IRS-assisted safety rate maximization design method under outage probability constraint

Abstract

The invention discloses an IRS-assisted safety rate maximization design method under outage probability constraint, and belongs to the technical field of wireless communication. In order to ensure that the outage probability of the secure transmission system is within a tolerable range, a probability constraint is introduced into the design of the secure transmission system in a transmitter, an intelligent reflecting surface, an expected receiver and a stealer communication system model; a safety rate maximization problem containing probability constraints is provided, wherein phase shift, power distribution and transmission rate are coupled in an objective function and the probability constraints. The method comprises the following steps of: firstly, converting a probability constraint which is difficult to process into a deterministic constraint by means of exponential distribution of received signal power; then, an alternating optimization (AO) method is used for fixing a beam forming matrix of a transmitter and a phase shift matrix of an intelligent reflecting surface respectively, and through inequality transformation and Rayleigh quotient solving, the problem that under the condition that a channel is uncertain, probability constraints of coupling variables are difficult to process, and the probability constraints of the coupling variables are difficult to process is well solved. And the problems of optimal phase shift, power distribution and transmission rate design of secure transmission are solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to an IRS-assisted safety rate maximization design method under interruption probability constraints. Background Art

[0002] The massive data transmission in the era of the Internet of Things has caused serious concerns about security issues, and physical layer security has become a key consideration in the design of future wireless networks. In recent years, in order to improve physical layer security, intelligent reflective surfaces (IRS) have been introduced. IRS usually consists of a certain number of low-cost reflective elements, each of which has the ability to reflect the reconfigurable phase shift and amplitude of the incident electromagnetic wave. IRS-based communication has significant advantages in improving spectrum efficiency and enhancing physical layer security. Specifically, when the legitimate receiver and the eavesdropper are in the same direction as the base station (BS), the channel response of the legitimate user will be highly correlated with the channel response of the eavesdropper. This makes traditional beamforming, which directs energy to the legitimate receiver, also beneficial to the eavesdropper. Therefore, it is difficult to ensure security by using beamforming only on the transceiver. Since the deployment of IRS provides greater freedom for additional transmission links for the legitimate receiver while eliminating the direction for the eavesdropper, it can significantly improve the quality of service for the legitimate user and reduce information leakage, thereby improving the security transmission performance.

[0003] Based on the many advantages of IRS, IRS has been widely considered for application in physical layer security to improve the security rate performance. It is assumed that the eavesdropper's channel has perfect channel state information (CSI) and the eavesdropper is regarded as an unscheduled active user in the network. Under this assumption, secure transmission using IRS can achieve higher security rates compared to transmission with random phase shifts or fixed phase shift matrices. However, even if the eavesdropper is an unscheduled active user in the network, CSI estimation and acquisition are more challenging than traditional communication systems due to the cascaded channels brought by IRS. Although channel estimation schemes for IRS systems have been proposed recently, it is still difficult to assume the perfect CSI of the eavesdropper. Due to the uncertainty of the eavesdropper's CSI, the interruption of secure transmission needs to be considered.

[0004] Therefore, in order to ensure that the interruption probability of the secure transmission system is within a tolerable range, the secure transmission system design of the present invention introduces a probability constraint, which is challenging for further analysis. In addition, phase shift, power allocation and transmission rate are coupled in the objective function and the probability constraint. In order to meet the above challenges, this paper first converts the intractable probability constraint into a deterministic constraint with the help of the exponential distribution of the received signal power. An optimization problem based on the receiver security rate and the eavesdropper redundancy rate is constructed under the condition of SOP constraints and transmission power budget. Then the variables are optimized by an alternating iterative optimization algorithm to obtain a considerable optimal solution. Summary of the invention

[0005] The purpose of the present invention is to design an optimal secure transmission to maximize the achievable secure rate under SOP constraints and transmission power budget.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A design method for maximizing the safety rate of IRS assistance under interruption probability constraints specifically includes the following steps:

