A secure communication method for full-duplex wireless energy-carrying network assisted by intelligent reflective surface

Abstract

The present invention discloses a secure communication method for a full-duplex wireless energy-carrying network assisted by an intelligent reflector. The method comprises the following steps: adding full-duplex wireless energy-carrying users to a wireless communication network model assisted by an intelligent reflector to construct a full-duplex user-cooperative intelligent reflector-assisted wireless energy-carrying network model; establishing a mathematical model for the optimization problem based on the energy of the transmitter and users, the phase shift of the intelligent reflector, and the security rate at the legitimate user; and maximizing the security rate of the legitimate receiver by jointly optimizing the transmitter's transmit beamforming vector, the phase shift matrix of the intelligent reflector, and the power of the artificial noise signal transmitted by the legitimate receiver. This method can effectively improve the quality of service for the legitimate receiver and ensure the security of the network's physical layer.

Description

[0001] The present invention relates to the field of wireless transmission technology, and in particular to a smart reflective surface-assisted full-duplex wireless energy-carrying network secure communication method. Background Art

[0002] Driven by the green, efficient, and secure development philosophy of next-generation wireless communication technologies (B5G and 6G), smart reflective surfaces, as a low-cost, efficient, and energy-saving emerging technology, have attracted widespread research interest in the wireless communications field. Specifically, thanks to new breakthroughs in materials technology, smart reflective surfaces are a new type of passive device composed of a large number of low-cost reflective elements. Each element can independently change the amplitude and phase of the incident signal, achieving three-dimensional passive beamforming and changing the wireless signal transmission environment. Research by S. Hu et al. in the paper "The potential of using large antenna arrays on intelligent surfaces" shows that the channel capacity achieved by smart reflective surface-assisted communication is linearly proportional to the transmit power, while the channel capacity and transmit power achieved by conventional multiple-input, multiple-output (MIMO) architectures are logarithmically proportional. Therefore, smart reflective surfaces can be widely used in traditional wireless relay networks to replace relays, improve network coverage, and reduce network deployment costs.

[0003] However, due to the dynamic topology of wireless networks, wireless communications are more vulnerable to eavesdropping, and users are increasingly concerned about information security. Therefore, improving the physical layer security of intelligent reflecting surface-assisted networks is crucial. M. Cui et al. first studied the impact of intelligent reflecting surfaces on physical layer security in their paper "Secure wireless communication via intelligent reflecting surface." They proposed an intelligent reflecting surface cooperative algorithm and a maximum ratio transmission algorithm to improve the physical layer security of intelligent reflecting surface networks. To further enhance network physical layer security, J. Huang et al. proposed a cooperative scheme using external jammers to emit artificial noise in their paper "Robust Secure Transmission in MISO Channels Based on Worst-Case Optimization," which effectively improves network physical layer security. S. Hong et al. proposed a cooperative scheme using base stations to emit artificial noise in their paper "Artificial-Noise-Aided Secure MIMO Wireless Communications via Intelligent Reflecting Surface," which also improves physical layer security. However, using cooperative jammers increases the difficulty of network deployment. Artificial noise generated by other devices can also interfere with users, reducing the signal-to-interference-to-noise ratio (SINR) received by legitimate users and increasing the complexity of decoding information for legitimate users. Generating artificial noise also results in additional energy consumption and reduces the lifespan of equipment. To address this issue, X. Li et al. proposed an artificial noise scheme for full-duplex user collaboration in the paper "Robust secure beamforming for swipt-aided relay systems with full-duplex receiver and imperfect CSI." Users employ a full-duplex receiver architecture, generating artificial noise to interfere with eavesdroppers while receiving signals to collect energy and information. Since the received artificial noise is emitted by the device itself, self-interference cancellation can be used to eliminate the received self-interference signal. The energy collected from the signal can be used to replenish the device's energy and extend its lifespan. However, the algorithm proposed in this paper is only applicable to relay collaboration networks and does not take into account the phase shift adjustability of the smart transmitting surface, making it unsuitable for smart reflector collaboration networks.

[0004] In this paper, we focus on the impact of full-duplex user collaboration on the security of a smart reflector-assisted wireless energy-carrying network. Specifically, in the presence of a passive eavesdropper, a multi-antenna transmitter transmits signals to legitimate full-duplex users via a smart reflector. Full-duplex users receive signals using wireless energy-carrying receivers, simultaneously collecting energy and decoding information. They also transmit artificial noise to interfere with eavesdroppers, improving network security. We maximize the network security rate by jointly optimizing the transmitter's transmit beamforming, the smart reflector's phase shift, and the artificial noise signal power generated by legitimate users. We also propose an iterative algorithm based on the alternating direction multiplier method, which solves the original non-convex problem using a semidefinite algorithm and the Charnes-Cooper transform. We also provide a closed-form solution for the optimal artificial noise signal power, effectively improving physical layer security and the communication quality of legitimate users. Summary of the Invention

