Method, device and terminal for enhancing transmission security by using channel reconfigurability

Abstract

The invention discloses a method, device and terminal for enhancing transmission security by using channel reconfigurability, and the method comprises the steps: constructing a target communication system, and constructing a target signal model based on the target communication system, the target communication system comprises a target base station configured with a movable antenna, a target obstacle, a target user, a target eavesdropper and a virtual line-of-sight link established through a target RIS; a target constraint condition is obtained based on the target communication system, a target problem is constructed based on the target constraint condition, and the target problem is used for designing the maximum safety rate of the target communication system in combination with a beam forming matrix, a sensing signal, a phase shift matrix of the target RIS and the antenna position of the target base station; and solving the target problem to obtain the maximum security rate of the target communication system. According to the method, the problem solving difficulty is reduced while the channel freedom degree is fully mined, and the safety communication level of the user is further improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method, apparatus and terminal for enhancing transmission security by utilizing channel reconfigurability. Background Technology

[0002] Currently, Integrated Sensing and Communication (ISAC) technology, as one of the six core application scenarios of future 6G networks, is becoming a research hotspot in academia and industry due to its unique advantages of hardware resource sharing and spectrum sharing. This technology achieves dual functions of environmental perception and data transmission through a single signal, making it particularly suitable for converged application scenarios requiring high reliability, such as intelligent transportation and the Industrial Internet of Things. However, this dual-function characteristic also brings serious security challenges—because the sensing requirements necessitate continuous exposure of the communication signal to an open environment, traditional encryption methods are insufficient to completely prevent the risk of signal interception or tampering.

[0003] To address this challenge, an innovative solution combining a reconfigurable intelligent reflective surface (Target RIS) and a movable antenna (MA) is proposed in existing technologies. Specifically, the Target RIS technology intelligently reconfigures the wireless propagation environment by dynamically adjusting the phase and amplitude of a large number of reflective elements, significantly enhancing the signal strength of legitimate receivers while suppressing the signal quality of illegitimate receivers. Building upon this, MA technology is introduced to further expand the adjustable dimension of the channel state by dynamically adjusting the physical position of the base station antenna array. This not only improves the spatial freedom of the system but also continuously optimizes the secure communication link through positional changes, forming a spatiotemporal dual-dimensional security protection system. This synergistic mechanism of hardware reconfiguration and positional adjustability provides a new technical path for the physical layer security of ISAC systems.

[0004] However, while introducing movable antennas (MAs) can improve communication security by dynamically adjusting base station locations, this improvement also introduces new problems: the transmit beam design, target RIS phase modulation, and antenna location optimization are highly coupled, leading to a significant increase in system design complexity. Traditional optimization algorithms struggle to efficiently solve such strongly coupled problems, and the computational difficulty increases dramatically with system scale.

[0005] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention

[0006] To address the aforementioned shortcomings of existing technologies, this invention provides a method, apparatus, terminal, and storage medium for enhancing transmission security by utilizing channel reconfigurability, aiming to solve the problem that traditional optimization algorithms in the prior art struggle to efficiently solve such strongly coupled issues.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A first aspect of the present invention provides a method for enhancing transmission security using channel reconfigurability, the method comprising:

[0009] Construct a target communication system, and construct a target signal model based on the target communication system. The target communication system includes a target base station with a movable antenna, a target obstacle, a target user, a target eavesdropper, and a virtual line-of-sight link established through the target RIS.

[0010] Based on the target communication system, target constraints are obtained, and a target problem is constructed based on the target constraints. The target problem is used to design the maximum safe rate of the target communication system by combining the beamforming matrix, the sensing signal, the phase shift matrix of the target RIS, and the antenna position of the target base station.

[0011] The target problem is solved to obtain the maximum secure rate of the target communication system.

[0012] In one implementation, the target problem is:

[0013]

[0014] ||w c || 2 +tr(R r )≤P max ,

[0015]

[0016] Among them, R s The security rate of the target communication system; R represents the beamforming matrix used for communication; r For x r The covariance matrix, Represents the sensing signal vector; This represents the phase shift matrix of the target RIS. Represents the phase shift of the nth reflecting unit. t represents the antenna position vector of the linear MA array in the target base station. m Let m be the position of the m-th antenna, m1 and m2 represent the m1-th and m2-th antennas respectively, and m1 ≠ m2. min P represents the minimum distance between antennas to prevent antenna mutual coupling effects. ε represents the minimum radar beamforming gain in sensing performance. max This indicates the maximum transmit power of the target base station. Let {1, ..., N} be the set of MAs of the target base station, {1, ..., N} be the set of MAs of the target RIS, and L represent the number of channel paths between the base station and the RIS.

[0017] In one implementation, solving the target problem to obtain the maximum secure rate of the target communication system includes:

[0018] The objective problem is decomposed into a performance optimization problem under a given antenna position and an antenna position optimization problem;

[0019] Based on SCA technology, the performance optimization problem at a given antenna position is solved, and the optimized beamforming matrix, the phase shift matrix of the sensed signal and the target RIS are obtained.

[0020] The antenna position optimization problem is solved based on the optimized beamforming matrix, the sensed signal, and the phase shift matrix of the target RIS, and the optimized antenna position is obtained.

[0021] The maximum secure rate of the target communication system is obtained based on the optimized beamforming matrix, the sensed signal, the phase shift matrix of the target RIS, and the antenna position.

[0022] In one implementation, the step of solving the performance optimization problem at a given antenna position based on SCA technology to obtain the optimized beamforming matrix, the phase shift matrix of the sensed signal, and the target RIS includes:

[0023] Given the phase shift matrix of the target RIS, solve for the first target solution, which is the optimal solution for the beamforming matrix and the sensing signal under the given antenna target position and the phase shift matrix of the target RIS;

[0024] The phase shift matrix of the target RIS is optimized based on the first target solution.

[0025] In one implementation, solving the first objective solution includes:

[0026] The first target SDP problem is to solve the beamforming matrix and the sensing signal when the antenna target position and the phase shift matrix of the target RIS are fixed.

[0027] The first objective SDP problem is simplified into a first objective convex problem;

[0028] The first objective convex problem is solved using the SCA technique to obtain the first objective solution.