[0008] S1. Construct a communication system model: select the transmitter (Alice), the intelligent reflecting surface (IRS), the eavesdropper (Eve) and the receiver (Bob) as the entity research objects of the communication system model;

[0009] S2. Derivation of the probability of security interruption of the receiver (Bob) due to the eavesdropper (Eve): The signal-to-noise ratio distribution function is calculated based on the statistical channel information of the eavesdropper (Eve), and the redundant rate is constructed to characterize the probability of security interruption; Based on the exponential distribution of the received signal power, the intractable probability constraint is converted into a deterministic constraint;

[0010] S3. Construct an optimization problem: Construct the problem of maximizing the achievable safety rate under the constraints of safety interruption probability and transmission power budget;

[0011] S4, simplify the optimization problem: Analyze and simplify the optimization problem constructed in S3, and simplify the safety rate maximization problem constrained by the safety interruption probability into an optimization problem related to only two scalars;

[0012] S5, alternating optimization variables: Based on the simplified optimization problem in S4, the AO algorithm is used to further simplify the optimization problem, and the optimization variables phase shift matrix and beamforming matrix are fixed respectively. First, the beamforming matrix is ​​fixed to obtain the optimal phase shift matrix; then the obtained phase shift matrix is ​​fixed to obtain the optimal beamforming matrix, and it is iterated until the objective function value converges, and then the phase shift matrix and beamforming matrix of the optimal objective function are derived and determined;

[0013] S6. Simulation experiment: Design a simulation experiment and perform simulation according to the method proposed in S5 to verify the effectiveness of the proposed security rate maximization transmission design method.

[0014] Preferably, the S1 specifically includes the following contents:

[0015] Assume that the transmitter is multi-antenna, and the receiver and the eavesdropper are both expected to have a single antenna and the number is 1; use smart reflective surfaces to improve the security transmission performance, and ignore the signals reflected twice or more by the smart reflective surfaces; consider the impact of quasi-static flat fading on all channels in the system, and express the baseband equivalent channels from Alice-IRS, Alice-Bob and IRS-Bob as follows: Based on the passive and unauthorized nature of the eavesdropper, Alice only knows the statistical channel state information of the IRS-Eve channel and the Alice-Eve channel respectively.

[0016] Let x ij represents the large-scale path loss, Where β0 represents the channel power gain at the reference distance of 1m; d ij Represents the distance between node i and node j; α ij represents the corresponding path loss index, ij∈{ar,ab,ae,rb,re} corresponds to different channels, and and is the corresponding small-scale fading coefficient;

[0017] Assuming that the phase of the smart reflector element changes continuously and the signal does not suffer any power loss from each reflector element, the diagonal reflection matrix of the smart reflector phase shift coefficient is expressed as:

[0018]

[0019] Assuming that the amplitude of each unit is equal to 1, θ n ∈[0,2π) is the phase shift of the nth element of the combined incident signal, n=1,...,N, so the transmitter's transmitted signal is expressed as:

[0020] x=ws (1)

[0021] Among them, s:CN(0,1) represents an independent information signal, represents the beamforming vector;

[0022] Assume that the transmitter has a maximum transmit power budget P max , then w H w≤P max ;

[0023] The receiver's received signal y b It is expressed as:

[0024]

[0025] The eavesdropper's received signal y eIt is expressed as:

[0026]

[0027] in, represents the channel vector from the transmitter to the desired receiver, represents the channel vector from the smart reflector to the desired receiver, H ar represents the channel vector from the transmitter to the smart reflection surface; n b represents the Gaussian white noise of the receiver, which obeys n b : That is, zero mean circular symmetry, the variance is The noise vector.

[0028] Preferably, S2 specifically includes the following contents:

[0029] The channel capacity of the eavesdropper is expressed as:

[0030]

[0031] in, represents the channel vector from the transmitter to the eavesdropper, represents the channel vector from the smart reflection surface to the desired receiver; n e represents the Gaussian white noise of the receiver, which obeys n e : That is, zero mean circular symmetry, the variance is The noise vector.

