A full-space PLS transmission method and system for STAR-RIS assisted wireless communication system

Abstract

This invention provides a full-space PLS transmission method and system for a STAR-RIS-assisted wireless communication system. In this system, the reflection coefficient and transmission coefficient of the STAR-RIS system, as well as the precoding vectors assigned by the base station to security users and public users, are obtained by solving a non-convex optimization problem. The non-convex optimization problem maximizes the weighted sum of the security rate of security users and the achievable rate of public users, with base station power as a constraint. The security rate of security users is obtained by performing a large-scale system analysis of their average security rate. With the assistance of STAR-RIS, the proposed scheme can achieve robust full-space physical layer security transmission.

Description

Technical Field

[0001] The present invention relates to the field of new generation wireless communication technology, and in particular to a robust full-space physical layer security transmission method and system for a STAR-RIS assisted wireless communication system. Background Art

[0002] With the advent of the 5G era, driven by advanced communications and data processing technologies, people are increasingly reliant on wireless communications. Large amounts of important sensitive information, such as identity information and confidential documents, are transmitted over open wireless networks, increasing the risk of eavesdropping. Consequently, information security is receiving increasing attention. Physical layer security (PLS), based on an information-theoretic framework, is a promising solution. It exploits the inherent unpredictability of physical media, particularly the random variations in both legitimate and potential eavesdropping channels, to protect the security of wireless information transmission.

[0003] While existing PLS transmission schemes can effectively improve security between base stations and users, they are based on uncontrollable, random channels, severely limiting the performance gains they can bring. To overcome this challenge, a PLS scheme based on reconfigurable smart surfaces (RIS) has been proposed. RIS can dynamically manipulate the electromagnetic properties of the incident signal to build an end-to-end controllable virtual channel between the base station and the user, significantly improving security.

[0004] However, in the existing traditional RIS-assisted PLS scheme, RIS can only achieve reflection modulation of the incident signal, which means that the transceiver terminal equipment must be located on the same side as the RIS. This will greatly limit the coverage of the wireless network and the flexibility of deploying RIS. In order to solve the above obstacles, a more advanced RIS, called simultaneous transmission and reflection RIS (STAR-RIS), was proposed, because STAR-RIS can split the incident signal into two parts, one part of the incident signal is reflected back to the same side, and the other part is transmitted to the opposite side. Therefore, STAR-RIS has shown great application potential in wireless networks. At present, some PLS schemes based on STAR-RIS-assisted communication systems have been proposed. However, these PLS schemes generally have the following shortcomings: (1) These PLS schemes usually assume that the eavesdropper's activity area (R zone or T zone) within the STAR-RIS service area has been predetermined, which enables the system to effectively solve the eavesdropping risk by designing appropriate security measures. It is worth noting that this assumption is unrealistic, because in practice, intelligent eavesdroppers can make full use of the full spatial coverage characteristics of STAR-RIS to enhance their ability to intercept confidential information. Specifically, they can move freely within the R and T zones to increase the probability of successful eavesdropping, which poses a severe challenge to current PLS schemes in maintaining information security. (2) These existing PLS schemes can only guarantee the security of semi-space wireless communications because they take into account fixed eavesdroppers in a specific area and mainly guarantee security within the R or T zones. Note that the full spatial coverage of STAR-RIS makes the STAR-RIS-assisted communication system encounter more serious inherent security issues than the traditional RIS-assisted communication system because eavesdroppers can conduct 360° monitoring of the communication between the base station and the legitimate user. Summary of the Invention

[0005] In order to solve the above-mentioned problems of the prior art, the present invention provides a full-space PLS transmission method and system for a STAR-RIS assisted wireless communication system.

[0006] The present invention is achieved through the following technical solutions:

[0007] A full-space PLS transmission method for a STAR-RIS assisted wireless communication system, wherein the reflection coefficient and transmission coefficient of STAR-RIS and the precoding vectors allocated by the base station to security users and public users in the STAR-RIS assisted wireless communication system are obtained by solving a non-convex optimization problem;

[0008] The non-convex optimization problem aims to maximize the weighted sum of the secure rate of the secure user and the achievable rate of the public user, with the base station power as a constraint. The secure rate of the secure user is obtained by performing a large-scale system analysis of the average secure rate of the secure user.