[0029] In one implementation, optimizing the phase shift matrix of the target RIS based on the first target solution includes:

[0030] Construct a second objective SDP problem, which is a problem of optimizing the phase shift matrix of the objective RIS based on the solution of the first objective problem;

[0031] The second objective SDP problem is simplified into a second objective convex problem;

[0032] The second target convex problem is solved using SCA technology to obtain the optimized phase shift matrix of the target RIS.

[0033] In one implementation, solving the antenna position optimization problem based on the optimized beamforming matrix, the sensed signal, and the phase shift matrix of the target RIS to obtain the optimized antenna position includes:

[0034] Based on the particle swarm optimization algorithm, particles are used to represent antenna positions;

[0035] The performance of each particle is evaluated based on the fitness function;

[0036] Obtain the antenna position constraints, and based on the antenna position constraints, obtain the optimal particle corresponding to each antenna to obtain the solution to the antenna position optimization problem.

[0037] A second aspect of the present invention provides a transmission security enhancement device utilizing channel reconfigurability, comprising:

[0038] A communication system construction module is used to construct a target communication system and construct a target signal model based on the target communication system. The target communication system includes a target base station with a movable antenna, a target obstacle, a target user, a target eavesdropper, and a virtual line-of-sight link established through the target RIS.

[0039] The target problem framework module is used to obtain target constraints based on the target communication system, and construct a target problem based on the target constraints. The target problem is used to design the maximum security rate of the target communication system by combining the beamforming matrix, the sensing signal, the phase shift matrix of the target RIS, and the antenna position of the target base station.

[0040] The solution module is used to solve the target problem and obtain the maximum secure rate of the target communication system.

[0041] A third aspect of the present invention provides a terminal, the terminal including a processor and a computer-readable storage medium communicatively connected to the processor, the computer-readable storage medium being adapted to store a plurality of instructions, the processor being adapted to invoke the instructions in the computer-readable storage medium to perform the steps of implementing the method for enhancing transmission security using channel reconfigurability as described in any of the preceding claims.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps of the method for enhancing transmission security using channel reconfigurability as described in any of the preceding claims.

[0043] Compared with existing technologies, the present invention provides a method, apparatus, and terminal for enhancing transmission security using channel reconfigurability. The method involves constructing a target communication system, building a target signal model based on the target communication system, and defining the target communication system as a target base station with a movable antenna, target obstacles, target users, target eavesdroppers, and a virtual line-of-sight link established through a target RIS. Then, target constraints are obtained based on the target communication system, and a target problem is constructed based on these constraints. This target problem is used to design the maximum security rate of the target communication system by combining beamforming matrices, sensing signals, the phase shift matrix of the target RIS, and the antenna position of the target base station. Finally, the target problem is solved to obtain the maximum security rate of the target communication system. The proposed method for enhancing transmission security using channel reconfigurability constructs a communication system comprising a base station with movable antennas, obstacles, users, eavesdroppers, and a virtual line-of-sight link established via a RIS (Relational Interference System). Based on this communication system, a target problem is constructed. Through deep problem solving, the maximum secure rate of the communication system is obtained. This effectively overcomes the performance limitations of existing technologies with fixed antenna settings, as well as the high complexity and difficulty of solving the problem after introducing movable antennas. By combining heuristic algorithms, the coupling problems of transmit beam, RIS phase shift, and antenna position are effectively solved. While fully exploiting the channel degrees of freedom, the difficulty of problem solving is reduced, thereby further improving the user's secure communication level and breaking through the bottleneck of existing technologies in ensuring wireless communication security. Attached Figure Description

[0044] Figure 1 A flowchart illustrating an embodiment of the method for enhancing transmission security using channel reconfigurability provided by the present invention;

[0045] Figure 2 A communication system structure diagram illustrating an embodiment of the method for enhancing transmission security using channel reconfigurability provided by the present invention;

[0046] Figure 3 A beam gain reference is shown in the simulation experiment of an embodiment of the transmission security enhancement device utilizing channel reconfigurability provided by the present invention.

[0047] Figure 4 The effect of transmit power on security rate in a simulation experiment of an embodiment of the transmission security device utilizing channel reconfigurability provided by the present invention is shown in the figure.

[0048] Figure 5 Figure 1 shows the effect of the number of RIS and the security rate in a simulation experiment of an embodiment of the transmission security enhancement device utilizing channel reconfigurability provided by the present invention.

[0049] Figure 6 Figure 1 shows the effect of the number of antennas on the security rate in a simulation experiment of an embodiment of the transmission security device that utilizes channel reconfigurability provided by the present invention.

[0050] Figure 7 The influence of the perception threshold ε and the security rate in a simulation experiment of an embodiment of the transmission security enhancement device utilizing channel reconfigurability provided by the present invention is shown in the figure.

[0051] Figure 8 The influence of antenna movement range and security rate in a simulation experiment of an embodiment of the transmission security enhancement device utilizing channel reconfigurability provided by the present invention is shown in the figure.

[0052] Figure 9 A simulation experiment diagram showing the influence of the number of transmission paths on the security rate in an embodiment of the transmission security device utilizing channel reconfigurability provided by the present invention.

[0053] Figure 10 A schematic diagram of the structural principle of an embodiment of the transmission security enhancement device utilizing channel reconfigurability provided by the present invention;

[0054] Figure 11 A schematic diagram illustrating the principle of an embodiment of the terminal provided by the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0056] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms "a," "an," "the," and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, "connected" or "coupled" as used herein can include wireless connections or wireless coupling. The term "and / or" as used herein includes all or any units and all combinations of one or more associated listed items.

[0057] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0058] The method for enhancing transmission security by utilizing channel reconfigurability provided by this invention can be applied to terminals with computing capabilities. The terminal can execute the method for enhancing transmission security by utilizing channel reconfigurability provided by this invention to perform transmit beamforming, RIS phase shift design, and movable antenna position design.