[0032] When the channel capacity of the eavesdropper is C e Exceeding the user's redundancy rate R e When a security interruption event occurs at the transmitter, the probability that the receiver is security interrupted due to an eavesdropper is:

[0033]

[0034] Among them, SOP represents the probability of interruption caused by Rayleigh fading; using the exponential distribution of the received signal power in the Rayleigh fading environment, the closed expression of SOP is derived. The specific derivation process is as follows:

[0035] First define for:

[0036]

[0037] in, and (H ar w) n Respectively and H ar The nth element of w, Follows a complex Gaussian distribution with mean 0 and variance δ;

[0038] in,

[0039] Following the exponential distribution, we get:

[0040]

[0041] In summary, the expression of SOP is:

[0042]

[0043] Among them, χ ae and χ re They represent the large-scale path loss from the receiver to the eavesdropper and the large-scale path loss from the smart reflector to the eavesdropper respectively.

[0044] Preferably, S3 specifically includes the following contents:

[0045] Based on the communication model constructed by S1, the channel capacity at the receiver is given by:

[0046]

[0047] Therefore, the safety rate maximization problem is expressed as:

[0048]

[0049] Among them, ε∈(0,1) is a predefined upper bound, indicating the maximum tolerable SOP; P max Indicates the maximum transmit power at the receiver.

[0050] Preferably, the S4 specifically includes the following contents:

[0051] A1. According to formula (8), the interruption probability constraint p so ≤ε is equivalent to:

[0052]

[0053] A2. Simplify the optimization problem by using the properties of the function. In problem (10), R e decreases, the value of the objective function increases, and when the C1 inequality reaches equality, we get R e The optimal value of is expressed as:

[0054]

[0055] Substituting formula (12) into the optimization problem (10), we get:

[0056]

[0057] Preferably, the S5 specifically includes the following contents:

[0058] B1. Use the AO algorithm to solve the optimization problem, fix the transmit beamforming w, and optimize the phase shift matrix Θ; by applying the variable The optimization problem is expressed as:

[0059]

[0060] where v = [v1,...,v N ] T ;

[0061] According to the inequality property, the objective function (14) satisfies the following inequality:

[0062]

[0063] Among them, if and only if The equation holds true; arg(·) represents the component direction phase of the complex vector; then the optimal θ of the optimal problem in formula (14) is:

[0064]

[0065] B2. Fix the phase shift matrix Θ to find the beamforming matrix w. The optimization problem is expressed as:

[0066]

[0067] Among them, Design the transmitter’s beamforming vector, written as in, represents the transmitter unit beamforming carrier; P w represents the corresponding power allocation factor;

[0068] Will Substituting into formula (17), the original optimization problem is transformed into the following form:

[0069]

[0070] The safe rate must be greater than or equal to 0, that is, the objective function in formula (18) must be greater than or equal to 1, so the inequality is obtained:

[0071]

[0072] When the inequality in formula (19) holds, for any given beamforming vector The objective function in the optimization problem (17) is about P w Monotonically increasing; consider P w ≤P max The optimal power allocation factor is obtained as follows:

[0073]

[0074] Then, to determine the optimal element beamforming vector The original optimization problem is transformed into the following form:

[0075]

[0076] The optimization problem (21) is a standard Rayleigh quotient problem, and its optimal unit beamforming vector is is the unit eigenvector corresponding to the largest eigenvalue of the matrix given by:

[0077]

[0078] The optimal w is

[0079] Finally, w obtained in B2 * As w is given in B1 to find Θ, the loop is iterated until the objective function value converges to obtain the optimal w and Θ.

[0080] Compared with the prior art, the present invention provides an IRS-assisted safety rate maximization design method under interruption probability constraints, which has the following beneficial effects:

[0081] (1) The present invention fully considers the uncertainty of the eavesdropper's CSI, and improves the maximum secure transmission rate while ensuring that the interruption probability of the secure transmission system is within a tolerable range.

[0082] (2) The present invention solves the problem of coupling phase shift, beamforming matrix and transmission rate in objective function and probability constraints through mathematical properties.

