproblem is triggered.

[0058] When the objective function converges or the number of alternating optimizations exceeds a preset threshold, the output is a hybrid reconfigurable smart surface coefficient and a base station beamforming vector that can dynamically adjust the signal propagation path.

[0059] Thirdly, this application provides an intelligent assisted security communication device, including: a memory and a processor;

[0060] The memory stores instructions that the computer executes;

[0061] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0062] Fourthly, this application provides a wireless communication system, comprising:

[0063] Hybrid reconfigurable smart surfaces contain active and passive components. The active components are used to adjust the signal amplitude and signal phase, while the passive components are used to adjust the signal phase.

[0064] And, as mentioned in the third aspect above, intelligent auxiliary security communication devices.

[0065] In one possible implementation, the number of active elements in the hybrid reconfigurable smart surface is less than the number of passive elements.

[0066] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a device such as a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0067] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed by a device such as a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0068] The intelligent assisted secure communication method, system, and apparatus provided in this application are applied to wireless communication systems. The wireless communication system deploys a hybrid reconfigurable smart surface, which includes active and passive components. This allows the hybrid reconfigurable smart surface to simultaneously possess the control capabilities of both active and passive components, providing precise security-guided control for communication security. Based on the structural information of the hybrid reconfigurable smart surface and the communication scenario (including users, eavesdropping targets, and interference sources), a joint optimization problem for the wireless communication system is constructed with the goal of maximizing the system security rate. This suppresses the eavesdropping target's analysis of valid communication content, ensuring communication security. By constructing the joint optimization problem for the wireless communication system, direct signal leakage to eavesdropping targets / interference sources is reduced, and reflection paths to eavesdropping targets are cut off, ensuring the security of signal propagation. An alternating optimization framework is adopted to decouple the joint optimization problem into a base station beamforming subproblem and a hybrid reconfigurable smart surface coefficient subproblem. These are iteratively solved to obtain the hybrid reconfigurable smart surface coefficients and base station beamforming vectors corresponding to the wireless communication system. This reduces the dynamic risk of signal leakage while continuously enhancing the signal superposition effect for users and suppressing the signal interference effect for eavesdropping targets, adapting to real-time changes in the communication scenario. During the iterative solution process, the two sub-problems feed back to each other and gradually approach the optimal solution, ensuring that the security performance of the communication system remains at a high level. Based on the hybrid reconfigurable smart surface coefficients and base station beamforming vectors obtained from the iterative solution, the signal propagation path can be dynamically adjusted to meet both anti-interference and anti-eavesdropping security requirements, thereby ensuring communication security. Attached Figure Description

[0069] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0070] Figure 1 This is a schematic diagram of the structure of a wireless communication system provided in an embodiment of this application;

[0071] Figure 2 A flowchart illustrating the intelligent assisted secure communication method provided in this application embodiment;

[0072] Figure 3 A schematic diagram of the alternating optimization framework provided in the embodiments of this application;

[0073] Figure 4 A flowchart illustrating the joint optimization process provided in this application embodiment;

[0074] Figure 5 A schematic diagram of the structure of the intelligent assisted security communication device provided in the embodiments of this application;

[0075] Figure 6 A schematic diagram of the structure of an intelligent assisted security communication device provided in an embodiment of this application.

[0076] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0077] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0078] To overcome the limitations of existing secure communication methods, this application provides an intelligent assisted secure communication method. By combining a joint optimization algorithm of HR-RIS with base station beamforming and dynamic adjustment of HR-RIS coefficients, the method maximizes the system security rate of a wireless communication system in complex communication environments with eavesdropping targets and interference sources. Specifically, it leverages the amplitude adjustment capability of a small number of active components and the phase reconstruction capability of passive components in HR-RIS to overcome the double fading problem of traditional passive RIS. Furthermore, by using an alternating optimization framework, the highly non-convex joint optimization problem is decoupled into two iteratively solvable sub-problems. This enables dynamic channel reconstruction and signal enhancement in scenarios where eavesdropping and interference coexist, ensuring communication security.

[0079] It is understandable that Hybrid Reconfigurable Intelligent Surface (HR-RIS) refers to a new type of reconfigurable intelligent surface architecture that integrates active and passive components, enabling each component to adjust the signal amplitude and phase while compensating for losses.

[0080] This application is applicable to wireless communication scenarios with high security requirements, such as Industrial Internet of Things (IIoT), autonomous vehicle communication, and drone communication. In these scenarios, users and base stations may simultaneously face threats from eavesdropping targets (attempting to intercept confidential information) and interference sources (attempting to disrupt communication links). The system architecture includes a base station equipped with multiple antennas, an HR-RIS (integrating active and passive reflectors), a single-antenna user, a single-antenna eavesdropping target, and a multi-antenna interference source. The HR-RIS dynamically adjusts the amplitude and phase of its active elements, combined with the base station's beamforming strategy, to reconstruct the signal propagation path to enhance the user's received signal strength, while suppressing the impact of eavesdropping targets and interference sources.

[0081] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0082] The intelligent assisted secure communication method provided in this application is applied to a wireless communication system. The wireless communication system deploys a hybrid reconfigurable intelligent surface. The components in the hybrid reconfigurable intelligent surface include active and passive components. The active components are used to adjust the signal amplitude and signal phase, while the passive components are used to adjust the signal phase. It can be understood that HR-RIS is an intelligent device with passive reflection and active amplification functions, which regulates the direction and intensity of signal propagation by adjusting the coefficients (phase and amplitude) of its own components.

[0083] Figure 1 This is a schematic diagram of the structure of a wireless communication system provided in an embodiment of this application. Figure 1 As shown, this wireless communication system is an HR-RIS-assisted communication system. The system includes a base station equipped with M antennas for transmitting signals to a single-antenna user. Simultaneously, the system also includes a single-antenna eavesdropping target and an interference source with L antennas. Furthermore, an HR-RIS is deployed within the system. Additionally, it includes legitimate links, eavesdropping links, and interference links.

[0084] It should be noted that the intelligent assisted security communication method provided in this application embodiment can also be extended to multi-user scenarios, such as those involving multiple users or multiple eavesdropping targets.

[0085] Figure 2 This is a flowchart illustrating an intelligent assisted secure communication method provided in an embodiment of this application. Figure 2 As shown, the intelligent assisted security communication method includes:

[0086] S101. Based on the structural information and communication scenario of the hybrid reconfigurable smart surface, a joint optimization problem of the wireless communication system is constructed with the goal of maximizing the system security rate of the wireless communication system. In the joint optimization problem, the optimization variables are coupled, and the objective function and the unit modulus constraint of the passive components are non-convex. The optimization variables include the base station beamforming vector and the coefficient matrix of the hybrid reconfigurable smart surface. The communication scenario includes users, eavesdropping targets and interference sources, and the interference source and the eavesdropping target cooperate.

[0087] Still Figure 1 As shown, this wireless communication system reflects the communication scenario of HR-RIS, which includes a user, an eavesdropping target, and an interference source. There is a cooperative relationship between the eavesdropping target and the interference source; for example, the interference signal received by the eavesdropping target can be eliminated.