[0059] Example 1

[0060] Currently, in research on Intelligent Reflective Surface (RIS)-assisted Communication-Sensing Integration (ISAC), traditional solutions typically employ fixed antenna layouts. This design limits the full exploitation of channel degrees of freedom, resulting in fixed performance bottlenecks in the system. Specifically, fixed beam direction and antenna position reduce spatial multiplexing gain, making it difficult to effectively guarantee secure user communication. While introducing movable antennas (MA) can improve communication security by dynamically adjusting the base station position, this improvement also introduces new problems: a high degree of coupling exists between transmit beam design, RIS phase modulation, and antenna position optimization, significantly increasing system design complexity. Traditional optimization algorithms struggle to efficiently solve such strongly coupled problems, and the computational difficulty increases dramatically with system scale.

[0061] To address the aforementioned challenges, this application employs a fusion of heuristic intelligent algorithms to break the strong coupling between the transmit beam, target RIS phase shift configuration, and antenna position. This fully releases channel degrees of freedom while reducing the difficulty of problem-solving. This embodiment not only overcomes the performance limits of fixed antenna systems but also effectively solves the complex coupling problems caused by movable antennas, thereby constructing a more robust and secure communication mechanism and providing a new technical path for improving the security performance of wireless communication systems.

[0062] like Figure 1 As shown, in one embodiment of the method for enhancing transmission security using channel reconfigurability provided by the present invention, the enhanced transmission security using channel reconfigurability includes the following steps:

[0063] S100. Construct a target communication system and build a target signal model based on the target communication system. The target communication system includes a target base station with a movable antenna, a target obstacle, a target user, a target eavesdropper, and a virtual line-of-sight link established through the target RIS.

[0064] This embodiment focuses on addressing the dual challenges of information security and spectrum efficiency in wireless communication. In real-world scenarios, base station signals are easily intercepted by third parties, threatening user privacy and security; simultaneously, with the proliferation of smart devices, spectrum resource congestion is becoming increasingly prominent. It is worth noting that communication and radar systems, due to their highly similar hardware architectures, often employ integrated deployments to improve resource utilization, but this also creates new physical layer security vulnerabilities. To address this issue, this embodiment combines intelligent reflective surface (target RIS) technology to dynamically regulate the wireless propagation environment and construct a secure communication model, as detailed in [reference needed]. Figure 2 This allows for the use of the target RIS's electromagnetic wave manipulation capabilities to enhance signal anti-interception capabilities; simultaneously, it overcomes the performance bottlenecks of traditional fixed antenna architectures; furthermore, by establishing a new security protection mechanism in communication-radar fusion scenarios, the information security level of the ISAC system can be systematically improved.

[0065] Specifically, in real-world communication scenarios, base station broadcast signals are easily intercepted by eavesdroppers, threatening user information security. Simultaneously, with the surge in the number of smart devices, competition for spectrum resources is becoming increasingly fierce, making it difficult for traditional communication architectures to meet the demands of massive access. It is worth noting that communication and radar systems, due to the high similarity of their hardware platforms, often achieve efficient resource utilization through integrated deployment; however, this collaboration also introduces new physical layer security vulnerabilities. To address these challenges, this embodiment proposes a dynamic environment control scheme based on intelligent reflective surfaces (target RIS), which constructs, for example... Figure 2 The communication model shown utilizes the target RIS's real-time reconstruction capability of electromagnetic propagation paths to enhance signal anti-interception and alleviate spectrum congestion, while providing physical layer security protection for the communication-radar fusion system, thereby systematically improving the information security level in the ISAC scenario.

[0066] Specifically, Figure 2 The scenario includes an ISAC base station equipped with a linear movable antenna transmitter array of length F, along which the positions of M antennas can be flexibly adjusted. However, a single-antenna target, namely the eavesdropper Eve, attempts to intercept the base station's information. Furthermore, due to obstruction, its line-of-sight links are blocked. Therefore, communication is achieved through a virtual line-of-sight link established by a reconfigurable intelligent surface target (RIS), where the RIS consists of a uniform planar array of N reflective elements.

[0067] In this embodiment, the antenna position vector of the linear MA array in the base station can be represented as: Where t m Let the coordinates of the m-th antenna be denoted as and the MA sets of the base station and the target RIS be denoted as . and A minimum spacing d needs to be set between different antennas. min To prevent antenna mutual coupling, that is, the distance between antennas must satisfy the distance condition:

[0068]

[0069] in, and Let m1 and m2 represent the positions of the m1-th and m2-th antennas, respectively, where m1 ≠ m2. In the channel modeling, a planar far-field channel model is considered, and it is assumed that the channel undergoes quasi-static flat fading. Therefore, assuming perfect knowledge of the channel state information, the channel between the target RIS and the target base station can be represented as:

[0070]

[0071] in, is the transmit field response vector in the base station, where L represents the number of channel paths between the base station and the target RIS. This corresponds to the transmit field response vector from the m-th movable antenna to the target RIS;

[0072] θ m,l ∈[0,2π] is the elevation angle of the l-th path of the m-th antenna, λ is the wavelength, and j represents the imaginary unit;

[0073] Where, ν l It is the complex response of the l-th path;

[0074] R represents the received field response vector in the target RIS segment. n =(x n ,y n () represents the coordinates of the nth reflecting unit in the target RIS;

[0075] Where i represents the index of the receiving path, and the i-th receiving path is located at position r of the reflection unit. n The difference in signal propagation distance between the origin and the target RIS is determined by... express;

[0076] Channel from the target RIS to the target user Following Ricean decay, it is represented as:

[0077]

[0078] Where ι is the path loss factor at a reference distance ι0 = 1 meter; d{r,b} κ represents the distance between the target RIS and the user (target user); κ is the Rice factor. This represents the non-line of sight (NLoS) component;

[0079] Given the azimuth angle θ from the target RIS to the target user r,b ∈(0,2π] and elevation angle ζ r,b ∈(-π / 2,π / 2], according to the distance condition, the line-of-sight component Represented as:

[0080]

[0081] Where, η b,1 =sin(θ) r,b sin(ζ) r,b ), η b,2 =sin(θ) r,b cos(ζ) r,b ), Represents the floor function, N x d represents the number of target RIS units per row. r This represents the spacing between adjacent reflecting units. Assuming the virtual line-of-sight channel created by the target RIS is stronger than the non-line-of-sight channel, let θ... r,e and ζ r,e These represent the azimuth and departure angles of the target relative to the target RIS, respectively. Similarly, the term of its steering vector can be expressed as:

[0082]

[0083] Where η e,1 =sin(θ) r,e sin(ζ) r,e ), η e,2 =sin(θ) r,e cos(ζ) r,e Therefore, the channel between the target RIS and the target eavesdropper can be represented as... d {r,e} This indicates the distance between the target RIS and the target eavesdropper.

