Method for improving safety rate of intelligent reflector-assisted WPCN system

Abstract

The invention discloses a method for improving the safety rate of an intelligent reflector-assisted WPCN (Wireless Power Control Network) system, which comprises the following steps of: providing the intelligent reflector-assisted WPCN system, and dividing the transmission process of the WPCN system into two stages of downlink energy transmission and uplink information transmission; establishing an optimization problem of maximizing the system security rate according to the WPCN system; aiming at a complex optimization problem, an effective method using one-dimensional search, alternate optimization, a Dinkelbach algorithm and an MM algorithm is designed to optimize a base station energy beam forming vector, intelligent reflecting surface shifts in uplink and downlink stages, a time allocation factor and the position of an intelligent reflecting surface; according to the method, the safety rate of the WPCN system can be effectively improved, and the algorithm complexity is low.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method for improving the safety rate of a WPCN system assisted by an intelligent reflective surface. Background Art

[0002] Wireless Powered Communication Networks (WPCN) use energy harvesting technology to wirelessly power devices in the network and extend their service life. This can solve the energy limitation problem in traditional wireless communication networks and has broad application prospects. Physical Layer Security (PLS) uses the physical characteristics of wireless channels to ensure the security of communication systems. Compared with traditional security technologies that rely on cryptography and work on upper-layer protocols, it has stronger confidentiality. Intelligent Reflecting Surface (IRS) consists of a large number of low-cost passive reflective elements, each of which can independently change the phase shift of the incident signal. By deploying IRS, the reflection link is superimposed on the direct link to achieve constructive or destructive addition of the received signal, thereby improving the overall performance of the wireless communication system. Applying IRS in the WPCN system can effectively improve the security rate of the system.

[0003] At present, the physical layer security problem of IRS-assisted WPCN system is a hot topic in wireless communication. The patent "A physical layer secure communication method of RIS-assisted WPCN system" applied by Ma Shuaifei et al. proposed a method to maximize the physical layer secure transmission rate of RIS-assisted WPCN system, but it did not consider optimizing the position of the smart reflector, and used semidefinite relaxation (SDR) and convex optimization tool CVX to solve the original problem, which requires Gaussian randomization to improve the quality of the solution and has high computational complexity. Summary of the invention

[0004] The purpose of the present invention is to solve the above defects in the prior art and provide a method for improving the safe rate of the intelligent reflector-assisted WPCN system. Aiming at the optimization problem of maximizing the safe rate of the IRS-assisted WPCN system, an effective method using one-dimensional search, alternating optimization, Dinkelbach algorithm and MM algorithm is designed to optimize the base station energy beamforming vector, the phase shift of the intelligent reflector in the uplink and downlink stages, the time allocation factor and the position of the intelligent reflector to improve the safe rate of the system.