[0009] Preferably, the non-convex optimization problem is as follows:

[0010]

[0011] Where ω1,ω2∈(0,1] represent the weighting factors of security users and public users respectively; P tmax Indicates the maximum transmit power at the base station; λ represents the number of quantization bits; and Respectively represent the precoding vectors allocated by the base station to the security user and the public user; U r and U t denote the reflection coefficient and transmission coefficient of STAR-RIS respectively; and denote the magnitudes of the STAR-RIS reflection and transmission coefficients, respectively, satisfying denote the phases of the STAR-RIS reflection coefficient and transmission coefficient respectively; j is the imaginary unit; R c Indicates the achievable communication rate for public users; Indicates the security rate of secure users.

[0012] Furthermore, the safety rate of safe users for:

[0013]

[0014] Among them, H BR Indicates the channel between the base station and STAR-RIS; represents the noise power at the eavesdropping user; R b Indicates the achievable communication rate of secure users; l re represents the large-scale path loss experienced by the signal from STAR-RIS to the eavesdropping user; P1 represents the probability that the eavesdropping user is located in the R region of STAR-RIS; P0 represents the probability that the eavesdropping user is located in the T region of STAR-RIS, P0 = 1-P1.

[0015] Furthermore, the achievable communication rate R of the secure user b and the achievable communication rate R for public users c They are as follows:

[0016]

[0017] Among them, h rb and h rc The channels between STAR-RIS and security users and public users are represented respectively. and denote the noise power at the safety user and public user respectively.

[0018] Furthermore, the method for solving the non-convex optimization problem is:

[0019] Decomposing the non-convex optimization problem into an active beamforming subproblem and a passive beamforming subproblem, and solving the active beamforming subproblem and the passive beamforming subproblem respectively;

[0020] The active beamforming subproblem is expressed as:

[0021]

[0022] The passive beamforming subproblem is expressed as:

[0023]

[0024] Furthermore, the method for solving the active beamforming subproblem is:

[0025] The active beamforming subproblem is solved using an iterative algorithm based on minimum mean square error. In the (t+1)th iteration, the active beamforming subproblem is formulated as the following convex optimization problem:

[0026]

[0027]

[0028] and is the auxiliary variable in the tth iteration;

[0029] The Lagrange multiplier method is used to solve the convex optimization problem and obtain the optimal solution.

[0030] Furthermore, the method for solving the passive beamforming subproblem is to use a cross-entropy optimized iterative algorithm to solve the passive beamforming subproblem.

[0031] Furthermore, the joint sampling distribution of the cross entropy optimization framework is constructed as follows:

[0032]

[0033]

[0034] in, represents the sampled solution, represents the skew parameter of the joint sampling distribution; the matrix P pq Represents φ p Select Collection The qth item in the The probability of 1 {·} An indicator function representing an event, represents the p-th entry of vector a; and denote the standard deviation and mean vector, σ m and μ m is the parameter that generates the amplitude of the mth unit of STAR-RIS; and denote the magnitudes of the STAR-RIS reflection and transmission coefficients, respectively, satisfying represents a discrete phase shift variable; denote the phases of the STAR-RIS reflection and transmission coefficients, respectively.

[0035] Furthermore, based on the joint sampling distribution, the cross entropy minimization optimization problem is:

[0036]

[0037] The Lagrange multiplier method is used to solve the above cross entropy minimization optimization problem and obtain the optimal solution.