[0005] The purpose of the present invention is to provide a secure communication method for a full-duplex wireless energy-carrying network assisted by an intelligent reflector. In a wireless communication network assisted by an intelligent reflector, users using full-duplex wireless energy-carrying receivers generate artificial noise while receiving signals to interfere with eavesdroppers, reducing the eavesdroppers' signal-to-interference-noise ratio (SINR). Wireless energy-carrying technology is also used to replenish energy for users. By jointly optimizing the transmitting end's transmit beamforming, the phase shift of the intelligent reflector, and the power of the artificial noise signal generated by legitimate users, the maximum network security rate is achieved. An effective iterative algorithm based on the alternating direction multiplier method is proposed, which solves the original non-convex problem using a semi-positive definite algorithm and the Charnes-Cooper transform. A closed-form solution for the optimal artificial noise signal power is provided, effectively improving physical layer security and the communication quality of legitimate users.

[0006] To achieve the above objectives, the intelligent reflective surface-assisted full-duplex wireless energy-carrying network secure communication method of the present invention includes:

[0007] Step 1: Construct a wireless energy-carrying network system model assisted by a smart reflective surface, which includes a transmitter equipped with M transmitting antennas forming a uniform linear array, a smart reflective surface equipped with N reflecting units forming a uniform rectangular array, a legitimate user U equipped with two independent antennas, and an eavesdropper E equipped with a single antenna who hopes to decode the confidential information sent by the transmitter. In this network, the signals received by user U and eavesdropper Eve are composed of two parts: one part is the signal that reaches the receiver through the direct transmission link between the transmitter and the user, and the other part is the signal transmitted by the transmitter and reaches the receiver through the reflection link of the smart reflective surface.

[0008] set up represents the complex channel coefficient vector of the direct link from the transmitter to user U, [] H Represents the conjugate transpose of a vector, CM×1 Represents an M×1 dimensional complex set, G∈C N×M represents the complex channel coefficient vector from the transmitter to the smart reflector, represents the complex channel coefficient vector from the smart reflector to the legitimate receiving user U, represents the complex channel coefficient vector from the smart reflective surface to the eavesdropper E, h UE represents the complex channel coefficient between the legitimate user U and the eavesdropper Eve;

[0009] Step 2: In each transmission time slot, after the transmitter transmits the signal, the signal received by the legitimate user U and the eavesdropper E contains two parts: the direct signal and the reflected signal. Specifically, the transmitter sends the confidential signal s, and determines the signal y received by the user. U , the signal y received by the eavesdropper E ;

[0010] Legitimate users collect the signal y U Divided into two parts, one Used to collect information, the other part Used to collect energy, where ρ is the energy allocation factor. The user uses the collected energy to generate artificial noise to interfere with the eavesdropper; determine the energy collected by the user E H , the energy required by the user to generate artificial noise The self-interference elimination circuit consumes energy E SIC ;In order to avoid reducing the service life of the user, it is necessary to ensure that the energy collected by the user, the energy consumed by the user and the energy consumed by the self-interference circuit meet the energy constraints;

[0011] Step 3: Receive signal y according to the legitimate user U U , determine the signal-to-interference-and-noise ratio (SINR) of the signal received by the legitimate user U U , according to Shannon's theorem, determine the channel capacity r of the legitimate user U U ; According to the eavesdropper E receives the signal y E , determine the signal-to-interference-and-noise ratio (SINR) of the signal received by the eavesdropper E E , according to Shannon's theorem, determine the channel capacity r of the eavesdropper E E , the channel capacity r of the legitimate user U U Subtract the channel capacity r of the eavesdropper E E Get the system confidentiality rate r S ;

[0012] Step 4: Based on the above definition, determine the problem P1 of maximizing the confidentiality rate of the smart reflector-assisted wireless energy-carrying network with full-duplex user cooperation under the constraints of the energy of the legitimate users and the phase shift of the smart reflector, so that the system can achieve the best physical layer security without affecting the service life of the legitimate users.

[0013] Step 5: Based on the problem P1 of maximizing the full-duplex user cooperation security rate of the smart reflector-assisted wireless energy network described in Step 4, decompose it into three optimization problems using the alternating direction multiplier method. The three optimization problems are solved using the semidefinite relaxation algorithm and the Charnes-Cooper transform algorithm, respectively, to obtain the transmitting end beamforming matrix, the smart reflector, the artificial noise signal power, and the maximum security rate.

