Optimization method for active STAR-RIS assisted NOMA secure communication transmission

Abstract

The invention discloses an optimization method of active STAR-RIS auxiliary NOMA (Non-Orthogonal Multiple Access) secure communication transmission. The method comprises the following steps: S1, constructing an active STAR-RIS auxiliary uplink NOMA secure wireless communication system; and S2, the link of the user 1-BS and the link of the user 2-BS are blocked due to obstacles or a long distance, and the legal user transmits information to the base station of the receiving end through STAR-RIS. S3, in order to improve the safety of the system, the base station sends the useful signal and the artificial interference noise at the same time; under the scene that the total emission energy of a base station is limited, an optimization equation which is based on beam forming, artificial noise and IRS phase shift parameters and is used for maximizing the system safety rate sum of optimization parameters is constructed. And performing decoupling optimization solution on active beam forming, artificial noise and IRS phase shift parameters of the base station based on an alternating optimization algorithm. According to the invention, the total safety rate of the system can be obviously improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications and relates to an optimization method for active STAR-RIS-assisted NOMA secure communication transmission. Background Art

[0002] With the widespread application and rapid development of wireless communication technology, the demand for related technologies such as the Internet of Things, virtual reality and autonomous driving is constantly increasing, and the requirements for speed and reliability in wireless transmission are becoming more and more stringent. It is expected that the Beyond-5th Generation Mobile Communication Technology (B5G) and the Sixth Generation Mobile Communication Technology (6G) will have higher communication requirements, such as high spectrum efficiency, massive connections, high density, low latency, low energy consumption, flexible network deployment and high reliability communication. Therefore, the challenges faced by mobile communications in the future are multifaceted, such as how to simultaneously meet the needs of large-capacity user information transmission and reliable communication under limited spectrum resources. In order to improve the reliability of the transmission channel, the currently widely used relay and large-scale Multiple-Input Multiple-Output (MIMO) usually require complex hardware implementation, which inevitably increases power consumption and signal processing complexity. In addition, these technologies can only passively adapt to the propagation environment, but cannot actively utilize or control the propagation environment, which also makes it impossible to reliably guarantee the quality of service (QoS) of users in harsh propagation environments.

[0003] STAR-RIS technology has the function of polarization and magnetization current for receiving and / or transmitting incident signals. Compared with traditional RIS, which can only provide semi-planar coverage, STAR-RIS can use transmission and reflection functions to provide services to users on both sides of RIS at the same time, achieving 360° full space coverage. Therefore, STAR-RIS can provide higher degrees of freedom in wireless transmission.

[0004] On the other hand, NOMA technology has attracted extensive attention from academia and industry because it can meet the communication needs of multiple users at the same time and effectively alleviate the shortage of wireless spectrum resources. Unlike traditional orthogonal multiple access (OMA) technology, NOMA has higher spectrum utilization efficiency, better robustness and lower transmission delay. In addition, with the increasing complexity of wireless network environments and the increasing demand for communication service quality, it is extremely important to ensure the transmission security of communication systems, especially to use the characteristics of the physical layer to meet the security of information transmission. Therefore, studying the secure transmission scheme of STAR-RIS and NOMA at the physical layer can fully combine the advantages of both, and improve the reliability and energy efficiency of system transmission on the basis of ensuring the security of communication systems and meeting simultaneous multiple access. Summary of the invention

[0005] The purpose of the present invention is to provide an optimization method for active STAR-RIS assisted NOMA secure communication transmission. By deploying STAR-RIS to collaboratively assist the communication between multiple single-antenna users and multi-antenna base stations. The user transmit power coefficient, base station-side receive beamforming vector, STAR-RIS amplification factor, and reflection and transmission coefficient matrix parameters are jointly optimized to maximize the total security rate of the system while ensuring the quality of service for legitimate users.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] An optimization method for active STAR-RIS assisted NOMA secure communication transmission, the method comprising the following steps:

[0008] S1. Construct a secure wireless communication system, which includes a base station with several antennas, a single-antenna reflection user 1, a single-antenna transmission user 2, a single-antenna eavesdropping user and an active STAR-RIS;

[0009] S2, when the user 1-BS and user 2-BS links are blocked due to obstacles or long distances, the legitimate user transmits information to the receiving base station through STAR-RIS;

[0010] S3. The base station sends artificial interference noise while sending useful signals. In the scenario where the total transmission energy of the base station is limited, an optimization equation for maximizing the system safety rate and based on beamforming, artificial noise, and IRS phase shift parameters as optimization parameters is constructed. The active beamforming, artificial noise, and IRS phase shift parameters of the base station are decoupled and optimized based on the alternating optimization algorithm.