[0088] Optionally, the structural information of the HR-RIS includes the distribution layout of active and passive components, the control capability boundaries, and hardware constraints. Specifically, active components have dual-dimensional control capabilities for signal amplitude and phase, and must meet preset power consumption upper limit constraints. Passive components only support flexible phase adjustment and must strictly adhere to the inherent physical constraints of unit modulus.

[0089] For example, based on the structural information and communication scenario of the HR-RIS, a joint optimization problem for the wireless communication system is constructed with the goal of maximizing the system security rate of the wireless communication system. The optimization variables in the joint optimization problem include the base station transmit beamforming vector and the HR-RIS coefficient matrix, which are strongly coupled. Specifically, the base station transmit beamforming vector determines the signal energy distribution and phase state incident on each unit of the HR-RIS, directly affecting the fundamental effect of subsequent reflection modulation by the HR-RIS. The HR-RIS coefficient matrix, through amplitude and phase modulation of the incident signal, inversely determines the final propagation direction of the base station beam energy, thus affecting the user's received signal-to-interference-plus-noise ratio and the signal acquisition quality of the eavesdropping target.

[0090] This joint optimization problem is non-convex, which means that the objective function with the system security rate as the core is itself non-convex, and the unit modulus constraint of passive components is a non-convex constraint in the complex domain space. Therefore, it is necessary to design targeted optimization algorithms to effectively decouple and solve the problem.

[0091] S102. Using an alternating optimization framework, the joint optimization problem is decoupled into two sub-problems for iterative solution, thereby obtaining the hybrid reconfigurable smart surface coefficients and base station beamforming vectors that can dynamically adjust the signal propagation path corresponding to the wireless communication system. The two sub-problems include the base station beamforming sub-problem and the hybrid reconfigurable smart surface coefficient sub-problem.

[0092] For example, using an alternating optimization framework, the joint optimization problem is decoupled into a base station beamforming subproblem and an HR-RIS coefficient subproblem. By iteratively solving the two decoupled subproblems, the global optimal solution is gradually approximated through multiple iterations, thereby obtaining the HR-RIS coefficients and base station beamforming vector corresponding to the wireless communication system.

[0093] Optionally, the obtained HR-RIS coefficients and base station beamforming vectors can be used to dynamically adjust the signal propagation path. Specifically, the optimized HR-RIS coefficient matrix and base station transmit beamforming vector are sent to the hardware to ensure the timeliness of dynamic adjustment of the signal propagation path. For the HR-RIS coefficient matrix, it is sent to each unit of the HR-RIS, and real-time parameter configuration is achieved through programmable phase shifters and amplifiers. For the base station beamforming vector, it is sent to the multi-antenna array of the base station, and the transmit phase and amplitude of each antenna are adjusted in real time through the radio frequency link to form a directional beam.

[0094] The intelligent assisted secure communication method provided in this application is applied to a wireless communication system. The system deploys a hybrid reconfigurable smart surface, which includes active and passive components. This allows the hybrid reconfigurable smart surface to simultaneously control both active and passive components, providing precise security-guided control for communication security. Based on the structural information of the hybrid reconfigurable smart surface and the communication scenario (including users, eavesdropping targets, and interference sources), a joint optimization problem for the wireless communication system is constructed with the goal of maximizing the system's security rate. This suppresses the eavesdropping target's ability to interpret valid communication content, ensuring communication security. By constructing the joint optimization problem, direct signal leakage to the eavesdropping target / interference source is reduced, and the reflection path to the eavesdropping target is cut off, ensuring the security of signal propagation. An alternating optimization framework is used to decouple the joint optimization problem into a base station beamforming subproblem and a hybrid reconfigurable smart surface coefficient subproblem. These are iteratively solved to obtain the hybrid reconfigurable smart surface coefficients and base station beamforming vectors corresponding to the wireless communication system. This reduces the dynamic risk of signal leakage while continuously enhancing the signal superposition effect for users and suppressing the signal interference effect for eavesdropping targets, adapting to real-time changes in the communication scenario. During the iterative solution process, the two sub-problems feed back to each other and gradually approach the optimal solution, ensuring that the security performance of the communication system remains at a high level. Based on the hybrid reconfigurable smart surface coefficients and base station beamforming vectors obtained from the iterative solution, the signal propagation path can be dynamically adjusted to meet both anti-interference and anti-eavesdropping security requirements, thereby ensuring communication security.

[0095] Based on the above embodiments, S101, based on the structural information of the hybrid reconfigurable smart surface and the communication scenario, and with the objective of maximizing the system security rate of the wireless communication system, constructs a joint optimization problem for the wireless communication system. This can further include: constructing a hybrid reconfigurable smart surface coefficient matrix based on the structural information of the hybrid reconfigurable smart surface; constructing a base station beamforming vector based on the structural information and the communication scenario; constructing a user-end signal model and an eavesdropping target signal model based on the hybrid reconfigurable smart surface coefficient matrix and the base station beamforming vector. The user-end signal model includes base station signals, interference from interference sources, and noise, while the eavesdropping target signal model only includes base station signals and noise. Based on the user-end signal model and the eavesdropping target signal model, with the objective of maximizing the system security rate of the wireless communication system, a joint optimization problem for the wireless communication system is constructed.

[0096] For example, the structural information of an HR-RIS deployed in a wireless communication system includes the following:

[0097] HR-RIS consists of N components, specifically including K active components and A passive component. A modular design is used. This represents the set of indices of active components on the HR-RIS, with the number of elements being... In terms of functionality, passive components operate through phase shifters, while active components can dynamically adjust the amplitude and phase of the incident signal.

[0098] For this HR-RIS, its coefficient matrix is ​​defined. For one A complex diagonal matrix, i.e.:

[0099]

[0100] in, It is the coefficient corresponding to the nth element in HR-RIS. and These represent amplitude and phase shift, respectively. For passive components (i.e.... Its amplitude is fixed as . It can be decomposed into ,in The coefficient used to represent the passive component. The coefficients used to represent active components are, where, Indicates an indicator function.

[0101] Furthermore, based on structural information and communication scenarios, a base station beamforming vector is constructed. Specifically, combining the structural information and communication scenarios of the HR-HIS, an initial form of the base station beamforming vector is preliminarily constructed. Its dimension corresponds to the number of antennas M of the base station, and the amplitude and phase of the vector need to be adapted to the requirement of "directing the signal energy to the effective elements of HR-RIS", while avoiding the direction of the eavesdropping target and the interference source, and reducing the invalid leakage of the signal.

[0102] For example, based on the HR-RIS coefficient matrix and the base station beamforming vector, a user-end signal model and an eavesdropping target-end signal model are constructed. Specifically, the following is defined: , , and These are the channel matrices between the base station and the HR-RIS, the channel matrix between the interference source and the HR-RIS, the channel vector between the HR-RIS and the user, and the channel vector between the HR-RIS and the eavesdropping target. Furthermore, Represented as a slave node To the node The channel vector, where These represent the base station, the interference source, the user, and the target of eavesdropping, respectively.