[0014] Step 6: Based on the three optimization problems proposed in step 5, an alternating optimization algorithm is proposed to obtain the optimal transmit beamforming matrix W for problem P1. * , smart reflection surface phase shift matrix Θ * , artificial noise signal power and maximum confidentiality rate And obtain the optimal transmitting end beamforming vector w according to the eigendecomposition algorithm or Gaussian randomization technology * , and finally obtain the optimal solution to the original problem P1;

[0015] Furthermore, in step 2 of the intelligent reflective surface-assisted full-duplex wireless energy-carrying network secure communication method of the present invention, the following steps are included:

[0016] The signal y received by the legitimate user U U for:

[0017]

[0018] in is the phase shift matrix of the smart reflection surface, w is the transmitting end beamforming vector, n SI Represents the residual self-interference noise signal received by the legitimate user, n U represents the noise signal received by the legitimate user. s represents the confidential signal transmitted by the transmitter, G represents the conjugate transpose of the complex channel coefficient vector from the transmitter to the smart reflector, Represents the conjugate transpose of the complex channel coefficient vector from the smart reflection surface to the legitimate user. represents the conjugate transpose of the complex channel coefficient vector of the direct link from the transmitter to user U.

[0019] The signal y received by the eavesdropper E E for:

[0020]

[0021] where s AN represents the artificial noise signal received by the eavesdropper, n E represents the noise signal received by the eavesdropper, represents the conjugate transpose of the complex channel coefficient vector from the smart reflective surface to the eavesdropper E, represents the conjugate transpose of the complex channel coefficient vector from the transmitter to the eavesdropper E, h UE represents the complex channel coefficient between the legitimate user U and the eavesdropper Eve;

[0022] Energy E collected by legitimate user U U It can be expressed as:

[0023]

[0024] Where η = λ(1-ρ), ρ∈(0,1) represents the energy allocation proportional factor, λ∈(0,1] represents the energy harvesting efficiency of the legitimate user, || 2 Represents the square of the modulus.

[0025] In order not to reduce the service life of the legitimate user U, the energy consumed by the legitimate user U needs to be less than the collected energy E U , that is, the following constraints need to be satisfied

[0026]

[0027] Furthermore, in step 3 of the intelligent reflective surface-assisted full-duplex wireless energy-carrying network secure communication method of the present invention, the following steps are included:

[0028] The signal-to-interference-and-noise ratio (SINR) of the signal received by the legitimate user U U for:

[0029]

[0030] in represents the power of the self-interference signal received by the user, Indicates the noise signal power received by the user, Represents the noise signal power caused by information decoding.

[0031] The channel capacity r of the legitimate user U U for:

[0032]

[0033] SINR of the signal received by the eavesdropper E It can be expressed as:

[0034]

[0035] in represents the power of the artificial noise signal received by the eavesdropper, Represents the noise signal power received by the eavesdropper.

[0036] The eavesdropper's channel capacity r S for:

[0037]

[0038] The system confidentiality rate is:

[0039] r S =r U -r E

[0040] Furthermore, in step 4 of the intelligent reflective surface-assisted full-duplex wireless energy-carrying network secure communication method of the present invention, the following steps are included:

[0041] The problem P1 of maximizing the confidentiality rate of full-duplex user cooperation in smart reflective surface-assisted wireless energy-carrying networks can be expressed as:

[0042] P1

[0043] st||w|| 2 ≤P

[0044]

[0045]

[0046] in Ensure that the obtained confidentiality rate value is non-negative, ||w|| 2 means taking the square of the Euclidean norm of the internal value, For all n belonging to N, P represents the maximum transmit power of the transmitter.

[0047] Furthermore, in step 5 of the intelligent reflective surface assisted full-duplex wireless energy-carrying network secure communication method of the present invention, the following steps are included:

[0048] The three-level optimization problem constructed based on the alternating direction multiplier method can be expressed as problems P2, P3 and P4, respectively, as shown below;

[0049] (1) Fixed smart reflector phase shift matrix Θ and artificial noise signal power Solve the transmitting end beamforming vector w when the smart reflector phase shift matrix Θ and the artificial noise signal power are fixed After that, the original optimization problem P1 becomes

[0050]

[0051] st||w|| 2 ≤P

[0052]

[0053] in

[0054] Since the quadratic form of the objective function in the above formula cannot be solved directly, we define it through the semi-definite relaxation algorithm: Relax at the same time Rank() represents the rank of the matrix, and the above formula can be transformed into

[0055]

[0056] st

[0057]

[0058] in Tr() represents the trace of the matrix, and ≥ represents positive semidefinite.

[0059] Then, we use the Charnes-Cooper transformation algorithm to solve the fractional form in the above problem, by defining The above problem can be transformed into P2

[0060] P2

[0061] stTr(W)≤μP

[0062]

[0063]

[0064] This problem is a standard convex optimization problem and can be solved efficiently using interior point methods or convex optimization toolkits.