[0038] A STAR-RIS assisted wireless communication system, wherein a reflection coefficient and a transmission coefficient of a STAR-RIS and a precoding vector allocated by a base station to a security user and a public user in the STAR-RIS assisted wireless communication system are obtained by solving a non-convex optimization problem;

[0039] The non-convex optimization problem aims to maximize the weighted sum of the secure rate of the secure user and the achievable rate of the public user, with the base station power as a constraint. The secure rate of the secure user is obtained by performing a large-scale system analysis of the average secure rate of the secure user.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] This paper addresses the challenge of full-space eavesdropping faced by wireless communication systems supported by STAR-RIS (Star-Related Informatics) (STAR-RIS). This paper proposes a robust full-space physical layer security transmission scheme. Unlike existing PLS schemes, this scheme considers the statistical channel information state of an eavesdropper at the base station and the uncertainty of the eavesdropper's location within the STAR-RIS-assisted service area. Using large-scale system analysis, it derives an asymptotic expression for the average security rate at the secure user as the secure rate of the secure user. It then formulates an optimization problem to maximize the weighted sum of the secure rate of the secure user and the achievable rate of the public user. This paper is applicable to the field of reconfigurable smart surface-assisted communication technology. Compared to traditional physical layer security schemes, the proposed scheme, assisted by STAR-RIS, can achieve robust full-space physical layer security transmission. Simulation results demonstrate that the proposed robust PLS scheme effectively mitigates information leakage within the STAR-RIS system coverage area. Compared to a baseline scheme assisted by traditional reconfigurable smart surfaces, it achieves higher performance gains, further validating the effectiveness of the robust PLS scheme and iterative algorithm.

[0042] Furthermore, by optimizing the joint design of active and passive beamforming variables under the constraints of power budget, equality constraints and actual implementation of STAR-RIS, in order to effectively solve the non-convex optimization problem with discrete variables, an iterative algorithm based on minimum mean square error (MMSE) and cross entropy optimization (CEO) is proposed. The proposed algorithm can be applied to the field of solving discrete optimization problems. At the same time, the algorithm proposed in the present invention has low complexity and excellent performance. It is particularly good at solving discrete optimization problems and has high engineering application value. The above advantages will help to cope with the challenges of high capacity and high security faced by future 6G networks, and bring significant economic benefits to the performance improvement of wireless communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 Schematic diagram of the STAR-RIS assisted wireless communication system of the present invention;

[0045] Figure 2 For the embodiment 1 of the present invention, P1=P0=0.5, ω1=ω2=0.5, P tmax =30dBm,λ=2 and different N tSchematic diagram of the performance of RIS configuration with respect to the number of units, weights, and rate changes under the condition of ;

[0046] Figure 3 This is how the solution proposed in this invention suppresses the leakage of STAR-RIS service area information. DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present invention through specific examples. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention.

[0048] It should be noted that the process equipment or devices not specifically specified in the following embodiments are all conventional equipment or devices in the art.

[0049] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses. Furthermore, unless otherwise specified, the numbering of each method step is merely a convenient tool for identifying each method step, and is not intended to limit the order of arrangement of each method step or to define the scope of the invention. Changes or adjustments to their relative relationships, without substantially changing the technical content, should also be considered within the scope of the invention.

[0050] The present invention proposes a robust full-space PLS transmission method for improving the security of the STAR-RIS assisted wireless communication system.

[0051] like Figure 1 As shown, a STAR-RIS assisted wireless communication system is constructed, including a t Consider a base station (BS) with M antennas, a STAR-RIS with M cells, and three different users: secure user Bob, public user Carol, and eavesdropping user Eve. Assume that secure user Bob is located in region R of STAR-RIS, while public user Carol uses the communication service in region T. Physical obstacles such as buildings and trees prevent direct communication between the base station and the users.

[0052] Based on the established STAR-RIS assisted wireless communication system, the asymptotically secure rate of the secure user Bob is derived, and a non-convex optimization problem is constructed with the weighted sum of the asymptotically secure rate of the secure user Bob and the achievable rate of the public user Carol as the optimization objective, while ensuring the base station power constraint.

[0053] The established non-convex optimization problem is decomposed into the first non-convex subproblem and the second non-convex subproblem. An iterative algorithm based on the minimum mean square error and cross entropy optimization framework is designed to effectively solve the first and second non-convex subproblems and obtain the optimization results.

