A method for improving the safety rate of a smart reflector-assisted WPCN system

Abstract

This invention discloses a method for improving the security rate of a smart reflector-assisted WPCN system. The method includes the following steps: proposing a smart reflector-assisted WPCN system, dividing the WPCN system transmission process into two stages: downlink energy transmission and uplink information transmission; establishing an optimization problem to maximize the system's security rate based on the WPCN system; and designing an effective method using one-dimensional search, alternating optimization, Dinkelbach algorithm, and MM algorithm to optimize the base station energy beamforming vector, the smart reflector phase shift in the uplink and downlink stages, the time allocation factor, and the position of the smart reflector for the complex optimization problem. This invention effectively improves the security rate of the WPCN system with low algorithm complexity.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically to a method for improving the security rate of a smart reflector-assisted WPCN system. Background Technology

[0002] Wireless Powered Communication Networks (WPCNs) utilize energy harvesting technology to wirelessly power devices within the network, extending their lifespan and solving the energy constraints of traditional wireless communication networks, thus offering broad application prospects. Physical Layer Security (PLS) leverages the physical characteristics of wireless channels to ensure the security of the communication system, providing stronger confidentiality compared to traditional security technologies that rely on cryptography and operate at higher layers. Intelligent Reflecting Surfaces (IRS) consist of numerous low-cost passive reflective elements, each capable of independently altering the phase shift of the incident signal. By deploying IRSs, reflected and direct links are superimposed, allowing for constructive or destructive addition of received signals, improving the overall performance of the wireless communication system. Applying IRSs to WPCN systems can effectively enhance the system's security rate.

[0003] Currently, physical layer security in IRS-assisted WPCN systems is a hot topic in wireless communication. The patent application by Ma Shuaifei et al., "A RIS-assisted WPCN System Physical Layer Security Communication Method," proposes a method to maximize the physical layer security transmission rate of a RIS-assisted WPCN system. However, it does not consider optimizing the position of the intelligent reflector and uses semidefinite relaxation (SDR) and the convex optimization tool CVX to solve the original problem, requiring Gaussian randomization to improve the solution quality, resulting in high computational complexity. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned deficiencies in the prior art and provide a method for improving the security rate of a smart reflector-assisted WPCN system. For the optimization problem of maximizing the security rate of an IRS-assisted WPCN system, an efficient method is designed using one-dimensional search, alternating optimization, Dinkelbach algorithm, and MM algorithm to optimize the base station energy beamforming vector, the smart reflector phase shift during uplink and downlink phases, the time allocation factor, and the position of the smart reflector, thereby improving the system's security rate.

[0005] The objective of this invention can be achieved by adopting the following technical solutions:

[0006] A method for improving the security rate of a smart reflector-assisted WPCN system, the WPCN system comprising a base station equipped with M antennas, a smart reflector with N reflective elements, a user equipped with a single antenna, and an eavesdropping terminal equipped with a single antenna. During the downlink phase, the user receives an energy signal transmitted by the base station; during the uplink phase, the user uses this energy to transmit an information signal to the base station, while the eavesdropping terminal simultaneously intercepts the information. The method includes the following steps:

[0007] S1. Before transmission begins, channel estimation is performed to obtain the channel between the base station and the user. Channel between base station and smart reflector Channel between intelligent reflector and user Channel between the intelligent reflector and the eavesdropping device Channel between the user and the eavesdropping device , Represents the field of complex numbers;

[0008] S2. The optimization problem of maximizing the safe speed of the system is defined as follows:

[0009]

[0010]

[0011]

[0012]

[0013]

[0014]

[0015]

[0016] in, For the base station energy beamforming vector, This represents the weight on the m-th antenna. Let the vector be the energy reflection coefficient of the intelligent reflective surface. It is the energy reflection coefficient of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the phase of the nth reflecting unit of the smart reflector during the downlink phase. This represents the reflection coefficient vector of the intelligent reflective surface. It is the information reflection coefficient of the nth reflecting unit of the intelligent reflective surface during the uplink phase. It is the phase of the nth reflecting unit of the smart reflector in the uplink phase. Assign a time vector, assuming the transmission time is normalized to . Second, It is the percentage of time allocated to the base station for transmitting energy signals. It represents the percentage of time allocated to transmitting information signals to the user, where x is the horizontal axis of the intelligent reflective surface. , These are the minimum and maximum x-coordinates of the intelligent reflector, respectively, and R is the secure transmission rate of the WPCN system. This represents the maximum transmit power of the base station.