[0083] (3) The present invention can make full use of the alternating iterative optimization algorithm to solve the variables in sequence, which greatly reduces the difficulty of solving the problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 A system schematic diagram of an IRS-assisted safety rate maximization design method under interruption probability constraints proposed in Embodiment 1 of the present invention;

[0085] Figure 2 This is a schematic diagram of an algorithm flow of an IRS-assisted safety rate maximization design method under interruption probability constraints proposed in Embodiment 1 of the present invention;

[0086] Figure 3 A schematic diagram of a curve showing a change in safety rate versus the number of transmitter antennas in an IRS-assisted safety rate maximization design method under an outage probability constraint proposed in Embodiment 2 of the present invention;

[0087] Figure 4 A schematic diagram of a curve showing a change in safety rate versus the horizontal position of an intelligent reflective surface in an IRS-assisted safety rate maximization design method under an interruption probability constraint proposed in Example 2 of the present invention; DETAILED DESCRIPTION

[0088] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0089] Embodiment 1:

[0090] See also Figure 1-2 The present invention proposes an IRS-assisted safety rate maximization design method under interruption probability constraints, which specifically includes the following steps:

[0091] Step 1: Construct a communication system model: Select the transmitter (Alice), the intelligent reflecting surface (IRS), the eavesdropper (Eve) and the receiver (Bob) as the entity research objects of the communication system model, including the following contents:

[0092] Assume that the transmitter is multi-antenna, and the receiver and the eavesdropper are both expected to have a single antenna and the number is 1; use smart reflective surfaces to improve the security transmission performance, and ignore the signals reflected twice or more by the smart reflective surfaces; consider the impact of quasi-static flat fading on all channels in the system, and express the baseband equivalent channels from Alice-IRS, Alice-Bob and IRS-Bob as follows: Based on the passive and unauthorized nature of the eavesdropper, Alice only knows the statistical channel state information of the IRS-Eve channel and the Alice-Eve channel respectively.

[0093] Let x ij represents the large-scale path loss, Where β0 represents the channel power gain at the reference distance of 1m; d ij Represents the distance between node i and node j; α ij represents the corresponding path loss index, ij∈{ar,ab,ae,rb,re} corresponds to different channels, and and is the corresponding small-scale fading coefficient;

[0094] Assuming that the phase of the smart reflector element changes continuously and the signal does not suffer any power loss from each reflector element, the diagonal reflection matrix of the smart reflector phase shift coefficient is expressed as:

[0095]

[0096] Assuming that the amplitude of each unit is equal to 1, θ n ∈[0,2π) is the phase shift of the nth element of the combined incident signal, n=1,...,N, so the transmitter's transmitted signal is expressed as:

[0097] x=ws (1)

[0098] Among them, s:CN(0,1) represents an independent information signal, represents the beamforming vector;

[0099] Assume that the transmitter has a maximum transmit power budget P max , then w H w≤P max ;

[0100] The receiver's received signal y b It is expressed as:

[0101]

[0102] The eavesdropper's received signal y e It is expressed as:

[0103]

[0104] in, represents the channel vector from the transmitter to the desired receiver, represents the channel vector from the smart reflector to the desired receiver, H ar represents the channel vector from the transmitter to the smart reflection surface; n b represents the Gaussian white noise of the receiver, which obeys n b : That is, zero mean circular symmetry, the variance is The noise vector.

[0105] Step 2: Derivation of the probability of security interruption of the receiver (Bob) due to the eavesdropper (Eve): The signal-to-noise ratio distribution function is calculated based on the statistical channel information of the eavesdropper (Eve), and the redundant rate is constructed to characterize the security interruption probability; according to the exponential distribution of the received signal power, the intractable probability constraints are converted into deterministic constraints, which specifically include the following:

[0106] The channel capacity of the eavesdropper is expressed as:

[0107]

[0108] in, represents the channel vector from the transmitter to the eavesdropper, represents the channel vector from the smart reflection surface to the desired receiver; n e represents the Gaussian white noise of the receiver, which obeys n e : That is, zero mean circular symmetry, the variance is The noise vector.