[0065] (2) Fixed transmitter beamforming vector w and artificial noise signal power Solve the phase shift matrix Θ of the smart reflection surface

[0066] When the transmitting end beamforming vector w and the artificial noise signal power are fixed After that, the original optimization problem P1 is equivalent to:

[0067]

[0068] st

[0069]

[0070] in diag() means to diagonalize the vector into a matrix. By defining

[0071]

[0072]

[0073] Then the following equation holds:

[0074]

[0075]

[0076] Where q = [v T , 1] T ,

[0077] According to the above definition, problem P1 is equivalent to

[0078]

[0079] st

[0080]

[0081] In order to solve the quadratic and fractional forms of the above problem, we use the semi-positive relaxation and Charnes-Cooper transformation algorithm to define τ=(Tr(q H H E q)+g E +1) -1 , the above problem can be transformed into the standard convex optimization form P3:

[0082] P3

[0083] st

[0084] Tr(H E Q)+τ(g E +1)=1,Q≥0

[0085]

[0086] Among them E n represents an all-zero matrix of dimension N whose n-th diagonal element is 1. This problem can be solved efficiently using standard convex optimization toolkits.

[0087] (3) Fix the transmitting end beamforming vector w and the smart reflector phase shift matrix Θ and solve the artificial noise signal power

[0088] When the transmitting end beamforming vector w and the smart reflection surface phase shift matrix Θ are fixed, the original optimization problem P1 can be transformed into P4

[0089] P4

[0090] st

[0091] in

[0092] Assume that the objective function of problem P4 is Then its first-order derivative is Since its first-order derivative is greater than 0, it means its original function along with Increase monotonically, due to the existence of constraints in problem P4, The optimal solution of problem P4 is

[0093] Furthermore, step 6 of the intelligent reflective surface-assisted full-duplex wireless energy-carrying network secure communication method of the present invention includes:

[0094] The alternating algorithm for solving the three-level optimization problem can be described as Algorithm A

[0095] A1. Set the initial value parameter w of problem (P1) 0 =w MRT ,Θ 0 =diag(1),q 0 =1, Convergence accuracy ε=10 -3 and iteration counter k=1;

[0096] A2. According to solving problem P2, obtain the transmitting end beamforming matrix w k ;

[0097] A3. Solve problem P3 and obtain the smart reflector phase shift matrix Θ k ;

[0098] A4. Solve problem P4 and obtain the artificial noise transmission power of the legal user

[0099] A5. According to r S =r U -r E Calculate the confidentiality rate of this iteration

[0100] A6. Determine whether Where || represents the absolute value. If the value is satisfied, jump to step A7; if not, return to step A2.

[0101] A7. Output the optimal value w * ,Θ * , and

[0102] Beneficial effects of the present invention:

[0103] Through the above technical solution, the present invention addresses the issues of existing smart reflector-assisted wireless power networks that fail to consider the use of full-duplex transceivers and user-generated noise collaborative interference. By jointly designing the transmit beamforming vector, the smart reflector phase shift matrix, and the power of the user-generated noise signal, the present invention effectively improves the physical layer security of the smart reflector-assisted wireless power network and the quality of service for legitimate users. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0105] Figure 1 This is a network model diagram of the present invention.

[0106] Figure 2 This is a structural diagram of the wireless energy-carrying receiver used by the full-duplex user of the present invention.

[0107] Figure 3 Flow chart of the method of the present invention.

[0108] Figure 4 This is a comparison chart of the confidentiality rate obtained by the method of the present invention as it changes with the transmitting end power and the traditional method. DETAILED DESCRIPTION

[0109] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0110] The intelligent reflective surface-assisted full-duplex wireless energy-carrying network secure communication method of the present invention comprises the following steps:

[0111] Step 1: Construct a wireless energy-carrying network system model assisted by a smart reflective surface. The model consists of a transmitter AP equipped with M transmitting antennas forming a uniform thread array, a smart reflective surface IRS equipped with N reflecting units forming a uniform rectangular array, a legitimate user U equipped with two independent antennas, and an eavesdropper E equipped with a single antenna who desires to decode the confidential information sent by the transmitter. In this network, the signals received by user U and eavesdropper E are composed of two parts: one part is the signal that reaches the receiver through the direct transmission link between the transmitter and the user, and the other part is the signal transmitted by the transmitter and reaches the receiver through the reflection link of the smart reflective surface.

[0112] set up represents the complex channel coefficient vector of the direct link from the transmitter to user U, [] H Represents the conjugate transpose of a vector, C M×1 Represents an M×1 dimensional complex set, G∈C N×M represents the complex channel coefficient vector from the transmitter to the smart reflector, represents the complex channel coefficient vector from the smart reflector to the legitimate receiving user U, represents the complex channel coefficient vector from the smart reflective surface to the eavesdropper E, h UE represents the complex channel coefficient between the legitimate user U and the eavesdropper Eve;

[0113] Step 2: In each transmission time slot, after the transmitter transmits the signal, the signal received by the legitimate user U and the eavesdropper E contains two parts: the direct signal and the reflected signal. Specifically, the transmitter sends the confidential signal s, and the user receives the signal y U for

[0114]

[0115] in is the phase shift matrix of the smart reflection surface, w is the transmitting end beamforming vector, n SI Represents the residual self-interference noise signal received by the legitimate user, n U represents the noise signal received by the legitimate user, s represents the confidential signal transmitted by the transmitter, G represents the conjugate transpose of the complex channel coefficient vector from the transmitter to the smart reflector, Represents the conjugate transpose of the complex channel coefficient vector from the smart reflection surface to the legitimate user. represents the conjugate transpose of the complex channel coefficient vector of the direct link from the transmitter to user U.