[0084] Furthermore, in this embodiment, for the signal model, let... This indicates the communication signal sent to the target user. This represents a sensing signal vector, which is generated independently of the communication signal.

[0085] Specifically, x r The covariance matrix is:

[0086]

[0087] The transmitted signal of the ISAC base station is represented as follows:

[0088] x = w c s c +x r ,

[0089] in, This represents the beamforming matrix used for communication.

[0090] The covariance matrix of the transmitted signal x is expressed as:

[0091]

[0092] Perceived direction θ r,e The beamforming gain is used as a radar performance indicator, and it is expressed as:

[0093]

[0094] in, This represents the phase shift matrix of the target RIS. This represents the phase shift of the nth reflecting unit.

[0095] For simplicity, the amplitude effect of the target RIS is ignored in this embodiment. The signals received by the target user and the target eavesdropper Eve are as follows:

[0096]

[0097] in, This indicates that the mean is 0 and the variance is σ. 2 The cyclic symmetric complex Gaussian distribution characteristics. These represent the noise received at Eve by the target user and the target eavesdropper, respectively.

[0098] Let h b (t,Φ)=H(t)Φ H h r,b and h e (t,Φ)=H(t)Φ H h r,e Let represent the equivalent channels from the base station to the target user and from the base station to Eve, respectively. Then, the achievable data rate for the target user is expressed as:

[0099]

[0100] Based on this, the system's safe speed is:

[0101] R s (w c,R r ,Φ,t)=[R b -R e ] + ,

[0102] in,[·] + =max{·,0} represents the maximum value function, which takes the larger of the value within the parentheses and 0.

[0103] At this point, the target communication system has been constructed. We will now proceed to the next step:

[0104] S200. Obtain target constraints based on the target communication system, and construct a target problem based on the target constraints. The target problem is used to design the maximum security rate of the target communication system by combining the beamforming matrix, the sensing signal, the phase shift matrix of the target RIS, and the antenna position of the target base station.

[0105] The target problem is:

[0106]

[0107] ||w c || 2 +tr(R r )≤P max ,

[0108]

[0109] Among them, R s The security rate of the target communication system; R represents the beamforming matrix used for communication; r For x r The covariance matrix, Represents the sensing signal vector; This represents the phase shift matrix of the target RIS. Represents the phase shift of the nth reflecting unit. t represents the antenna position vector of the linear MA array in the target base station. m Let m be the position of the m-th antenna, m1 and m2 represent the m1-th and m2-th antennas respectively, and m1 ≠ m2. min P represents the minimum distance between antennas to prevent antenna mutual coupling effects. ε represents the minimum radar beamforming gain in sensing performance. max This indicates the maximum transmit power of the target base station. Let {1, ..., N} be the set of MAs of the target base station, {1, ..., N} be the set of MAs of the target RIS, and L represent the number of channel paths between the base station and the RIS.

[0110] Specifically, in this embodiment, by constructing the target problem, w is jointly designed. c R r The parameters Φ and t are used to maximize the safe rate of the target communication system, ensure minimum beamforming gain, and meet the constraints of base station transmit power and movable antenna position.

[0111] Specifically, the target problem is:

[0112]

[0113] ||w c || 2 +tr(R r )≤P max ,

[0114]

[0115] Among them, R s The security rate of the target communication system; R represents the beamforming matrix used for communication; r For x r The covariance matrix, Represents the sensing signal vector; This represents the phase shift matrix of the target RIS. Represents the phase shift of the nth reflecting unit. t represents the antenna position vector of the linear MA array in the target base station. m Let m be the position of the m-th antenna, m1 and m2 represent the m1-th and m2-th antennas respectively, and m1 ≠ m2. min P represents the minimum distance between antennas to prevent antenna mutual coupling effects. ε represents the minimum radar beamforming gain in sensing performance. max This indicates the maximum transmit power of the target base station. Let {1, ..., N} be the set of MAs of the target base station, {1, ..., N} be the set of MAs of the target RIS, and L represent the number of channel paths between the base station and the RIS.

[0116] It can be seen that in the aforementioned objective problem, due to the objective function Both the constraints and conditions are non-convex, and the target problem is highly non-convex.

[0117] Among them, the radar beamforming gain under the constraint condition equation The constraint condition (‖w) is given in the text. c || 2 +tr(R r )≤P max The symbol ) represents the transmit power constraint limit of the target base station; furthermore, the constraint condition formula is... The phase shift constraint of the target RIS has been addressed; while the constraint condition (|t) i -t j |≥d min ) and formula This indicates the positional constraint of the movable antenna.

[0118] After constructing the target problem based on the target constraints, the following steps are also included:

[0119] S300. Solve the target problem to obtain the maximum secure rate of the target communication system.

[0120] Solving the target problem to obtain the maximum secure rate of the target communication system includes:

[0121] S310. Decompose the target problem into a performance optimization problem and an antenna position optimization problem under a given antenna position.

[0122] In this embodiment, the target problem is solved by decomposing the original problem into two sub-problems. Specifically, the target problem is decomposed into a performance optimization problem under a given antenna position and an antenna position optimization problem. Under any given antenna position, SDR and SCA techniques are used to optimize the performance optimization problem. c ,R r and Φ, then based on the obtained w c ,R r And Φ, the antenna position is optimized using the particle swarm optimization algorithm.

[0123] S320. Based on SCA technology, solve the performance optimization problem at a given antenna position to obtain the optimized beamforming matrix, the phase shift matrix of the sensing signal and the target RIS.