[0103] In one example, the user-end signal model (including base station signal, interference from interference sources, and noise) can be represented as:

[0104]

[0105] in, Let x represent the base station signal vector. Let x represent the transmitted symbol of the base station signal and satisfy the following conditions: ,and This represents the beamforming vector of the base station. The base station's transmit power constraint (meaning the signal cannot be transmitted without limit) is... ,Right now , This represents the maximum transmit power of the base station signal.

[0106] Similarly, This indicates the interference source, the interference signal, and its maximum power is... ,Right now .also, and Let represent the noise of HR-RIS and the user, respectively, both following a cyclically symmetric Gaussian distribution. The variance of HR-RIS noise, It is an N-dimensional identity matrix. The variance of the user noise. This is the user's equivalent noise. (Assume...) You can get .in, This indicates the conjugate transpose.

[0107] In another example, since there is a potential cooperative relationship between the interference source and the eavesdropping target, the interference signal received by the eavesdropping target can be canceled. Therefore, the signal model at the eavesdropping target end (containing only base station signals and noise) can be represented as:

[0108]

[0109] in, The equivalent noise representing the object being eavesdropped on.

[0110] In some embodiments, the system security rate of the wireless communication system can be expressed as:

[0111]

[0112] in, ,express The non-negative part; R represents the system's security rate; U and R E These represent the reachable rate at the user's end and the eavesdropping rate at the target's end, respectively.

[0113] Optionally, let , , These represent the equivalent channel vectors between the user and the base station, the equivalent channel vector between the user and the interference source, and the equivalent channel vector between the eavesdropping target and the base station, respectively.

[0114] Therefore, R U and R E It can be obtained based on the signal-to-noise ratio, and is expressed as follows:

[0115]

[0116]

[0117] It is important to note that by jointly optimizing the base station beamforming vector... and HR-RIS coefficient This is to maximize the system security rate of the wireless communication system. Furthermore, considering that the maximized system security rate should be non-negative, the formula for the system security rate... Operators can be removed.

[0118] Furthermore, based on the user-end signal model and the target eavesdropping signal model, a joint optimization problem for the wireless communication system is constructed with the goal of maximizing the system security rate. For example, the joint optimization problem can be expressed as:

[0119]

[0120] Among them, the joint optimization problem The following constraint must be met: the base station signal transmission power does not exceed the maximum value. Actual power consumption of HR-RIS active components Not exceeding the maximum value The amplitude of the passive components of HR-RIS is fixed at 1.

[0121] For example, the actual power consumption of HR-RIS active components This can be expressed by the following formula:

[0122]

[0123] in, This indicates the power of the base station → HR-RIS signal; This indicates the power of the interference source → HR-RIS signal; This indicates the inherent power consumption of the HR-RIS's own circuitry.

[0124] By establishing a model of a wireless communication system covering scenarios where eavesdropping and interference coexist, the correlation between the system's security rate and the HR-RIS coefficient and the base station beamforming vector is clarified, providing a mathematical framework for subsequent optimization.

[0125] By constructing the HR-RIS coefficient matrix and base station beamforming vector, and based on these, user-end signal models and target-end signal models are built to accurately depict the actual signal environment of legitimate communication and eavesdropping, ensuring the authenticity of rate calculations. Based on these two models, a joint optimization problem for the wireless communication system is constructed with the goal of maximizing the system's secure rate. This provides a precise security performance optimization framework for subsequent solutions, further enhancing the system's anti-eavesdropping capability and secure transmission stability.

[0126] As one possible implementation, in the iterative solution process: after fixing the hybrid reconfigurable smart surface coefficients, the base station beamforming subproblem is transformed into a convex problem using semidefinite relaxation and continuous convex approximation techniques to obtain a suboptimal base station beamforming vector; after fixing the base station beamforming vector, the hybrid reconfigurable smart surface coefficient subproblem is solved using continuous convex approximation techniques. In each inner loop, the continuous convex approximation technique approximates the objective function as a linear objective function and relaxes the unit modulus constraint.

[0127] Since the joint optimization problem is a highly non-convex optimization problem with coupled variables, an alternating optimization framework is adopted to decompose the original problem into two subproblems and solve them iteratively through an outer loop.

[0128] Figure 3 This is a schematic diagram of the alternating optimization framework provided in an embodiment of this application. Figure 3 As shown, firstly, the number of iterations, i.e., the number of outer loop iterations T, is set to 0, and the base station beamforming vector is initialized. and HR-RIS coefficient vector Then, the iterative solution begins. Specifically, within a fixed... Subsequently, the base station beamforming subproblem is transformed into a convex problem using semi-definite relaxation (SDR) and successful convex approximation (SCA) techniques. The base station beamforming subproblem is then solved to obtain the suboptimal base station beamforming vector. In fixed Then, the SCA technique is used to solve the HR-RIS coefficient subproblem in order to obtain the suboptimal HR-RIS coefficient vector. .

[0129] In each inner loop, the SCA technique approximates the non-convex objective function as a linear objective function to transform the objective function into a convex function. It also employs constraint relaxation to relax the unit modulus constraint of passive components to transform the non-convex constraint into a convex constraint, ultimately transforming the non-convex optimization problem into a convex optimization problem.

[0130] It is important to note that since the joint optimization problem is split into two subproblems for alternating optimization rather than global optimization, the solution obtained through iteration is suboptimal rather than globally optimal.

[0131] By iterating through the solutions to the two subproblems, the suboptimal base station beamforming vector and HR-HIS coefficient matrix can be obtained. According to Cauchy's theorem, since both the base station beamforming vector and the HR-HIS coefficient matrix are constrained, the objective function has monotonically convergent properties.

[0132] It is understandable that the base station beamforming vector at the current iteration number can be used as a fixed parameter for solving the HR-RIS coefficient subproblem at the current iteration number, and the HR-RIS coefficient matrix at the current iteration number can be used as a fixed parameter for solving the base station beamforming subproblem at the next iteration number, and so on.

[0133] By decomposing complex problems through alternating optimization frameworks, computational complexity is reduced. At the same time, by combining SDR and SCA technologies, non-convex problems are transformed into convex problems, making the original coupled non-convex problems operable and gradually approaching the global optimal solution. This provides a feasible and efficient solution path for maximizing the system's security rate and ensures the performance implementation of intelligent assisted secure communication.

[0134] Optionally, the base station beamforming subproblem can be transformed into a convex problem using semidefinite relaxation and continuous convex approximation techniques to obtain a suboptimal base station beamforming vector. This can include: using semidefinite relaxation techniques to transform the base station beamforming subproblem into a relaxation problem; using the Channes-Cooper transform technique to transform the objective function into an equivalent non-fractional form of the objective function; using a penalty method to transform the unit modulus constraint into an inequality, and based on the inequality, transforming the equivalent non-fractional form of the objective function into an objective function containing a penalty term; using continuous convex approximation techniques to perform a first-order Taylor expansion on the objective function containing the penalty term, and based on the objective function after the first-order Taylor expansion, transforming the base station beamforming subproblem into a convex problem to obtain a suboptimal base station beamforming matrix; and using eigenvalue decomposition techniques to transform the suboptimal base station beamforming matrix into a suboptimal base station beamforming vector.