[0017] S3. The optimization problem of maximizing the system's security rate is solved using the convex optimization tool CVX to obtain the optimal solution for the base station energy beamforming vector. Optimal solution of energy reflection coefficient vector of intelligent reflective surface Optimal solution of intelligent reflective surface information reflection coefficient vector Optimal solution of time allocation vector Optimal solution for the abscissa of the intelligent reflective surface ,in, This represents the optimal weight on the m-th antenna of the base station. It is the optimal energy reflection coefficient of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the optimal phase of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the optimal information reflection coefficient of the nth reflecting unit of the intelligent reflective surface during the uplink phase. It is the optimal phase of the nth reflecting unit of the smart reflector during the uplink phase. It is the optimal time allocation for base stations to transmit energy signals. It is the optimal time allocation for transmitting information signals to users;

[0018] S4. The base station generates an energy-carrying signal s, which follows a complex Gaussian distribution with zero mean and unit variance. The energy signal transmitted by the base station is: ,according to , Adjust the phase of the reflection unit in the downward and upward phases of the intelligent reflector separately, according to... and To allocate the time proportion of downlink transmission energy and the time proportion of uplink transmission information, according to Adjust the position of the intelligent reflective surface.

[0019] The channel estimation process is as follows: Before transmission begins, the base station sends a command to the user. After receiving the command, the user sends a training signal to the base station. The base station performs channel estimation based on the received training signal to obtain the channel between the base station and the user. ; Continue to estimate the channel state information between the base station and the smart reflector by sending training signals. Channel state information between the intelligent reflector and the user Based on the quasi-static stationary fading characteristics, the channel state information between the intelligent reflector and the eavesdropping terminal... Channel state information between the user and the eavesdropping device It is calculated using statistical information from the channel.

[0020] Furthermore, the secure transmission rate R of the WPCN system is expressed as: ,in It refers to the information transmission rate of the base station. It is the received signal-to-noise ratio at the base station. This is the actual power received by the user. For energy harvesting efficiency, It is the total channel gain between the base station and the user during the downlink phase. To collect saturated power for users' energy, This represents the total channel gain between the user and the base station during the uplink phase. It is the noise power at the base station. It refers to the information transmission rate of the eavesdropping device. It's the signal-to-noise ratio of the eavesdropping device. The total channel gain between the user and the eavesdropping device. It is the noise power of the eavesdropping device.

[0021] Furthermore, step S3 is as follows:

[0022] S3.1 Initialize the position change step size of the smart reflective surface Convergence tolerance Minimum and maximum x-coordinates of the intelligent reflective surface , ,make ;

[0023] S3.2, Initialize the number of iterations k=1, and set the base station energy beamforming vector. Intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector Time allocation vector The initial values ​​are denoted as follows: , , , Calculate the safe rate ,in , Let V represent the energy beamforming vector of the base station in the k-th iteration and the weights on the m-th antenna, respectively. , Let these represent the energy reflection coefficient vector of the smart reflective surface in the k-th iteration and the energy reflection coefficient on the n-th reflective unit, respectively. , Let the information reflection coefficient vector of the smart reflective surface in the k-th iteration and the information reflection coefficient on the n-th reflective unit be represented respectively. These represent the proportions of energy allocated to the base station for transmission and time allocated to the user for transmission in the k-th iteration, respectively.