[0005] The purpose of the present invention can be achieved by adopting the following technical solutions: A method for improving the safety rate of a WPCN system assisted by an intelligent reflective surface, wherein the WPCN system includes a base station equipped with M antennas, an intelligent reflective surface with N reflective units, a user equipped with a single antenna, and an eavesdropping terminal equipped with a single antenna. In the downlink phase, the user receives an energy signal sent by the base station, and in the uplink phase, the user uses the energy to transmit an information signal to the base station, while the eavesdropping terminal eavesdrops on the information. The method includes the following steps: S1. Perform channel estimation before transmission starts to obtain the channel between the base station and the user. , the channel between the base station and the smart reflector , the channel between the smart reflective surface and the user , the channel between the intelligent reflective surface and the eavesdropping end , the channel between the user and the eavesdropping end , represents a complex domain; S2. Define the optimization problem of maximizing the system safety rate as follows: in, is the base station energy beamforming vector, represents the weight on the mth antenna, is the energy reflection coefficient vector of the smart reflector, is the energy reflection coefficient of the nth reflection unit of the intelligent reflection surface in the downlink phase, is the phase of the nth reflector unit of the intelligent reflector surface in the downlink phase, is the reflection coefficient vector of the smart reflective surface information, is the information reflection coefficient of the nth reflection unit of the intelligent reflection surface in the uplink phase, is the phase of the nth reflector unit of the intelligent reflector surface in the uplink phase, is the time allocation vector, assuming that the transmission time is normalized to Second, is the proportion of time allocated to the base station to transmit energy signals, is the proportion of time allocated to users to transmit information signals, x is the horizontal coordinate of the smart reflective surface, , are the minimum and maximum horizontal coordinates of the intelligent reflector, R is the safe transmission rate of the WPCN system, is the maximum transmit power of the base station; S3. Use the convex optimization tool CVX to solve the optimization problem of maximizing the system safety rate and obtain the optimal solution of the base station energy beamforming vector. , Optimal solution of energy reflection coefficient vector of intelligent reflective surface , Optimal solution of the reflection coefficient vector of the intelligent reflective surface information , optimal solution of time allocation vector , Optimal solution of the horizontal coordinate of the intelligent reflection surface ,in, represents the optimal weight of the mth antenna of the base station, is the optimal energy reflection coefficient of the nth reflection unit of the intelligent reflection surface in the downlink phase, is the optimal phase of the nth reflector unit of the intelligent reflector in the downlink phase, is the optimal information reflection coefficient of the nth reflection unit of the intelligent reflection surface in the uplink phase, is the optimal phase of the nth reflector unit of the intelligent reflector in the uplink phase, is the optimal time ratio allocated to the base station to transmit energy signals, It is the optimal time proportion allocated to users to transmit information signals; S4, the base station generates an energy-carrying signal s, s obeys a complex Gaussian distribution with zero mean and unit variance. The energy signal sent by the base station is: ,according to , Adjust the phase of the reflector unit in the downlink and uplink stages of the intelligent reflector surface respectively, 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 smart reflective surface.

[0006] The process of channel estimation is as follows: before the transmission starts, the base station sends an instruction to the user. After receiving the instruction, 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 reflective surface and the user ; According to the quasi-static steady fading characteristics, the channel state information between the intelligent reflective surface and the eavesdropping end , the channel state information between the user and the eavesdropping end Calculated using channel statistics.

[0007] Furthermore, the safe transmission rate R of the WPCN system is expressed as ,in is the information transmission rate of the base station, is the received signal-to-noise ratio at the base station, is the power actually received by the user, is the energy harvesting efficiency, is the total channel gain between the base station and the user in the downlink phase, Energy harvesting saturation power for users, is the total channel gain between the user and the base station in the uplink phase, is the noise power at the base station, is the information transmission rate of the eavesdropping end, is the signal-to-noise ratio at the eavesdropping end, is the total channel gain between the user and the eavesdropping end, is the noise power at the eavesdropping end.