[0109] When the channel capacity of the eavesdropper is C e Exceeding the user's redundancy rate R e When a security interruption event occurs at the transmitter, the probability that the receiver is security interrupted due to an eavesdropper is:

[0110]

[0111] Among them, SOP represents the probability of interruption caused by Rayleigh fading; using the exponential distribution of the received signal power in the Rayleigh fading environment, the closed expression of SOP is derived. The specific derivation process is as follows:

[0112] First define for:

[0113]

[0114] in, and (H ar w) n Respectively and H ar The nth element of w, Follows a complex Gaussian distribution with mean 0 and variance δ;

[0115] in,

[0116] Following the exponential distribution, we get:

[0117]

[0118] In summary, the expression of SOP is:

[0119]

[0120] Among them, χ ae and χ re They represent the large-scale path loss from the receiver to the eavesdropper and the large-scale path loss from the smart reflector to the eavesdropper respectively.

[0121] Step 3: Construct an optimization problem: Under the constraints of the safety interruption probability and the transmission power budget in step 2, construct the problem of maximizing the achievable safety rate, which specifically includes the following:

[0122] The goal of the present invention is to design an optimal secure transmission that maximizes the achievable secure rate under SOP constraints and transmission power budget.

[0123] Based on the communication model constructed by S1, the channel capacity at the receiver is given by:

[0124]

[0125] Therefore, the safety rate maximization problem is expressed as:

[0126]

[0127] Among them, ε∈(0,1) is a predefined upper bound, indicating the maximum tolerable SOP; P max Indicates the maximum transmit power at the receiver.

[0128] Step 4: Simplify the optimization problem: Analyze and simplify the optimization problem constructed in step 3, and simplify the problem of maximizing the safety rate constrained by the safety interruption probability into an optimization problem related to only two scalars, including the following:

[0129] Step 4.1: According to formula (8), the safety interruption probability constraint p can be so ≤ε is equivalent to:

[0130]

[0131] Step 4.2: Simplify the optimization problem by using the properties of the function, and pay attention to reducing R in (10) e This will result in an increase in the value of the objective function. Therefore, when the C1 inequality is equal, we get R e The optimal value of is expressed as:

[0132]

[0133] Substituting formula (12) into the optimization problem (10), we can obtain:

[0134]

[0135] Step 5, alternately iterate the optimization variables: Based on the simplified optimization problem in step 4, it is proposed to use the AO algorithm to further simplify the optimization problem, fix the optimization variables phase shift matrix and beamforming matrix respectively, first fix the beamforming matrix, and find the optimal phase shift matrix. Then fix the obtained phase shift matrix to obtain the optimal beamforming matrix, iterate until the objective function value converges, and then derive and determine the phase shift matrix and beamforming matrix of the optimal objective function, which specifically includes the following contents:

[0136] Step 5.1: Use the AO algorithm to solve the optimization problem. First, fix the transmit beamforming w to optimize the phase shift matrix Θ. By applying the variable The optimization problem is expressed as:

[0137]

[0138] where v = [v1,...,v N ] T ;

[0139] According to the inequality property, the objective function (14) satisfies the following inequality:

[0140]

[0141] Among them, if and only if The equation holds true; arg(·) represents the component direction phase of the complex vector; then the optimal θ of the optimal problem in formula (14) is:

[0142]

[0143] Step 5.2: Fix the phase shift matrix Θ to find the beamforming matrix w. The optimization problem is expressed as:

[0144]

[0145] Among them, Design the transmitter’s beamforming vector, written as in, represents the transmitter unit beamforming carrier; P w represents the corresponding power allocation factor;

[0146] Will Substituting into formula (17), the original optimization problem is transformed into the following form:

[0147]

[0148] The safe rate must be greater than or equal to 0, that is, the objective function in formula (18) must be greater than or equal to 1, so the inequality is obtained:

[0149]

[0150] When the inequality in formula (19) holds, for any given beamforming vector The objective function in the optimization problem (17) is about P w Monotonically increasing; consider P w ≤P max The optimal power allocation factor is obtained as follows:

[0151]

[0152] Then, to determine the optimal element beamforming vector , convert the original optimization problem into the following form:

[0153]

[0154] The optimization problem (21) is a standard Rayleigh quotient problem, and its optimal unit beamforming vector is is the unit eigenvector corresponding to the largest eigenvalue of the matrix given by:

[0155]

[0156] The optimal w is

[0157] Finally, w obtained in B2 * As w is given in B1 to find Θ, the loop is iterated until the objective function value converges to obtain the optimal w and Θ.