[0054] In the STAR-RIS assisted wireless communication system, the signal model of each user includes:

[0055] The signals received by secure user Bob and public user Carol can be expressed as:

[0056]

[0057] Among them, h rb and h rc are the channels between STAR-RIS and the secure user Bob and the public user Carol, respectively. BR Indicates the channel between the base station and STAR-RIS. b and s c The precoding vectors assigned by the base station to the secure user Bob and the public user Carol are respectively and and are the additive white Gaussian noise at the secure user Bob and the public user Carol, and are the noise powers at the secure user Bob and the public user Carol respectively. r and U t are the reflection coefficient and transmission coefficient of STAR-RIS, which can be expressed as:

[0058]

[0059] in, and denote the magnitudes of the STAR-RIS reflection and transmission coefficients, respectively, which satisfy are the phases of the STAR-RIS reflection and transmission coefficients, respectively. j is an imaginary unit.

[0060] When the eavesdropping user Eve moves in the service area of ​​the STAR-RIS assisted wireless communication system, it can eavesdrop on signals in different areas. In other words, the eavesdropping user Eve may perform eavesdropping activities in area R or in area T. In order to represent the location area of ​​the eavesdropping user Eve, a Bernoulli variable b is defined. Specifically, when b = 1 and the probability is P1, it means that the eavesdropping user Eve is located in area R of STAR-RIS. Conversely, when b = 0 and the probability P0 = 1-P1, it means that the eavesdropping user Eve is located in area T of STAR-RIS. Therefore, the signal received by the eavesdropping user Eve can be expressed as:

[0061]

[0062] in, represents the additive Gaussian white noise at the eavesdropping user Eve, represents the noise power at the eavesdropping user Eve, h re Indicates the channel between Eve and STAR-RIS.

[0063] The achievable communication rates of secure user Bob and public user Carol can be expressed as:

[0064]

[0065] The eavesdropping rate of Eve on the communication between the base station and the secure user Bob can be expressed as:

[0066]

[0067] Therefore, the secure rate of secure user Bob can be expressed as:

[0068]

[0069] Among them, operation [x] + represents max{0,x}.

[0070] Considering that the eavesdropping user Eve remains hidden and moves freely within the entire coverage area of ​​the STAR-RIS assisted wireless communication system, it is challenging for the base station to obtain accurate information about the location area of ​​the eavesdropping user Eve and perfect channel state information (CSI). Therefore, the present invention chooses to use the secure user Bob in b and h re The average security rate on represents the security performance of the secure user Bob in the STAR-RIS assisted wireless communication system, which is given by the following formula:

[0071]

[0072] in,

[0073]

[0074] In fact, due to the addition of random variables h in the eavesdropping rate expression re , leading to the derivation The precise mathematical formula for is difficult to parse. To address this problem, the present invention employs large-system analysis techniques, a common method for evaluating the performance limits of wireless communication systems. Specifically, assuming that STAR-RIS is equipped with a large number of passive elements, it can be deduced that:

[0075]

[0076] Among them, (a) is obtained by using large system analysis technology. It is worth noting that by performing such an operation, In h re Therefore, the asymptotic expression of the average safety rate can be obtained, that is,

[0077]

[0078] Therefore, the present invention will utilize The asymptotic result of To represent the secure rate of secure user Bob.

[0079] The optimization problem is as follows:

[0080]

[0081] Among them, ω1, ω2∈(0,1] are the weighting factors of security users and public users respectively; P tmax Indicates the maximum transmit power at the base station; λ represents the number of quantization bits. In fact, the objective function of the above optimization problem is non-convex, contains equality constraints related to amplitude, involves discrete variables, and exhibits significant coupling between variables, making it difficult to solve. To address this issue, we first use an alternating strategy to decompose the original optimization problem into two subproblems: the active beamforming subproblem and the passive beamforming subproblem. Based on this, we propose an iterative algorithm based on minimum mean square error (MMSE) and cross-entropy optimization (CEO) to effectively solve the above problem.

