Wireless communication system safety performance enhancement method based on multi-RIS assistance

Abstract

The invention belongs to the technical field of wireless communication, and provides a wireless communication system safety performance enhancement method based on multi-RIS assistance, which comprises the steps of single-user downlink communication system model construction, transmission model establishment, target optimization problem determination, target optimization problem improvement and experimental simulation. According to the invention, by introducing a multi-RIS synergistic effect and designing a multi-hop cascade reflection path, a plurality of passive beam forming gains are formed, the problem of path loss caused by single reflection is effectively overcome, and the transmission capability and confidentiality of signals are enhanced; through the combination of an alternating iterative optimization algorithm, a continuous convex approximation technology and a semi-definite relaxation technology, the beam forming vector of a base station and the reflection phase shift matrix of each RIS node are optimized, the channel gain of legal users is improved, the signal receiving quality of eavesdroppers is reduced, the secrecy rate of a communication system is improved, and the user experience is improved. And the safety performance of the system is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method for enhancing the security performance of a wireless communication system assisted by multiple RIS. Background Art

[0002] Reconfigurable Intelligent Surface (RIS) is the full name of Reconfigurable Intelligent Surface. With the rapid development of 5G and the upcoming 6G communication networks, wireless communications have put forward higher requirements for capacity, coverage and energy efficiency. However, traditional enhancement technologies, such as repeaters and amplifiers, can improve signal strength, but are limited by their high deployment costs and energy consumption. In recent years, as a new low-cost and low-energy technology, RIS has attracted widespread attention due to its great potential in improving signal transmission performance, network energy efficiency and security. RIS is a programmable surface composed of a large number of passive reflection units. It can intelligently reflect and regulate the incident signal by dynamically adjusting the phase shift of each reflection unit, thereby reconstructing the wireless propagation environment. By combining with the active beamforming of the base station, RIS technology can significantly improve the gain of the received signal, while weakening the interference signal, and enhancing the reliability and confidentiality of communication. This feature makes the application of RIS in physical layer secure communication a hot topic in recent years. Related studies have shown that RIS can effectively suppress the signal reception of eavesdroppers through the controllability of the channel and improve the confidentiality rate of legitimate users. This security performance improvement method based on channel optimization has become a research hotspot in academia and industry.

[0003] However, most existing research focuses on the application of a single RIS. Although a single RIS can significantly improve the confidentiality rate of the system, its coverage and performance are still limited in actual large-scale communication networks. For example, the performance of a single RIS will be greatly reduced in an environment where the direct signal path is blocked or the reflection path is complex. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a method for enhancing the security performance of a wireless communication system based on multi-RIS assistance, so as to solve the problem that signal attenuation and path loss are prone to occur in a complex communication environment and the security of the communication system is vulnerable to threats.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for enhancing the security performance of a wireless communication system based on multi-RIS assistance, comprising:

[0007] Constructing a single-user downlink communication system model assisted by deploying multiple RIS; the single-user downlink communication system model includes: a plurality of RIS;

[0008] Establish a multi-hop cascade transmission model, define the channel model and signal transmission path between nodes in the transmission model, and determine the expression of the signal-to-noise ratio from the base station to the user, the signal-to-noise ratio from the base station to the eavesdropper, the transmission rate from the base station to the user, the transmission rate from the base station to the eavesdropper, and the confidentiality rate of the system;

[0009] Determine the target optimization problem of the system according to the transmission model; the first constraint of the target optimization problem includes: a beamforming vector; the second constraint of the target optimization problem includes: a reflection phase of the first RIS to a reflection phase of the Lth RIS;

[0010] Using a pre-designed alternating iterative optimization algorithm, combined with continuous convex approximation technology and semidefinite relaxation technology, the beamforming vector of the base station and the reflection phase shift matrix of each RIS node in the target optimization problem are transformed to obtain an improved optimization problem; the improved optimization problem includes: a second optimization problem, a third optimization problem, a fourth optimization problem and a fifth optimization problem;

[0011] The improved optimization problem is experimentally simulated according to the single-user downlink communication system model and the transmission model to obtain simulation results, and the simulation results are determined as the deployment basis for the design of the actual deployment plan of multiple RIS nodes.

[0012] Preferably, the single-user downlink communication system model includes: a base station BS, L RISs, a target user and an eavesdropper; the base station BS is equipped with M antennas; the target user and the eavesdropper are each equipped with a single antenna; each RIS includes N reflection units;

[0013] L RIS are deployed in sequence to form a multi-hop link; the reflection unit is connected to a preset controller; an obstacle is set between the base station BS and the target user and the eavesdropper respectively.

[0014] Preferably, the expression of the signal-to-noise ratio from the base station to the user is:

[0015]

[0016] The expression of the signal-to-noise ratio from the base station to the eavesdropper is:

[0017]

[0018] The expression of the transmission rate from the base station to the user is:

[0019]

[0020] The expression of the transmission rate from the base station to the eavesdropper is:

[0021]

[0022] The expression of the confidentiality rate is:

[0023]

[0024] in, γ U H is the signal-to-noise ratio from the base station to the user; U is an equivalent channel from the base station BS to the target user via L RISs; is the noise power received by the target user; γ E is the signal-to-noise ratio from the base station to the eavesdropper; H E is the equivalent channel from the base station to the eavesdropper via L RISs; is the noise power received by the eavesdropper; R U R is the transmission rate from the base station to the user; E is the transmission rate from the base station to the eavesdropper; R sec is the confidentiality rate; [f(·)] + represents the maximum value between f(·) and 0.