[0135] For example, in the Tth alternating iteration optimization process, the HR-RIS coefficient vector is fixed. , The optimization subproblem can be expressed as:

[0136]

[0137] in, , .and This represents the remaining power budget after subtracting interference signals and inherent power consumption from the total power budget, which is the remaining power budget of the HR-RIS active components. (Question) This includes the following constraints: base station transmit power constraints and HR-RIS active power constraints. Because... The function has the property of monotonically increasing. Symbols can be omitted.

[0138] In view of the problem Due to the non-convex nature of the problem, the base station beamforming problem is transformed into a relaxation problem using SDR technology. For example, this relaxation problem can be expressed by the following formula:

[0139]

[0140] Among them, matrix Defined as . express It is a positive semi-definite matrix; express traces; express Rank.

[0141] because The objective function and Constraints, Problems This still falls under the category of nonconvex problems. Specifically, it involves positive semi-definite matrices. If the rank is 1, the rank constraint is non-convex, and the objective function is in fractional form, then... It is still non-convex.

[0142] Furthermore, the Charnes-Cooper Transformation (CCT) technique is employed to transform the objective function into its equivalent non-fractional form. Specifically, let... , and will Convert to the following form:

[0143]

[0144] in, This represents the equality constraint introduced by CCT; This represents the transformed form of the base station's transmit power constraint. This represents the transformation form of HR-HIS active power constraint.

[0145] However, due to the problem The problem remains non-convex, as the rank-1 constraint, i.e., the unit modulus constraint of the passive element, still exists.

[0146] Optionally, a penalty method can be used to constrain the unit modulus. Transform into inequalities .in, express traces, express The largest eigenvalue.

[0147] Furthermore, based on the above inequalities, the equivalent non-fractional form of the objective function is transformed into an objective function with a penalty term, and the following problem is constructed:

[0148]

[0149] in, As a penalty weight, when When the value of is large enough, and The suboptimal solution is the same. That is, in order to incorporate the rank-1 constraint into the objective function, a penalty method is used to... The maximization objective is transformed into minimizing the negative objective, with a penalty term added. When Large enough, the penalty will force Otherwise, the objective function would become infinitely large. and They have the same suboptimal solution.

[0150] Optionally, the SCA technique is used to perform a first-order Taylor expansion on the objective function containing the penalty term. Because Since it is not differentiable, we use the second gradient, i.e. ,get The first-order Taylor expansion of the objective function is shown in the following formula:

[0151]

[0152] in, It is the first The solution obtained from the SCA algorithm iteration; yes The eigenvector corresponding to the largest eigenvalue. Furthermore, based on the objective function after the first-order Taylor expansion, the base station beamforming problem is transformed into the following convex problem:

[0153]

[0154] Therefore, a suboptimal solution can be found for this convex problem. That is, since the objective function is linear (after Taylor expansion), The terms are approximated as linear, and all constraints are convex (including positive semidefinite constraints and linear inequality / equality constraints). Through the analysis of... By iteratively solving the problem, the suboptimal base station beamforming matrix can be obtained.

[0155] Furthermore, the suboptimal base station beamforming matrix can be transformed into a suboptimal base station beamforming vector using eigenvalue decomposition techniques, such as singular value decomposition (SVD). .

[0156] It is important to note that the problem The minimum objective value is the problem An upper bound on the suboptimal value, that is... The corresponding objective function is to use SCA. The corresponding upper bound approximation function for the objective function is the result of using SCA technology. SDR relaxes the non-convex constraints of base station beamforming into convex constraints, while SCA approximates the non-convex objective function into a convex function, thus transforming the base station beamforming subproblem into a solvable convex problem.

[0157] In solving the base station beamforming problem, the original problem is transformed into a preliminary solvable relaxation problem using SDR (Self-Depth Reduction). CCT (Corrective Coding) technology eliminates the fractional non-convexity of the objective function. The penalty method transforms the unit modulus constraint into an inequality and embeds it into the objective function. SCA (Self-Aided Coding) technology transforms the non-convex objective function containing the penalty term into a convex function to solve for the suboptimal beam matrix. Finally, eigenvalue decomposition completes the reconstruction of the beam matrix into beam vectors. The entire solution process ensures feasibility and efficiency while outputting directional guidance signal energy and suboptimal beam parameters that maximize the system's security rate.

[0158] Furthermore, solving the subproblem of hybrid reconfigurable smart surface coefficients using continuous convex approximation techniques can include: transforming the objective function into a power-related objective function based on a fixed base station beamforming vector; reconstructing the hybrid reconfigurable smart surface coefficient variables to be optimized into real-valued vectors, each containing amplitude and phase; equivalently representing the power-related objective function as a real-valued vector objective function, and solving for the partial derivatives of the amplitude and phase corresponding to the real-valued vector objective function; performing a first-order Taylor expansion of the real-valued objective function using continuous convex approximation techniques based on the partial derivatives, thus transforming the real-valued objective function into a convex function; relaxing the unit module constraint, transforming the unit module constraint into a convex constraint; and using a convex optimization solver based on the convex function and convex constraints to solve the subproblem of hybrid reconfigurable smart surface coefficients.

[0159] For example, in the Tth alternating iteration optimization process, the base station beamforming vector obtained from solving the base station beamforming subproblem is fixed. According to the fixed The objective function is transformed into a power-related objective function, that is, the problem This can be transformed into the following question:

[0160]

[0161] Optionally, It can be represented as:

[0162]

[0163] in, , and It is a vector and The nth element. Let and For vectors and The nth element, and let For matrix The nth line, For matrix The nth row. Therefore, the equivalent channels for the user, the eavesdropping target, and the interference source are respectively represented as... , , . Again , , , and .in , , , and These represent the base station signal power received by the user, the interference signal power received by the user, the noise power at the user's end, the base station signal power received by the eavesdropping target, and the noise power at the eavesdropping target's end, respectively.

[0164] Based on the above formula, the problem can be solved. Transformed into the following problem :

[0165]

[0166] Therefore, the problem is... It is a highly non-convex optimization problem. Its non-convexity comes from its non-convex objective function and unit module constraint. That is, the objective function contains quadratic / fractional combinations and is non-convex, and the unit module constraint of the passive component is also non-convex.

[0167] Furthermore, All independent variables that need optimization are reconstructed into one 3D real vector In other words, real number vectors It contains the amplitudes of K active elements (the amplitude of passive elements is fixed at 1 and does not need to be optimized) and the phases of all N elements (including active and passive elements) of HR-RIS. For example, this real vector... It can be represented as:

[0168]

[0169] Optionally, The corresponding objective function, i.e., the power-related objective function. Equivalently denoted as a real vector objective function And solve for the partial derivatives of its corresponding amplitude and phase. That is, to solve and .in , The specific form is obtained from the following formula:

[0170]

[0171] in, , , , .symbol The conjugate operator for complex numbers. This indicates the index of the active element in HR-RIS.