[0008] Furthermore, the process of step S3 is as follows: S3.1. Initialize the position change step of the smart reflective surface , Convergence Tolerance , the minimum and maximum horizontal coordinates of the smart reflective surface , ,make ; S3.2, initialization iteration number k=1, base station energy beamforming vector , smart reflective surface energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , the initial values ​​are recorded as , , , , calculate the safe rate ,in , They represent the energy beamforming vector of the k-th iteration base station and the weight on the m-th antenna, , They represent the energy reflection coefficient vector of the k-th iteration smart reflection surface and the energy reflection coefficient on the n-th reflection unit, respectively. , They represent the information reflection coefficient vector of the k-th iteration smart reflection surface and the information reflection coefficient on the n-th reflection unit, respectively. They represent the time proportions allocated to the base station transmission energy and the user information transmission energy in the kth iteration respectively; S3.3. Fixed smart reflector energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , solve the optimization problem in step S2 to obtain the optimal solution of the k-th iteration base station energy beamforming vector, denoted as ; S3.4. Fixed base station energy beamforming vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , the Dinkelbach algorithm and the MM algorithm are used to solve the optimization problem described in step S2 to obtain the optimal solution of the energy reflection coefficient vector of the k-th iteration smart reflector, which is recorded as ; S3.5. Fixed base station energy beamforming vector , smart reflective surface energy reflection coefficient vector , time allocation vector , the Dinkelbach algorithm and the MM algorithm are used to solve the optimization problem described in step S2 to obtain the optimal solution of the k-th iteration of the intelligent reflector information reflection coefficient vector, which is recorded as ; S3.6, Fixed Base Station Energy Beamforming Vector , smart reflective surface energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , using the convex optimization tool CVX to solve the optimization problem in step S2, we get the optimal solution of the k-th iteration time allocation vector, denoted as ; S3.7. Calculation ,judge If established, stop the iteration and let , ; Otherwise, let k=k+1 and return to step S3.3; S3.8. Judgment Is it satisfied? If satisfied, then , return to step S3.2, otherwise, output the optimal solution for the position of the smart reflective surface , the optimal solution of the base station energy beamforming vector , the optimal solution of the energy reflection coefficient vector of the smart reflector , the optimal solution of the reflection coefficient vector of the intelligent reflective surface information , the optimal solution of the time allocation vector .

[0009] Compared with the prior art, the present invention has the following advantages and effects: 1. The present invention adopts the Dinkelbach algorithm and the MM algorithm to obtain a semi-closed solution for solving the energy reflection coefficient vector and the information reflection coefficient vector of the intelligent reflection surface. Compared with the semi-definite relaxation method that requires Gaussian randomization, the algorithm complexity is lower.

[0010] 2. The present invention proposes a method for improving the safety rate of the IRS-assisted WPCN system under the condition of optimizing the position of the intelligent reflective surface. Compared with the random placement of the intelligent reflective surface, the system safety transmission rate is higher.

[0011] 3. The present invention proposes a method for improving the safety rate of the IRS-assisted WPCN system while considering optimizing the position of the intelligent reflecting surface. 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 stages, the time allocation factor and the position of the intelligent reflecting surface, which is simple to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 It is a model diagram of the intelligent reflector-assisted WPCN system in the present invention; Figure 2 It is a flow chart of a method for improving the safety rate of a WPCN system assisted by an intelligent reflective surface disclosed in the present invention; Figure 3 It is a flow chart for solving the optimization problem in the present invention; Figure 4 is a graph showing the relationship between the CPU execution time of different embodiments and the number of IRS reflection units in two cases of solving the IRS energy reflection coefficient vector using the Dinkelbach-based MM algorithm and the SDR algorithm in the present invention; Figure 5 It is a relationship diagram of the system security transmission rate of different embodiments of the present invention with the maximum transmission power of the base station under the two situations of optimized IRS position and random IRS position. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Example 1 The IRS-assisted WPCN system model diagram, the method flow chart for improving the system safety rate, and the flow chart for solving the optimization problem in this embodiment are shown in the following figures respectively. Figure 1 , 2 , as shown in Figure 3.

[0015] In this embodiment, the specific parameters are set as follows: Assume that the WPCN system is located in a three-dimensional coordinate system, and the coordinates of the base station, intelligent reflector, user, and eavesdropping end are (0m, 3m, 15m), ( m,6m,15m), (4m,0m,15m), (8m,0m,15m); the base station is equipped with M=8 antennas, the intelligent reflector has N=10 reflective units, and the maximum transmission power , energy collection efficiency , the energy harvesting saturation power of the user , the noise power at the base station and the noise power of the eavesdropping end Both , the position change step of the smart reflective surface , convergence tolerance , the minimum horizontal coordinate of the smart reflective surface , the maximum horizontal coordinate ; The channels are all Rice channels, the Rice factor is 10, and the path loss model is ,in The reference distance The path loss at and the path loss exponents are , , , , .