[0011] Furthermore, the method for obtaining the safe rate of a legitimate user is as follows:

[0012] First, the signal of legitimate user 1 is decoded by treating the signal of legitimate user 2 as interference, and then the signal of legitimate user 2 is decoded by continuous interference cancellation technology. The signal-to-interference-to-noise ratios of legitimate user 1 and user 2 at the base station receiving end are:

[0013]

[0014] According to Shannon's formula, the receiving channel capacity of a legitimate user is:

[0015] R k =log2(1+γ k ), k∈{1,2}(5)

[0016] Signal-to-interference-noise ratio of the eavesdropping user:

[0017]

[0018]

[0019] According to Shannon's formula, the channel capacity of the eavesdropping user is:

[0020] R E，k =log2(1+γ E，k ), k∈{1,2} (8)

[0021] Therefore, the safe rate of user k is:

[0022] R sec，k =[R k -R E，k ] + , k∈{1,2} (9).

[0023] Furthermore, in step S2, the optimization problem is proposed as follows:

[0024]

[0025] Among them, P max is the maximum transmit power of each user, P total is the maximum limit on the total power consumption of the active STAR-RIS, p n is the maximum power consumption limit of the nth component on the active STAT-RIS, R minis the minimum safe rate requirement for each user; C1 represents the minimum quality of service (QoS) requirement for legitimate users, C2 represents the beamforming constraint at the base station receiver, C3 represents the system cascade channel gain relationship, C4 represents the transmit power constraint for each user, C5 and C6 represent the total power constraint of active STAR-RIS and the power constraint of each element, respectively, and C7 and C8 represent the amplitude and phase shift constraints of STAR-RIS.

[0026] Furthermore, through the alternating iterative optimization algorithm, the original problem is decomposed into four sub-problems, which are: base station receive beamforming optimization; active STAR-RIS amplification factor optimization; STAR-RIS reflection transmission phase shift optimization; user end transmit power coefficient optimization.

[0027] Beneficial effects of the present invention:

[0028] 1. The present invention proposes an active STAR-RIS assisted NOMA uplink communication system, and designs an active STAR-RIS combined with NOMA assisted uplink secure communication system for the first time. Compared with traditional RIS assisted wireless communication, the use of STAR-RIS can not only effectively improve the freedom of signal propagation, but also expand the coverage system transmission range.

[0029] 2. The present invention proposes a novel and low-complexity alternating iterative optimization algorithm. Compared with the traditional method (converting the non-convex problem into a convex problem, using SDR to relax the rank-one constraint and then further using the CVX toolbox to solve the optimization variables), the algorithm proposed in this article effectively reduces the simulation running time complexity.

[0030] 3. This paper proposes a novel and effective alternating iterative optimization algorithm. The simulation proves that the active STAR-RIS-NOMA scheme proposed in this paper achieves better transmission performance for the active STAR-RIS-NOMA uplink security communication system compared with the active STAR-RIS-assisted NOMA scheme with a random phase shift matrix and the passive STAR-RIS-assisted NOMA scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] Figure 1 It is a system model diagram of the present invention;

[0033] Figure 2 It is a simulation diagram of the present invention;

[0034] Figure 3FIG. 4 is a graph showing the relationship between the total safety rate of the system of the present invention and the number N of active STAR-RIS elements.

[0035] Figure 4 Graph showing the relationship between the total safety rate of the system of the present invention and the number M of base station antennas.

[0036] Figure 5 is the total safety rate and transmission power P of the system of the present invention max relationship diagram. DETAILED DESCRIPTION

[0037] 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.

[0038] An optimization method for active STAR-RIS assisted NOMA secure communication transmission, the method comprising the following steps:

[0039] S1. Construct an active STAR-RIS assisted uplink NOMA secure wireless communication system, which consists of a base station with M antennas, a single-antenna reflection user 1, a single-antenna transmission user 2, a single-antenna eavesdropping user and an active STAR-RIS, where the number of components of the active STAR-RIS is N;

[0040] S2. This paper considers a challenging scenario where the user 1-BS and user 2-BS (direct) links are Figure 1 The obstacles shown are blocked or the distance is too far, so they can be ignored. The legitimate user transmits information to the receiving base station through STAR-RIS. For simplicity, this paper assumes that the legitimate user can obtain accurate channel state information.