[0024] S3.3, Fixed intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector Time allocation vector Solving the optimization problem described in step S2 yields the optimal solution for the base station energy beamforming vector in the k-th iteration, denoted as... ;

[0025] S3.4, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface information reflection coefficient vector Time allocation vector The optimal solution for the energy reflection coefficient vector of the smart reflective surface in the k-th iteration is obtained by solving the optimization problem described in step S2 using the Dinkelbach algorithm and the MM algorithm, denoted as . ;

[0026] S3.5, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface energy reflection coefficient vector Time allocation vector The optimal solution for the intelligent reflective surface information reflection coefficient vector in the k-th iteration is obtained by using the Dinkelbach algorithm and the MM algorithm to solve the optimization problem described in step S2, denoted as . ;

[0027] S3.6, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector The optimal solution to the time allocation vector in the k-th iteration is obtained by using the convex optimization tool CVX to solve the optimization problem described in step S2, denoted as . ;

[0028] S3.7 Calculation ,judge If true, stop iterating and let... , Otherwise, let k = k + 1 and return to step S3.3;

[0029] S3.8, Judgment Does it meet the requirements? If satisfied, then let Otherwise, return to step S3.2; otherwise, output the optimal solution for the position of the intelligent reflector. Optimal solution of base station energy beamforming vector The optimal solution of the energy reflection coefficient vector of the intelligent reflective surface The optimal solution for the information reflection coefficient vector of the intelligent reflective surface Optimal solution of time allocation vector .

[0030] The present invention has the following advantages and effects compared with the prior art:

[0031] 1. This invention uses the Dinkelbach algorithm and the MM algorithm to obtain a semi-closed solution for solving the energy reflection coefficient vector and information reflection coefficient vector of the intelligent reflective surface. Compared with the semidefinite relaxation method that requires Gaussian randomization, the algorithm has lower complexity.

[0032] 2. This invention proposes a method to improve the security rate of an IRS-assisted WPCN system by considering the optimization of the smart reflector position. Compared with randomly placed smart reflectors, the system has a higher security transmission rate.

[0033] 3. This invention proposes a method to improve the security rate of an IRS-assisted WPCN system while considering the optimization of the smart reflector position. For the constructed non-convex optimization problem, a tight-boundary semi-closed solution based on the Dinkelbach algorithm and the MM algorithm is proposed. An effective method using one-dimensional search and alternating optimization is designed to optimize the base station energy beamforming vector, the IRS phase shift in the uplink and downlink phases, the time allocation factor, and the position of the smart reflector. The implementation is simple. Attached Figure Description

[0034] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0035] Figure 1 This is a model diagram of the intelligent reflective surface-assisted WPCN system in this invention;

[0036] Figure 2 This is a flowchart of a method for improving the safety rate of a smart reflector-assisted WPCN system disclosed in this invention;

[0037] Figure 3 This is a flowchart of solving the optimization problem in this invention;

[0038] Figure 4This is a graph showing the relationship between the CPU execution time and the number of IRS reflection units in two different embodiments of this invention: the MM algorithm based on Dinkelbach and the SDR algorithm for solving the IRS energy reflection coefficient vector.

[0039] Figure 5 This is a graph showing the relationship between the system secure transmission rate and the maximum transmit power of the base station under two scenarios: optimized IRS location and random IRS location, in different embodiments of this invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Example 1

[0042] In this embodiment, the IRS-assisted WPCN system model diagram, the flowchart of the method for improving system security speed, and the flowchart for solving the optimization problem are respectively as follows: Figure 1 , 2 As shown in Figure 3.

[0043] In this embodiment, the specific parameter settings are as follows:

[0044] The WPCN system is set in a three-dimensional coordinate system, with the coordinates of the base station, smart reflector, user, and eavesdropping terminal being (0m, 3m, 15m), (...). (m, 6m, 15m), (4m, 0m, 15m), (8m, 0m, 15m); the base station is equipped with M=8 antennas, and the intelligent reflector has N=10 reflective elements, with a maximum transmit power of Energy harvesting efficiency User's energy harvesting saturation power Noise power at the base station and the noise power of the eavesdropping device All The step size of the position change of the intelligent reflective surface Convergence tolerance The minimum x-coordinate of the intelligent reflective surface Maximum x-coordinate All channels are Ricean channels with a Ricean factor of 10, and the path loss model is as follows: ,in For reference distance The path loss at point , and the path loss exponents are respectively , , , , .