[0124] Based on the aforementioned objective problem, given the antenna location, the main problem is simplified to the performance optimization problem:

[0125]

[0126] tr(W c )+tr(R r )≤Pmax,

[0127] R r ≥0,W c ≥0,

[0128] rank(W c ) = 1;

[0129] It is easy to see that the performance optimization problem remains non-convex. Similarly, it needs to be solved into two sub-problems: a joint optimization problem of communication beamforming and sensing covariance matrix, and an IRS phase shift design problem. In the first sub-problem, SCA (Self-Controlled Aspect-Oriented Algorithm) is used, followed by the SDR (Self-Controlled Reduction) algorithm, where the rank-one constraint is ignored, and a construction method is used to satisfy the rank-one constraint. Similarly, the second sub-problem is also handled as a combination of SCA and SDR. Then, due to the limitations of traditional Gaussian randomization, the SRCR (Self-Controlled Reduction) algorithm is used to handle the rank-one constraint.

[0130] Specifically, the process of solving the performance optimization problem at a given antenna position based on SCA technology to obtain the optimized beamforming matrix, the phase shift matrix of the sensed signal, and the target RIS includes:

[0131] S321. Given the phase shift matrix of the target RIS, solve for the first target solution, which is the optimal solution of the beamforming matrix and the sensing signal under the given antenna target position and the phase shift matrix of the target RIS.

[0132] S322. Optimize the phase shift matrix of the target RIS based on the first target solution.

[0133] Solving for the first objective solution includes:

[0134] The first target SDP problem is to solve the beamforming matrix and the sensing signal when the antenna target position and the phase shift matrix of the target RIS are fixed.

[0135] The first objective SDP problem is simplified into a first objective convex problem;

[0136] The first objective convex problem is solved using the SCA technique to obtain the first objective solution.

[0137] Specifically, with the antenna position vector t of the linear MA array in the target base station and the phase shift matrix Φ of the target RIS fixed, the following is defined: Among them W c ≥0 and rank(W) c ) = 1, and define as well as We can obtain:

[0138]

[0139] We can obtain:

[0140]

[0141] Therefore, with the phase shift matrix Φ of the target RIS fixed, the performance optimization problem can be effectively constructed as the first target SDP problem:

[0142] R b (w c ,R r )-R e (w c ,R r );

[0143]

[0144] tr(W c )+tr(R r )≤P max ,

[0145] R r ≥0,W c ≥0,

[0146] rank(W c ) = 1;

[0147] The first objective SDP problem is to solve the beamforming matrix and the sensing signal when the antenna target position and the phase shift matrix of the target RIS are fixed. Since the objective function includes a non-convex objective term, the first objective SDP problem is still non-convex. To solve this problem, in this embodiment, the function R... b (W c ,R r Rewrite it in the difference form of a convex function, that is:

[0148] R b (W c ,R r )=F1(W c ,R r )-F2(R r ),

[0149] in,

[0150] At the same time, the function R e (W c ,R r Rewritten as a difference form of a convex function, the eavesdropping rate is:

[0151] R e (W c ,R r )=f1(W c ,R r )-f2(R r ),

[0152] in,

[0153] Due to the non-convexity of the objective function, in this embodiment, the SCA technique is used, which is combined with the classic optimization minimization (MM) algorithm to find a concave surrogate function for the objective function, and the surrogate function is used alternately to solve the problem.

[0154] Specifically, the SCA technique leverages the ease of solving convex optimization problems to transform a non-convex problem into a series of convex subproblems. An approximate solution to the original problem is obtained by alternately solving these subproblems. Therefore, through a first-order Taylor expansion, the concave function F2(R) is... r ) and f1(W c ,R r Solving for the given information yields:

[0155]

[0156] in and In the k1th iteration, W c and R r The value, and

[0157] Based on the above concave function F2(R) r ) and f1(W c ,R r A lower bound function is constructed for the objective function and used as a surrogate function. Therefore, in this embodiment, the first objective SDP problem is approximated as a first objective transition problem:

[0158]

[0159] tr(W c )+tr(R r )≤P max ,

[0160] R r ≥0,W c ≥0,

[0161] rank(W c ) = 1;

[0162] Use the SDR method to remove the rank-1 constraint (rank(W)). c If ) = 1), then the first objective SDP problem is transformed into the first objective convex problem:

[0163]

[0164] tr(W c )+tr(R r )≤P max ,

[0165] R r ≥0,W c ≥0;

[0166] This is a convex problem, which can be solved using CVX. Specifically, let... and This represents the optimal solution to the first objective convex problem. However, due to the lack of a rank-1 constraint, the obtained solution may not be the optimal solution to the first objective transition problem. Therefore, based on existing technology, it is necessary to construct a solution that satisfies the rank-1 constraint (rank(W)). c The solution to the constraint ) = 1).

[0167] Specifically, given the optimal solution to the first objective convex problem and The optimal solution to the first objective transition problem can be derived as follows:

[0168]

[0169] In other words, the solution to the first objective SDP problem, which is also the solution to the first objective, is:

[0170]

[0171] In this embodiment, the proof of the first objective solution is also included:

[0172] Specifically, define (Rank 1 constraint) It is the optimal solution to the first objective SDP problem.

[0173] According to the formula for the first objective solution, Therefore, it satisfies the target's power constraint. It is not difficult to prove... Therefore, the proof is now complete as the perception performance indicators that meet the target are met.

[0174] Referring to Table 1, the methods for solving w are summarized in Table 1. c and R r The detailed algorithm, the first objective algorithm, is given, where ∈1 represents the convergence precision.

[0175] Table 1: SCA algorithm for solving w c and R r :

[0176]

[0177] The optimization of the phase shift matrix of the target RIS based on the first target solution includes:

[0178] Construct a second objective SDP problem, which is a problem of optimizing the phase shift matrix of the objective RIS based on the solution of the first objective problem;

[0179] The second objective SDP problem is simplified into a second objective convex problem;

[0180] The second target convex problem is solved using SCA technology to obtain the optimized phase shift matrix of the target RIS.

[0181] Specifically, the first target solution w obtained based on the first target algorithm c and R r ,definition consider Where i ∈ (b, e). By introducing V = vv H You can get here For i∈(b,e), similarly, for have in i∈(b,e). Therefore, the optimization problem can be expressed as the second objective SDP problem:

[0182]

[0183] V≥0;

[0184] rank(V) = 1;

[0185]

[0186] Based on the properties of the objective function, the objective function can be transformed into the first objective function based on the second objective SDP problem:

[0187]

[0188] By approximating its convexity using the SCA method, we examine its first-order Taylor expansions on I1 and I2 to obtain their upper bounds, which can be expressed as the target Taylor expansion formula:

[0189]

[0190] in, V is the local optimum solution in the k2th iteration. By replacing the upper bounds of I1(V) and I2(V) in the objective Taylor expansion formula and omitting the constant terms in the first objective function, the second objective SDP problem can be optimized as follows:

[0191]

[0192] V≥0;

[0193] rank(V) = 1;

[0194]

[0195] Based on this, the convex problem can be effectively solved by relaxing the rank 1 constraint in (rank(V)=1).