[0116] The signal y received by the eavesdropper E for

[0117]

[0118] where sAN represents the artificial noise signal received by the eavesdropper, n E represents the noise signal received by the eavesdropper, represents the conjugate transpose of the complex channel coefficient vector from the transmitter to the eavesdropper E, represents the conjugate transpose of the complex channel coefficient vector from the smart reflective surface to the eavesdropper E, h UE represents the complex channel coefficient between the legitimate user U and the eavesdropper E;

[0119] Legitimate users collect the signal y U Divided into two parts, one Used to collect information, the other part Used to collect energy, where ρ∈(0,1) is the energy allocation factor. Users use the collected energy to generate artificial noise to interfere with eavesdroppers; the energy collected by legitimate users E H It can be expressed as

[0120]

[0121] Where η = λ(1-ρ), λ∈(0,1] represents the energy harvesting efficiency of the legitimate user, || 2 Indicates taking the square of the modulus of the internal value.

[0122] In order not to reduce the service life of the legitimate user U, the energy consumed by the legitimate user U needs to be less than the collected energy E U , that is, the following constraints need to be satisfied

[0123]

[0124] Step 3: Receive signal y according to the legitimate user U U , determine the signal-to-interference-and-noise ratio (SINR) of the signal received by the legitimate user U U for:

[0125]

[0126] in represents the power of the self-interference signal received by the user, Indicates the noise signal power received by the user, Represents the noise signal power caused by information decoding.

[0127] The channel capacity r of the legitimate user U U for:

[0128]

[0129] According to the eavesdropper E receives the signal y E , determine the signal-to-interference-and-noise ratio (SINR) of the signal received by the eavesdropper EE for:

[0130]

[0131] in represents the artificial noise signal power received by the eavesdropper E, Represents the noise signal received by the eavesdropper E

[0132] Power. The channel capacity r of the eavesdropper E E for:

[0133]

[0134] The channel capacity r of the legitimate user U U Subtract the channel capacity r of the eavesdropper E E Get the system confidentiality rate r S For: r S =r U -r E

[0135] Step 4: Based on the above definition, determine the problem P1 of maximizing the confidentiality rate of the smart reflector-assisted wireless energy network with full-duplex user cooperation under the constraints of the energy constraint of the legitimate user and the phase shift constraint of the smart reflector, so that the system can obtain the best physical layer security without affecting the service life of the legitimate user. P1 can be expressed as

[0136] P1

[0137] st||w|| 2 ≤P

[0138]

[0139]

[0140] in Ensure that the obtained confidentiality rate value is non-negative, ||w|| 2 means taking the square of the Euclidean norm of the internal value, For all n belonging to N, P represents the maximum transmit power of the transmitter.

[0141] Step 5: Based on the problem P1 of maximizing the full-duplex user cooperation in the smart reflective surface-assisted wireless energy network confidentiality rate described in step 4, it is decomposed into three optimization problems P2, P3, and P4 according to the alternating direction multiplier method, which can be expressed as:

[0142] (1) Fixed smart reflector phase shift matrix Θ and artificial noise signal power Solve the transmitting end beamforming vector w

[0143] When the smart reflector phase shift matrix Θ and the artificial noise signal power are fixed After that, the original optimization problem P1 becomes

[0144]

[0145] st||w|| 2 ≤P

[0146]

[0147] in

[0148] In order to solve the quadratic form of the objective function in the above formula, we define the semi-definite relaxation algorithm as Relax at the same time Rank() represents the rank of the matrix, and the above formula can be transformed into

[0149]

[0150] st

[0151]

[0152] in Tr() represents the trace of the matrix, and ≥ represents positive semidefinite.

[0153] Then, we use the Charnes-Cooper transformation algorithm to solve the fractional form in the above problem, by defining The above problem can be transformed into P2

[0154] P2

[0155] stTr(W)≤μP

[0156]

[0157]

[0158] This problem is a standard convex optimization problem and can be solved efficiently using interior point methods or convex optimization toolkits.

[0159] (2) Fixed transmitter beamforming vector w and artificial noise signal power Solve the phase shift matrix Θ of the smart reflection surface

[0160] When the transmitting end beamforming vector w and the artificial noise signal power are fixed After that, the original optimization problem P1 is equivalent to:

[0161]

[0162] st

[0163]

[0164] in diag() means to diagonalize the vector into a matrix. By defining

[0165]

[0166]

[0167] Then the following equation holds:

[0168]

[0169]

[0170] Where q = [v T ,1] T , v T Represents the transpose of the diagonal elements of the phase shift matrix.