[0158] Embodiment 2:

[0159] Based on Example 1, but with a difference, in order to demonstrate the correctness and effectiveness of the above-mentioned derivation results, the advantages of the proposed safety performance optimization algorithm for safety interruption probability protection are further verified by simulation. For practical purposes, the specific simulation parameters are set as follows: a three-dimensional coordinate system is considered. Alice, Bob, Eve and IRS are located at (0, 0, 0), (70, 10, 0), (70, -10, 0)) and (70, 0, 5), respectively. The path loss index from Alice to IRS is set to 2.4, the path loss index from Alice to Bob and Alice to Willie is set to 3.6, and the path loss index from IRS to Eve and IRS to Bob is 3.0. Unless otherwise specified, the values ​​of the remaining parameters are as follows:

[0160] The number of IRS elements N = 50, the number of transmitter antennas M = 8, and the noise level at the receiver (Bob) and the eavesdropper (Eve) is expected to be equal (i.e. ), the upper bound of the interruption probability is ε = 0.05. The target difference of the algorithm's iteration termination condition is 0.0001, and the achievable safe rate is obtained by averaging the safe rates corresponding to 500 random channels.

[0161] See also Figure 3 ,like Figure 3 The figure shows the curve of the average security rate of the system with the number of reflective units of the intelligent reflective surface under different upper bounds of the security interruption probability ε, which reflects the relationship between the average security rate performance and the tolerable security interruption probability (SOP). The simulation results show that when other conditions are the same, a smaller ε will lead to a decrease in the achievable security rate performance. In fact, this result is completely consistent with the actual communication situation, because the size of ε measures the security level and the smaller its value, the larger the redundant rate R will be to avoid the occurrence of security interruption events, thereby reducing the achievable security rate performance. In addition, when the upper bounds of the security interruption probability ε are equal, it can be observed that the average security rate of the system increases with the increase in the number of IRS. This is because as the number of IRS reflective units increases, the strength of the reflected signal is also increasing, thereby better interfering with eavesdroppers, which shows that IRS can effectively improve the security of wireless communication systems.

[0162] See also Figure 4 , Figure 4The figure shows the curve of the average security rate of the system changing with the horizontal position of the IRS under different upper bounds of the security interruption probability ε. It can be observed that when the IRS is at the horizontal position 70, the system security rate reaches the peak. In order to explain this phenomenon, it is necessary to review the horizontal coordinates of each node and find that the horizontal coordinates of the eavesdropper Eve and the legal receiver Bob are both at 70. The principle that IRS can improve the security of the wireless system is that it can weaken the signal of the eavesdropper Eve and enhance the signal of the legal receiver at the same time. When the IRS is also at the horizontal position 70, it is closest to the eavesdropper Eve and the distance to the legal receiver Bob is also the closest. Therefore, at this time, the path loss of the IRS reflected signal is the smallest, the signal weakening effect on the eavesdropper and the signal enhancement effect on the legal receiver in the system are the strongest, the superposition of various gains of the system is the maximum, and the security rate naturally reaches the highest peak. It can be seen that after reaching the peak, the farther the IRS horizontal position is from the transmitter Alice, the smaller its security rate gain effect on the wireless communication system. This is because as the IRS is farther away from the transmitter, due to path attenuation and other environmental factors, the received signal becomes weaker and weaker, so the reflected signal of the IRS is also weaker and weaker, and the weakening effect on the eavesdropper's signal and the strengthening effect on the legitimate receiver are getting smaller and smaller, which explains why the security rate is getting lower and lower when the horizontal distance is farther away from 70. It can be imagined that as the distance between the IRS and the transmitter increases further, its gain effect on the security rate of the entire wireless communication system will also become smaller and smaller, until it is close to the security rate and transmission power that can be achieved by the wireless communication system without deploying IRS. Therefore, the optimal horizontal position of IRS deployment should be a compromise position that takes into account the eavesdropper Eve and the legitimate receiver Bob, and should avoid being deployed too far away from the transmitter Alice. Finally, it can be directly observed that in each position in the figure, compared with the wireless communication system without deploying IRS, the wireless communication system assisted by IRS can achieve a higher security rate under the premise of meeting the security interruption constraint and power constraint, proving that IRS can effectively improve the security performance of the system.