[0025] Preferably, the expression of the channel model is:

[0026]

[0027] Among them, G l is the channel matrix from the base station BS to the lth RIS; C0 represents the path loss at the reference distance d0; d l represents the distance from the base station BS to the lth RIS; α l is the path loss index; β l is the Rician factor; G l,LOS Represents the visible transmission component of the channel; G l,NLOS Represents the non-visible transmission component of the channel.

[0028] Preferably, the expression of the target optimization problem includes:

[0029]

[0030] Wherein, P1 represents the target optimization problem; φ l is the reflection phase of the lth RIS; BS is the maximum transmission power of the base station; l,nis the phase of the nth reflection unit in the lth RIS; L is the number of the RIS; and N is the number of the reflection units in each RIS.

[0031] Preferably, a pre-designed alternating iterative optimization algorithm is used in combination with a continuous convex approximation technique and a semidefinite relaxation technique to transform the beamforming vector of the base station and the reflection phase shift matrix of each RIS node in the target optimization problem to obtain an improved optimization problem, including:

[0032] Initializing the beamforming vector, the reflection phase of the first RIS to the reflection phase of the Lth RIS to obtain a data set to be optimized;

[0033] Optimizing the target optimization problem and the beamforming vectors in the data set to be optimized using the continuous convex approximation technology according to the reflection phase of the first RIS to the reflection phase of the Lth RIS in the data set to be optimized, to obtain the second optimization problem and the optimized beamforming vectors;

[0034] According to the optimized beamforming vector and the reflection phases of the second RIS to the Lth RIS in the data set to be optimized, the second optimization problem and the reflection phase of the first RIS are optimized using the semidefinite relaxation technique to obtain the third optimization problem and the optimized reflection phase of the first RIS;

[0035] According to the optimized beamforming vector, the reflection phase of the first RIS to the reflection phase of the i-1th RIS, and the reflection phase of the i+1th RIS to the Lth RIS in the data set to be optimized, the third optimization problem and the reflection phase of the i-th RIS are optimized using the semidefinite relaxation technique to obtain the fourth optimization problem and the optimized reflection phase of the i-th RIS; wherein i=2, 3, ..., L-1;

[0036] According to the optimized beamforming vector and the reflection phases of the first RIS to the L-1th RIS, the fourth optimization problem and the reflection phase of the Lth RIS are optimized using the semidefinite relaxation technique to obtain the fifth optimization problem and the optimized reflection phase of the Lth RIS;

[0037] The confidentiality rate is calculated using the optimized beamforming vector and the reflection phase of the first RIS to the reflection phase of the L-th RIS, and the optimized beamforming vector and the reflection phase of the first RIS to the L-th RIS are updated as the data set to be optimized, and the step of "optimizing the target optimization problem and the beamforming vector in the data set to be optimized using the continuous convex approximation technology according to the reflection phase of the first RIS to the L-th RIS in the data set to be optimized to obtain the second optimization problem and the optimized beamforming vector" is returned to perform the next round of iteration. If the confidentiality rate obtained in the current iteration is less than the confidentiality rate obtained in the previous iteration, the iteration is completed.

[0038] Preferably, the second optimization problem includes:

[0039]

[0040] in, tr(W)≤P BS ; tr(WA)≥0;tr(WB)≥0; W=ww H ∈￡ M×M ; tr(·) is denoted as the trace of the matrix.

[0041] Preferably, the third optimization problem includes:

[0042]

[0043] in,

[0044] tr(U E1 η1)+λ1·(|v E1 | 2 +σ 2 )=1;η1=λ1S1;

[0045] U B1 , U E1 are system matrices from the base station to the target user and the eavesdropper via the first RIS; B1 、v E1 are signal vectors from the base station to the target user and the eavesdropper through the first RIS respectively; mis a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0; φ1 is the reflection phase shift vector of the first RIS; η1 is the first auxiliary variable introduced.

[0046] Preferably, the fourth optimization problem includes:

[0047]

[0048] in, tr(U Ei η i )+λ i ·(|v Ei | 2 +σ 2 )=1; η i =λ i S i ; U Bi , U Ei point

[0049] is a system matrix from the base station to the target user and the eavesdropper via the i-th RIS; v Bi 、v Ei are the signal vectors from the base station to the target user and the eavesdropper through the i-th RIS respectively; m is a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0; φ i is the reflection phase shift vector of the i-th RIS; η i is the i-th auxiliary variable introduced.

[0050] Preferably, the fifth optimization problem includes:

[0051]

[0052] in,

[0053] tr(U EL η L )+λ L ·(|v EL | 2 +σ 2 )=1; η L =λ L S L ; U BL 、v BL are the system matrices from the base station to the target user and the eavesdropper through the Lth RIS respectively; BL 、vEL are signal vectors from the base station to the target user and the eavesdropper through the Lth RIS respectively; m is a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0; φ L is the reflection phase shift vector of the Lth RIS; η L is the Lth auxiliary variable introduced.