[0171] According to the above definition, problem P1 is equivalent to

[0172]

[0173] st

[0174]

[0175] In order to solve the quadratic and fractional forms of the above problem, we use the semi-positive relaxation and Charnes-Cooper transformation algorithm to define Q = τqq H ,τ=(Tr(q H H E q)+g E +1) -1 , the above problem can be transformed into the standard convex optimization form P3:

[0176]

[0177] st

[0178] Tr(H E Q)+τ(g E +1)=1,Q≥0

[0179]

[0180] Among them E n represents an all-zero matrix of size N whose n-th diagonal element is 1. This problem can be solved efficiently using standard convex optimization toolkits.

[0181] (3) Fix the transmitting end beamforming vector w and the smart reflector phase shift matrix Θ and solve the artificial noise signal power

[0182] When the transmitting end beamforming vector w and the smart reflection surface phase shift matrix Θ are fixed, the original optimization problem P1 can be transformed into P4

[0183] P4

[0184] st

[0185] in

[0186] Assume that the objective function of problem P4 is Then its first-order derivative is Since its first-order derivative is greater than 0, it means its original function along with Increase monotonically, due to the existence of constraints in problem P4, Therefore, the optimal solution of problem P4 is

[0187] Step 6: Based on the three optimization problems proposed in step 5, an alternating optimization algorithm is proposed to obtain the optimal transmit beamforming matrix W for problem P1. * , smart reflection surface phase shift matrix Θ * , artificial noise signal power and maximum confidentiality rate And obtain the optimal transmitting end beamforming vector w according to the eigendecomposition algorithm or Gaussian randomization technology * ; The alternating algorithm for solving the three-level optimization problem can be described as Algorithm A

[0188] A1. Set the initial value parameter w of problem (P1) 0 =w MRT ,Θ 0 =diag(1),q 0 =1, Convergence accuracy ε=10 -3 and iteration counter k=1;

[0189] A2. According to solving problem P2, obtain the transmitting end beamforming matrix w k ;

[0190] A3. Solve problem P3 and obtain the smart reflector phase shift matrix Θ k ;

[0191] A4. Solve problem P4 and obtain the artificial noise transmission power of the legal user

[0192] A5. According to r S =r U -r E Calculate the confidentiality rate of this iteration

[0193] A6. Determine whether Where || represents the absolute value. If the value is satisfied, jump to step A7; if not, return to step A2.

[0194] A7. Output the optimal value w * ,Θ * , and

[0195] Experimental process:

[0196] 1. Simulation environment settings

[0197] like Figure 1 As shown in Figure 2, we assume that the entire simulation scenario is within a rectangular area. If the transmitting AP is used as the Cartesian coordinate system origin (0,0), the legitimate user U is located at (60,0) on the X-axis, the smart reflector is located at (30,40), and the eavesdropper is located at (70,10). Similar to related work, the channel model is assumed to include large-scale path loss and small-scale multipath fading. The path loss model is given by the following equation:

[0198]

[0199] The meaning of the parameters in the above formula and other parameter settings of this experiment are shown in the following table:

[0200] Table 1. Experimental parameters

[0201]

[0202]

[0203] 2. Experimental process

[0204] Set the experimental parameters according to the table above, and set the three comparison algorithms as follows:

[0205] 1. Maximum ratio transmission algorithm, a classic algorithm that does not consider base station transmit beamforming. For details, see the document "Secure wireless communication via intelligent reflecting surface";

[0206] 2. Artificial noise cooperation algorithm, which considers the impact of artificial noise emitted by base stations on physical layer security. For details, see the paper "Artificial-noise-aided secure MIMO wireless communications via intelligent reflecting surface";

[0207] 3. Intelligent reflecting surface collaboration algorithm, which considers the impact of intelligent reflecting surfaces on physical layer security. For details, see the document "Secure wireless communication via intelligent reflecting surface";

[0208] The simulation experiment under the same parameters obtained the following data:

[0209] Table 2. Experimental data

[0210]

[0211] From the above experimental data, we can see that the algorithm proposed in this invention can achieve the highest confidentiality rate, which proves the superiority of our algorithm.

[0212] Through the above technical solution, the present invention addresses the issues of existing smart reflector-assisted wireless power networks that fail to consider the use of full-duplex transceivers and user-generated noise collaborative interference. By jointly designing the transmit beamforming vector, the smart reflector phase shift matrix, and the power of the user-generated noise signal, the present invention effectively improves the physical layer security of the smart reflector-assisted wireless power network and the quality of service for legitimate users.