[0082] The active beamforming subproblem can be expressed as:

[0083]

[0084] Note that the above active beamforming subproblem is a non-convex optimization problem, since the objective function is in terms of (wrt)wb and w c To solve this problem, the present invention first converts Converted to the following form:

[0085]

[0086] Then use the MMSE method to equivalently transform f1, f2, f3, f4 and f5 into

[0087]

[0088] in,

[0089]

[0090] Here, W1, W2, W3, W4, W5, u1, u2 and u3 are auxiliary variables introduced; l re represents the large-scale path loss experienced by the signal from STAR-RIS to the eavesdropping user Eve. In fact, these auxiliary variables can be easily solved by setting the first-order partial derivatives of the objective function with respect to these variables to zero. The optimal solution can be given as:

[0091]

[0092] According to the above analysis, the present invention can be and In the case of , we obtain the concave lower bounds of f1, f2, f3, f4, and f5 in the (t+1)th iteration, which can be expressed as:

[0093]

[0094] It is worth noting that and is the effective beamforming solution obtained at the tth iteration and Calculated. Therefore, in the (t+1) iteration, The concave lower bound of can be expressed as:

[0095]

[0096] Similarly, in the (t+1)th iteration, R c can be equivalently converted to

[0097]

[0098] in,

[0099]

[0100] Therefore, the above active beamforming subproblem is reformulated as the following convex optimization problem in the (t+1)th iteration,

[0101]

[0102] The present invention further utilizes the Lagrange multiplier method to solve the above convex optimization problem. Specifically, the Lagrange function of the above problem can be expressed as:

[0103]

[0104] in, represents the Lagrange multiplier (dual variable) associated with the constraint. b and w c The optimal solution of the Lagrangian problem can be expressed as:

[0105]

[0106] Among them, the operator Represents a pseudo-inverse operation.

[0107]

[0108]

[0109] The optimal solution given above shows that the optimal active beamforming and dual variables For any given The present invention can directly calculate the optimal closed-form solution to active beamforming. Therefore, to obtain the optimal active beamforming of the original problem, solving its dual problem and obtaining the optimal dual variable is crucial, as this is the key step in achieving the optimal active beamforming of the original problem.

[0110] First, the dual problem of the active beamforming subproblem is given:

[0111]

[0112] in, represents the dual function.

[0113] Regarding the dual problem, the present invention will use the subgradient algorithm to solve it. In the (l+1)th inner loop iteration, the dual variable The update rule can be expressed as:

[0114]

[0115] in, Represents the dual variable update algorithm Positive increment of .

[0116] The passive beamforming subproblem can be expressed as:

[0117]

[0118] In practice, solving the passive beamforming subproblem mentioned above poses a challenge due to the non-convexity of the objective function, the equality constraints on the STAR-RIS amplitude, and the discrete phase shifts involved. To address this issue, the CEO method is used to design the passive beamforming variables. Unlike traditional convex optimization algorithms, the CEO method utilizes probabilistic learning techniques to solve complex problems. Its basic idea for dealing with complex optimization problems can be summarized as follows: (1) design appropriate sampling distributions characterized by tilt parameters for different types of variables; (2) generate expected solutions from the sampling distributions and evaluate these expected solutions by calculating the associated objective function values; (3) adjust the tilt parameters of the sampling distributions by optimizing the cross entropy between the existing sampling distributions and the benchmark sampling distributions established from these significantly effective expected solutions; (4) iterate new feasible samples using the modified tilt parameters until the difference in the objective function values ​​between consecutive iterations drops to a predefined threshold.