[0172]

[0173] in, , , . This represents the real part operator.

[0174]

[0175]

[0176] in, , . This represents the iteration count index corresponding to the HR-RIS subproblem.

[0177]

[0178]

[0179] in, .

[0180] Furthermore, according to Using SCA technology Perform a first-order Taylor expansion to transform the real variables... The objective function is transformed into a convex function, which expands as follows:

[0181]

[0182] in This represents the j-th iteration in the SCA technique. .

[0183] At this point, the HR-RIS coefficient subproblem only involves the non-convex unit modulus constraint, i.e., the unit modulus of the passive component is 1. To solve this problem, by relaxing the unit modulus constraint, the following constraint is obtained:

[0184]

[0185] At this point, the unit modulus constraint is transformed into a convex constraint, and the objective function is transformed into a convex function. Therefore, the HR-RIS coefficient subproblem is transformed into a standard convex optimization problem. In other words, since the transformed HR-RIS coefficient subproblem has a linear objective and a fully convex constraint set (including active power constraints and relaxed passive amplitude constraints), it is a standard convex optimization problem. Based on the transformed convex function and convex constraints, a convex optimization solver is used to solve the HR-RIS coefficient subproblem.

[0186] In solving the base station beamforming problem, the objective function is transformed into a power-dependent form, and the complex coefficient variables are reconstructed into real-number vectors containing amplitude and phase, achieving an equivalent transformation of the objective function in the real domain. By solving the partial derivatives of the objective function with respect to amplitude and phase, and combining SCA technology with first-order Taylor expansion, the original non-convex objective function is transformed into a convex function. At the same time, the non-convex constraint of the unit mode length of passive components is relaxed to a convex constraint. Finally, a convex optimization solver is used to quickly solve the problem and output HR-RIS coefficients adapted to the current base station beamforming, providing accurate support for the collaborative optimization of the base station and HR-RIS, and helping to maximize the system's security rate.

[0187] In some embodiments, S102, employing an alternating optimization framework, decouples the joint optimization problem into two sub-problems for iterative solving to obtain the hybrid reconfigurable smart surface coefficients and base station beamforming vectors corresponding to the wireless communication system with dynamically adjustable signal propagation paths. This may further include: during the iterative solution process, if the base station beamforming sub-problem satisfies the first iterative convergence condition, fixing the base station beamforming vector obtained from solving the base station beamforming sub-problem, and solving the hybrid reconfigurable smart surface coefficient sub-problem. The first iterative convergence condition is the internal iterative convergence condition of the base station beamforming sub-problem. Furthermore, if the hybrid reconfigurable smart surface coefficient sub-problem satisfies the second iterative convergence condition, triggering an outer loop. The outer loop is one alternating optimization process, and the second iterative convergence condition is the internal iterative convergence condition or the number of iterations condition of the hybrid reconfigurable smart surface coefficient sub-problem.

[0188] If the base station beamforming subproblem does not meet the first iteration convergence condition, the iterative solution of the base station beamforming subproblem is triggered; and / or, if the hybrid reconfigurable smart surface coefficient subproblem does not meet the second iteration convergence condition, the iterative solution of the hybrid reconfigurable smart surface coefficient subproblem is triggered.

[0189] When the objective function converges or the number of alternating optimizations exceeds a preset threshold, the output is a hybrid reconfigurable smart surface coefficient and a base station beamforming vector that can dynamically adjust the signal propagation path.

[0190] Figure 4 This is a flowchart illustrating the joint optimization process provided in an embodiment of this application. Figure 4 As shown, the entire iterative solution process of the joint optimization problem is illustrated, including the beamforming subproblem and the HR-RIS coefficient subproblem.

[0191] First, initialize the HR-RIS coefficient vector. Base station beamforming vector And beamforming problem The iteration count T corresponding to the joint optimization problem and the inner loop count j corresponding to the HR-RIS coefficient optimization subproblem are set to 0. By solving the beamforming subproblem and the HR-RIS coefficient subproblem separately, the two subproblems are iterated alternately to obtain the suboptimal base station beamforming vector and HR-RIS coefficient vector.

[0192] During the iterative solution process, if the base station beamforming subproblem (index k) satisfies the first iterative convergence condition, the base station beamforming vector obtained from solving the base station beamforming subproblem is fixed, and the hybrid reconfigurable smart surface coefficient subproblem is solved. The first iterative convergence condition is the internal iterative convergence condition of the base station beamforming subproblem. For example, the internal iterative convergence condition of the base station beamforming subproblem is:

[0193]

[0194] in, It is the convergence tolerance. This formula is used to determine whether the solution after semi-definite relaxation can be reduced to a reasonable base station beamforming vector.

[0195] Specifically, if the base station beamforming subproblem satisfies the first iteration convergence condition, then the base station beamforming vector obtained by solving it is... As fixed parameters for the HR-RIS coefficient subproblem, the HR-RIS coefficient optimization subproblem is solved.

[0196] When solving the HR-RIS coefficient subproblem (index j), fix First, the optimization variables are reconstructed into real number vectors. And calculate its corresponding objective function. gradient corresponding to the objective function Construct a new objective function using the objective function and gradient. And solve the convex optimization problem to obtain the real vector of solutions to the HR-RIS coefficient subproblems. .

[0197] If the HR-RIS coefficient subproblem satisfies the second iteration convergence condition, the outer loop is triggered. The outer loop is a single iteration optimization process. The second iteration convergence condition is either the internal iteration convergence condition or the iteration count condition of the HR-RIS coefficient subproblem. For example, the internal iteration convergence condition of the HR-RIS coefficient subproblem is: .in, To achieve convergence tolerance. This indicates that the difference between the optimization variables obtained in the (j+1)th iteration and the jth iteration is less than or equal to the convergence tolerance. This indicates that the changes in variables during the iteration process are sufficiently small, and the iteration can be determined to be convergent. The iteration number condition for the HR-RIS coefficient subproblem is: . This indicates the iteration count condition for the HR-RIS coefficient subproblem, that is, the number of iterations corresponding to the subproblem is greater than the preset maximum number of iterations.

[0198] Specifically, if the HR-RIS coefficient subproblem (index j) satisfies the convergence condition of the second iteration, then the real vector obtained from its solution is... Restore to This triggers the outer loop, i.e., the alternating iterative process.

[0199] Optionally, if the base station beamforming subproblem does not satisfy the convergence condition of the first iteration, based on the given... and In the problem of updating base station beamforming and This triggers the iterative solution of the base station beamforming sub-problem. For example, let... Then proceed to the next iteration (index k+1).

[0200] In some embodiments, if the HR-RIS coefficient subproblem does not satisfy the second iteration convergence condition, based on the given... and Update the HR-RIS coefficient subproblem This triggers the iterative solution of the HR-RIS coefficient subproblems. For example, let... Then proceed to the next iteration (index j+1).