[0041] S3. In order to improve the security of the system, the base station sends artificial interference noise (AN) while sending useful signals. In the scenario where the total transmission energy of the base station is limited, an optimization equation for maximizing the system safety rate and based on beamforming, artificial noise, and IRS phase shift parameters as optimization parameters is constructed. The active beamforming, artificial noise, and IRS phase shift parameters of the base station are decoupled and optimized based on the alternating optimization algorithm;

[0042] The S1 includes the following contents:

[0043] The received signal at the base station can be expressed as:

[0044]

[0045] The received signal of the eavesdropping user can be expressed as:

[0046]

[0047] Among them, the channel coefficients between legitimate user 1 to STAR-RIS, legitimate user 2 to STAR-RIS, STAR-RIS to base station and STAR-RIS to eavesdropping user are: G∈C M×N and g∈C 1×N , all channels obey the small-scale Rician fading channel model. is the additive complex Gaussian white noise at the base station, represents the additional noise generated by the active equipment of STAR-RIS, which cannot be ignored. P1 and P2 represent the transmit power of user 1 and user 2 respectively. s1 is the transmit signal at user 1, and s2 is the transmit signal at user 2. w H ∈C 1×M represents the receiving beamforming vector at the base station;

[0048] The reflection and transmission phase shift matrices are: Θ i =diag[θ i ], and represents the amplitude and phase shift of the nth reflection or transmission unit on STAR-RIS. In addition, the amplification factor matrix of STAR-RIS is A=diag[a],

[0049] Without loss of generality, we assume that the decoding order is consistent with the conditions of the cascaded channel. Specifically, the cascaded channel gain of the two users satisfies

[0050] According to the decoding principle of uplink NOMA, the BS first decodes the signal of legitimate user 1 by treating the signal of legitimate user 2 as interference, and then performs continuous interference cancellation technology to decode the signal of legitimate user 2. Therefore, the signal-to-interference-to-noise ratios of legitimate user 1 and user 2 at the base station receiving end are:

[0051]

[0052] According to Shannon's formula, the receiving channel capacity of a legitimate user is:

[0053] R k =log2(1+γ k ), k∈{1,2} (5)

[0054] Signal-to-interference-noise ratio of the eavesdropping user:

[0055]

[0056] According to Shannon's formula, the channel capacity of the eavesdropping user is:

[0057] R E，k =log2(1+γ E，k ), k∈{1,2} (8)

[0058] Therefore, the safe rate of user k is:

[0059] R sec，k =[R k -R E，k ] + , k∈{1,2} (9)

[0060] In S2, in order to improve the system security rate (SSR), an optimization problem is proposed:

[0061]

[0062] Among them, P max is the maximum transmit power of each user, P total is the maximum limit on the total power consumption of the active STAR-RIS, p n is the maximum power consumption limit of the nth component on the active STAT-RIS, R min is the minimum safe rate requirement for each user. C1 represents the minimum quality of service (QoS) requirement for legitimate users, C2 represents the beamforming constraint at the base station receiving end, C3 represents the system cascade channel gain relationship, C4 represents the transmit power constraint for each user, C5 and C6 represent the total power constraint of active STAR-RIS and the power constraint of each element, respectively, and C7 and C8 represent the amplitude and phase shift constraints of STAR-RIS.

[0063] In step S3, it can be seen from the statement of the above problem (P1-0) that there are a total of four types of variables that need to be optimized, namely the transmission power coefficient allocation problem, the base station receiving beamforming problem, the STAR-RIS end amplification coefficient problem, and the transmission and reflection coefficient problem. However, since the variables of the four optimization sub-problems are coupled with each other, it is difficult to directly solve the above problem (P1-0). In order to solve this non-convex coupling problem, this paper proposes an alternating iterative optimization algorithm, which decomposes the original problem into 4 sub-problems, and uses the AGM inequality to solve the base station receiving beamforming vector; the STAR-RIS end amplification coefficient and the reflection and transmission phase shift matrix are approximately solved by the first-order Taylor expansion; finally, the semidefinite relaxation method and the CVX toolbox are used to obtain the optimal solution. That is, it is divided into four sub-problems:

[0064] (1) Sub-problem 1: Optimization of receive beamforming at the base station

[0065]

[0066] Optimization process:

[0067] The objective function of problem (P1-1) is non-concave, so auxiliary variables ξ1 and ξ2 are introduced to transform the above optimization problem into:

[0068]

[0069] Where: ξ1 and ξ2 represent the signal-to-noise ratios of reflected user 1 and transmitted user 2, respectively. In addition, W = ww H , However, in problem (P1-1) and The constraints are still non-convex. In order to solve this problem, the arithmetic-geometric mean (AGM) inequality is used to transform the above non-convex constraints.