[0196] Specifically, while the SDR and Gaussian randomization algorithms can obtain approximate solutions for v, they cannot guarantee the convergence of the entire algorithm. SRCR, unlike traditional algorithms, completely abandons the rank-one constraint. Instead, it relaxes the parameters... To adjust the constraints. When At this point, it effectively abandons the rank-one constraint, allowing the discovery of feasible points. With As the value steadily increases from 0, the rank-one constraint is gradually satisfied until it eventually approaches the true set of rank-one constraints. Therefore, using SRCR, (rank(V)=1) is equivalently transformed into:

[0197]

[0198] in This represents the feasible solution obtained in the k2th iteration. Corresponding to... The eigenvector of the largest eigenvector is represented as Furthermore, by iteratively sequentially... The relaxation parameters are increased from 0 to 1 to gradually approach a rank-1 solution. After each iteration, the relaxation parameters can be updated as follows:

[0199]

[0200] in, express The largest eigenvalue, and This represents the step size used for updating the weight parameters. The optimization problem at step k2 is represented as the second objective convex problem:

[0201]

[0202] V≥0;

[0203] rank(V) = 1;

[0204]

[0205] The second target convex problem is solved using the CVX tool.

[0206] Specifically, in this embodiment, the detailed algorithm steps for solving Φ based on the SCA algorithm are shown in Table 2, where ∈2 represents a predefined threshold, when When less than ∈2, the objective value of the second objective convex problem converges.

[0207] Table 2: Solving Φ based on SCA algorithm:

[0208]

[0209]

[0210] S330. Solve the antenna position optimization problem based on the optimized beamforming matrix, the phase shift matrix of the sensed signal and the target RIS, and obtain the optimized antenna position.

[0211] The process of solving the antenna position optimization problem based on the optimized beamforming matrix, the sensed signal, and the phase shift matrix of the target RIS to obtain the optimized antenna position includes:

[0212] Based on the particle swarm optimization algorithm, particles are used to represent antenna positions;

[0213] The performance of each particle is evaluated based on the fitness function;

[0214] Obtain the antenna position constraints, and based on the antenna position constraints, obtain the optimal particle corresponding to each antenna to obtain the solution to the antenna position optimization problem.

[0215] Specifically, based on the obtained beamforming matrix w c The sensing signal R r The phase shift matrix Φ of the target RIS is used to optimize the antenna position t. Given the antenna position, the objective function can be expressed as R. Sec Based on this, the objective problem can be transformed into the antenna position optimization problem:

[0216]

[0217] |t i -t j |≥d min ;

[0218]

[0219] As can be seen, the antenna position optimization problem is highly non-convex. Therefore, when using search-based methods, the high-dimensional transmit response matrix of the moving antenna significantly increases the complexity of the problem. Thus, in this embodiment, a particle swarm optimization algorithm is used to solve the antenna position optimization problem.

[0220] Specifically, the basic principle of the particle swarm optimization algorithm is to use a particle to represent every potential solution in the solution space. These particles fly at a certain speed. During flight, they continuously adjust their positions based on their individual optimal positions and the globally optimal positions found by the entire particle swarm, constantly searching for the globally optimal solution.

[0221] Specifically, firstly, S-particles are introduced, and their positions and velocities are initialized to... and in This represents the initial velocity of particle s. Let represent the initial position of particle s, and each particle represents a possible solution for the antenna position, i.e.:

[0222]

[0223] in Let represent the possible positions of the k-th antenna of particle s within a finite motion region. The particle swarm optimization algorithm can efficiently search for the optimal particle from S particles and identify it as the solution to the antenna position optimization problem.

[0224] Specifically, the fitness function is used to evaluate the performance of each particle, that is, to determine whether the particle's position can achieve the desired performance requirements. Considering the sensing constraints... Antenna position constraints (|t) i -t j |≥d min In this embodiment, a target penalty function is introduced:

[0225]

[0226] in τ represents the antenna position of the s-th particle in the q-th iteration; Q represents the maximum number of iterations; τ t and τ r The antenna position constraints (|t) are respectively represented by i -t j |≥d min ) and the sensing constraint The penalty factor is used to adjust the severity of the punishment; δ(·) is an indicator function that equals 1 when the condition is met, and 0 otherwise.

[0227] In this embodiment, the fitness function is:

[0228]

[0229] At the same time, τ t and τ r Location required The penalty function is used to drive the particles to satisfy the relevant constraints.

[0230] Therefore, as the number of iterations increases, the penalty function... It will converge to 0.

[0231] The position of the s-th particle is determined by its own local optimum. And the global best position p among all particles ＊ The global optimal position is evaluated using a fitness function. Therefore, according to existing techniques, the position and velocity update of the s-th particle can be expressed as:

[0232]

[0233] Where c1 and c2 are individual and global learning factors, respectively, used to determine the step size for each particle to move toward the optimal position; r2 and r3 are two random parameters uniformly distributed in [0, 1], the purpose of which is to increase the randomness of the search to escape local optima. This represents the inertia weight. To balance the search speed and accuracy of particles, its value gradually decreases as the number of iterations increases. Represented as:

[0234]

[0235] in, and yes The maximum and minimum values.

[0236] Because the antenna position cannot exceed the movement range, i.e., a constraint. Therefore, the coordinates of points outside this range are mapped to the corresponding maximum / minimum values:

[0237]

[0238] Therefore, according to the mapping function It ensures that the antenna position remains within the feasible area throughout the entire iteration process.