[0045] This method includes the following steps:

[0046] S1. Before transmission begins, the base station sends a command to the user. After receiving the command, the user sends a training signal to the base station. The base station performs channel estimation based on the received training signal to obtain the channel between the base station and the user. ; Continue to estimate the channel state information between the base station and the smart reflector by sending training signals. Channel state information between the intelligent reflector and the user Based on the quasi-static stationary fading characteristics, the channel state information between the intelligent reflector and the eavesdropping terminal... Channel state information between the user and the eavesdropping device Calculated using channel statistics;

[0047] S2. The optimization problem of maximizing the safe speed of the system is defined as follows:

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] in, For the base station energy beamforming vector, This represents the weight on the m-th antenna. Let the vector be the energy reflection coefficient of the intelligent reflective surface. It is the energy reflection coefficient of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the phase of the nth reflecting unit of the smart reflector during the downlink phase. The vector of reflection coefficients of the intelligent reflective surface. It is the information reflection coefficient of the nth reflecting unit of the intelligent reflective surface during the uplink phase. It is the phase of the nth reflecting unit of the smart reflector in the uplink phase. Assign a time vector, assuming the transmission time is normalized to . Second, It is the percentage of time allocated to the base station for transmitting energy signals. It represents the percentage of time allocated to transmitting information signals to the user, where x is the horizontal axis of the intelligent reflective surface. , These are the minimum and maximum x-coordinates of the intelligent reflective surface, respectively. This is the maximum transmit power of the base station. ,in It refers to the information transmission rate of the base station. It is the received signal-to-noise ratio at the base station. This is the actual power received by the user. For energy harvesting efficiency, It is the total channel gain between the base station and the user during the downlink phase. To collect saturated power for users' energy, This represents the total channel gain between the user and the base station during the uplink phase. It is the noise power at the base station. It refers to the information transmission rate of the eavesdropping device. It's the signal-to-noise ratio of the eavesdropping device. The total channel gain between the user and the eavesdropping device. It is the noise power of the eavesdropping device;

[0056] S3. The optimization problem of maximizing the system's security rate is solved using the convex optimization tool CVX to obtain the optimal solution for the base station energy beamforming vector. Optimal solution of energy reflection coefficient vector of intelligent reflective surface Optimal solution of intelligent reflective surface information reflection coefficient vector Optimal solution of time allocation vector Optimal solution for the abscissa of the intelligent reflective surface ,in, This represents the optimal weight on the m-th antenna of the base station. It is the optimal energy reflection coefficient of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the optimal phase of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the optimal information reflection coefficient of the nth reflecting unit of the intelligent reflective surface during the uplink phase. It is the optimal phase of the nth reflecting unit of the smart reflector during the uplink phase. It is the optimal time allocation for base stations to transmit energy signals. It represents the optimal time allocation for transmitting information signals to users. The specific solution process is as follows:

[0057] S3.1 Initialize the position change step size of the smart reflective surface Convergence tolerance The minimum and maximum x-coordinates of the intelligent reflective surface , ,make ;

[0058] S3.2, Initialize the number of iterations k=1, and set the base station energy beamforming vector. Intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector Time allocation vector The initial values ​​are denoted as follows: , , , Calculate the safe rate ,in , Let V represent the energy beamforming vector of the base station in the k-th iteration and the weights on the m-th antenna, respectively. , Let these represent the energy reflection coefficient vector of the smart reflective surface in the k-th iteration and the energy reflection coefficient on the n-th reflective unit, respectively. , Let the information reflection coefficient vector of the smart reflective surface in the k-th iteration and the information reflection coefficient on the n-th reflective unit be represented respectively. These represent the proportions of energy allocated to the base station for transmission and time allocated to the user for transmission in the k-th iteration, respectively.