[0070] Furthermore, problem (12) can be transformed into:

[0071]

[0072] In order to solve this problem, a semidefinite relaxation method is used to relax the rank-one constraint, and then problem (13) can be directly solved using a convex optimization toolbox such as CVX.

[0073] (2) Sub-problem 2: Optimization of active STAR-RIS amplification factor:

[0074] Given the receive beamforming w, STAR-RIS transmission and reflection coefficients θ r ,θ t and the transmitting end power allocation coefficient P k The STAR-RIS amplification factor A optimization problem in the case of . Therefore, the original problem can be simplified to the following form:

[0075]

[0076] Optimization process:

[0077] The objective function and constraints of problem (P1-2) are non-convex, defined as: Equivalent to Since log2(2 a / 2 b )=ab, so we introduce auxiliary variable a k ,b k,c k ,d k (k=1,2) Transform the objective function and constraints of problem (P1-2).

[0078]

[0079] in:

[0080]

[0081] Non-convex constraints (15-2), (15-3), (15-6) and (15-7) can be transformed into convex constraints by first-order Taylor expansion approximation;

[0082]

[0083] in, xt represents the value of x at the tth iteration. Therefore, problem (14) is transformed into:

[0084]

[0085] in:

[0086]

[0087] Note that problem (16) removes the rank-one constraint in the equivalent condition, so problem (15-12) is convex. At this time, we can use some convex optimization tools (such as CVX) to solve it. Finally, when we get the optimal solution of problem (15-12) Finally, a feasible solution is obtained using the Gaussian randomization process. We further obtain the optimization problem (14): (3) Sub-problem 3: STAR-RIS reflection-transmission phase shift optimization:

[0088] Given the receive beamforming w, STAR-RIS amplification factor A and the transmit power allocation factor P k STAR-RIS transmission and reflection coefficients θ for the case r ,θ t Optimization problem. Therefore, the original problem can be simplified to the following form:

[0089]

[0090] Optimization process:

[0091] The objective function and constraints of problem (P1-3) are non-convex, defined as: Equivalent to Q i ≥0,rank(Q i )=1,diag(Q i)=β i Since log2(2 a / 2 b )=ab, so we introduce auxiliary variable a k ,b k ,c k ,d k (k=1,2) Transform the objective function and constraints of problem (16).

[0092]

[0093] in:

[0094]

[0095] The non-convex constraints (18-2), (18-3), (18-6) and (18-7) can be transformed into convex constraints by first-order Taylor expansion approximation.

[0096]

[0097] in, x t represents the value of x at the tth iteration. Therefore, problem (17) is transformed into:

[0098]

[0099] in:

[0100]

[0101] Note that problem (19) removes the rank-one constraint in the equivalent condition, so problem (19) is convex and can be solved with the help of some convex optimization tools (e.g., CVX). Finally, when the optimal solution of problem (19) is obtained Finally, a feasible solution is obtained using the Gaussian randomization process. We further obtain the optimization problem (17)

[0102] (4) Sub-problem 4: Optimization of user-side transmission power coefficient:

[0103] In this subsection, we first focus on the given receive beamforming w, STAR-RIS amplification factor A, and STAR-RIS transmission and reflection coefficients θ r ,θ t The transmitting end power allocation coefficient P under the condition k Optimization problem. Therefore, the original problem can be simplified to the following form:

[0104]

[0105] Note that the constraints of problem (20) are all linear, and can be solved with the help of some convex optimization tools (e.g., CVX).

[0106] The practical effects of the present invention are described in detail below in conjunction with simulation.