[0239] Specifically, the overall algorithm framework of the particle swarm optimization algorithm includes:

[0240] The S-particle is initialized with a random position and velocity under predefined system constraints. For the antenna configuration of each particle, the transmitted beamforming w is iteratively optimized via Algorithm 1 and Algorithm 2. c Sensing covariance matrix R r The variable is the phase shift Φ of the RIS, where the corresponding security rate R is... sec Used as a fitness metric. Afterwards, the local optimum is updated through fitness comparison. and the global optimal position p＊ During the Q-iteration process, the inertia weight It is dynamically scaled, and then the velocity and position vectors are updated using PSO kinematic rules. Each update triggers a change in the kinematics. and p ＊ A reassessment. At convergence, the optimal antenna configuration t is determined from p. ＊ This is derived, thus producing a joint solution w that satisfies all security and sensing constraints. c R r And Φ.

[0241] This gives us the optimized antenna position.

[0242] S340. Based on the optimized beamforming matrix, the sensed signal, the phase shift matrix of the target RIS, and the antenna position, the maximum secure rate of the target communication system is obtained.

[0243] Specifically, after obtaining the optimized beamforming matrix, the sensed signal, the phase shift matrix of the target RIS, and the antenna position, the maximum secure rate of the target communication system is obtained based on the optimized beamforming matrix, the sensed signal, the phase shift matrix of the target RIS, and the antenna position. The overall algorithm details for solving the target problem are shown in Table 3.

[0244] Table 3: Detailed Algorithm Details of PSO-SCA

[0245]

[0246]

[0247] This embodiment also includes simulation experiments on the algorithms in Table 3.

[0248] Specifically, including:

[0249] The ISAC base station was fixed at coordinates (0m, 0m), while the RIS base station was placed at (50m, 0m). The user and base station were positioned two meters away from the RIS, with the user's azimuth relative to the RIS at π / 6 and the target's azimuth relative to the RIS at -π / 6. Furthermore, other relevant channel parameters, particle swarm optimization parameters, and various other necessary parameters were set as shown in Table 4, which clearly presents the data to provide comprehensive data support for in-depth analysis of algorithm performance.

[0250] Table 4: System Main Parameter Settings

[0251]

[0252]

[0253] Specifically, in the simulation comparison, three basic schemes are introduced and compared with the model, among which:

[0254] Proposed: This refers to the method proposed in this embodiment for enhancing transmission security by utilizing channel reconfigurability.

[0255] FPA: Indicates that antenna position optimization is ignored, and a traditional fixed antenna array is selected. Joint optimization of RIS phase shift, communication beamforming, and sensing covariance matrix is ​​performed.

[0256] Fixed RIS: This means that phase shift optimization of RIS is ignored, a fixed RIS phase shift is selected, and only antenna position, communication beamforming and sensing covariance matrix are optimized.

[0257] ZF: This indicates that the communication beamforming and sensing covariance are solved using the zero-forcing algorithm, and the RIS phase shift and antenna position optimization are jointly optimized.

[0258] Among them, beam gain reference Figure 3 The influence between transmit power and safe rate is referenced. Figure 4 The impact of the number of RIS on the safety rate is referenced. Figure 5 The influence between the number of antennas and the safe speed is referenced. Figure 6 The influence between the perception threshold ε and the safe rate is referenced. Figure 7 The influence between antenna movement range and safe speed is referenced. Figure 8 The impact of the number of transmission paths on the secure rate is referenced. Figure 9 .

[0259] In summary, this embodiment provides a method for enhancing transmission security using channel reconfigurability. It involves constructing a target communication system, building a target signal model based on the target communication system, and defining the target communication system as a target base station with a movable antenna, target obstacles, target users, target eavesdroppers, and a virtual line-of-sight link established through a target RIS. Then, target constraints are obtained based on the target communication system, and a target problem is constructed based on these constraints. This target problem is used to design the maximum secure rate of the target communication system by combining the beamforming matrix, the sensed signal, the phase shift matrix of the target RIS, and the antenna position of the target base station. Finally, the target problem is solved to obtain the maximum secure rate of the target communication system. The proposed method for enhancing transmission security using channel reconfigurability constructs a communication system comprising a base station with movable antennas, obstacles, users, eavesdroppers, and a virtual line-of-sight link established via a RIS (Relational Information System). Based on this communication system, a target problem is constructed. Through deep problem solving, the maximum secure rate of the communication system is obtained. This effectively overcomes the performance limitations of existing technologies with fixed antenna settings, as well as the high complexity and difficulty of solving the problem after introducing movable antennas. By combining heuristic algorithms, the coupling problem between transmit beam, RIS phase shift, and antenna position is effectively solved. While fully exploiting the channel's degrees of freedom, the difficulty of solving the problem is reduced, thereby further improving the user's secure communication level and breaking through the bottleneck of existing technologies in ensuring wireless communication security.

[0260] It should be understood that although the steps in the flowcharts shown in the accompanying drawings are displayed sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0261] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0262] Example 2

[0263] Based on the above embodiments, the present invention also provides a transmission security enhancement device utilizing channel reconfigurability, the functional module of which is shown in the figure below. Figure 10 As shown, the transmission security enhancement device utilizing channel reconfigurability includes:

[0264] The communication system construction module is used to construct a target communication system and construct a target signal model based on the target communication system. The target communication system includes a target base station with a movable antenna, a target obstacle, a target user, a target eavesdropper, and a virtual line-of-sight link established through the target RIS, as specifically described in Embodiment 1.

[0265] The target problem framework module is used to obtain target constraints based on the target communication system, and construct a target problem based on the target constraints. The target problem is used to design the maximum security rate of the target communication system by combining the beamforming matrix, the sensing signal, the phase shift matrix of the target RIS, and the antenna position of the target base station, as described in Embodiment 1.

[0266] The solution module is used to solve the target problem and obtain the maximum security rate of the target communication system, as described in Embodiment 1.

[0267] Example 3

[0268] Based on the above embodiments, the present invention also provides a terminal, such as... Figure 11 As shown, the terminal includes a processor 10 and a memory 20. Figure 11 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0269] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard. Furthermore, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a channel reconfigurability-enhanced transmission security program 30, which can be executed by the processor 10 to implement the channel reconfigurability-enhanced transmission security method of this application.

[0270] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other chip, used to run program code stored in the memory 20 or process data, such as executing the method for enhancing transmission security by utilizing channel reconfigurability.