[0054] The present invention discloses the following technical effects:

[0055] The present invention provides a method for enhancing the security performance of a wireless communication system assisted by multiple RIS. By introducing the synergistic effect of multiple RIS and designing a multi-hop cascade reflection path, the path loss problem caused by a single reflection is solved, and the transmission capacity and confidentiality of the signal are enhanced. By combining an alternating iterative optimization algorithm, a continuous convex approximation technology and a semidefinite relaxation technology, the defect that the security of the communication system is vulnerable to threats is solved, and the beamforming vector of the base station and the reflection phase shift matrix of each RIS node are optimized, and the signal reception quality of the eavesdropper is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0057] Figure 1 A schematic diagram of a process for enhancing the security performance of a wireless communication system based on multi-RIS assistance provided by an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of a multi-RIS-assisted single-user downlink communication system model provided in an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of the relationship between the maximum transmission power of a base station BS and the average security rate of the system provided in an embodiment of the present invention;

[0060] Figure 4 A schematic diagram of the relationship between the number of RISs and the average security rate of the system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] The purpose of the present invention is to provide a method for enhancing the security performance of a wireless communication system based on multi-RIS assistance, so as to solve the problem that signal attenuation and path loss are prone to occur in a complex communication environment and the security of the communication system is vulnerable to threats.

[0063] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0064] Figure 1 A schematic diagram of a wireless communication system security performance enhancement process based on multi-RIS assistance provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a method for enhancing the security performance of a wireless communication system based on multi-RIS assistance, comprising:

[0065] Step 100: construct a single-user downlink communication system model assisted by deploying multiple RISs; the single-user downlink communication system model includes: a plurality of RISs;

[0066] Step 200: Establish a multi-hop cascade transmission model, define the channel model and signal transmission path between nodes in the transmission model, and determine the expression of the signal-to-noise ratio from the base station to the user, the signal-to-noise ratio from the base station to the eavesdropper, the transmission rate from the base station to the user, the transmission rate from the base station to the eavesdropper, and the confidentiality rate of the system;

[0067] Step 300: determining a target optimization problem of the system according to the transmission model; the first constraint of the target optimization problem includes: a beamforming vector; the second constraint of the target optimization problem includes: a reflection phase from the first RIS to the reflection phase of the Lth RIS;

[0068] Step 400: using a pre-designed alternating iterative optimization algorithm, combined with a continuous convex approximation technique and a semidefinite relaxation technique, transforming the beamforming vector of the base station and the reflection phase shift matrix of each RIS node in the target optimization problem to obtain an improved optimization problem; the improved optimization problem includes: a second optimization problem, a third optimization problem, a fourth optimization problem, and a fifth optimization problem;

[0069] Step 500: Experimentally simulate the improved optimization problem according to the single-user downlink communication system model and the transmission model to obtain simulation results, and determine the simulation results as the deployment basis for designing the actual deployment plan of multiple RIS nodes.

[0070] Preferably, the single-user downlink communication system model includes: a base station BS, L RISs, a target user and an eavesdropper; the base station BS is equipped with M antennas; the target user and the eavesdropper are each equipped with a single antenna; each RIS includes N reflection units;

[0071] L RIS are deployed in sequence to form a multi-hop link; the reflection unit is connected to a preset controller; an obstacle is set between the base station BS and the target user and the eavesdropper respectively.

[0072] Specifically, the expression of the signal-to-noise ratio from the base station to the user is:

[0073]

[0074] The expression of the signal-to-noise ratio from the base station to the eavesdropper is:

[0075]

[0076] The expression of the transmission rate from the base station to the user is:

[0077]

[0078] The expression of the transmission rate from the base station to the eavesdropper is:

[0079]

[0080] The expression of confidentiality rate is:

[0081]

[0082] in, γ U is the signal-to-noise ratio from the base station to the user; H U is the equivalent channel from the base station BS to the target user via L RIS; is the noise power received by the target user; γ E is the signal-to-noise ratio from the base station to the eavesdropper; H E is the equivalent channel from the base station to the eavesdropper via L RISs; is the noise power received by the eavesdropper; R U is the transmission rate from the base station to the user; R E is the transmission rate from the base station to the eavesdropper; R sec is the confidentiality rate; [f(·)] + represents the maximum value between f(·) and 0.

[0083] Furthermore, the expression of the channel model is:

[0084]

[0085] Among them, G l is the channel matrix from the base station BS to the lth RIS; C0 represents the path loss at the reference distance d0; d l represents the distance from the base station BS to the lth RIS; α l is the path loss index; β l is the Rician factor; G l,LOS Represents the visible transmission component of the channel; G l,NLOS Represents the non-visible transmission component of the channel.

[0086] Specifically, the expression of the target optimization problem includes:

[0087]

[0088] Among them, P1 represents the target optimization problem; φ l is the reflection phase of the lth RIS; P BS is the maximum transmission power of the base station; φ l,n is the phase of the nth reflection unit in the lth RIS; L is the number of RIS; N is the number of reflection units in each RIS.

[0089] Preferably, a pre-designed alternating iterative optimization algorithm is used in combination with a continuous convex approximation technique and a semidefinite relaxation technique to transform the beamforming vector of the base station and the reflection phase shift matrix of each RIS node in the target optimization problem to obtain an improved optimization problem, including:

[0090] Initialize the beamforming vector, the reflection phase of the first RIS to the reflection phase of the Lth RIS, and obtain the data set to be optimized;

[0091] According to the reflection phase of the first RIS to the reflection phase of the Lth RIS in the data set to be optimized, the target optimization problem and the beamforming vector in the data set to be optimized are optimized by using the continuous convex approximation technology to obtain the second optimization problem and the optimized beamforming vector;

[0092] According to the optimized beamforming vector and the reflection phase of the second RIS to the reflection phase of the Lth RIS in the data set to be optimized, the second optimization problem and the reflection phase of the first RIS are optimized using a semidefinite relaxation technique to obtain a third optimization problem and the optimized reflection phase of the first RIS;