[0201] It is important to note that the outer loop can only be triggered when the base station beamforming subproblem satisfies the first iterative convergence condition and when the HR-RIS coefficient subproblem satisfies the second iterative convergence condition. Optionally, if either the base station beamforming subproblem or the HR-RIS coefficient subproblem fails to meet the preset conditions, the outer loop cannot be triggered; only its corresponding iterative solution will be triggered until both the base station beamforming subproblem and the HR-RIS coefficient subproblem satisfy their respective iterative convergence conditions, at which point the outer loop can be triggered.

[0202] For example, the convergence condition of the outer loop is: the objective function converges or the number of alternating optimization iterations T exceeds a preset threshold, i.e., the maximum number of iterations. In other words, when the convergence condition of the outer loop is met, the iteration stops, and the HR-RIS coefficients and base station beamforming vector, which can dynamically adjust the signal propagation path, are output. Optionally, when the convergence condition of the outer loop is not met, the outer loop is triggered, and the next alternating iteration (T+1) is executed until the convergence condition of the outer loop is met.

[0203] The inner loop iterates separately on individual sub-problems of base station beamforming or HR-RIS coefficients, ensuring that each sub-problem is fully solved. The outer loop performs alternating optimization when both sub-problems meet preset conditions, ensuring the efficiency of their collaborative optimization. Furthermore, by using termination rules based on objective function convergence or alternating optimization thresholds, the base station beamforming vector and HR-RIS coefficients are output in a timely manner to dynamically adjust the signal propagation path, ensuring that the optimization results support the intelligent assisted secure communication performance of the system.

[0204] In summary, the intelligent assisted secure communication method provided in this application has at least the following advantages:

[0205] First, the intelligent assisted secure communication method provided in this application is applied to a wireless communication system deployed with HR-RIS. The HR-RIS includes active and passive components, enabling it to jointly control the phase and / or amplitude of corresponding coefficients of the components to improve transmission security. Based on the structural information of the HR-RIS and the communication scenario (including users, eavesdropping targets, and interference sources), an HR-RIS coefficient matrix and a base station beamforming vector are constructed. Based on these, user-end signal models and eavesdropping target-end signal models are constructed to accurately depict the actual signal environment of legitimate communication and eavesdropping, ensuring the accuracy of the system's secure rate calculation. Based on these two models, with the goal of maximizing the system's secure rate, a joint optimization problem for the wireless communication system is constructed, providing a precise security performance optimization framework for subsequent solutions and further improving the system's anti-eavesdropping capability and secure transmission stability.

[0206] Second, by employing an alternating optimization framework, the joint optimization problem is decoupled into a base station beamforming subproblem and a hybrid reconfigurable smart surface coefficient subproblem, which are then solved iteratively to reduce computational complexity. In solving the base station beamforming subproblem, SDR transforms the original problem into a preliminary solvable relaxation problem; CCT technology eliminates the fractional nonconvexity of the objective function; a penalty method transforms the unit modulus constraint into an inequality and embeds it into the objective function; SCA technology transforms the nonconvex objective function containing the penalty term into a convex function to solve for the suboptimal beam matrix; and finally, eigenvalue decomposition completes the reconstruction of the beam matrix into beam vectors. The entire solution process ensures feasibility and efficiency while outputting directional guidance signal energy and suboptimal beam parameters that maximize the system's security rate.

[0207] Third, in solving the base station beamforming subproblem, the objective function is transformed into a power-dependent form, and the complex coefficient variables are reconstructed into real-valued vectors containing amplitude and phase, achieving an equivalent transformation of the objective function in the real domain. By solving the partial derivatives of the objective function with respect to amplitude and phase, and combining SCA technology with first-order Taylor expansion, the original non-convex objective function is transformed into a convex function. At the same time, the non-convex constraint of the unit module length of passive components is relaxed to a convex constraint. Finally, a convex optimization solver is used to quickly solve the problem and output HR-RIS coefficients adapted to the current base station beamforming, providing precise support for the collaborative optimization of the base station and HR-RIS, and helping to maximize the system's security rate.

[0208] Fourth, through the inner loop, individual sub-problems such as base station beamforming or HR-RIS coefficients are iterated separately to ensure that each sub-problem is fully solved. Through the outer loop, both sub-problems are alternately optimized when they meet preset conditions, ensuring the efficiency of their collaborative optimization. Furthermore, by using termination rules based on the convergence of the objective function or the threshold for alternate optimization, the base station beamforming vector and HR-RIS coefficients that can dynamically adjust the signal propagation path are output in a timely manner, ensuring that the optimization results support the intelligent assisted secure communication performance of the system. Based on the HR-RIS coefficients and base station beamforming vectors obtained through iterative solutions, the system's security rate can be improved, and the signal propagation path can be dynamically adjusted, taking into account both anti-interference and anti-eavesdropping security requirements, thereby ensuring communication security.

[0209] Fifth, the HR-RIS provided in this application not only leverages the advantages of traditional passive RIS in adjusting phase to reconstruct the channel, but also utilizes the advantages of active components in amplifying signal amplitude, effectively overcoming the "double fading" problem faced by traditional passive RIS. Compared to fully active RIS solutions, the HR-RIS provided in this application significantly reduces hardware costs, achieving near-full active RIS performance results with only a few active components activated. Activating only a few RIS units can significantly improve the safety rate.

[0210] Figure 5 This is a schematic diagram of the structure of the intelligent assisted safety communication device provided in an embodiment of this application. The intelligent assisted safety communication device provided in this application is applied to a wireless communication system. The wireless communication system deploys a hybrid reconfigurable smart surface. The components in the hybrid reconfigurable smart surface include active components and passive components. The active components are used to adjust the signal amplitude and signal phase, and the passive components are used to adjust the signal phase.

[0211] like Figure 5 As shown, the intelligent assisted safety communication device 20 provided in this application embodiment includes:

[0212] Module 201 is used to construct a joint optimization problem of a wireless communication system based on the structural information and communication scenario of a hybrid reconfigurable smart surface, with the goal of maximizing the system security rate of the wireless communication system. In the joint optimization problem, the optimization variables are coupled, and the objective function and the unit modulus constraints of the passive components are non-convex. The optimization variables include the base station beamforming vector and the coefficient matrix of the hybrid reconfigurable smart surface. The communication scenario includes users, eavesdropping targets and interference sources, with the interference source and the eavesdropping target cooperating.

[0213] The processing module 202 is used to decouple the joint optimization problem into two sub-problems by adopting an alternating optimization framework and solve them iteratively to obtain the hybrid reconfigurable smart surface coefficients and base station beamforming vectors that can dynamically adjust the signal propagation path corresponding to the wireless communication system. The two sub-problems include the base station beamforming sub-problem and the hybrid reconfigurable smart surface coefficient sub-problem.

[0214] In one possible implementation, the building module 201 is specifically used for:

[0215] Based on the structural information of the hybrid reconfigurable smart surface, a hybrid reconfigurable smart surface coefficient matrix is ​​constructed.

[0216] Based on structural information and communication scenarios, base station beamforming vectors are constructed.