[0119] The sampling distribution is designed as follows:

[0120] In order to effectively use the cross entropy optimization framework to solve the optimization problem, we must first establish two appropriate sampling distribution models to generate discrete phase shift variables. and continuous amplitude variables φ p The sampling distribution model of is designed as follows:

[0121] To generate feasible phase shifts, the present invention can utilize the obtained or predetermined discrete probability distribution {P p1 ,…,P pq ,…,P pQ}In parallel and independently for each φ p , Sampling is performed, where P pq Represents φ p Select Collection The qth item in the Therefore, the sampling distribution for generating φ can be given by:

[0122]

[0123] In the formula, the matrix An indicator function representing an event, represents the p-th entry of vector a.

[0124] Continuous amplitude variable sampling distribution model: Unlike discrete phase shift, the amplitude variable is a continuous variable. Here, Gaussian distribution is the best choice for generating the amplitude variable. Therefore, the sampling distribution for generating random amplitude can be expressed as

[0125]

[0126] in, and denote the standard deviation and mean vector, σ m and μ m is the key parameter for generating the amplitude of the mth unit. Also, In practice, the magnitude of the transmission coefficient can be determined using the constraint Direct calculation, that is The sampling distribution may generate amplitudes outside the range of (0,1]. To solve this problem, the present invention can use the following techniques to ensure that the generated amplitudes are feasible: (1) remove infeasible amplitudes and randomly select amplitudes within the required range for replacement; (2) iteratively generate replacement values ​​for each infeasible amplitude until a feasible amplitude is obtained. and μ represent the variance and mean, respectively. It should be emphasized that the randomly generated f0 may exceed [f min ,f max ] range.

[0127] Joint sampling distribution model: Based on the above analysis, the joint sampling distribution for the optimization problem can be expressed as:

[0128]

[0129] In this case, the sampling solution is sampled from the joint distribution generated in represents the skew parameter of the joint sampling distribution.

[0130] The tilt parameter update criteria are as follows:

[0131] As mentioned above, the CEO framework updates the tilt parameter by minimizing the cross entropy problem between the current sampling distribution and the benchmark sampling distribution derived from the best performing expected solution. Specifically, the present invention first generates K feasible samples based on the joint sampling distribution Then calculate the objective function value R(Ξ k ). In order to identify these efficient solutions, the candidate solutions generated Arrange them in descending order according to their objective function values. The candidate solution with the kth largest objective function value is denoted as Ξ [k] , satisfying R(Ξ [1] )≥R(Ξ [2] )≥…≥R(Ξ[k] )≥…≥R(Ξ [K] ). Before selection samples form an elite set in Indicates the percentage of the best performing samples in the selected elite set.

[0132] The tilt parameter is adjusted by minimizing the cross entropy between the current sampling distribution and the new sampling distribution, which are derived from the elite samples in the CEO framework. The cross entropy minimization optimization problem can be equivalently transformed into:

[0133]

[0134] In fact, the Lagrange multiplier method can effectively handle the above optimization problem. In order to utilize this method, the present invention first constructs the Lagrangian function of the problem, which is expressed as:

[0135]

[0136] Among them, ∈ p is the Lagrange multiplier associated with the equality constraints given in the above optimization problem. The first-order partial derivatives of and μ can be derived as:

[0137]

[0138] let and It can be introduced:

[0139]

[0140] In order to reduce the risk of converging to a local optimum in the CEO algorithm, a smoothing method commonly used in reinforcement learning is usually used to adjust the tilt parameter in successive iterations. The tilt parameter of the i-th iteration is modified according to the following method:

[0141]

[0142] μ (i) ←ξμ (i) +(1-ξ)μ (i-1) ,

[0143] where ξ∈(0,1) represents a smoothing parameter. Under the adjusted tilt parameter, K new feasible candidate solutions are generated, and then an elite solution is selected to prepare for the next round of updating the tilt parameter. The proposed algorithm, based on the CEO framework, converges to the optimal solution in successive iterations once the difference in the optimal objective function value between consecutive iterations falls below a predetermined threshold.

[0144] Example 1

[0145] This embodiment examines P1=P0=0.5,ω1=ω2=0.5,P tmax =30dBm,λ=2 and different N t Considering the change in the number of antennas, the impact of the number of units deployed on STAR-RIS on the weighted sum rate is as follows: Figure 2 In particular, the present invention observed that as M increases, all cases show an upward trend, while the growth rate experiences a gradual decline. This is because more units can provide more degrees of freedom to reconfigure the wireless propagation environment.

