Secrecy rate optimization method for simultaneous wireless information and power transfer system

Abstract

Disclosed in the present invention is a secrecy rate optimization method for a simultaneous wireless information and power transfer system. The method comprises: constructing a simultaneous wireless information and power transfer system, which includes an intelligent reflecting surface apparatus, an access point, a plurality of legitimate users and a plurality of eavesdroppers, wherein the intelligent reflecting surface apparatus is provided with a plurality of reflecting elements, and adding artificial noise to an information signal transmitted by the access point; for the simultaneous wireless information and power transfer system, establishing a secrecy rate optimization problem, and in the secrecy rate optimization problem, taking interference between the users into consideration and describing channel state information of the eavesdroppers; and for the secrecy rate optimization problem, jointly optimizing a power splitting ratio, transmission beamforming, a covariance matrix for the artificial noise, and a phase shift matrix for the intelligent reflecting surface apparatus, wherein the power splitting ratio is used for limiting a legitimate user to splitting a power into an information decoding part and an energy harvesting part. The present invention is more applicable to an actual communication scenario, reduces the eavesdropping rate, and thus increases the overall rate of a system.

Description

A method for optimizing the safety rate of wireless energy-carrying communication system Technical Field The present invention relates to the field of communication technology, and more specifically, to a method for optimizing the safety rate of a wireless energy-carrying communication system. Background Art The fifth generation of wireless mobile communication networks (5G) can provide faster data transmission, lower latency and more reliable connections. 5G networks can be applied to various devices, such as smartphones, smart home devices, IoT devices, etc., which can improve the efficiency and quality of wireless communications and support more device connections to meet people's growing needs. 5G networks also provide broader opportunities in areas such as autonomous driving, smart cities, and healthcare, making many new technologies and innovations possible. However, the high returns brought by 5G networks are also accompanied by problems of high power consumption and high costs, which are very challenging issues for the continued development of the next generation of mobile communication networks. Wireless power transfer technology (SWIPT) is considered one of the main solutions to this problem. Traditional wireless communication technology can only transmit information, while SWIPT allows the simultaneous transmission of information and energy. This technology can be applied to charging various devices such as mobile devices, electric vehicles, and drones, without the need for traditional plugs and chargers, greatly facilitating people's lives. In addition, SWIPT also helps to charge devices in places that are inaccessible to people. Like traditional wireless communication technology, wireless power communication technology will also experience problems such as signal attenuation and co-channel interference. To overcome these problems, the intelligent reflecting surface (IRS) technology is introduced. The intelligent reflecting surface is a reconfigurable metasurface composed of a large number of low-cost reflective elements. By dynamically adjusting the phase shift of the reflective elements, the signal propagation environment can be optimized and the system performance can be enhanced. By superimposing the reflected signal of the intelligent reflecting surface with the direct signal from the base station, the signal-to-interference-to-noise ratio of the receiver can be significantly improved. Combining the two technologies of wireless power communication and intelligent reflecting surface can establish a more efficient wireless propagation channel and achieve more reliable communication, which will benefit future mobile communication networks in areas such as intelligent transportation and green communications. In addition, due to the broadcast characteristics of wireless signals, the transmission of signals in wireless networks often faces the risk of being eavesdropped. And because eavesdroppers often hide their information, it is difficult to obtain channel state information related to eavesdroppers. Therefore, how to optimize the system rate based on the imperfect channel of eavesdroppers becomes a very difficult problem. Regarding the combination of smart reflective surfaces and wireless energy-carrying communication technology, the literature (Y. Jin, R. Guo, L. Zhou and Z. Hu, "Secure Beamforming for IRS-Assisted Nonlinear SWIPT Systems With Full-Duplex User," in IEEE Communications Letters, vol. 26, no. 7, pp. 1494-1498, July 2022.) proposed a secure wireless energy-carrying communication system assisted by a smart reflective surface, considering a single-user, single-eavesdropper system model. To suppress eavesdroppers, full-duplex users also emit artificial noise while receiving information and energy. However, this scheme only considers the ideal scenario of a single user and a single eavesdropper, but in actual scenarios, there are often multiple users and multiple eavesdroppers in a system. And the interference between users is ignored in this scenario, but in actual scenarios, the interference between users has a great impact on the performance of the system. In addition, this scheme considers the perfect channel state information of the eavesdropper, but due to the concealment of the eavesdropper, the channel state information related to it is often difficult to obtain. Moreover, although this solution adopts the SWIPT technology of the PS (power splitting) architecture, it does not jointly optimize the PS factor with other optimization variables. The literature (H.Niu, Z.Chu, F.Zhou, Z.Zhu, L.Zhen and K.