[0107] 1) Simulation conditions

[0108] This section verifies the effectiveness of the proposed active STAR-RIS-assisted NOMA secure transmission scheme through simulation. Figure 2 As shown in the figure, in the simulation system model, this paper assumes that the BS, eavesdropper and active STAR-RIS are located at (0,0,0), (0,3,0) and (0,50,0) respectively. The reflected users and the transmitted users are randomly distributed on a semicircle around the active STAR-RIS with a radius r of 3m. In addition, this paper sets the number of base station antennas M = 4, the number of elements on the active STAR-RIS N = 10, and the additional noise generated by the active equipment The maximum transmission power limit of the user end is Pmax = 30dBm, and the additive complex Gaussian white noise of the base station end and the eavesdropping end is and

[0109] 2) Simulation results

[0110] This section designs two benchmark schemes for comparison with the active STAR-RIS-assisted NOMA scheme (aSTAR-RIS-NOMA) proposed in this chapter. Scheme 1: active STAR-RIS-assisted NOMA scheme with random phase shift matrix (aSTAR-RIS-RPS); Scheme 2: passive STAR-RIS-assisted NOMA scheme (pSTAR-RIS-NOMA).

[0111] Figure 3The influence of the number of active STAR-RIS components on the total security rate of the system is analyzed. The simulation compares the system performance differences between the aSTAR-RIS-NOMA scheme proposed in this paper and the benchmark schemes (i.e., the aSTAR-RIS-RPS scheme and the pSTAR-RIS-NOMA scheme). It can be seen from the figure that: (1) As the number of STAR-RIS components increases, the number of transmission links between STAR-RIS and the base station increases, so the total security rate of the system of the three schemes increases. In addition, the algorithm scheme proposed in this paper is significantly better than the benchmark scheme, which further demonstrates the superiority of the algorithm scheme proposed in this paper. (2) As the number of components increases, the advantages of the aSTAR-RIS-NOMA scheme proposed in this paper are more obvious than the benchmark scheme. This shows that the active STAR-RIS based on the optimization algorithm of this paper combined with the NOMA technology-assisted transmission strategy can provide better security transmission performance. It can be further concluded that the performance gap between passive STAR-RIS and active STAR-RIS can be made up by increasing the number of components.

[0112] Figure 4 The relationship between the total system security rate and the number of BS antennas M of the aSTAR-RIS-NOMA scheme and the benchmark schemes (i.e., the aSTAR-RIS-RPS scheme and the pSTAR-RIS-NOMA scheme) is compared. It can be seen from the figure that: (1) The total system security rate of the STAR-RIS-NOMA scheme and the benchmark scheme increases with the increase in the number of base station antennas. This is because the increase in the number of base station antennas increases the spatial degrees of freedom and further provides a more flexible receive beamforming design, thereby achieving better reception performance. (2) The aSTAR-RIS-NOMA scheme proposed in this paper is significantly better than the aSTAR-RIS-RPS scheme and the pSTAR-RIS-NOMA scheme, which shows that due to the severe multiplicative fading effect, the passive STAR-RIS scheme has limited improvement on the total system security rate. In addition, the aSTAR-RIS-NOMA scheme optimizes the phase shift matrix to obtain the optimal reflection and transmission coefficients, while the pSTAR-RIS-NOMA scheme uses random phase shifts, which has large uncertainties, further confirming the superiority of the scheme proposed in this paper.

[0113] Figure 5 The total system security rate and total transmission power P of the aSTAR-RIS-NOMA scheme and the benchmark schemes (i.e., aSTAR-RIS-RPS scheme and pSTAR-RIS-NOMA scheme) are analyzed. max From the figure, we can see that: (1) For the three schemes, the total security rate of the system increases with the total transmission power P maxThe aSTAR-RIS-NOMA scheme proposed in this paper has better system performance than the aSTAR-RIS-RPS scheme and the pSTAR-RIS-NOMA scheme, which further confirms the effectiveness of the algorithm proposed in this paper. (2) As the total transmission power P max As the rate of the aSTAR-RIS-NOMA scheme increases, the total system security rate of the aSTAR-RIS-NOMA scheme becomes more advantageous than that of the pSTAR-RIS-NOMA scheme. This is because the active STAR-RIS amplifies the incident signal during the uplink transmission phase, so that the base station can receive more accurate signals.

[0114] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. An optimization method for active STAR-RIS-assisted NOMA secure communication transmission, characterized in that: The method comprises the following steps: S1. Construct a secure wireless communication system, which includes a base station with several antennas, a single-antenna reflection user 1, a single-antenna transmission user 2, a single-antenna eavesdropping user and an active STAR-RIS; S2, when the user 1-BS and user 2-BS links are blocked due to obstacles or long distances, the legitimate user transmits information to the receiving base station through STAR-RIS; S3. The base station sends artificial interference noise while sending useful signals. In the scenario where the total transmission energy of the base station is limited, an optimization equation for maximizing the system safety rate and based on beamforming, artificial noise, and IRS phase shift parameters as optimization parameters is constructed. The active beamforming, artificial noise, and IRS phase shift parameters of the base station are decoupled and optimized based on the alternating optimization algorithm.