[0146] In addition, the present invention also discovered an interesting phenomenon that when the value of M is low, the zero forcing (ZF) scheme shows better performance improvement, and under the same configuration, as the number of units increases, the scheme proposed by the present invention is better than the ZF scheme. This is because the basic design goal of the ZF scheme is to eliminate interference between users. Therefore, even if the control capability of STAR-RIS is limited, it can still achieve significant performance improvement. On the contrary, when the control capability of STAR-RIS is limited, that is, when M is small, the scheme proposed by the present invention faces challenges in eliminating interference between users. As M increases, STAR-RIS's ability to adjust the input signal is enhanced, which can not only effectively eliminate interference between users, but also improve the quality of signals received by different users, thereby further improving the communication rate. In addition, under the same conditions, by comparing with the simulation results of the SDR scheme, the significant advantages of the algorithm proposed by the present invention are demonstrated, and the effectiveness of the algorithm proposed by the present invention is verified. Note that compared with other benchmark schemes, the traditional RIS-assisted system shows the smallest performance improvement, even compared with the system with more stringent system conditions (i.e., N t =5) is even worse. This demonstrates the remarkable capability of STAR-RIS in effectively implementing full spatial signal modulation.

[0147] Example 2

[0148] Reference Figure 3 To verify the effectiveness of the proposed PLS scheme in suppressing information leakage in the full spatial coverage area of ​​the STAR-RIS-assisted wireless communication system, we first generated 500 random eavesdropping channels and calculated the corresponding eavesdropping rates with and without security policies. This was accomplished using optimized active and passive beamforming solutions. Figure 3(a) and (b) represent the eavesdropping rates in the R region and the T region, respectively. As can be seen from the figure, compared with the results without considering the security policy, the eavesdropping rate of the PLS scheme proposed in the present invention under different CSIs of the eavesdropping user Eve is significantly suppressed, which shows that the robust full-space PLS scheme proposed in the present invention can effectively suppress the eavesdropping user Eve from eavesdropping on the communication between the base station and the secure user Bob. In addition, the present invention can find that these eavesdropping rates are still significantly increased in the channels of some eavesdropping user Eve, mainly due to the high coupling between these channels and the communication channel of the secure user Bob. These simulation results also show that the reflection area is more susceptible to eavesdropping than the transmission area, which can be evidenced by the eavesdropping rate values.

[0149] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0150] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A full-space PLS transmission method for a STAR-RIS assisted wireless communication system, characterized in that: The reflection coefficient and transmission coefficient of STAR-RIS in the STAR-RIS assisted wireless communication system as well as the precoding vectors allocated by the base station to the security users and public users are obtained by solving a non-convex optimization problem; The non-convex optimization problem aims to maximize the weighted sum of the secure rate of the secure user and the achievable rate of the public user, with the base station power as the constraint. The secure rate of the secure user is obtained by performing a large-scale system analysis of the average secure rate of the secure user. The non-convex optimization problem is as follows: in, denote the weighting factors of security users and public users respectively; Indicates the maximum transmit power at the base station; ; Indicates the number of quantization bits; and denote the precoding vectors allocated by the base station to security users and public users respectively; and denote the reflection coefficient and transmission coefficient of STAR-RIS respectively; and denote the magnitudes of the STAR-RIS reflection and transmission coefficients, respectively, satisfying ; denote the phases of the STAR-RIS reflection and transmission coefficients, respectively; Indicates the achievable communication rate for public users; Indicates the safe rate of safe users; Safe rate for secure users for: in, Indicates the channel between the base station and STAR-RIS; represents the noise power at the eavesdropping user; Indicates the achievable communication rate for secure users; represents the large-scale path loss experienced by the signal from STAR-RIS to the eavesdropping user; represents the probability that the eavesdropping user is located in the R region of STAR-RIS; represents the probability that the eavesdropping user is located in the T region of STAR-RIS, .