-K.Wong,"Robust Design for Intelligent Reflecting Surface-Assisted Secrecy SWIPT Network,"in IEEE Transactions on Wireless Communications,vol.21,no.6,pp.4133-4149,June 2022.) provides a wireless energy-carrying communication system assisted by an intelligent reflective surface based on an imperfect channel. In this system, there are information users and energy users, among which energy users are regarded as potential eavesdroppers, and an optimization algorithm for maximizing the system and rate is proposed based on this model. However, this scheme also does not jointly optimize the PS factor with other optimization variables, and the PS factor is an important parameter for dividing the RF signal into the energy collection part and the information decoding part. In addition, the energy receiving device and the information receiving device in this scheme are two independent devices, which is not suitable for scenarios where the device needs to receive both information and energy. Summary of the invention The purpose of the present invention is to overcome the above-mentioned defects of the prior art and provide a wireless energy-carrying communication system A safety rate optimization method for a system. The method comprises the following steps: Constructing a wireless energy-carrying communication system, comprising an intelligent reflective surface device, an access point, a plurality of legitimate users, and a plurality of eavesdroppers, wherein the intelligent reflective surface device is provided with a plurality of reflective elements, and artificial noise is added to the information signal transmitted by the access point; For the wireless energy-carrying communication system, a security rate optimization problem is established, wherein the security rate optimization problem takes into account interference between users and describes the channel state information of the eavesdropper; In view of the security rate optimization problem, the power division ratio, the covariance matrix of the transmit beamforming and artificial noise, and the phase shift matrix of the intelligent reflector device are jointly optimized, wherein the power division ratio is used to limit the legitimate user to divide the power into an information decoding part and an energy collection part. Compared with the prior art, the advantages of the present invention are that it takes into account the wireless energy-carrying communication system with multiple legitimate users and multiple eavesdroppers, and when modeling the security rate optimization problem of the system, it takes into account the interference problem between users, which is more in line with the real scene, and introduces a non-perfect eavesdropping channel, which can more accurately describe the channel state information of the eavesdropper. In addition, the present invention jointly optimizes the power split ratio with the beamforming, the covariance matrix of the artificial noise and the phase shift matrix of the intelligent reflector device, which is more suitable for a system composed of devices that need to receive both energy and information, thereby improving the overall security rate of the system while ensuring the communication quality. Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention. 1 is a flow chart of a method for optimizing a safety rate of a wireless energy-carrying communication system according to an embodiment of the present invention; FIG2 is a schematic diagram of a model of a wireless energy-carrying communication system assisted by a smart reflective surface according to an embodiment of the present invention; FIG. 3 is a schematic diagram of experimental results according to an embodiment of the present invention. DETAILED DESCRIPTION Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures. Referring to FIG1 , the provided method for optimizing the safety rate of a wireless energy-carrying communication system includes the following steps: Step S110, constructing a wireless energy-carrying communication system, which includes an intelligent reflective surface device, multiple legal users, multiple eavesdroppers, and adding artificial noise to the information signal transmitted by the access point. As shown in Figure 2, the constructed wireless energy-carrying communication system includes a multi-antenna access point (Access Point, AP), an intelligent reflective surface device including L reflective elements, K single-antenna legitimate users (Legitimate Users, LUs) and N single-antenna eavesdroppers (Eves). Considering the limited energy stored in mobile devices, the PS architecture is adopted at the legitimate user end to divide the received RF signal into two parts, which are used for information decoding and energy collection respectively. At the same time, considering that the presence of eavesdroppers will reduce the achievable rate of the system. In order to reduce the impact of eavesdroppers on the system, artificial noise (Artificial Noise, AN) is added to the information signal transmitted by the AP to suppress the eavesdropper's eavesdropping rate. In addition, AN can also be used as an energy source to provide energy for legitimate users. Step S120, for the wireless power-carrying communication system, a security rate optimization problem is established, and interference between users is considered in the optimization problem, and a non-perfect eavesdropping channel is introduced. For the wireless energy-carrying communication system of FIG2 , the mixed transmission signal can be expressed as: Among them, w k and k They represent the transmit beamforming vector and information-bearing signal transmitted by the access point to the legitimate user k, respectively. is AN, K is the number of legal users. The signal received at the legitimate user LUk is expressed as: in, represents the channel vector between IRS and LUk, represents the channel vector between AP and LUk, and G represents the channel matrix between AP and IRS. represents the phase shift matrix of IRS, where Represents the phase