[0163] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A design method for maximizing the safety rate of IRS assistance under interruption probability constraints, characterized in that: The specific steps include: S1. Construct a communication system model: select a transmitter, a smart reflective surface, a spy and a receiver as the entity research objects of the communication system model; S2. Derivation of the probability of security interruption of the receiver due to the eavesdropper: The signal-to-noise ratio distribution function is calculated based on the statistical channel information of the eavesdropper, and the redundant rate is constructed to characterize the probability of security interruption; according to the exponential distribution of the received signal power, the difficult-to-handle probability constraint is converted into a deterministic constraint; S3. Construct an optimization problem: Construct the problem of maximizing the achievable safety rate under the constraints of safety interruption probability and transmission power budget; S4, simplify the optimization problem: Analyze and simplify the optimization problem constructed in S3, and simplify the safety rate maximization problem constrained by the safety interruption probability into an optimization problem related to only two scalars; S5, alternating optimization variables: Based on the simplified optimization problem in S4, the AO algorithm is used to further simplify the optimization problem, and the optimization variables phase shift matrix and beamforming matrix are fixed respectively. First, the beamforming matrix is ​​fixed to obtain the optimal phase shift matrix; then the obtained phase shift matrix is ​​fixed to obtain the optimal beamforming matrix, and it is iterated until the objective function value converges, and then the phase shift matrix and beamforming matrix of the optimal objective function are derived and determined; S6. Simulation experiment: Design a simulation experiment and perform simulation according to the method proposed in S5 to verify the effectiveness of the proposed security rate maximization transmission design method.

2. According to claim 1, a design method for maximizing the safety rate assisted by IRS under interruption probability constraints is characterized in that: The S1 specifically includes the following contents: Assume that the transmitter is multi-antenna, and the receiver and the eavesdropper are both expected to have a single antenna and the number is 1; use smart reflective surfaces to improve the security transmission performance, and ignore the signals reflected twice or more by the smart reflective surfaces; consider the impact of quasi-static flat fading on all channels in the system, and express the baseband equivalent channels from Alice-IRS, Alice-Bob and IRS-Bob as follows: Based on the passive and unauthorized nature of the eavesdropper, Alice only knows the statistical channel state information of the IRS-Eve channel and the Alice-Eve channel respectively. Let x ij represents the large-scale path loss, Where β0 represents the channel power gain at the reference distance of 1m; d ij Represents the distance between node i and node j; α ij represents the corresponding path loss index, ij∈{ar,ab,ae,rb,re} corresponds to different channels, and and is the corresponding small-scale fading coefficient; Assuming that the phase of the smart reflector element changes continuously and the signal does not suffer any power loss from each reflector element, the diagonal reflection matrix of the smart reflector phase shift coefficient is expressed as: Assuming that the amplitude of each unit is equal to 1, θ n ∈[0,2π) is the phase shift of the nth element of the combined incident signal, n=1,...,N, so the transmitter's transmitted signal is expressed as: x=ws (1) Among them, s:CN(0,1) represents an independent information signal, represents the beamforming vector; Assume that the transmitter has a maximum transmit power budget P max , then w H w≤P max ; The receiver's received signal y b It is expressed as: The eavesdropper's received signal y e It is expressed as: in, represents the channel vector from the transmitter to the desired receiver, represents the channel vector from the smart reflector to the desired receiver, H ar represents the channel vector from the transmitter to the smart reflection surface; n b represents the Gaussian white noise of the receiver, obeying That is, zero mean circular symmetry, the variance is The noise vector.