[0059] S3.3, Fixed intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector Time allocation vector Solving the optimization problem described in step S2 yields the optimal solution for the base station energy beamforming vector in the k-th iteration, denoted as... ;

[0060] S3.4, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface information reflection coefficient vector Time allocation vector The optimal solution for the energy reflection coefficient vector of the smart reflective surface in the k-th iteration is obtained by solving the optimization problem described in step S2 using the Dinkelbach algorithm and the MM algorithm, denoted as . ;

[0061] S3.5, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface energy reflection coefficient vector Time allocation vector The optimal solution for the intelligent reflective surface information reflection coefficient vector in the k-th iteration is obtained by using the Dinkelbach algorithm and the MM algorithm to solve the optimization problem described in step S2, denoted as . ;

[0062] S3.6, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector The optimal solution to the time allocation vector in the k-th iteration is obtained by using the convex optimization tool CVX to solve the optimization problem described in step S2, denoted as . ;

[0063] S3.7 Calculation ,judge If true, then stop iterating and let... , Otherwise, let k = k + 1 and return to step S3.3;

[0064] S3.8, Judgment Does it meet the requirements? If satisfied, then let Otherwise, return to step S3.2; otherwise, output the optimal solution for the position of the intelligent reflector. Optimal solution of base station energy beamforming vector The optimal solution of the energy reflection coefficient vector of the intelligent reflective surface The optimal solution for the information reflection coefficient vector of the intelligent reflective surface Optimal solution of time allocation vector ;

[0065] S4. The base station generates an energy-carrying signal s, which follows a complex Gaussian distribution with zero mean and unit variance. The energy signal transmitted by the base station is: ,according to , Adjust the phase of the reflection unit in the downward and upward phases of the intelligent reflector separately, according to... and To allocate the time proportion of downlink transmission energy and the time proportion of uplink transmission information, according to Adjust the position of the intelligent reflective surface.

[0066] Example 2

[0067] In this embodiment, the IRS-assisted WPCN system model diagram, the flowchart of the method for improving system security speed, and the flowchart for solving the optimization problem are respectively as follows: Figure 1 , 2 As shown in Figure 3.

[0068] In this embodiment, the specific parameter settings are as follows:

[0069] The WPCN system is set in a three-dimensional coordinate system, with the coordinates of the base station, smart reflector, user, and eavesdropping terminal being (0m, 3m, 15m), (...). (m, 6m, 15m), (4m, 0m, 15m), (8m, 0m, 15m); the base station is equipped with M=16 antennas, and the intelligent reflector has N=10 reflective elements, with a maximum transmit power of Energy harvesting efficiency User's energy harvesting saturation power Noise power at the base station and the noise power of the eavesdropping device All The step size of the position change of the intelligent reflective surface Convergence tolerance The minimum x-coordinate of the intelligent reflective surface Maximum x-coordinate All channels are Ricean channels with a Ricean factor of 10, and the path loss model is as follows: ,in For reference distance The path loss at point , and the path loss exponents are respectively , , , , .

[0070] The method includes the following steps:

[0071] S1. Before transmission begins, the base station sends a command to the user. After receiving the command, the user sends a training signal to the base station. The base station performs channel estimation based on the received training signal to obtain the channel between the base station and the user. ; Continue to estimate the channel state information between the base station and the smart reflector by sending training signals. Channel state information between the intelligent reflector and the user Based on the quasi-static stationary fading characteristics, the channel state information between the intelligent reflector and the eavesdropping terminal... Channel state information between the user and the eavesdropping device Calculated using channel statistics;

[0072] S2. The optimization problem of maximizing the safe speed of the system is defined as follows:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] in, For the base station energy beamforming vector, This represents the weight on the m-th antenna. Let the vector be the energy reflection coefficient of the intelligent reflective surface. It is the energy reflection coefficient of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the phase of the nth reflecting unit of the smart reflector during the downlink phase. The vector of reflection coefficients of the intelligent reflective surface. It is the information reflection coefficient of the nth reflecting unit of the intelligent reflective surface during the uplink phase. It is the phase of the nth reflecting unit of the smart reflector in the uplink phase. Assign a time vector, assuming the transmission time is normalized to . Second, It is the percentage of time allocated to the base station for transmitting energy signals. It represents the percentage of time allocated to transmitting information signals to the user, where x is the horizontal axis of the intelligent reflective surface. , These are the minimum and maximum x-coordinates of the intelligent reflective surface, respectively. This is the maximum transmit power of the base station. ,in It refers to the information transmission rate of the base station. It is the received signal-to-noise ratio at the base station. This is the actual power received by the user. For energy harvesting efficiency, It is the total channel gain between the base station and the user during the downlink phase. To collect saturated power for users' energy, This represents the total channel gain between the user and the base station during the uplink phase. It is the noise power at the base station. It refers to the information transmission rate of the eavesdropping device. It's the signal-to-noise ratio of the eavesdropping device. The total channel gain between the user and the eavesdropping device. It is the noise power of the eavesdropping device;