[0093] The third optimization problem and the reflection phase of the i-th RIS are optimized using the semidefinite relaxation technique according to the optimized beamforming vector, the reflection phase of the first RIS to the reflection phase of the i-1th RIS, and the reflection phase of the i+1th RIS to the Lth RIS in the data set to be optimized, to obtain the fourth optimization problem and the optimized reflection phase of the i-th RIS; wherein, i=2,3,...,L-1;

[0094] According to the optimized beamforming vector, the reflection phase of the first RIS to the reflection phase of the L-1 RIS, the fourth optimization problem and the reflection phase of the L RIS are optimized by using the semidefinite relaxation technique to obtain the fifth optimization problem and the optimized reflection phase of the L RIS;

[0095] The confidentiality rate is calculated using the optimized beamforming vector and the reflection phase of the first RIS to the reflection phase of the L-th RIS, and the optimized beamforming vector and the reflection phase of the first RIS to the L-th RIS are updated as the data set to be optimized, and the step of "optimizing the target optimization problem and the beamforming vector in the data set to be optimized using the continuous convex approximation technology according to the reflection phase of the first RIS to the L-th RIS in the data set to be optimized to obtain the second optimization problem and the optimized beamforming vector" is returned to perform the next round of iteration. If the confidentiality rate obtained by the current iteration is less than the confidentiality rate obtained by the previous iteration, the iteration is completed.

[0096] Specifically, the second optimization problem includes:

[0097]

[0098] in, tr(W)≤P BS ; tr(WA)≥0;tr(WB)≥0; W=ww H ∈￡ M×M ; tr(·) is denoted as the trace of the matrix.

[0099] Furthermore, the third optimization problem includes:

[0100]

[0101] in,

[0102] tr(U E1 η1)+λ1·(|v E1 | 2 +σ 2 )=1;η1=λ1S1;

[0103] U B1 , U E1 are the system matrices from the base station to the target user and the eavesdropper through the first RIS; v B1 、v E1 are the signal vectors from the base station to the target user and the eavesdropper through the first RIS respectively; E m is a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0; φ1 is the reflection phase shift vector of the first RIS; η1 is the first auxiliary variable introduced.

[0104] Specifically, the fourth optimization problem includes:

[0105]

[0106] in, tr(U Ei η i )+λ i ·(|v Ei | 2 +σ 2 )=1;

[0107] η i =λ i S i ; U Bi , U Ei point

[0108] is the system matrix from the base station to the target user and the eavesdropper through the i-th RIS; v Bi 、v Ei are the signal vectors from the base station to the target user and the eavesdropper through the i-th RIS respectively; E m is a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0; φ i is the reflection phase shift vector of the i-th RIS; η i is the i-th auxiliary variable introduced.

[0109] Furthermore, the fifth optimization problem includes:

[0110]

[0111] in,

[0112] tr(U EL η L )+λ L ·(|v EL |2 +σ 2 )=1; η L =λ L S L ; U BL 、v BL are the system matrices from the base station to the target user and the eavesdropper through the Lth RIS; v BL 、v EL are the signal vectors from the base station to the target user and the eavesdropper through the Lth RIS respectively; E m is a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0; φ L is the reflection phase shift vector of the Lth RIS; η L is the Lth auxiliary variable introduced.

[0113] Furthermore, the target user refers to the end user who needs to be provided with services in the communication network; the eavesdropper refers to an individual or device that illegally monitors the user's network communications; the target user and the eavesdropper are both equipped with a single antenna.

[0114] Specifically, e b It means that at a certain iteration point b% is approximated by the first-order Taylor expansion:

[0115] refer to Figure 2The model consists of a base station BS, L RIS, a user User and an eavesdropper Eve; the L RIS are deployed in sequence to form a multi-hop link. It is assumed that the base station BS is equipped with M antennas, and the user and the eavesdropper are equipped with single antennas. Each RIS has N reflection units, which are all connected to a controller for adjusting its phase shift to achieve control of its phase shift; in the model, there are obstacles (such as tall buildings, trees, etc.) between the base station BS and the user User, and between the base station BS and the eavesdropper Eve, which block the direct link between them. At the same time, the model is also applicable to the situation where the distance between the base station BS and the user User is far; in this embodiment, L reconfigurable smart surfaces (RIS) are deployed in sequence to form a multi-hop link to assist the communication between the base station BS and the user User; specifically, the direct channel between the base station BS and the user User may be limited by obstacles, resulting in a decrease in signal quality, and even enabling the eavesdropper Eve to effectively eavesdrop on the communication data; in order to overcome this problem, multiple RIS are used, and through reasonable design and control, the reflected signal of each RIS is coordinated with other RIS to form a multi-hop cascade link. These RIS gradually transmit signals to users through multiple reflection paths, while also effectively avoiding the risk of information leakage in direct link channels; each RIS can not only improve the signal transmission quality by optimizing its own reflection phase, but also cooperate with other RIS to perform collaborative optimization on multiple signal links, significantly enhancing the system's anti-eavesdropping capabilities.