[0016] This method comprises the following steps: S1. Before the transmission starts, the base station sends an instruction to the user. After receiving the instruction, the user sends a training signal to the base station. The base station estimates the channel based on the received training signal and obtains 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 reflective surface and the user ; According to the quasi-static steady fading characteristics, the channel state information between the intelligent reflective surface and the eavesdropping end , the channel state information between the user and the eavesdropping end Calculated through the statistical information of the channel; S2. Define the optimization problem of maximizing the system safety rate as follows: in, is the base station energy beamforming vector, represents the weight on the mth antenna, is the energy reflection coefficient vector of the smart reflector, is the energy reflection coefficient of the nth reflection unit of the intelligent reflection surface in the downlink phase, is the phase of the nth reflector unit of the intelligent reflector surface in the downlink phase, is the reflection coefficient vector of the smart reflective surface information, is the information reflection coefficient of the nth reflection unit of the intelligent reflection surface in the uplink phase, is the phase of the nth reflector unit of the intelligent reflector surface in the uplink phase, is the time allocation vector, assuming that the transmission time is normalized to Second, is the proportion of time allocated to the base station to transmit energy signals, is the proportion of time allocated to users to transmit information signals, x is the horizontal coordinate of the smart reflective surface, , are the minimum and maximum horizontal coordinates of the smart reflective surface, is the maximum transmission power of the base station, ,in is the information transmission rate of the base station, is the received signal-to-noise ratio at the base station, is the power actually received by the user, is the energy harvesting efficiency, is the total channel gain between the base station and the user in the downlink phase, Energy harvesting saturation power for users, is the total channel gain between the user and the base station in the uplink phase, is the noise power at the base station, is the information transmission rate of the eavesdropping end, is the signal-to-noise ratio at the eavesdropping end, is the total channel gain between the user and the eavesdropping end, is the noise power at the eavesdropping end; S3. Use the convex optimization tool CVX to solve the optimization problem of maximizing the system safety rate and obtain the optimal solution of the base station energy beamforming vector. , Optimal solution of energy reflection coefficient vector of intelligent reflective surface , Optimal solution of the reflection coefficient vector of the intelligent reflective surface information , optimal solution of time allocation vector , Optimal solution of the horizontal coordinate of the intelligent reflection surface ,in, represents the optimal weight of the mth antenna of the base station, is the optimal energy reflection coefficient of the nth reflection unit of the intelligent reflection surface in the downlink phase, is the optimal phase of the nth reflector unit of the intelligent reflector in the downlink phase, is the optimal information reflection coefficient of the nth reflection unit of the intelligent reflection surface in the uplink phase, is the optimal phase of the nth reflector unit of the intelligent reflector in the uplink phase, is the optimal time ratio allocated to the base station to transmit energy signals, is the optimal time ratio allocated to users to transmit information signals. The specific solution process is as follows: S3.1. Initialize the position change step of the smart reflective surface , Convergence Tolerance , the minimum and maximum horizontal coordinates of the smart reflective surface , ,make ; S3.2, initialization iteration number k=1, base station energy beamforming vector , smart reflective surface energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , the initial values ​​are recorded as , , , , calculate the safe rate ,in , They represent the energy beamforming vector of the k-th iteration base station and the weight on the m-th antenna, , They represent the energy reflection coefficient vector of the k-th iteration smart reflection surface and the energy reflection coefficient on the n-th reflection unit, respectively. , They represent the information reflection coefficient vector of the k-th iteration smart reflection surface and the information reflection coefficient on the n-th reflection unit, respectively. They represent the time proportions allocated to the base station transmission energy and the user information transmission energy in the kth iteration respectively; S3.3. Fixed smart reflector energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , solve the optimization problem in step S2 to obtain the optimal solution of the k-th iteration base station energy beamforming vector, denoted as ; S3.4. Fixed base station energy beamforming vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , the Dinkelbach algorithm and the MM algorithm are used to solve the optimization problem described in step S2 to obtain the optimal solution of the energy reflection coefficient vector of the k-th iteration smart reflector, which is recorded as ; S3.5. Fixed base station energy beamforming vector , smart reflective surface energy reflection coefficient vector , time allocation vector , the Dinkelbach algorithm and the MM algorithm are used to solve the optimization problem described in step S2 to obtain the optimal solution of the k-th iteration of the intelligent reflector information reflection coefficient vector, which is recorded as ; S3.6, Fixed Base Station Energy Beamforming Vector , smart reflective surface energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , using the convex optimization tool CVX to solve the optimization problem in step S2, we get the optimal solution of the k-th iteration time allocation vector, denoted as ; S3.7. Calculation ,judge If established, stop the iteration and let , ; Otherwise, let k=k+1 and return to step S3.3; S3.8. Judgment Is it satisfied? If satisfied, then , return to step S3.2, otherwise, output the optimal solution for the position of the smart reflective surface , the optimal solution of the base station energy beamforming vector , the optimal solution of the energy reflection coefficient vector of the smart reflector , the optimal solution of the reflection coefficient vector of the intelligent reflective surface information , the optimal solution of the time allocation vector ; S4, the base station generates an energy-carrying signal s, s obeys a complex Gaussian distribution with zero mean and unit variance. The energy signal sent by the base station is: ,according to , Adjust the phase of the reflector unit in the downlink and uplink stages of the intelligent reflector surface respectively, 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 smart reflective surface.