[0081] S3. The optimization problem of maximizing the system's security rate is solved using the convex optimization tool CVX to obtain the optimal solution for the base station energy beamforming vector. Optimal solution of energy reflection coefficient vector of intelligent reflective surface Optimal solution of intelligent reflective surface information reflection coefficient vector Optimal solution of time allocation vector Optimal solution for the abscissa of the intelligent reflective surface ,in, This represents the optimal weight on the m-th antenna of the base station. It is the optimal energy reflection coefficient of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the optimal phase of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the optimal information reflection coefficient of the nth reflecting unit of the intelligent reflective surface during the uplink phase. It is the optimal phase of the nth reflecting unit of the smart reflector during the uplink phase. It is the optimal time allocation for base stations to transmit energy signals. It represents the optimal time allocation for transmitting information signals to users. The specific solution process is as follows:

[0082] S3.1 Initialize the position change step size of the smart reflective surface Convergence tolerance Minimum and maximum x-coordinates of the intelligent reflective surface , ,make ;

[0083] S3.2, Initialize the number of iterations k=1, and set the base station energy beamforming vector. Intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector Time allocation vector The initial values ​​are denoted as follows: , , , Calculate the safe rate ,in , Let V represent the energy beamforming vector of the base station in the k-th iteration and the weights on the m-th antenna, respectively. , Let these represent the energy reflection coefficient vector of the smart reflective surface in the k-th iteration and the energy reflection coefficient on the n-th reflective unit, respectively. , Let the information reflection coefficient vector of the smart reflective surface in the k-th iteration and the information reflection coefficient on the n-th reflective unit be represented respectively. These represent the proportions of energy allocated to the base station for transmission and time allocated to the user for transmission in the k-th iteration, respectively.

[0084] S3.3, Fixed intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector Time allocation vector Solving the optimization problem described in step S2 yields the optimal solution for the base station energy beamforming vector in the k-th iteration, denoted as... ;

[0085] S3.4, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface information reflection coefficient vector Time allocation vector The optimal solution for the energy reflection coefficient vector of the smart reflective surface in the k-th iteration is obtained by solving the optimization problem described in step S2 using the Dinkelbach algorithm and the MM algorithm, denoted as . ;

[0086] S3.5, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface energy reflection coefficient vector Time allocation vector The optimal solution for the intelligent reflective surface information reflection coefficient vector in the k-th iteration is obtained by using the Dinkelbach algorithm and the MM algorithm to solve the optimization problem described in step S2, denoted as . ;

[0087] S3.6, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector The optimal solution to the time allocation vector in the k-th iteration is obtained by using the convex optimization tool CVX to solve the optimization problem described in step S2, denoted as . ;

[0088] S3.7 Calculation ,judge If true, stop iterating and let... , Otherwise, let k = k + 1 and return to step S3.3;

[0089] S3.8, Judgment Does it meet the requirements? If satisfied, then let Otherwise, return to step S3.2; otherwise, output the optimal solution for the position of the intelligent reflector. Optimal solution of base station energy beamforming vector The optimal solution of the energy reflection coefficient vector of the intelligent reflective surface The optimal solution for the information reflection coefficient vector of the intelligent reflective surface Optimal solution of time allocation vector ;

[0090] S4. The base station generates an energy-carrying signal s, which follows a complex Gaussian distribution with zero mean and unit variance. The energy signal transmitted by the base station is: ,according to , Adjust the phase of the reflection unit in the downward and upward phases of the intelligent reflector separately, according to... and To allocate the time proportion of downlink transmission energy and the time proportion of uplink transmission information, according to Adjust the position of the intelligent reflective surface.