[0116] Furthermore, a multi-hop cascade transmission model is established, the channel model between each node is defined, the signal transmission path is set, and the signal-to-noise ratio and rate of the received signal at the legitimate user and the eavesdropper are calculated; in this system transmission model, BS represents the base station, Eve represents the eavesdropper, Obstacles represents the obstacles between the base station and the user and the eavesdropper, and RIS L represents the Lth RIS; let the channel gains from BS to the lth RIS, the lth RIS to the user User, and the lth RIS to the eavesdropper Eve be represented as G l ∈￡ N×M , g l ∈￡ 1×N ,h l ∈￡ 1×N , l = 1, 2, 3..., L, the channel gain from the k-1th RIS to the kth RIS is expressed as H k-1,k ∈￡ N×N , where k = 2, 3..., L. The reflection phase shift matrix of the lth RIS is expressed as Φ l =diag(φ l )∈￡ N×N , where φ l =[φ l,1 ,φ l,2,.......,φ l,N ] T is the corresponding reflection vector of the lth RIS, is the reflection amount of the nth reflection unit of the lth RIS, where θ l,n ∈[0,2π),β l,n ∈[0,1] respectively represent the reflection phase shift and amplitude reflection coefficient of the nth reflection unit of the lth RIS to the incident signal. In general, let β l,n =1, which means that all units of the RIS fully reflect the incident signal. Accordingly, consider that the base station BS and each RIS have perfect channel state information. The signal transmitted by the base station BS is represented by s with a mean of zero and a variance of 1. The beamforming vector of the BS is represented by w∈￡ M×1 Represents, and satisfies ||w|| 2 ≤P BS , where P BS is the maximum allowed transmission power of the base station. The equivalent channel from the base station BS to the user through L RIS can be expressed as

[0117]

[0118] The above formula specifically describes the entire transmission process of the signal from the base station to the user, including the direct reflection path and the multi-hop cascade path. Among them, the first direct reflection path represents the path of the signal from the base station through each RIS to the user, and the reflection characteristics of each RIS acting alone. The second multi-hop cascade path represents the path of the signal from the base station to the user through the cascade reflection (multi-hop path) of multiple RIS. The RISs are transmitted in coordination with each other, reflecting the interaction effect between RISs.

[0119] Specifically, the signal received by the legitimate user can be expressed as

[0120] y U =H U ws+n U

[0121] Among them, n U The mean at the user is zero and the variance is The equivalent channel from the base station BS to the eavesdropper through L RIS can be expressed as

[0122]

[0123] The signal received by the eavesdropper can be expressed as

[0124] y E =H E ws+n E

[0125] Among them, n E The mean at the eavesdropper is zero and the variance is Additive white Gaussian noise.

[0126] According to the above formula, the signal-to-noise ratios received by the user and the eavesdropper are

[0127]

[0128] Here, define Therefore, according to Shannon's theorem, the achievable rate of the user and the eavesdropper is

[0129]

[0130] Accordingly, the confidentiality rate can be defined by the difference between the achievable rates of the user and the eavesdropper, that is, R sec =[R U -R E ]+, where [x] + =max(0,x).

[0131] Therefore, the expression of the confidentiality rate is as follows

[0132]

[0133] Furthermore, it is defined that all channels follow Rician fading, the antenna units at the base station BS form a uniform linear array, and the reflection units at the RIS form a uniform rectangular array. Due to the spatiality of the simulation environment, each RIS is represented by N = N in the spatial coordinate system. x ×N y Reflection units, where N x represents the number of RIS reflection units along the x-axis, N y The channel matrix G from the base station BS to the lth RIS is l The modeling is as follows

[0134]

[0135] Among them, β l is channel G l The corresponding Rician factor. G l,LOS , G l,NLOS They represent the line-of-sight (LOS) and non-line-of-sight (NLOS) transmission components of the channel, respectively. The LOS component G l,LOS Expressed as

[0136]

[0137] where a r represents the receive array response of RIS, a t represents the transmit array response of the BS. r 、a t is defined as

[0138]

[0139] In the above formula, λ represents the signal wavelength; d r and d t represents the cell spacing of the receiving array and the transmitting array; and are the departure angle and arrival angle; N and M are the number of reflective elements at the receiving end and the number of antennas at the transmitting end.

[0140] NLOS component G l,NLOS It obeys Rayleigh fading. Correspondingly, other channels in the system can also be modeled in the same way, such as H k-1,k , g l 、h l According to their different positions, the distance between their nodes, departure angle and arrival angle are adjusted to achieve modeling of their channels.

[0141] Furthermore, according to the established system model, the objectives and constraints of system optimization are clarified. The optimization objective is to maximize the confidentiality rate of the system. The main objective of this embodiment is to optimize the beamforming w of the base station and the phase shifts φ1, φ2, ..., φ of the multiple RISs in cooperation with each other. L To maximize the confidentiality rate. Therefore, under the constraints of the maximum transmission power of the base station BS and the phase shift unit modulus of the RIS reflection unit, the optimization problem is expressed as

[0142]

[0143] st||w|| 2 ≤P BS

[0144]

[0145] Because the beamforming vector w in the objective function and the phase shifts φ1, φ2, ..., φ of the RIS L There is coupling with the constraints, so it can be seen that the optimization problem is a non-convex problem and there may be multiple local optimal solutions.

[0146] Preferably, this embodiment proposes an optimization algorithm based on alternating iteration, combining continuous convex approximation (SCA) and semidefinite relaxation (SDR) technology to optimize the beamforming vector of the base station and the reflection phase shift matrix of each RIS node respectively; an effective algorithm is designed to maximize the system confidentiality rate. In the optimization problem of P1, the first constraint contains the variable w, and the second constraint contains L variables φ1, φ2, ..., φ L This will enable the optimization of w and φ1, φ2, ..., φ by alternating iterations. L Specifically, the overall optimization problem can be divided into several sub-problems. First, given φ1, φ2, ..., φ L Optimize w under the given condition, and then optimize φ1, φ2, ..., φ respectively under the given condition L Since there are L RIS working together, this will generate many optimization sub-problems. The specific details will be described below.