shift of the lth reflecting element in the IRS. It is Additive White Gaussian Noise (AWGN). Assuming PS ratio and They represent the proportion of the signal received by the legitimate user k used for information decoding and energy collection. Definition Then the parts of the signal received by LUk used for information decoding and energy collection are expressed as: Among them, n ID is AWGN. Therefore, the received signal-to-interference-plus-noise ratio (SINR) and received power at LUk can be expressed as: Among them, γ LU，k represents the signal-to-interference-noise ratio received at LUk, P EH，k represents the power received at LUk, represents the variance of additive white Gaussian noise, represents the variance of the additive Gaussian white noise generated during the information decoding process, ρ k represents the power split ratio of legitimate user k, w k represents the transmit beamforming vector of LUk. It is worth noting that the AN signal is a known sequence for legitimate users. The noise is eliminated by using interference cancellation technology, so AN will not have any impact on the achievable rate of legitimate users. The achievable rate of LUk is expressed as: R LU,k =log 2 (1+γ LU,k ) (7) When the nonlinear energy harvesting model is adopted, the energy collected by LUk can be expressed as: in: Among them, Lu，k Indicates the received RF power P EH，k The relevant logic function, M k represents the maximum received power of the EH (energy harvesting) receiver when the EH circuit is saturated, Γ k is a constant, a k and bx represent constants related to the breakdown voltage of the indicated resistor, capacitor, and diode. Similarly, the signal received at Even can be expressed as: in, and Respectively represent the channel vectors between IRS and Even and between AP and Even, It is AWGN. definition and Where, f = [f 1 ,f 2 ,...,f L ] H , Then the receiving SINR of Even eavesdropped LUk can be expressed as: Among them, f and f l are vectors and matrices defined for calculations. Correspondingly, the eavesdropping rate of Even eavesdropping LUk is: R Eve,n =log 2 (1+γ n,k ) (12) Therefore, the achievable rate of the system is expressed as: R k ≤[R LU,k -max{R Eve,1 ,...,R Eve,N}] + (13) Among them, [a] + =max{0,a}. Due to the concealment of the eavesdropper in the system, its perfect channel state information (CSI) is often difficult to obtain. Therefore, a statistical CSI model of the eavesdropper is considered, which assumes that the estimation error of the channel obeys a cyclically symmetric complex Gaussian random distribution. So we have: Among them, H e，n Indicates the inclusion of eavesdroppers and the cascaded channel coefficients of G, represents the estimation of the cascade channel state information, ΔH e，n represents the statistical error vector generated in the estimation process, Σ e，n represents the positive semidefinite covariance matrix of the error vector, h d，n represents the channel vector between AP and Even, represents the estimation of the channel state information, Δh d，n represents the statistical error vector generated in the estimation process, Σ d，n represents the positive semidefinite covariance matrix of the error vector. At this time, due to the introduction of random variables, (13) can no longer be directly used to represent the safety rate of the system. In this case, the safety outage probability (SOP) constraint is introduced: in, represents the eavesdropping rate of the eavesdropper n when eavesdropping on the information of the legitimate user k, δ represents the SOP threshold, let δ n =1-(1-δ) 1 / N , so we have: Define θ = [θ 1 ,θ 2 ,...,θ L ], then the optimization problem P1 can be expressed as: 0<ρ k <1, (18e) Among them, PA represents the maximum transmit power constraint of the AP, E min represents the minimum energy collection constraint of the legal user, formula (18e) is the constraint related to the PS ratio of the legal user, and formula (18g) is the phase shift constraint of the IRS. V represents the covariance matrix of the artificial noise, and Tr(V) represents the trace of the matrix V. From formula (13), we know that R K Must be less than R LU,k , therefore, R K It can be expressed as R LU,k Subtract a positive value. So, the slack variable is introduced Transform formula (17) into: Among them, τ k satisfy: R k ≥R LU,k -τ k , (20) Therefore, the optimization problem P1 can be equivalently transformed into the optimization problem P2: st(18b)-(18c), (19), (18e)-(18g). (21b) By introducing the auxiliary variable t, the optimization problem p2 can be transformed into the optimization problem P3: (18b)-(18c), (19), (18e)-(18g). (22c) Step S130 , for the safety rate optimization problem, jointly optimize the PS ratio, transmit beamforming, the covariance matrix of the artificial noise, and the phase shift matrix of the smart reflective surface. Due to the tight coupling between variables, the optimization problem P3 is still a non-convex optimization problem. Therefore, in a preferred embodiment, an alternating optimization algorithm is used to decouple the optimization variables, decompose the original optimization problem into three sub-optimization problems, and then alternately optimize {ρ k ,τ k},{w k ,V} and one of θ until the algorithm converges. First, optimize the transmit beamforming w k and the covariance matrix V of the artificial noise, the optimization subproblem is marked as P4: For constraint (23d), due to Γ k Does not affect the design of the optimization algorithm, then Ψ LU,k can be directly used to represent the power received by LUk, so Ψ in formula (9) LU,k The inverse function of can be expressed as: definition and f=[f 1 ,f 2 ,...,f L ] H ,in make in, According to formula (24), constraint (23d) can be transformed into: Substituting formula (14) and formula (15) into constraint (23e), we can obtain: definition and in and where β∈{(e,n),(d,n)}. Let and Then constraint (23e) can be transformed into: in C 2 =A 2 +C 1 as well as: The Bernstein-type inequality is introduced as follows: Theorem 1.(teh Bernstein-type ijequality