2. The full-space PLS transmission method for a STAR-RIS assisted wireless communication system according to claim 1, characterized in that: Achievable communication rate for secure users and the achievable communication rate for public users They are as follows: in, and The channels between STAR-RIS and security users and public users are represented respectively. and denote the noise power at the safety user and public user respectively.

3. The full-space PLS transmission method for a STAR-RIS assisted wireless communication system according to claim 2, characterized in that: The method for solving the non-convex optimization problem is: Decomposing the non-convex optimization problem into an active beamforming subproblem and a passive beamforming subproblem, and solving the active beamforming subproblem and the passive beamforming subproblem respectively; The active beamforming subproblem is expressed as: The passive beamforming subproblem is expressed as: 。 4. The full-space PLS transmission method for a STAR-RIS assisted wireless communication system according to claim 3, characterized in that: The method to solve the active beamforming subproblem is: The active beamforming subproblem is solved using an iterative algorithm based on the minimum mean square error, where The active beamforming subproblem in the iteration is formulated as the following convex optimization problem: , , , , , , and For the Auxiliary variables in the iteration; The Lagrange multiplier method is used to solve the convex optimization problem and obtain the optimal solution.

5. The full-space PLS transmission method for a STAR-RIS assisted wireless communication system according to claim 3, characterized in that: The method for solving the passive beamforming subproblem is to use an iterative algorithm based on cross entropy optimization to solve the passive beamforming subproblem.

6. The full-space PLS transmission method for a STAR-RIS assisted wireless communication system according to claim 5, characterized in that: The joint sampling distribution of the cross entropy optimization framework is constructed as: in, represents the sampled solution, represents the skew parameter of the joint sampling distribution; the matrix ; express Select Collection Middle Item and meet probability; An indicator function representing an event, Represents a vector No. entries; and denote the standard deviation and mean vectors respectively, and This is the first step in generating STAR-RIS Parameters of unit amplitude; and denote the magnitudes of the STAR-RIS reflection and transmission coefficients, respectively, satisfying ; represents a discrete phase shift variable; denote the phases of the STAR-RIS reflection and transmission coefficients, respectively.

7. The full-space PLS transmission method for a STAR-RIS assisted wireless communication system according to claim 6, characterized in that: Based on the joint sampling distribution, the cross entropy minimization optimization problem is: The Lagrange multiplier method is used to solve the above cross entropy minimization optimization problem and obtain the optimal solution.

8. A STAR-RIS assisted wireless communication system, characterized in that: The reflection coefficient and transmission coefficient of STAR-RIS in the STAR-RIS assisted wireless communication system and the precoding vectors allocated by the base station to the security user and the public user are obtained by solving a non-convex optimization problem; The non-convex optimization problem aims to maximize the weighted sum of the secure rate of the secure user and the achievable rate of the public user, with the base station power as the constraint. The secure rate of the secure user is obtained by performing a large-scale system analysis of the average secure rate of the secure user. The non-convex optimization problem is as follows: in, denote the weighting factors of security users and public users respectively; Indicates the maximum transmit power at the base station; ; Indicates the number of quantization bits; and denote the precoding vectors allocated by the base station to security users and public users respectively; and denote the reflection coefficient and transmission coefficient of STAR-RIS respectively; and denote the magnitudes of the STAR-RIS reflection and transmission coefficients, respectively, satisfying ; denote the phases of the STAR-RIS reflection and transmission coefficients, respectively; Indicates the achievable communication rate for public users; Indicates the safe rate of safe users; Safe rate for secure users for: in, Indicates the channel between the base station and STAR-RIS; represents the noise power at the eavesdropping user; Indicates the achievable communication rate for secure users; represents the large-scale path loss experienced by the signal from STAR-RIS to the eavesdropping user; represents the probability that the eavesdropping user is located in the R region of STAR-RIS; represents the probability that the eavesdropping user is located in the T region of STAR-RIS, .

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