[0017] Example 2 The IRS-assisted WPCN system model diagram, the method flow chart for improving the system safety rate, and the flow chart for solving the optimization problem in this embodiment are shown in the following figures respectively. Figure 1 , 2 , as shown in Figure 3.

[0018] In this embodiment, the specific parameters are set as follows: Assume that the WPCN system is located in a three-dimensional coordinate system, and the coordinates of the base station, intelligent reflector, user, and eavesdropping end are (0m, 3m, 15m), ( m,6m,15m), (4m,0m,15m), (8m,0m,15m); the base station is equipped with M=16 antennas, the intelligent reflector has N=10 reflective units, and the maximum transmission power , energy collection efficiency , the energy harvesting saturation power of the user , the noise power at the base station and the noise power of the eavesdropping end Both , the position change step of the smart reflective surface , convergence tolerance , the minimum horizontal coordinate of the smart reflective surface , the maximum horizontal coordinate ; The channels are all Rice channels, the Rice factor is 10, and the path loss model is ,in The reference distance The path loss at and the path loss exponents are , , , , .

[0019] The method comprises the following steps: S1. Before the transmission starts, the base station sends an instruction to the user. After receiving the instruction, the user sends a training signal to the base station. The base station estimates the channel based on the received training signal and obtains 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 reflective surface and the user ; According to the quasi-static steady fading characteristics, the channel state information between the intelligent reflective surface and the eavesdropping end , the channel state information between the user and the eavesdropping end Calculated through the statistical information of the channel; S2. Define the optimization problem of maximizing the system safety rate as follows: in, is the base station energy beamforming vector, represents the weight on the mth antenna, is the energy reflection coefficient vector of the smart reflector, is the energy reflection coefficient of the nth reflection unit of the intelligent reflection surface in the downlink phase, is the phase of the nth reflector unit of the intelligent reflector surface in the downlink phase, is the reflection coefficient vector of the smart reflective surface information, is the information reflection coefficient of the nth reflection unit of the intelligent reflection surface in the uplink phase, is the phase of the nth reflector unit of the intelligent reflector surface in the uplink phase, is the time allocation vector, assuming that the transmission time is normalized to Second, is the proportion of time allocated to the base station to transmit energy signals, is the proportion of time allocated to users to transmit information signals, x is the horizontal coordinate of the smart reflective surface, , are the minimum and maximum horizontal coordinates of the smart reflective surface, is the maximum transmission power of the base station, ,in is the information transmission rate of the base station, is the received signal-to-noise ratio at the base station, is the power actually received by the user, is the energy harvesting efficiency, is the total channel gain between the base station and the user in the downlink phase, Energy harvesting saturation power for users, is the total channel gain between the user and the base station in the uplink phase, is the noise power at the base station, is the information transmission rate of the eavesdropping end, is the signal-to-noise ratio at the eavesdropping end, is the total channel gain between the user and the eavesdropping end, is the noise power at the eavesdropping end; S3. Use the convex optimization tool CVX to solve the optimization problem of maximizing the system safety rate and obtain the optimal solution of the base station energy beamforming vector. , Optimal solution of energy reflection coefficient vector of intelligent reflective surface , Optimal solution of the reflection coefficient vector of the intelligent reflective surface information , optimal solution of time allocation vector , Optimal solution of the horizontal coordinate of the intelligent reflection surface ,in, represents the optimal weight of the mth antenna of the base station, is the optimal energy reflection coefficient of the nth reflection unit of the intelligent reflection surface in the downlink phase, is the optimal phase of the nth reflector unit of the intelligent reflector in the downlink phase, is the optimal information reflection coefficient of the nth reflection unit of the intelligent reflection surface in the uplink phase, is the optimal phase of the nth reflector unit of the intelligent reflector in the uplink phase, is the optimal time ratio allocated to the base station to transmit energy signals, is the optimal time ratio allocated to users to transmit information signals. The specific solution process is as follows: S3.1. Initialize the position change step of the smart reflective surface , Convergence Tolerance , the minimum and maximum horizontal coordinates of the smart reflective surface , ,make ; S3.2, initialization iteration number k=1, base station energy beamforming vector , smart reflective surface energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , the initial values ​​are recorded as , , , , calculate the safe rate ,in , They represent the energy beamforming vector of the k-th iteration base station and the weight on the m-th antenna, , They represent the energy reflection coefficient vector of the k-th iteration smart reflection surface and the energy reflection coefficient on the n-th reflection unit, respectively. , They represent the information reflection coefficient vector of the k-th iteration smart reflection surface and the information reflection coefficient on the n-th reflection unit, respectively. They represent the time proportions allocated to the base station transmission energy and the user information transmission energy in the kth iteration respectively; S3.3. Fixed smart reflector energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , solve the optimization problem in step S2 to obtain the optimal solution of the k-th iteration base station energy beamforming vector, denoted as ; S3.4. Fixed base station energy beamforming vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , the Dinkelbach algorithm and the MM algorithm are used to solve the optimization problem described in step S2 to obtain the optimal solution of the energy reflection coefficient vector of the k-th iteration smart reflector, which is recorded as ; S3.5. Fixed base station energy beamforming vector , smart reflective surface energy reflection coefficient vector , time allocation vector , the Dinkelbach algorithm and the MM algorithm are used to solve the optimization problem described in step S2 to obtain the optimal solution of the k-th iteration of the intelligent reflector information reflection coefficient vector, which is recorded as ; S3.6, Fixed Base Station Energy Beamforming Vector , smart reflective surface energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , using the convex optimization tool CVX to solve the optimization problem in step S2, we get the optimal solution of the k-th iteration time allocation vector, denoted as ; S3.7. Calculation ,judge If established, stop the iteration and let , ; Otherwise, let k=k+1 and return to step S3.3; S3.8. Judgment Is it satisfied? If satisfied, then , return to step S3.2, otherwise, output the optimal solution for the position of the smart reflective surface , the optimal solution of the base station energy beamforming vector , the optimal solution of the energy reflection coefficient vector of the smart reflector , the optimal solution of the reflection coefficient vector of the intelligent reflective surface information , the optimal solution of the time allocation vector ; S4, the base station generates an energy-carrying signal s, s obeys a complex Gaussian distribution with zero mean and unit variance. The energy signal sent by the base station is: ,according to , Adjust the phase of the reflector unit in the downlink and uplink stages of the intelligent reflector surface respectively, 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 smart reflective surface.