3. The IRS-assisted safety rate maximization design method under interruption probability constraint according to claim 2 is characterized in that: The S2 specifically includes the following contents: The channel capacity of the eavesdropper is expressed as: in, represents the channel vector from the transmitter to the eavesdropper, represents the channel vector from the smart reflection surface to the desired receiver; n e represents the Gaussian white noise of the receiver, obeying That is, zero mean circular symmetry, the variance is The noise vector of When the channel capacity of the eavesdropper is C e Exceeding the user's redundancy rate R e When a security interruption event occurs at the transmitter, the probability that the receiver is security interrupted due to an eavesdropper is: Among them, SOP represents the probability of interruption caused by Rayleigh fading; using the exponential distribution of the received signal power in the Rayleigh fading environment, the closed expression of SOP is derived. The specific derivation process is as follows: First define for: in, and (H ar w) n Respectively and H ar The nth element of w, Follows a complex Gaussian distribution with mean 0 and variance δ; in, Following the exponential distribution, we get: In summary, the expression of SOP is: Among them, χ ae and χ re They represent the large-scale path loss from the receiver to the eavesdropper and the large-scale path loss from the smart reflector to the eavesdropper respectively.

4. The IRS-assisted safety rate maximization design method under interruption probability constraint according to claim 3 is characterized in that: The S3 specifically includes the following contents: Based on the communication model constructed by S1, the channel capacity at the receiver is given by: Therefore, the safety rate maximization problem is expressed as: Among them, ε∈(0,1) is a predefined upper bound, indicating the maximum tolerable SOP; P max Indicates the maximum transmit power at the receiver.

5. The IRS-assisted safety rate maximization design method under interruption probability constraint according to claim 4 is characterized in that: The S4 specifically includes the following contents: A1. According to formula (8), the interruption probability constraint P so ≤ε is equivalent to: A2. Simplify the optimization problem by using the properties of the function. In problem (10), R e decreases, the value of the objective function increases, and when the C1 inequality reaches equality, we get R e The optimal value of is expressed as: Substituting formula (12) into the optimization problem (10), we get:

6. The IRS-assisted safety rate maximization design method under interruption probability constraint according to claim 1 is characterized in that: The S5 specifically includes the following contents: B1. Use the AO algorithm to solve the optimization problem, fix the transmit beamforming w, and optimize the phase shift matrix Θ; by applying the variable The optimization problem is expressed as: where v = [v1,...,v N ] T ; According to the inequality property, the objective function (14) satisfies the following inequality: Among them, if and only if The equation holds true; arg(·) represents the component direction phase of the complex vector; then the optimal θ of the optimal problem in formula (14) is: B2. Fix the phase shift matrix Θ to find the beamforming matrix w. The optimization problem is expressed as: Among them, Design the transmitter’s beamforming vector, written as in, represents the transmitter unit beamforming carrier; P w represents the corresponding power allocation factor; Will Substituting into formula (17), the original optimization problem is transformed into the following form: The safe rate must be greater than or equal to 0, that is, the objective function in formula (18) must be greater than or equal to 1, so the inequality is obtained: When the inequality in formula (19) holds, for any given beamforming vector The objective function in the optimization problem (18) is about P w Monotonically increasing; consider P w ≤P max The optimal power allocation factor is obtained as follows: Then, to determine the optimal element beamforming vector The original optimization problem is transformed into the following form: The optimization problem (21) is a standard Rayleigh quotient problem, and its optimal unit beamforming vector is is the unit eigenvector corresponding to the largest eigenvalue of the matrix given by: The optimal w is Finally, w obtained in B2 * As w is given in B1 to find Θ, the loop is iterated until the objective function value converges to obtain the optimal w and Θ.

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