[0213] The present invention first provides a modeling method for smart reflector-assisted wireless networks. To improve the physical layer security of wireless network communications, the present invention introduces full-duplex user cooperative transmitter communications using a wireless power-carrying receiver. By jointly optimizing the transmitter beamforming vector, the smart reflector phase shift matrix, and the user artificial noise signal power, an optimization problem is established to achieve optimal communication performance for legitimate users. This problem is decomposed into three subproblems using the alternating direction multiplier method. These subproblems are converted into standard convex optimization problems using a semi-definite relaxation algorithm and a Charnes-Cooper transform algorithm, respectively. An iterative algorithm is designed to efficiently solve these problems, ultimately obtaining the optimal transmitter beamforming vector, smart reflector phase shift matrix, and user artificial noise signal power to maximize the user's confidentiality rate. This invention can significantly improve the confidentiality rate of smart reflector-assisted wireless power-carrying networks.

[0214] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for secure communication in a full-duplex wireless energy-carrying network assisted by an intelligent reflective surface, characterized in that: The following steps are involved: Step 1: Construct a smart reflector-assisted wireless energy network system model, which includes a transmitter AP equipped with M transmitting antennas forming a uniform linear array, a smart reflector IRS equipped with N reflecting units forming a uniform rectangular array, a legitimate user U equipped with two independent antennas, and an eavesdropper E equipped with a single antenna hoping to decode the confidential information sent by the transmitter. In this network, the signals received by user U and eavesdropper E are composed of two parts: one part is the signal from the direct transmission link between the transmitter and the receiver to the receiver, and the other part is the signal transmitted by the transmitter to the receiver through the reflection link of the smart reflector. set up represents the complex channel coefficient vector of the direct link from the transmitter to user U, [] H Represents the conjugate transpose symbol, C M×1 Represents an M×1 dimensional complex set, G∈C N×M represents the complex channel coefficient vector from the transmitter to the smart reflector, represents the complex channel coefficient vector from the smart reflector to the legitimate receiving user U, represents the complex channel coefficient vector from the smart reflective surface to the eavesdropper E, h UE ∈C represents the complex channel coefficient between the legitimate user U and the eavesdropper E; Step 2: In each transmission time slot, after the transmitter transmits the signal, the signal received by the legitimate user U and the eavesdropper E contains two parts: the direct link signal and the reflected link signal. Specifically, the transmitter sends the confidential signal s, and determines the signal y received by the user. U , the signal y received by the eavesdropper E ; Legitimate users collect the signal y U Divided into two parts, one Used to collect information, the other part Used to collect energy, where ρ∈(0,1) represents the energy allocation factor. The user uses the collected energy to generate artificial noise to interfere with the eavesdropper; determine the energy E collected by the legitimate user H , the energy required by the user to generate artificial noise The self-interference elimination circuit consumes energy E SIC In order not to reduce the service life of legitimate users, it is also necessary to ensure that the energy collected by legitimate users, the energy consumed by users, and the energy consumed by self-interference circuits meet the energy constraints; Step 3: Receive signal y according to the legitimate user U U , determine the signal-to-interference-and-noise ratio (SINR) of the signal received by the legitimate user U U , according to Shannon's theorem, determine the channel capacity r of the legitimate user U U ; According to the eavesdropper E receives the signal y E , determine the signal-to-interference-and-noise ratio (SINR) of the signal received by the eavesdropper E E , according to Shannon's theorem, determine the channel capacity r of the eavesdropper E E , the channel capacity r of the legitimate user U U Subtract the channel capacity r of the eavesdropper E E Get the system confidentiality rate r S ; Step 4: Based on the above definition, determine the problem P1 of maximizing the confidentiality rate of the smart reflector-assisted wireless energy-carrying network with full-duplex user cooperation under the constraints of the energy of the legitimate users and the phase shift of the smart reflector, so that the system can achieve the best physical layer security without affecting the service life of the legitimate users. In step 4, the problem P1 of maximizing the confidentiality rate of the smart reflective surface-assisted wireless energy-carrying network with full-duplex user cooperation is expressed as: s.t.‖‖w‖‖ 2 ≤P in Ensure that the obtained confidentiality rate value is non-negative, ||w|| 2 means taking the square of the Euclidean norm of w, For all n belonging to N, P represents the maximum transmit power of the transmitter; Step 5: Based on the problem P1 of maximizing the full-duplex user cooperation confidentiality rate of the smart reflector-assisted wireless energy network described in Step 4, since it is non-convex and difficult to solve, it is decomposed into three convex optimization problems using the alternating direction multiplier method. The three optimization problems are solved using the semi-definite relaxation algorithm and the Charnes-Cooper transform algorithm to obtain the transmitting end beamforming vector, the smart reflector phase shift matrix, and the artificial noise signal power. Step 6: Based on the three optimization problems proposed in step 5, an alternating optimization algorithm is proposed to obtain the optimal transmit beamforming matrix W for problem P1. * , smart reflection surface phase shift matrix Θ * , artificial noise signal power and maximum confidentiality rate And obtain the optimal transmitting end beamforming vector w according to the eigendecomposition algorithm or Gaussian randomization technology * , and finally obtain the optimal solution to the original problem P1.