2. According to claim 1, an optimization method for active STAR-RIS assisted NOMA secure communication transmission is characterized in that: The method for obtaining the safe rate of legitimate users is as follows: First, the signal of legitimate user 1 is decoded by treating the signal of legitimate user 2 as interference, and then the signal of legitimate user 2 is decoded by continuous interference cancellation technology. The signal-to-interference-to-noise ratios of legitimate user 1 and user 2 at the base station receiving end are: According to Shannon's formula, the receiving channel capacity of a legitimate user is: R k =log2(1+γ k ),k∈{1,2} (5) Signal-to-interference-noise ratio of the eavesdropping user: According to Shannon's formula, the channel capacity of the eavesdropping user is: R E，k =log2(1+γ E，k ),k∈{1,2} (8) Therefore, the safe rate of user k is: R sec，k =[R k -R E，k ] + ,k∈{1,2} (9)。 3. According to claim 2, an optimization method for active STAR-RIS assisted NOMA secure communication transmission is characterized in that: In step S2, the optimization problem is also proposed as follows: s.t.C1:R sec，k ≥R min , C2:w H in=1, C4:P k ≤P max , Among them, p max is the maximum transmit power of each user, P total is the maximum limit on the total power consumption of the active STAR-RIS, p n is the maximum power consumption limit of the nth component on the active STAT-RIS, R min is the minimum safe rate requirement for each user; C1 represents the minimum quality of service (QoS) requirement for legitimate users, C2 represents the beamforming constraint at the base station receiver, C3 represents the system cascade channel gain relationship, C4 represents the transmit power constraint for each user, C5 and C6 represent the total power constraint of active STAR-RIS and the power constraint of each element, respectively, and C7 and C8 represent the amplitude and phase shift constraints of STAR-RIS.

4. According to claim 3, an optimization method for active STAR-RIS assisted NOMA secure communication transmission is characterized in that: Through the alternating iterative optimization algorithm, the original problem is decomposed into four sub-problems, which are: base station receive beamforming optimization; active STAR-RIS amplification factor optimization; STAR-RIS reflection transmission phase shift optimization; user end transmit power coefficient optimization.

5. The optimization method for active STAR-RIS assisted NOMA secure communication transmission according to claim 4, characterized in that: The optimization problem of subproblem 1 is as follows: P1-1： s.t.C1:R sec，k ≥R min , C2:w H in=1, The specific method is: By introducing auxiliary variables ξ1 and ξ2, the above optimization problem is transformed into: Where: ξ1 and ξ2 represent the signal-to-noise ratios of reflected user 1 and transmitted user 2, respectively. In addition, W = ww H , The above non-convex constraints are transformed using the arithmetic-geometric mean inequality, and problem (12) is transformed into: The rank-one constraint is relaxed using a semidefinite relaxation method, and problem (13) is solved using a convex optimization toolbox such as CVX.

6. The optimization method for active STAR-RIS assisted NOMA secure communication transmission according to claim 4, characterized in that: The optimization problem of subproblem 2 is as follows: The optimization method is: definition: Equivalent to Since log2(2 a / 2 b )=ab, so we introduce auxiliary variable a k ,b k ,c k ,d k (k=1,2) Transform the objective function and constraints of problem (P1-2); in: The non-convex constraints (15-2), (15-3), (15-6) and (15-7) are transformed into convex constraints through the first-order Taylor expansion approximation; in, x t represents the value of x at the tth iteration; therefore, problem (14) is transformed into: in: Solve with the help of convex optimization tools; when the optimal solution of problem (15-12) is obtained Finally, a feasible solution is obtained using the Gaussian randomization process. We further obtain the optimization problem (14):

7. The method for optimizing active STAR-RIS-assisted NOMA secure communication transmission according to claim 6, characterized in that: The optimization problem of subproblem 3 is as follows: Transform problem (17) into: in: With the help of some convex optimization tools, we can finally get the optimal solution to problem (19) Finally, a feasible solution is obtained using the Gaussian randomization process. We further obtain the optimization problem (17) 8. The method for optimizing active STAR-RIS-assisted NOMA secure communication transmission according to claim 7, characterized in that: The optimization problem of subproblem 4 is as follows: s.t.C1:R set，k ≥R min , C4:P k ≤P max , The solution is obtained using convex optimization tools.

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