[0147] 1) Given φ1, φ2, ..., φ L , optimize w

[0148] First, fix φ1, φ2, ..., φ L , and problem P1 needs to be reformulated. Therefore, problem P1 will be rewritten as

[0149]

[0150] st||w|| 2 ≤P BS

[0151] By definition and W = ww H ∈￡ M×M , where W needs to satisfy the rank-one constraint, that is, Rank(W) = 1. For the rank-one constraint, the semidefinite relaxation (SDR) technique can be used to relax the constraint. Then we can get |H U w| 2 =tr(WA),|H E w| 2 =tr(WB). Therefore, the above problem can be rewritten as

[0152]

[0153] sttr(W)≤P BS

[0154]

[0155] However, the rewritten objective function is non-convex, so this part needs to be processed and new auxiliary variables are introduced as follows:

[0156]

[0157] By integrating the above formulas, the latest statement of the problem is:

[0158]

[0159]

[0160] tr(WA)≥0,tr(WB)≥0

[0161] The objective function after integration is an exponential function and is convex, but it can be observed that the above constraint is a non-convex constraint. In order to deal with this non-convex constraint, the continuous convex approximation (SCA) algorithm is used. In this method, e b At a certain iteration point At, it can be approximated by the first-order Taylor expansion into the following form:

[0162]

[0163] Through the above analysis, the problem can be restated as

[0164]

[0165] tr(WA)≥0,tr(WB)≥0

[0166] After the above discussion, the problem has been transformed into a convex optimization problem, which can be solved efficiently using the CVX toolbox. At the xth iteration, the convex approximation problem can be expressed as follows

[0167]

[0168] tr(WA)≥0,tr(WB)≥0

[0169] First, initialize the randomly generated w (0) , and then according to W (0) =w (0) (w (0) ) H Calculate W (0) Then, according to the existing formula, we can get b (0) . It should be noted that each iteration updates Both So we can get the initialized The algorithm is based on SCA technology and solves non-convex optimization problems through iterative optimization. It gradually optimizes the values ​​of W and b by linearizing or convexifying the objective function that is difficult to solve directly until the convergence condition is reached. Finally, by checking whether the rank of the matrix satisfies rank(W)=1, it decides to solve directly or introduce Gaussian randomization to obtain the optimal w.

[0170] 2) Given w, optimize φ1, φ2, φ3..., φ L

[0171] Given w, we need to calculate φ1, φ2, φ3, ..., φ L Optimize and divide φ1, φ according to the channel connection mode i ,φ L , i=2,3,...,L-1, and optimize for different channel characteristics respectively. Such division not only conforms to the physical characteristics of multi-hop multi-RIS system, but also can effectively reduce the complexity of system optimization. Based on the step-by-step optimization strategy of channel connection mode, the global non-convex optimization problem is decomposed into multiple independent sub-problems.

[0172] 21) Given w, φ2, φ3, ..., φ L , optimize φ1

[0173] Next, given w and φ2, φ3, ..., φ L When φ1 is optimized, the problem becomes

[0174]

[0175] Then, considering that the logarithmic function is monotonically increasing, it can be ignored. At this point, the problem can be simplified as

[0176]

[0177] in,

[0178]

[0179] Since the above problem is still a non-convex problem, the optimization problem needs to be further simplified. Here we introduce a new auxiliary variable t, Here t is usually set to 1. Therefore, in

[0180]

[0181] Now define a S1 must also satisfy the semi-positive definite matrix And rank(S1)=1.

[0182] The problem is then transformed into

[0183]

[0184] Among them, E m is a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0. Since rank(S1)=1 is a non-convex constraint, the Charnes-Cooper transformation is used to solve it. Definition and η1=λ1S1 transform a non-convex optimization problem into a non-fractional structural form:

[0185]

[0186] tr(U E1 η1)+λ1·(|v E1 | 2 +σ 2 )=1

[0187]

[0188] At this point, the optimization problem is transformed into a convex optimization problem, which can be solved efficiently using the existing convex optimization tool CVX. On this basis, a Gaussian randomization method is used to find a near-optimal solution from the relaxed solution.

[0189] 22) Given w,φ1,φ2,...,φ i-1 ,φ i+1 ,...,φ L , optimize φ i (i=2,3,...,L-1)

[0190] For given w and φ1, φ2, ..., φ i-1 ,φ i+1 ,...,φ L In the case of i , i=2,3,...,L-1 for optimization, the problem becomes

[0191]

[0192] in,

[0193]

[0194] The above problem is still a non-convex problem and needs to be further simplified. Similarly, we continue to introduce an auxiliary variable t for simplification. where t is an auxiliary variable, and in,

[0195]

[0196] Here is defined Where S i Needs to be satisfied And rank(S i )=1. Then the optimization problem is transformed into

[0197]

[0198] Similarly, continue to use Charnes-Cooper transformation to solve, let and η i =λ i S i , the above optimization problem is transformed into:

[0199]

[0200] tr(U Ei η i )+λ i ·(|v Ei | 2 +σ 2 )=1

[0201]

[0202] Then, the problem is a convex semidefinite programming problem, which can be solved using the toolbox CVX. Finally, the standard Gaussian randomization method is applied to obtain an approximate solution to the problem.