[0029] ,

[0030] .Forany μ∈(0,1], the following approximation holds: where x and y are slack variables. Using the Bernstein-type inequality, formula (27) can be transformed into: Furthermore, define Where β∈{(e,n),(d,n)}, so: At this point, the above optimization problem can be written as optimization problem P5: Rank(W k )=1, (32f) The left side of formula (32b) can be written as: Using the first-order Taylor expansion, we can get D 2 A lower bound of is expressed as: Using SDR technology to remove the rank-one constraint Rank(W k )=1, then the optimization problem can be written as optimization problem P6: (32c)-(32e), (32g)-(32i). (35c) At this point, the optimization problem P6 is already a convex optimization problem and can be solved using the CVX toolbox, etc. Next, optimize the PS ratio and the variable τ k , the optimization subproblem is marked as P7: 0<ρ k <1, (36c) According to the properties of the logarithmic function, formula (36b) can be written as: Among them, f 2 A lower bound solution of can be expressed as: Therefore, the optimization subproblem P7 can be approximated as the optimization subproblem P8: (36c)-(36g). (39c) P8 is already a convex optimization problem and can be solved using the CVX toolbox, for example. Finally, the phase shift of the IRS is optimized. definition in, In addition, the definition At the same time Rank(F) = 1. A in formula (26) 2 It can be written as Tr(FH 1 ),in Then constraints (30a) and (30b) are expressed as: in, Using SDR technology, we can eliminate the rank-one constraint Rank(F)=1, and the optimization subproblem is marked as P9: To solve the non-convex optimization subproblem P9, an auxiliary variable {χ k}, then the optimization subproblem P9 can be written as the optimization subproblem P10: (42c)-(42f). (43d) To solve the non-convex constraint (43c), it is transformed into: Among them, x k and k is an auxiliary variable, and Ω represents all constants that are independent of F. and The corresponding first-order Taylor expansion is used as the lower bound, that is and Therefore, a continuous convex approximation algorithm is used to solve the problem. Then a lower bound solution of the optimization subproblem P10 can be obtained by solving the following optimization subproblem P11. (42c)-(42f), (43b), (44a). (45d) in, The alternating optimization algorithm and the continuous convex approximation algorithm adopted by the present invention are one of the core contents, and the specific implementation method includes the following process: Step S1, initializing system parameters, including PS ratio Phase shift matrix f (0) and iteration counter i = 0; Step S2: and f (i) Substitute it into the optimization subproblem P6 and get the solution to this problem: and V (i+1) ; Step S3: V (i+1) and f (i) Substitute it into the optimization subproblem P8 and get the solution to the problem and Step S4: V (i+1) , and Substitute it into the optimization subproblem P10 and execute the continuous convex approximation algorithm. The specific process is as follows: (1) Initialization and iteration counter p = 1; (2) Substituting into the optimization subproblem P11, we get F (p) and (3) Update counter: p = p + 1; (4) Repeat the above process until the algorithm converges; (5) Decomposed from F Then calculate f; (6) The solution obtained by the above process is expressed as f (i+1) . Step S5, update counter i=i+1; Step S6, loop through the above process until the algorithm converges. After the above process, W k It is not guaranteed that and F are rank-one matrices that satisfy the constraints. Therefore, for the generated non-rank-one matrix, in one embodiment, a Gaussian randomization method can be used to approximate it to obtain a solution that meets the constraints. In summary, the present invention adopts Taylor's formula to transform and solve the PS ratio of legitimate users, adopts semi-positive definite relaxation algorithm for the covariance matrix of transmit beamforming and artificial noise, and adopts continuous convex approximation algorithm for the phase shift of the reflective element on the smart reflective surface. Furthermore, in order to verify the effect of the present invention, a simulation experiment was conducted. In the simulation experiment, the number of AP antennas was set to 2, the maximum transmit power of AP was set to 28dBm, the number of reflective elements of IRS was set to 10, the distance between AP and IRS was 50m, the distance between IRS and legitimate users was taken from the interval [10,30]m according to the current location of different users, and the distance between IRS and eavesdroppers was taken from the interval [10,30]m according to the current location of different eavesdroppers. The channels between the legitimate user and the AP and the channel between the eavesdropper and the AP are non-line-of-sight channels, while the channels between the AP and the smart reflective surface, between the smart reflective surface and the legitimate user, and between the smart reflective surface and the eavesdropper are all line-of-sight channels. In the simulation experiment, the present invention is compared and analyzed with the following benchmark methods, namely: 1) IRS phase shift randomization mode (Random phase): The elements of the IRS phase shift matrix Φ are given in the form of random amplitudes. 