2. The intelligent reflective surface-assisted full-duplex wireless energy-carrying network secure communication method according to claim 1, characterized in that: In step 2, the signal y received by the legitimate user U U for: in is the phase shift matrix of the smart reflection surface, diag() means to diagonalize the vector into a matrix, w is the transmitting end beamforming vector, n SI Represents the residual self-interference noise signal received by the legitimate user, n U represents the noise signal received by the legitimate user, s represents the confidential signal transmitted by the transmitter, represents the conjugate transpose of the complex channel coefficient vector from the smart reflection surface to the legitimate user; represents the conjugate transpose of the complex channel coefficient vector of the direct link from the transmitter to user U; The signal y received by the eavesdropper E E for: where s AN represents the artificial noise signal received by the eavesdropper, n E represents the noise signal received by the eavesdropper, represents the conjugate transpose of the complex channel coefficient vector from the transmitter to the eavesdropper E, represents the conjugate transpose of the complex channel coefficient vector from the smart reflective surface to the eavesdropper E, h UE ∈C represents the complex channel coefficient between the legitimate user U and the eavesdropper E; Energy E collected by legitimate user U U It can be expressed as: Where η = λ(1-ρ), ρ∈(0,1) represents the energy allocation factor, λ∈(0,1] represents the energy harvesting efficiency of the legitimate user, | | 2 It means taking the square of the internal value modulus; In order not to reduce the service life of the legitimate user U, the energy consumed by the legitimate user U needs to be less than the collected energy E U , that is, the following constraints need to be satisfied in Indicates the power of the artificial noise signal transmitted by the user, E SIC Indicates the power consumed by the user using the self-interference cancellation technology.

3. The intelligent reflective surface-assisted full-duplex wireless energy-carrying network secure communication method according to claim 2, characterized in that: In step 3, the signal to interference and noise ratio (SINR) of the signal received by the legitimate user U is U for: in represents the power of the self-interference signal received by the user, Indicates the noise signal power received by the user, Represents the noise signal power caused by information decoding; The channel capacity r of the legitimate user U U Expressed as: SINR of the signal received by the eavesdropper E Expressed as: in represents the power of the artificial noise signal received by the eavesdropper, represents the noise signal power received by the eavesdropper, ρ∈(0,1) represents the energy allocation factor; The channel capacity of the eavesdropper is expressed as: System confidentiality rate r S for: r S ＝r U -r E 。 4. The intelligent reflective surface-assisted full-duplex wireless energy-carrying network secure communication method according to claim 1, characterized in that: In step 5, the three-level optimization problem constructed based on the alternating direction multiplier method can be expressed as problems P2, P3 and P4 respectively; (1) Fixed smart reflector phase shift matrix Θ and artificial noise signal power Solve the transmitting end beamforming vector w When the smart reflector phase shift matrix Θ and the artificial noise signal power are fixed After that, the original optimization problem P1 becomes s.t.‖‖w‖‖ 2 ≤P in In order to solve the quadratic form of the objective function in the above formula, we define the semi-definite relaxation algorithm as Relax at the same time Rank( ) represents the rank of the matrix, and the above formula can be transformed into in Tr( ) represents the trace of the matrix, and ≥ represents semi-positive definite. Then, the Charnes-Cooper transformation algorithm is used to solve the fractional form in the above problem, by defining The above problem is transformed into P2 stTr(W)≤μP This problem is a standard convex optimization problem and can be solved efficiently using interior point methods or convex optimization toolkits. (2) Fixed transmitter beamforming vector w and artificial noise signal power Solve the phase shift matrix Θ of the smart reflection surface When the transmitting end beamforming vector w and the artificial noise signal power are fixed After that, the original optimization problem P1 is equivalent to: in represents the diagonal elements of the phase shift matrix Θ, [] T Represents the transpose symbol, diag() means diagonalizing the vector into a matrix; by defining Then the following equation holds: Where q = [v T ,1] T , v T represents the transpose of the diagonal elements of the phase shift matrix; According to the above definition, problem P1 is equivalent to In order to solve the quadratic and fractional forms of the above problem, we use the semi-positive relaxation and Charnes-Cooper transformation algorithm to define Q = τqq H ,τ=(Tr(q H H E q)+g E +1) -1 , the above problem is transformed into the standard convex optimization form P3: Among them E n represents an all-zero matrix of size N whose n-th diagonal element is 1. This problem is efficiently solved using standard convex optimization toolkits; (3) Fix the transmitting end beamforming vector w and the smart reflector phase shift matrix Θ and solve the artificial noise signal power When the transmitting end beamforming vector w and the smart reflector phase shift matrix Θ are fixed, the original optimization problem P1 is transformed into P4 in Assume that the objective function of problem P4 is Then its first-order derivative is Since its first-order derivative is greater than 0, it means its original function along with Increase monotonically, due to the existence of constraints in problem P4, The optimal solution of problem P4 is

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