[0203] 23) Given w, φ1, φ2, ..., φ L-1 , optimize φ L

[0204] For a given w,φ1,φ2,...,φ L-1 In the case of L Optimization. The optimization problem is reformulated as

[0205]

[0206] in,

[0207]

[0208]

[0209] Similarly, we continue to introduce the auxiliary variable t to simplify the above problem. Here we let in,

[0210]

[0211] definition Where S L Needs to be satisfied And rank(S L )=1. The optimization problem P5 is simplified to

[0212]

[0213] Because rank(S L )=1 is non-convex, and the Charnes-Cooper transformation is used to solve it. and η L =λ L S L , the above problem is transformed into

[0214]

[0215] tr(U EL η L )+λ L ·(|v EL | 2 +σ 2 )=1

[0216]

[0217] This problem is also a convex semidefinite programming problem, which can be solved first using the toolbox CVX, and then the standard Gaussian randomization method is applied to find an approximate solution to the P5 problem.

[0218] Furthermore, based on the above optimization scheme, experimental simulation was carried out using MATLAB software to verify the algorithm's effect on improving the confidentiality rate of the base station transmission power, as well as the impact of the number of RIS on the system confidentiality rate. Figure 3 By changing the transmission power of the base station, the change trend of the average confidentiality rate of the system under different transmission powers is observed; compared with the traditional single RIS optimization algorithm and the algorithm that does not consider multi-RIS collaborative optimization, it is proved that the joint optimization algorithm proposed in this embodiment can significantly improve the security performance of the system under different parameter configurations; Figure 4The influence of the number of RIS on the confidentiality rate of the system is further verified. When other system parameters remain unchanged, by changing the number of RIS, it is proved that as the number of RIS nodes increases, the confidentiality rate of the system also increases, and the two show a significant positive correlation; when multiple RISs are coordinated and optimized, especially when the number of RISs is large, the degree of improvement of the confidentiality rate of the system is more obvious, which proves the enhancing effect of multi-RIS coordinated transmission on communication security; the results show that the joint optimization algorithm proposed in this embodiment can significantly improve the confidentiality rate and security of the wireless communication system by introducing a multi-RIS coordinated optimization strategy; whether under different configurations of base station transmission power or under different settings of the number of RIS nodes, the confidentiality rate of the system shows a significant improvement, which verifies the significant enhancement of the security performance of the system under the assistance of multiple RISs; therefore, the optimization scheme of this embodiment has broad application potential in improving the security of wireless communication systems.

[0219] The beneficial effects of the present invention are as follows:

[0220] The present invention introduces the synergistic effect of multiple RIS and designs a multi-hop cascade reflection path to form multiple passive beamforming gains, effectively overcomes the path loss caused by a single reflection, and enhances the transmission capability and confidentiality of the signal; through the combination of an alternating iterative optimization algorithm, a continuous convex approximation technology, and a semidefinite relaxation technology, the beamforming vector of the base station and the reflection phase shift matrix of each RIS node are optimized, the channel gain of the legitimate user is improved, and the signal reception quality of the eavesdropper is reduced, thereby improving the confidentiality rate of the communication system and enhancing the security performance of the system.

[0221] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0222] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for enhancing the security performance of a wireless communication system based on multi-RIS assistance, characterized in that: include: Construct a single-user downlink communication system model with multiple RIS assistance. The single-user downlink communication system model includes: a plurality of RIS; Establish a multi-hop cascade transmission model, define the channel model and signal transmission path between nodes in the transmission model, and determine the expression of the signal-to-noise ratio from the base station to the user, the signal-to-noise ratio from the base station to the eavesdropper, the transmission rate from the base station to the user, the transmission rate from the base station to the eavesdropper, and the confidentiality rate of the system; Determine the target optimization problem of the system according to the transmission model; the first constraint of the target optimization problem includes: a beamforming vector; the second constraint of the target optimization problem includes: a reflection phase of the first RIS to a reflection phase of the Lth RIS; Using a pre-designed alternating iterative optimization algorithm, combined with continuous convex approximation technology and semidefinite relaxation technology, the beamforming vector of the base station and the reflection phase shift matrix of each RIS node in the target optimization problem are transformed to obtain an improved optimization problem; the improved optimization problem includes: a second optimization problem, a third optimization problem, a fourth optimization problem and a fifth optimization problem; The improved optimization problem is experimentally simulated according to the single-user downlink communication system model and the transmission model to obtain simulation results, and the simulation results are determined as the deployment basis for the design of the actual deployment plan of multiple RIS nodes.

2. According to claim 1, a method for enhancing the security performance of a wireless communication system based on multi-RIS assistance is characterized in that: The single-user downlink communication system model includes: a base station BS, L RISs, a target user and an eavesdropper; the base station BS is equipped with M antennas; the target user and the eavesdropper are each equipped with a single antenna; each RIS includes N reflection units; L RIS are deployed in sequence to form a multi-hop link; the reflection unit is connected to a preset controller; an obstacle is set between the base station BS and the target user and the eavesdropper respectively.

3. The method for enhancing the security performance of a wireless communication system based on multi-RIS assistance according to claim 2 is characterized in that: The expression of the signal-to-noise ratio from the base station to the user is: The expression of the signal-to-noise ratio from the base station to the eavesdropper is: The expression of the transmission rate from the base station to the user is: The expression of the transmission rate from the base station to the eavesdropper is: The expression of the confidentiality rate is: in, γ U H is the signal-to-noise ratio from the base station to the user; U is an equivalent channel from the base station BS to the target user via L RISs; is the noise power received by the target user; γ E H is the signal-to-noise ratio from the base station to the eavesdropper; E is the equivalent channel from the base station to the eavesdropper via L RISs; is the noise power received by the eavesdropper; R U is the transmission rate from the base station to the user; R E is the transmission rate from the base station to the eavesdropper; R sec is the confidentiality rate; [f(·)] + represents the maximum value between f(·) and 0.