[0271] In one embodiment, when the processor 10 executes the channel reconfigurability-enhanced transport security program 30 in the memory 20, the following steps are performed:

[0272] Construct a target communication system, and construct a target signal model based on the target communication system. The target communication system includes a target base station with a movable antenna, a target obstacle, a target user, a target eavesdropper, and a virtual line-of-sight link established through the target RIS.

[0273] Based on the target communication system, target constraints are obtained, and a target problem is constructed based on the target constraints. The target problem is used to design the maximum safe rate of the target communication system by combining the beamforming matrix, the sensing signal, the phase shift matrix of the target RIS, and the antenna position of the target base station.

[0274] The target problem is solved to obtain the maximum secure rate of the target communication system.

[0275] Example 3

[0276] The present invention also provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps of the method for enhancing transmission security using channel reconfigurability as described above.

[0277] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for enhancing transmission security by utilizing channel reconfigurability, characterized in that, The method for enhancing transmission security by utilizing channel reconfigurability includes: Construct a target communication system, and construct a target signal model based on the target communication system. The target communication system includes a target base station with a movable antenna, a target obstacle, a target user, a target eavesdropper, and a virtual line-of-sight link established through the target RIS. Based on the target communication system, target constraints are obtained, and a target problem is constructed based on the target constraints. The target problem is used to design the maximum safe rate of the target communication system by combining the beamforming matrix, the sensing signal, the phase shift matrix of the target RIS, and the antenna position of the target base station. The target problem is solved to obtain the maximum secure rate of the target communication system.

2. The method for enhancing transmission security using channel reconfigurability according to claim 1, characterized in that, The target problem is: ‖In c ‖ 2 +tr(R r )≤P max , Among them, R s The security rate of the target communication system; R represents the beamforming matrix used for communication; r For x r The covariance matrix, Represents the sensing signal vector; This represents the phase shift matrix of the target RIS. Represents the phase shift of the nth reflecting unit. t represents the antenna position vector of the linear MA array in the target base station. m Let m be the position of the m-th antenna, m1 and m2 represent the m1-th and m2-th antennas respectively, and m1 ≠ m2. min P represents the minimum distance between antennas to prevent antenna mutual coupling effects. ε represents the minimum radar beamforming gain in sensing performance. max This indicates the maximum transmit power of the target base station. Let {1, ..., N} be the set of MAs of the target base station, {1, ..., N} be the set of MAs of the target RIS, and L represent the number of channel paths between the base station and the RIS.

3. The method for enhancing transmission security using channel reconfigurability according to claim 1, characterized in that, Solving the target problem to obtain the maximum secure rate of the target communication system includes: The objective problem is decomposed into a performance optimization problem under a given antenna position and an antenna position optimization problem; Based on SCA technology, the performance optimization problem at a given antenna position is solved, and the optimized beamforming matrix, the phase shift matrix of the sensed signal and the target RIS are obtained. The antenna position optimization problem is solved based on the optimized beamforming matrix, the sensed signal, and the phase shift matrix of the target RIS, and the optimized antenna position is obtained. The maximum secure rate of the target communication system is obtained based on the optimized beamforming matrix, the sensed signal, the phase shift matrix of the target RIS, and the antenna position.

4. The method for enhancing transmission security using channel reconfigurability according to claim 3, characterized in that, The method of solving the performance optimization problem at a given antenna position based on SCA technology yields the optimized beamforming matrix, the phase shift matrix of the sensed signal, and the target RIS, including: Given the phase shift matrix of the target RIS, solve for the first target solution, which is the optimal solution for the beamforming matrix and the sensing signal under the given antenna target position and the phase shift matrix of the target RIS; The phase shift matrix of the target RIS is optimized based on the first target solution.

5. The method for enhancing transmission security using channel reconfigurability according to claim 4, characterized in that, Solving for the first objective solution includes: The first target SDP problem is to solve the beamforming matrix and the sensing signal when the antenna target position and the phase shift matrix of the target RIS are fixed. The first objective SDP problem is simplified into a first objective convex problem; The first objective convex problem is solved using the SCA technique to obtain the first objective solution.

6. The method for enhancing transmission security using channel reconfigurability according to claim 4, characterized in that, The optimization of the phase shift matrix of the target RIS based on the first target solution includes: Construct a second objective SDP problem, which is a problem of optimizing the phase shift matrix of the objective RIS based on the solution of the first objective problem; The second objective SDP problem is simplified into a second objective convex problem; The second target convex problem is solved using SCA technology to obtain the optimized phase shift matrix of the target RIS.

7. The method for enhancing transmission security using channel reconfigurability according to claim 3, characterized in that, The process of solving the antenna position optimization problem based on the optimized beamforming matrix, the sensed signal, and the phase shift matrix of the target RIS to obtain the optimized antenna position includes: Based on the particle swarm optimization algorithm, particles are used to represent antenna positions; The performance of each particle is evaluated based on the fitness function; Obtain the antenna position constraints, and based on the antenna position constraints, obtain the optimal particle corresponding to each antenna to obtain the solution to the antenna position optimization problem.

8. A device for enhancing transmission security by utilizing channel reconfigurability, characterized in that, The device includes: A communication system construction module is used to construct a target communication system and construct a target signal model based on the target communication system. The target communication system includes a target base station with a movable antenna, a target obstacle, a target user, a target eavesdropper, and a virtual line-of-sight link established through the target RIS. The target problem framework module is used to obtain target constraints based on the target communication system, and construct a target problem based on the target constraints. The target problem is used to design the maximum security rate of the target communication system by combining the beamforming matrix, the sensing signal, the phase shift matrix of the target RIS, and the antenna position of the target base station. The solution module is used to solve the target problem and obtain the maximum secure rate of the target communication system.

9. A terminal, characterized in that, The terminal includes: a processor and a computer-readable storage medium communicatively connected to the processor, the computer-readable storage medium being adapted to store a plurality of instructions, and the processor being adapted to invoke the instructions in the computer-readable storage medium to execute the steps of the method for enhancing transmission security using channel reconfigurability as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs that can be executed by one or more processors to implement the steps of the method for enhancing transmission security using channel reconfigurability as described in any one of claims 1-8.

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