[0091] Figure 4 This graph shows the relationship between CPU execution time and the number of IRS reflection units in two different implementations: the MM algorithm based on Dinkelbach and the SDR algorithm for solving the IRS energy reflection coefficient vector. As can be seen from the graph, the CPU execution time of both algorithms increases with the increase in the number of IRS reflection units. Furthermore, it can be seen that the SDR algorithm has a longer CPU execution time compared to the MM algorithm. This is because the SDR algorithm uses the CVX tool and requires Gaussian randomization to improve the solution quality, resulting in high computational complexity. This invention proposes a tight-boundary semi-closed solution based on the Dinkelbach and MM algorithms, which can effectively reduce CPU execution time.

[0092] Figure 5 This graph shows the relationship between the system secure transmission rate and the maximum transmit power of the base station under two scenarios: optimized IRS location and random IRS location. As can be seen from the graph, the secure transmission rate increases in both scenarios as the maximum transmit power of the base station increases. However, compared with randomly placed smart reflectors, the proposed solution in this invention has a higher system secure transmission rate.

[0093] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for improving the safety rate of a smart reflective surface-assisted WPCN system, characterized in that, The WPCN system includes a base station equipped with M antennas, a smart reflector with N reflector elements, a user equipped with a single antenna, and an eavesdropping terminal equipped with a single antenna. During the downlink phase, the user receives energy signals transmitted by the base station; during the uplink phase, the user uses this energy to transmit information signals back to the base station, while the eavesdropping terminal simultaneously intercepts the information. The method includes the following steps: S1. Before transmission begins, channel estimation is performed to obtain the channel between the base station and the user. Channel between base station and smart reflector Channel between intelligent reflector and user Channel between the intelligent reflector and the eavesdropping device Channel between the user and the eavesdropping device , Represents the field of complex numbers; S2. The optimization problem of maximizing the safe speed of the system is defined as follows: in, For the base station energy beamforming vector, This represents the weight on the m-th antenna. Let the vector be the energy reflection coefficient of the intelligent reflective surface. It is the energy reflection coefficient of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the phase of the nth reflecting unit of the smart reflector during the downlink phase. This represents the reflection coefficient vector of the intelligent reflective surface. It is the information reflection coefficient of the nth reflecting unit of the intelligent reflective surface during the uplink phase. It is the phase of the nth reflecting unit of the smart reflector in the uplink phase. Assign a time vector, assuming the transmission time is normalized to . Second, It is the percentage of time allocated to the base station for transmitting energy signals. It represents the percentage of time allocated to transmitting information signals to the user, where x is the horizontal axis of the intelligent reflective surface. , These are the minimum and maximum x-coordinates of the intelligent reflector, respectively, and R is the secure transmission rate of the WPCN system. This represents the maximum transmit power of the base station. S3. The optimization problem of maximizing the system's security rate is solved using the convex optimization tool CVX to obtain the optimal solution for the base station energy beamforming vector. Optimal solution of energy reflection coefficient vector of intelligent reflective surface Optimal solution of intelligent reflective surface information reflection coefficient vector Optimal solution of time allocation vector Optimal solution for the abscissa of the intelligent reflective surface Among them, the Dinkelbach algorithm and the MM algorithm are used to solve the optimization problem of maximizing the system's safety rate to obtain the optimal solution of the energy reflection coefficient vector of the intelligent reflector. The optimal solution for the information reflection coefficient vector of the intelligent reflector is obtained by solving the optimization problem of maximizing the system's safe rate using the Dinkelbach algorithm and the MM algorithm. , This represents the optimal weight on the m-th antenna of the base station. It is the optimal energy reflection coefficient of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the optimal phase of the nth reflecting unit of the intelligent reflector during the downlink phase. It is the optimal information reflection coefficient of the nth reflecting unit of the intelligent reflective surface during the uplink phase. It is the optimal phase of the nth reflecting unit of the smart reflector during the uplink phase. It is the optimal time allocation for base stations to transmit energy signals. It is the optimal time allocation for transmitting information signals to users; S4. The base station generates an energy-carrying signal s, which follows a complex Gaussian distribution with zero mean and unit variance. The energy signal transmitted by the base station is: ,according to , Adjust the phase of the reflection unit in the downward and upward phases of the intelligent reflector separately, according to... and To allocate the time proportion of downlink transmission energy and the time proportion of uplink transmission information, according to Adjust the position of the intelligent reflective surface.