4. The method for enhancing the security performance of a wireless communication system based on multi-RIS assistance according to claim 2, characterized in that: The expression of the channel model is: Among them, G l is the channel matrix from the base station BS to the lth RIS; C0 represents the path loss at the reference distance d0; d l represents the distance from the base station BS to the lth RIS; α l is the path loss index; β l is the Rician factor; G l,LOS Represents the visible transmission component of the channel; G l,NLOS Represents the non-visible transmission component of the channel.

5. The method for enhancing the security performance of a wireless communication system based on multi-RIS assistance according to claim 3 is characterized in that: The expression of the target optimization problem includes: s.t.||w|| 2 ≤P BS 、 Wherein, P1 represents the target optimization problem; φ l is the reflection phase of the lth RIS; BS is the maximum transmission power of the base station; φ l,n is the phase of the nth reflection unit in the lth RIS; L is the number of the RIS; and N is the number of the reflection units in each RIS.

6. The method for enhancing the security performance of a wireless communication system based on multi-RIS assistance according to claim 5, characterized in that: The beamforming vector of the base station and the reflection phase shift matrix of each RIS node in the target optimization problem are transformed by using a pre-designed alternating iterative optimization algorithm and combining the continuous convex approximation technology and the semidefinite relaxation technology to obtain an improved optimization problem, including: Initializing the beamforming vector, the reflection phase of the first RIS to the reflection phase of the Lth RIS to obtain a data set to be optimized; Optimizing the target optimization problem and the beamforming vectors in the data set to be optimized using the continuous convex approximation technology according to the reflection phase of the first RIS to the reflection phase of the Lth RIS in the data set to be optimized, to obtain the second optimization problem and the optimized beamforming vectors; According to the optimized beamforming vector and the reflection phases of the second RIS to the Lth RIS in the data set to be optimized, the second optimization problem and the reflection phase of the first RIS are optimized using the semidefinite relaxation technique to obtain the third optimization problem and the optimized reflection phase of the first RIS; According to the optimized beamforming vector, the reflection phase of the first RIS to the reflection phase of the i-1th RIS, and the reflection phase of the i+1th RIS to the Lth RIS in the data set to be optimized, the third optimization problem and the reflection phase of the i-th RIS are optimized using the semidefinite relaxation technique to obtain the fourth optimization problem and the optimized reflection phase of the i-th RIS; wherein i=2, 3, ..., L-1; According to the optimized beamforming vector and the reflection phases of the first RIS to the L-1th RIS, the fourth optimization problem and the reflection phase of the Lth RIS are optimized using the semidefinite relaxation technique to obtain the fifth optimization problem and the optimized reflection phase of the Lth RIS; The confidentiality rate is calculated using the optimized beamforming vector and the reflection phase of the first RIS to the reflection phase of the L-th RIS, and the optimized beamforming vector and the reflection phase of the first RIS to the L-th RIS are updated as the data set to be optimized, and the step of "optimizing the target optimization problem and the beamforming vector in the data set to be optimized using the continuous convex approximation technology according to the reflection phase of the first RIS to the L-th RIS in the data set to be optimized to obtain the second optimization problem and the optimized beamforming vector" is returned to perform the next round of iteration. If the confidentiality rate obtained in the current iteration is less than the confidentiality rate obtained in the previous iteration, the iteration is completed.

7. The method for enhancing the security performance of a wireless communication system based on multi-RIS assistance according to claim 6, characterized in that: The second optimization problem includes: in, tr(WA)≥0; tr(WB)≥0; tr(·) is denoted as the trace of the matrix.

8. The method for enhancing the security performance of a wireless communication system based on multi-RIS assistance according to claim 7, characterized in that: The third optimization problem includes: in, tr(U E1 η1)+λ1·(|v E1 | 2 +s 2 )=1; η1=λ1S1; U B1 , U E1 are system matrices from the base station to the target user and the eavesdropper via the first RIS; B1 、v E1 are signal vectors from the base station to the target user and the eavesdropper through the first RIS respectively; m is a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0; φ1 is the reflection phase shift vector of the first RIS; η1 is the first auxiliary variable introduced.

9. The method for enhancing the security performance of a wireless communication system based on multi-RIS assistance according to claim 8, characterized in that: The fourth optimization problem includes: in, tr(U Ei or i )+λ i ·(|v Ei | 2 +s 2 )=1; or i =λ i S i ; U Bi , U Ei are the system matrices from the base station to the target user and the eavesdropper through the i-th RIS respectively; Bi 、v Ei are the signal vectors from the base station to the target user and the eavesdropper through the i-th RIS respectively; m is a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0; φ i is the reflection phase shift vector of the i-th RIS; η i is the i-th auxiliary variable introduced.

10. The method for enhancing the security performance of a wireless communication system based on multi-RIS assistance according to claim 9, characterized in that: The fifth optimization problem includes: in, tr(U EL or L )+λ L ·(|v EL | 2 +s 2 )=1; or L =λ L S L ; U BL 、v BL are the system matrices from the base station to the target user and the eavesdropper through the Lth RIS respectively; BL 、v EL are signal vectors from the base station to the target user and the eavesdropper through the Lth RIS respectively; m is a (N+1)×(N+1) matrix whose mth main diagonal element is 1 and the rest of the elements are 0; φ L is the reflection phase shift vector of the Lth RIS; η L is the Lth auxiliary variable introduced.

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