[0020] Figure 4 It is a relationship diagram of the CPU execution time of different embodiments as a function of the number of IRS reflection units in two cases of solving the IRS energy reflection coefficient vector using the Dinkelbach-based MM algorithm and the SDR algorithm; it can be seen from the figure that as the number of IRS reflection units increases, the CPU execution time of the two algorithms increases accordingly; in addition, we can also see that compared with the MM algorithm, the CPU execution time of the SDR algorithm is longer, because the CVX tool is used in the SDR algorithm, and Gaussian randomization is required to improve the quality of the solution, and the computational complexity is high, and the present invention proposes a tight boundary semi-closed solution based on the Dinkelbach algorithm and the MM algorithm, which can effectively reduce the CPU execution time.

[0021] Figure 5 It is a relationship diagram of the system security transmission rate of different embodiments with the maximum transmission power of the base station under the two situations of optimized IRS position and random IRS position. It can be seen from the figure that as the maximum transmission power of the base station increases, the security transmission rate of both cases increases, but compared with the random placement of the intelligent reflective surface, the system security transmission rate of the solution proposed by the present invention is higher.

[0022] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for improving the safety rate of a WPCN system assisted by an intelligent reflective surface, characterized in that: The WPCN system includes a base station equipped with M antennas, an intelligent reflective surface with N reflective units, a user equipped with a single antenna, and an eavesdropping terminal equipped with a single antenna. In the downlink phase, the user receives an energy signal sent by the base station, and in the uplink phase, the user uses the energy to transmit an information signal to the base station, while the eavesdropping terminal eavesdrops on the information. The method includes the following steps: S1. Perform channel estimation before transmission starts to obtain the channel between the base station and the user. , the channel between the base station and the smart reflector , the channel between the smart reflective surface and the user , the channel between the intelligent reflective surface and the eavesdropping end , the channel between the user and the eavesdropping end , represents a complex domain; S2. Define the optimization problem of maximizing the system safety rate as follows: in, is the base station energy beamforming vector, represents the weight on the mth antenna, is the energy reflection coefficient vector of the smart reflector, is the energy reflection coefficient of the nth reflection unit of the intelligent reflection surface in the downlink phase, is the phase of the nth reflector unit of the intelligent reflector surface in the downlink phase, is the reflection coefficient vector of the smart reflective surface information, is the information reflection coefficient of the nth reflection unit of the intelligent reflection surface in the uplink phase, is the phase of the nth reflector unit of the intelligent reflector surface in the uplink phase, is the time allocation vector, assuming that the transmission time is normalized to Second, is the proportion of time allocated to the base station to transmit energy signals, is the proportion of time allocated to users to transmit information signals, x is the horizontal coordinate of the smart reflective surface, , are the minimum and maximum horizontal coordinates of the intelligent reflector, R is the safe transmission rate of the WPCN system, is the maximum transmit power of the base station; S3. Use the convex optimization tool CVX to solve the optimization problem of maximizing the system safety rate and obtain the optimal solution of the base station energy beamforming vector. , Optimal solution of energy reflection coefficient vector of intelligent reflective surface , Optimal solution of the reflection coefficient vector of the intelligent reflective surface information , optimal solution of time allocation vector , Optimal solution of the horizontal coordinate of the intelligent reflection surface ,in, represents the optimal weight of the mth antenna of the base station, is the optimal energy reflection coefficient of the nth reflection unit of the intelligent reflection surface in the downlink phase, is the optimal phase of the nth reflector unit of the intelligent reflector in the downlink phase, is the optimal information reflection coefficient of the nth reflection unit of the intelligent reflection surface in the uplink phase, is the optimal phase of the nth reflector unit of the intelligent reflector in the uplink phase, is the optimal time ratio allocated to the base station to transmit energy signals, It is the optimal time proportion allocated to users to transmit information signals; S4, the base station generates an energy-carrying signal s, s obeys a complex Gaussian distribution with zero mean and unit variance. The energy signal sent by the base station is: ,according to , Adjust the phase of the reflector unit in the downlink and uplink stages of the intelligent reflector surface respectively, 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 smart reflective surface.