[0217] Based on the hybrid reconfigurable smart surface coefficient matrix and the base station beamforming vector, a user-end signal model and an eavesdropping target signal model are constructed. The user-end signal model includes base station signal, interference from interference sources, and noise, while the eavesdropping target signal model only includes base station signal and noise.

[0218] Based on the user-side signal model and the eavesdropping target-side signal model, a joint optimization problem for the wireless communication system is constructed with the goal of maximizing the system security rate of the wireless communication system.

[0219] In one possible implementation, during the iterative solution process: after fixing the hybrid reconfigurable smart surface coefficients, a semidefinite relaxation and continuous convex approximation technique is used to transform the base station beamforming subproblem into a convex problem to obtain a suboptimal base station beamforming vector; after fixing the base station beamforming vector, a continuous convex approximation technique is used to solve the hybrid reconfigurable smart surface coefficient subproblem. In each inner loop, the continuous convex approximation technique approximates the objective function as a linear objective function and relaxes the unit modulus constraint.

[0220] In one possible implementation, a semidefinite relaxation and continuous convex approximation technique are used to transform the base station beamforming subproblem into a convex problem to obtain a suboptimal base station beamforming vector, including:

[0221] A semi-definite relaxation technique is used to transform the base station beamforming problem into a relaxation problem;

[0222] The Chauns-Cooper transform technique is used to transform the objective function into an equivalent non-fractional form.

[0223] The penalty method is adopted to transform the unit modulus constraint into an inequality, and based on the inequality, the equivalent non-fractional form of the objective function is transformed into an objective function containing a penalty term;

[0224] The continuous convex approximation technique is adopted to perform a first-order Taylor expansion on the objective function containing the penalty term. Based on the objective function after the first-order Taylor expansion, the base station beamforming subproblem is transformed into a convex problem to obtain the suboptimal base station beam matrix.

[0225] The suboptimal base station beam matrix is ​​transformed into a suboptimal base station beamforming vector using eigenvalue decomposition technology.

[0226] In one possible implementation, a continuous convex approximation technique is used to solve the hybrid reconfigurable smart surface coefficient subproblem, including:

[0227] Based on the fixed base station beamforming vector, the objective function is transformed into a power-related objective function;

[0228] The coefficient variables of the hybrid reconfigurable smart surface to be optimized are reconstructed into real vectors, which contain amplitude and phase.

[0229] The power-related objective function is equivalently denoted as a real vector objective function, and the partial derivatives of the magnitude and phase corresponding to the real vector objective function are solved;

[0230] Based on the partial derivatives, the first-order Taylor expansion of the objective function of the real variables is performed using the continuous convex approximation technique to transform the objective function of the real variables into a convex function;

[0231] Relax the unit module constraint and transform it into a convex constraint;

[0232] Based on convex functions and convex constraints, a convex optimization solver is used to solve the subproblem of hybrid reconfigurable smart surfaces.

[0233] In one possible implementation, the processing module 202 is specifically used for:

[0234] During the iterative solution process, if the base station beamforming subproblem satisfies the first iterative convergence condition, the base station beamforming vector obtained from solving the base station beamforming subproblem is fixed, and the hybrid reconfigurable intelligent surface coefficient subproblem is solved. The first iterative convergence condition is the internal iterative convergence condition of the base station beamforming subproblem. And, if the hybrid reconfigurable intelligent surface coefficient subproblem satisfies the second iterative convergence condition, the outer loop is triggered. The outer loop is an alternating optimization process. The second iterative convergence condition is the internal iterative convergence condition or the iteration number condition of the hybrid reconfigurable intelligent surface coefficient subproblem.

[0235] If the base station beamforming subproblem does not meet the first iteration convergence condition, the iterative solution of the base station beamforming subproblem is triggered; and / or, if the hybrid reconfigurable smart surface coefficient subproblem does not meet the second iteration convergence condition, the iterative solution of the hybrid reconfigurable smart surface coefficient subproblem is triggered.

[0236] When the objective function converges or the number of alternating optimizations exceeds a preset threshold, the output is a hybrid reconfigurable smart surface coefficient and a base station beamforming vector that can dynamically adjust the signal propagation path.

[0237] The intelligent assisted safety communication device provided in this application embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0238] Figure 6 This is a schematic diagram of the structure of an intelligent assisted security communication device provided in an embodiment of this application. Figure 6 As shown, the intelligent assisted safety communication device 30 provided in this embodiment includes at least one processor 301 and a memory 302. Optionally, the intelligent assisted safety communication device 30 further includes a communication component 303. The processor 301, memory 302, and communication component 303 are connected via a bus.

[0239] In a specific implementation, at least one processor 301 executes computer execution instructions stored in memory 302, causing at least one processor 301 to perform the above-described method.

[0240] The specific implementation process of processor 301 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0241] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0242] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0243] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0244] This application also provides a wireless communication system, including:

[0245] A hybrid reconfigurable smart surface, comprising active and passive components, wherein the active components are used to regulate signal amplitude and phase, and the passive components are used to regulate signal phase; and, as... Figure 6 The intelligent auxiliary security communication device shown.

[0246] It is understandable that HR-RIS includes active and passive components. The active components have both amplitude gain and phase adjustment capabilities (integrated power amplifiers that can actively amplify the reflected amplitude of the incident signal); the passive components only have phase adjustment capabilities (adjusting the reflected phase of the incident signal through a phase shifter), and the amplitude is fixed at 1 (no active power amplification).

[0247] In one possible implementation, the number of active elements in a hybrid reconfigurable smart surface is less than the number of passive elements.

[0248] It is understandable that passive components only have phase modulation capabilities, and have significant advantages such as low cost, low power consumption and simple structure. Through large-scale deployment, they can provide sufficient phase modulation freedom, perform distributed phase shaping on incident signals in a wide range, and build basic signal propagation path guidance capabilities, such as constructive interference for legitimate users and destructive interference for eavesdropping targets.

[0249] Alternatively, while active components can combine amplitude and phase modulation capabilities to achieve precise signal enhancement and suppression, they are limited by high hardware costs and power consumption. Therefore, they can only be deployed in a small number at key link nodes, such as providing amplitude gain on the main propagation path of legitimate users' signals, or specifically suppressing leaked signals in the core area of ​​eavesdropping threats.

[0250] For example, considering the power consumption and hardware cost of HR-RIS, assume the number of active components... ,in, This represents the total number of components in the HR-RIS.

[0251] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0252] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0253] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0254] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0255] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0256] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0257] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0258] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0259] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0260] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A smart assisted secure communication method, characterized in that, The invention relates to a wireless communication system in which a hybrid reconfigurable smart surface is deployed. The components in the hybrid reconfigurable smart surface include active components and passive components. The active components are used to adjust the signal amplitude and signal phase, and the passive components are used to adjust the signal phase. The intelligent assisted secure communication method includes: Based on the structural information and communication scenario of the hybrid reconfigurable smart surface, a joint optimization problem of the wireless communication system is constructed with the goal of maximizing the system security rate of the wireless communication system. In the joint optimization problem, the optimization variables are coupled, and the objective function and the unit modulus constraint of the passive component are both non-convex. The optimization variables include the base station beamforming vector and the hybrid reconfigurable smart surface coefficient matrix. The communication scenario includes the user, the eavesdropping target, and the interference source. The interference source cooperates with the eavesdropping target. By employing an alternating optimization framework, the joint optimization problem is decoupled into two sub-problems that are solved iteratively to obtain the hybrid reconfigurable smart surface coefficients and base station beamforming vectors that allow for dynamic adjustment of signal propagation paths for the wireless communication system. The two sub-problems include the base station beamforming sub-problem and the hybrid reconfigurable smart surface coefficient sub-problem.