2. The method for improving the safety rate of a smart reflector-assisted WPCN system according to claim 1, characterized in that, The channel estimation process is as follows: Before transmission begins, the base station sends a command to the user. After receiving the command, the user sends a training signal to the base station. The base station performs channel estimation based on the received training signal to obtain the channel between the base station and the user. ; Continue to estimate the channel state information between the base station and the smart reflector by sending training signals. Channel state information between the intelligent reflector and the user Based on the quasi-static stationary fading characteristics, the channel state information between the intelligent reflector and the eavesdropping terminal... Channel state information between the user and the eavesdropping device It is calculated using statistical information from the channel.

3. The method for improving the safety rate of a smart reflector-assisted WPCN system according to claim 1, characterized in that, The secure transmission rate R of the WPCN system is expressed as: ,in It refers to the information transmission rate of the base station. It is the received signal-to-noise ratio at the base station. This is the actual power received by the user. For energy harvesting efficiency, It is the total channel gain between the base station and the user during the downlink phase. To collect saturated power for users' energy, This represents the total channel gain between the user and the base station during the uplink phase. It is the noise power at the base station. It refers to the information transmission rate of the eavesdropping device. It's the signal-to-noise ratio of the eavesdropping device. The total channel gain between the user and the eavesdropping device. It is the noise power of the eavesdropping device.

4. The method for improving the safety rate of a smart reflector-assisted WPCN system according to claim 1, characterized in that, The process of step S3 is as follows: S3.1 Initialize the position change step size of the smart reflective surface Convergence tolerance Minimum and maximum x-coordinates of the intelligent reflective surface , ,make ; S3.2, Initialize the number of iterations k=1, and set the base station energy beamforming vector. Intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector Time allocation vector The initial values ​​are denoted as follows: , , , Calculate the safe rate ,in , Let V represent the energy beamforming vector of the base station in the k-th iteration and the weights on the m-th antenna, respectively. , Let these represent the energy reflection coefficient vector of the smart reflective surface in the k-th iteration and the energy reflection coefficient on the n-th reflective unit, respectively. , Let the information reflection coefficient vector of the smart reflective surface in the k-th iteration and the information reflection coefficient on the n-th reflective unit be represented respectively. These represent the proportions of energy allocated to the base station for transmission and time allocated to the user for transmission in the k-th iteration, respectively. S3.3, Fixed intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector Time allocation vector Solving the optimization problem described in step S2 yields the optimal solution for the base station energy beamforming vector in the k-th iteration, denoted as... ; S3.4, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface information reflection coefficient vector Time allocation vector The optimal solution for the energy reflection coefficient vector of the smart reflective surface in the k-th iteration is obtained by solving the optimization problem described in step S2 using the Dinkelbach algorithm and the MM algorithm, denoted as . ; S3.5, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface energy reflection coefficient vector Time allocation vector The optimal solution for the intelligent reflective surface information reflection coefficient vector in the k-th iteration is obtained by using the Dinkelbach algorithm and the MM algorithm to solve the optimization problem described in step S2, denoted as . ; S3.6, Fixed Base Station Energy Beamforming Vector Intelligent reflective surface energy reflection coefficient vector Intelligent reflective surface information reflection coefficient vector The optimal solution to the time allocation vector in the k-th iteration is obtained by using the convex optimization tool CVX to solve the optimization problem described in step S2, denoted as . ; S3.7 Calculation ,judge If true, then stop iterating and let... , Otherwise, let k = k + 1 and return to step S3.3; S3.8, Judgment Does it meet the requirements? If satisfied, then let Otherwise, return to step S3.2; otherwise, output the optimal solution for the position of the intelligent reflector. Optimal solution of base station energy beamforming vector The optimal solution of the energy reflection coefficient vector of the intelligent reflective surface The optimal solution for the information reflection coefficient vector of the intelligent reflective surface Optimal solution of time allocation vector .

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