2) Without IRS-assisted communication mode: In this communication mode, only the communication between AP and legitimate users, and between AP and eavesdroppers are considered, without the participation of IRS. 3) Communication mode without artificial noise (WithoutAN): In this communication mode, artificial noise is not added to the signal transmitted by the AP. Simulations were performed on the alternating optimization algorithm and the continuous convex approximation algorithm proposed in the present invention and the above communication modes. After ensuring the convergence of the algorithms, the simulation experimental results are shown in FIG3 . As can be seen from Figure 3, the present invention achieves the best performance. It can also be seen that compared with the mode without deploying IRS, the other three modes can achieve higher rates. The main reason is that deploying the intelligent reflective surface can provide an additional link, which can improve the connection between AP and legitimate users. In addition, the present invention and the WithoutAN algorithm have obvious advantages over the RandomPhase algorithm, which shows that it is very necessary to optimize the phase shift matrix of the intelligent reflection surface. Compared with the WithoutAN algorithm, the present invention can achieve better performance because after adding artificial noise, the receiving SINR of the eavesdropper can be suppressed, the eavesdropping rate can be reduced, and thus the overall rate of the system can be improved. In summary, compared with the prior art, the present invention has the following advantages: 1) The present invention studies a wireless energy-carrying communication system assisted by an intelligent reflective surface based on an imperfect channel, which is more in line with the actual situation than a communication system based on a perfect channel. 2) The present invention adopts a wireless energy-carrying communication system based on an intelligent reflective surface, which can achieve higher communication efficiency than traditional wireless energy-carrying communication systems. 3) The present invention divides the security rate optimization method into three sub-problems for joint optimization. The Taylor formula is used to transform and solve the PS ratio of legitimate users; a semi-positive definite relaxation algorithm is used for the transmit beamforming and covariance matrix; and a continuous convex approximation algorithm is used for the phase shift of the reflective element on the smart reflective surface. 4) The present invention introduces an artificial noise-assisted communication system to achieve secure transmission. 5) The present invention considers the system model with multiple legitimate users and multiple eavesdroppers. When modeling the problem, it considers the interference problem between users, which is more in line with the real scene. In addition, it introduces a non-perfect eavesdropping channel, which can more accurately describe the channel state information of the eavesdropper. 6) The present invention jointly optimizes the PS ratio with the beamforming, covariance matrix and phase shift matrix, which is more suitable for a system composed of devices that need to receive both energy and information, and achieves good performance. The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention. A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as punched card or raised structure in groove with instructions stored thereon, and any suitable combination of the above. Computer readable storage medium as used herein is not to be interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires. The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device. The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, Python, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention. Here, reference is made to methods, apparatus (systems) and computer program products according to embodiments of the present invention. The flowcharts and / or block diagrams in the drawings describe various aspects of the present invention. It should be understood that each block of the flowcharts and / or block diagrams and the combination of each block in the flowcharts and / or block diagrams can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram. Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram. The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware. The above descriptions of various embodiments of the present invention are exemplary and not exhaustive. The present invention is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terms used herein are selected to best explain the principles of the embodiments, practical applications or technical improvements in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method for optimizing the safety rate of a wireless energy-carrying communication system, comprising the following steps: Constructing a wireless energy-carrying communication system, comprising an intelligent reflective surface device, an access point, a plurality of legitimate users, and a plurality of eavesdroppers, wherein the intelligent reflective surface device is provided with a plurality of reflective elements, and artificial noise is added to the information signal transmitted by the access point; For the wireless energy-carrying communication system, a security rate optimization problem is established, wherein the security rate optimization problem takes into account interference between users and describes the channel state information of the eavesdropper; In view of the security rate optimization problem, the power division ratio, the covariance matrix of the transmit beamforming and artificial noise, and the phase shift matrix of the intelligent reflector device are jointly optimized, wherein the power division ratio is used to limit the legitimate user to divide the power into an information decoding part and an energy collection part.