2. A method for improving the safety rate of a WPCN system assisted by an intelligent reflective surface according to claim 1, characterized in that: The channel estimation process is as follows: before the transmission starts, the base station sends an instruction to the user, and after receiving the instruction, 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 reflective surface and the user ; According to the quasi-static steady fading characteristics, the channel state information between the intelligent reflective surface and the eavesdropping end , the channel state information between the user and the eavesdropping end Calculated using channel statistics.

3. A method for improving the safety rate of a WPCN system assisted by an intelligent reflective surface according to claim 1, characterized in that: The safe transmission rate R of the WPCN system is expressed as ,in is the information transmission rate of the base station, is the received signal-to-noise ratio at the base station, is the power actually received by the user, is the energy harvesting efficiency, is the total channel gain between the base station and the user in the downlink phase, Energy harvesting saturation power for users, is the total channel gain between the user and the base station in the uplink phase, is the noise power at the base station, is the information transmission rate of the eavesdropping end, is the signal-to-noise ratio at the eavesdropping end, is the total channel gain between the user and the eavesdropping end, is the noise power at the eavesdropping end.

4. A method for improving the safety rate of a WPCN system assisted by an intelligent reflective surface according to claim 1, characterized in that: The process of step S3 is as follows: S3.

1. Initialize the position change step of the smart reflective surface , Convergence Tolerance , the minimum and maximum horizontal coordinates of the smart reflective surface , ,make ; S3.2, initialization iteration number k=1, base station energy beamforming vector , smart reflective surface energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , the initial values ​​are recorded as , , , , calculate the safe rate ,in , denote the energy beamforming vector of the k-th iteration base station and the weight on the m-th antenna, respectively. , They represent the energy reflection coefficient vector of the k-th iteration smart reflection surface and the energy reflection coefficient on the n-th reflection unit, respectively. , They represent the information reflection coefficient vector of the k-th iteration smart reflection surface and the information reflection coefficient on the n-th reflection unit, respectively. They represent the proportion of time allocated to the base station transmission energy and the proportion of time allocated to the user transmission information in the kth iteration respectively; S3.

3. Fixed smart reflector energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , solve the optimization problem in step S2 to obtain the optimal solution of the k-th iteration base station energy beamforming vector, denoted as ; S3.

4. Fixed base station energy beamforming vector , intelligent reflective surface information reflection coefficient vector , time allocation vector , the Dinkelbach algorithm and the MM algorithm are used to solve the optimization problem described in step S2 to obtain the optimal solution of the energy reflection coefficient vector of the k-th iteration smart reflector, which is recorded as ; S3.

5. Fixed base station energy beamforming vector , smart reflective surface energy reflection coefficient vector , time allocation vector , the Dinkelbach algorithm and the MM algorithm are used to solve the optimization problem described in step S2 to obtain the optimal solution of the k-th iteration of the intelligent reflector information reflection coefficient vector, which is recorded as ; S3.6, Fixed Base Station Energy Beamforming Vector , smart reflective surface energy reflection coefficient vector , intelligent reflective surface information reflection coefficient vector , using the convex optimization tool CVX to solve the optimization problem in step S2, we get the optimal solution of the k-th iteration time allocation vector, denoted as ; S3.

7. Calculation ,judge If established, stop the iteration and let , ; Otherwise, let k=k+1 and return to step S3.3; S3.

8. Judgment Is it satisfied? If satisfied, then , return to step S3.2, otherwise, output the optimal solution for the position of the smart reflective surface , the optimal solution of the base station energy beamforming vector , the optimal solution of the energy reflection coefficient vector of the smart reflector , the optimal solution of the reflection coefficient vector of the intelligent reflective surface information , the optimal solution of the time allocation vector .

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