2. The intelligent assisted secure communication method according to claim 1, characterized in that, Based on the structural information and communication scenario of the hybrid reconfigurable smart surface, and with the objective of maximizing the system security rate of the wireless communication system, a joint optimization problem for the wireless communication system is constructed, including: Based on the structural information of the hybrid reconfigurable smart surface, a hybrid reconfigurable smart surface coefficient matrix is ​​constructed; Based on the structural information and the communication scenario, a base station beamforming vector is constructed; Based on the hybrid reconfigurable smart surface coefficient matrix and the base station beamforming vector, a user-end signal model and an eavesdropping target signal model are constructed. The user-end signal model includes base station signals, interference sources, and noise, while the eavesdropping target signal model only includes base station signals and noise.

3. The intelligent assisted secure communication method according to claim 1 or 2, characterized in that, During the iterative solution process: after fixing the hybrid reconfigurable smart surface coefficients, the base station beamforming subproblem is transformed into a convex problem by using semidefinite relaxation and continuous convex approximation techniques to obtain the suboptimal base station beamforming vector; After fixing the base station beamforming vector, the hybrid reconfigurable smart surface coefficient subproblem is solved using the continuous convex approximation technique. In each inner loop, the continuous convex approximation technique approximates the objective function as a linear objective function and relaxes the unit modulus constraint.

4. The intelligent assisted secure communication method according to claim 3, characterized in that, The method of transforming the base station beamforming subproblem into a convex problem using semi-definite relaxation and continuous convex approximation techniques to obtain a suboptimal base station beamforming vector includes: A semi-definite relaxation technique is used to transform the base station beamforming problem into a relaxation problem; The Chauns-Cooper transform technique is used to transform the objective function into an equivalent non-fractional form. The penalty method is used to transform the unit modulus constraint into an inequality, and based on the inequality, the equivalent non-fractional form of the objective function is transformed into an objective function containing a penalty term. The continuous convex approximation technique is used to perform a first-order Taylor expansion on the objective function containing the penalty term. Based on the objective function after the first-order Taylor expansion, the base station beamforming subproblem is transformed into a convex problem to obtain a suboptimal base station beam matrix. The suboptimal base station beam matrix is ​​transformed into a suboptimal base station beamforming vector using eigenvalue decomposition technology.

5. The intelligent assisted secure communication method according to claim 3, characterized in that, The method of solving the subproblem of hybrid reconfigurable smart surface coefficients using the continuous convex approximation technique includes: Based on the fixed base station beamforming vector, the objective function is transformed into a power-related objective function; The coefficient variables of the hybrid reconfigurable smart surface to be optimized are reconstructed into a real vector, which contains amplitude and phase. The power-related objective function is equivalently denoted as the objective function of the real number vector, and the partial derivatives of the magnitude and phase of the objective function of the real number vector are solved. Based on the partial derivatives, the objective function of the real variables is expanded using a first-order Taylor expansion using the continuous convex approximation technique, so as to transform the objective function of the real variables into a convex function. Relax the unit module length constraint and convert the unit module length constraint into a convex constraint; Based on the convex function and the convex constraint, a convex optimization solver is used to solve the subproblem of the hybrid reconfigurable smart surface coefficients.

6. The intelligent assisted secure communication method according to claim 1 or 2, characterized in that, The method employs an alternating optimization framework to decouple the joint optimization problem into two sub-problems that are solved iteratively. This yields the hybrid reconfigurable smart surface coefficients and base station beamforming vectors for dynamically adjustable signal propagation paths corresponding to the wireless communication system, including: During the iterative solution process, if the base station beamforming subproblem satisfies the first iterative convergence condition, the base station beamforming vector obtained from solving the base station beamforming subproblem is fixed, and the hybrid reconfigurable intelligent surface coefficient subproblem is solved. The first iterative convergence condition is the internal iterative convergence condition of the base station beamforming subproblem. And, if the hybrid reconfigurable intelligent surface coefficient subproblem satisfies the second iterative convergence condition, the outer loop is triggered. The outer loop is an alternating optimization process. The second iterative convergence condition is the internal iterative convergence condition or the iteration number condition of the hybrid reconfigurable intelligent surface coefficient subproblem. If the base station beamforming subproblem does not meet the first iteration convergence condition, the iterative solution of the base station beamforming subproblem is triggered; and / or, if the hybrid reconfigurable smart surface coefficient subproblem does not meet the second iteration convergence condition, the iterative solution of the hybrid reconfigurable smart surface coefficient subproblem is triggered. When the number of convergences or alternating optimizations of the objective function exceeds a preset threshold, the output is a hybrid reconfigurable smart surface coefficient and a base station beamforming vector that can dynamically adjust the signal propagation path.

7. An intelligent assisted safety communication device, characterized in that, The invention relates to a wireless communication system in which a hybrid reconfigurable smart surface is deployed. The components in the hybrid reconfigurable smart surface include active components and passive components. The active components are used to adjust the signal amplitude and signal phase, and the passive components are used to adjust the signal phase. The intelligent assisted security communication device includes: A construction module is used to construct a joint optimization problem of the wireless communication system based on the structural information and communication scenario of the hybrid reconfigurable smart surface, with the goal of maximizing the system security rate of the wireless communication system. In the joint optimization problem, the optimization variables are coupled, and the objective function and the unit modulus constraint of the passive component are both non-convex. The optimization variables include the base station beamforming vector and the coefficient matrix of the hybrid reconfigurable smart surface. The communication scenario includes the user, the eavesdropping target, and the interference source, and the interference source cooperates with the eavesdropping target. The processing module is used to decouple the joint optimization problem into two sub-problems using an alternating optimization framework and solve them iteratively to obtain the hybrid reconfigurable smart surface coefficients and base station beamforming vectors that can dynamically adjust the signal propagation path corresponding to the wireless communication system. The two sub-problems include the base station beamforming sub-problem and the hybrid reconfigurable smart surface coefficient sub-problem.

8. An intelligent auxiliary security communication device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 6.

9. A wireless communication system, characterized in that, include: A hybrid reconfigurable smart surface, wherein the elements in the hybrid reconfigurable smart surface include active elements and passive elements, wherein the active elements are used to adjust the signal amplitude and signal phase, and the passive elements are used to adjust the signal phase; And, the intelligent assisted security communication device as described in claim 8.

10. The wireless communication system according to claim 9, characterized in that, In the hybrid reconfigurable smart surface, the number of active elements is less than the number of passive elements.

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