2. The method according to claim 1, characterized in that The safety rate optimization problem is established according to the following steps: Establish the optimization problem P1: Among them, R k is the safe rate of legitimate user k, P A represents the maximum transmit power constraint of the access point, E min represents the minimum energy collection constraint of the legitimate user, V represents the covariance matrix of the artificial noise, Tr(V) represents the trace of the matrix V, θ=[θ1,θ2,...,θ L ], represents the phase shift of the lth reflective element in the smart reflective surface device, K represents the number of legal users, k is the index of the legal user, L represents the number of reflective elements, and E LU，k represents the energy collected by legitimate user k, w k is the beamforming transmitted by the access point to the legitimate user k, R represents the eavesdropping rate of eavesdropper n eavesdropping on legitimate user k, LU，k represents the rate that legal user k can achieve, ρ k represents the power split ratio of the legitimate user k, formula (18d) is the safety interruption probability constraint, δ represents the threshold of the safety interruption probability, and let δ n =1-(1-δ) 1 / N , N is the number of eavesdroppers; The optimization problem P1 is equivalently transformed into the optimization problem P2: Wherein, formula (19) is set as: in, is the introduced slack variable and satisfies: R k ≥R LU,k -t k , (20) Introducing auxiliary variable t, the optimization problem p2 is transformed into the optimization problem P3:

3. The method according to claim 2, characterized in that For the safety rate optimization problem, the joint optimization of the power split ratio, the covariance matrix of the transmit beamforming and artificial noise, and the phase shift matrix of the smart reflector device includes: Step S31, optimizing the covariance matrix of transmit beamforming and artificial noise according to the following process: Optimize transmit beamforming w k and the covariance matrix V of the artificial noise, the optimization subproblem is labeled P4: For constraint (23d), use Ψ LU,k represents the power received by legitimate user k, Ψ LU,k The inverse function of is expressed as: definition and f=[f1,f2,...,f L ] H ,in And order According to formula (24), the constraint (23d) is converted to: The formula Japanese formula Substituting into constraint (23e), we obtain: definition and in and β∈{(e,n),(d,n)}, and let and Then the constraint (23e) is transformed into: in C2 = A2 + C1 and: Using the Bernstein-type inequality, formula (27) is transformed into: Further definition Where β∈{(e,n),(d,n)}, so: Rewrite the optimization subproblem P4 as the optimization subproblem P5: The left side of formula (32b) is written as: Using the first-order Taylor expansion, we get a lower bound for D2, expressed as: Using SDR technology to remove the rank-one constraint Rank(W k )=1, then the optimization subproblem P5 is written as the optimization subproblem P6: Solve the optimization subproblem P6 to obtain the optimized covariance matrix of the optimized transmit beamforming and artificial noise; Step S32, optimizing the power split ratio and the introduced relaxation variable τ according to the following process: k : Label this optimization subproblem as P7: According to the properties of the logarithmic function, formula (36b) can be written as: Among them, a lower bound solution of f2 is expressed as: Approximate the optimization subproblem P7 to the optimization subproblem P8: Solve the optimization subproblem P8 to obtain the optimized power split ratio and the introduced slack variable τ k ; Step S33, optimizing the phase shift matrix of the intelligent reflective surface device according to the following process: definition in, And define At the same time Rank(F)=1, write A2 in formula (26) as Tr(FH1), where The constraints (30a) and (30b) are expressed as: in, Use SDR technology to eliminate the rank-one constraint Rank(F)=1, and mark the optimization subproblem as P9: For the optimization subproblem P9, we introduce the auxiliary variable {χ k }, then the optimization subproblem P9 is written as the optimization subproblem P10: By solving the optimization sub-problem P10, the phase shift matrix of the optimized smart reflector device is obtained; Among them, Lu，k Indicates the received RF power P EH，k The relevant logic function, M k represents the maximum received power of the energy harvesting receiver when the energy harvesting circuit is saturated, Γ k is a constant, a k and b k Represents a constant related to the breakdown voltage of indicated resistors, capacitors, and diodes, H e，n Indicates the inclusion of eavesdroppers and the cascaded channel coefficients of G, represents the estimation of the cascade channel state information, ΔH e，n represents the statistical error vector generated in the estimation process, Σ e，n represents the positive semidefinite covariance matrix of the error vector, h d，n represents the channel vector between the access point and the eavesdropper n, represents the estimation of the channel state information, Δh d，n represents the statistical error vector generated in the estimation process, Σ d，n represents the positive semidefinite covariance matrix of the error vector.

4. The method according to claim 3, characterized in that Also includes: To solve the non-convex constraint (43c), it is transformed into: Among them, x k and k are auxiliary variables, Ω represents all constants that are independent of F, and The corresponding first-order Taylor expansion is used as the lower bound. A lower bound solution to the optimization subproblem P10 is obtained by solving the following optimization subproblem P11: in, 5. The method according to claim 1, characterized in that: The transmission signal of the access point is expressed as: Among them, w k and k They represent the transmit beamforming vector and information-bearing signal reflected by the access point to the legitimate user k, respectively. is artificial noise, K is the number of legitimate users, and k is the index of the legitimate user.

6. The method according to claim 1, characterized in that The channel between the legal user and the access point, and the channel between the eavesdropper and the access point are non-line-of-sight channels, and the channel between the access point and the intelligent reflecting surface device, the channel between the intelligent reflecting surface device and the legal user, and the channel between the intelligent reflecting surface device and the eavesdropper are all line-of-sight channels.

7. The method according to claim 1, characterized in that The artificial noise is a signal sequence known to the legitimate user, which does not interfere with the information transmission of the legitimate user and serves as an energy source to provide energy for the legitimate user.

8. The method according to claim 1, characterized in that The access point is configured with multiple antennas, and the legal user is configured with a single antenna.

9. A computer-readable storage medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

10. An electronic device comprising a memory and a processor, wherein a computer program capable of running on the processor is stored in the memory, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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