Auxiliary RS-NOMA network covert communication optimization method based on STAR-RIS

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to an RS-NOMA network covert communication optimization method based on STAR-RIS assistance. The method comprises the following steps: 1) constructing a covert communication system model based on STAR-RIS assisted NOMA; 2) introducing a rate segmentation strategy at a sending end; 3) respectively obtaining detection error probabilities of illegal users on two sides of the STAR-RIS based on binary hypothesis testing; 4) respectively obtaining corresponding signal transmission rates according to the received signal expressions of the hidden users; 5) based on physical layer security, respectively acquiring security interruption probabilities of hidden users at two sides of the STAR-RIS; 6) constructing an optimization problem which takes system hidden transmission rate maximization as an optimization target and takes detection error probability constraint and security interruption probability constraint of illegal users as constraint conditions; and 7) the power distribution coefficient and the rate distribution coefficient of the sending end and the energy segmentation coefficient of the STAR-RIS are jointly optimized based on an adaptive chaotic particle swarm optimization algorithm, and the hidden transmission rate of the system is maximized.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network. Background Technology

[0002] With the rapid development of sixth-generation wireless communication and the Internet of Things (IoT), the number of smart devices accessing networks has surged, leading to an exponential increase in data traffic and increasingly strained spectrum resources, posing a severe challenge to wireless networks. To alleviate this problem, Non-Orthogonal Multiple Access (NOMA) technology has attracted widespread attention. NOMA allows multiple users to share the same time-frequency resources and separates signals through serial interference cancellation technology, thereby significantly improving spectrum efficiency and meeting the demands for high speed, low latency, and massive connectivity. However, NOMA systems still face significant security risks due to the open nature of wireless channels. Currently, there are two main strategies for improving wireless communication security: one is to use physical layer security mechanisms to resist eavesdropping, and the other is to conceal transmission activities through covert communication to prevent information from being detected.

[0003] In recent years, Reconfigurable Smart Surfaces (RIS) have been widely studied as a key technology for enhancing communication performance and security. RIS can intelligently adjust the phase and amplitude of incident signals, thereby optimizing the propagation environment, improving the communication quality of legitimate users, and suppressing malicious eavesdropping. However, most RIS schemes assume that the transmitter and receiver are located on the same side, limiting their practical application. To overcome this limitation, Omnidirectional Smart Surfaces (STAR-RIS) have emerged. STAR-RIS achieves full-space coverage by allowing signals to refract and reflect on both sides, further expanding the flexibility of beamforming. Combining STAR-RIS with NOMA can enhance system security while improving transmission performance. In addition, the Rate Split (RS) strategy has also been introduced to improve physical layer security and covert communication performance. RS technology provides more flexibility for covert transmission by dividing user information into public and private parts, and when used in conjunction with NOMA, it can significantly improve the security performance of the system.

[0004] The main goal of covert communication is to maximize the transmission rate while concealing communication behavior. To improve the transmission capability of covert information in STAR-RIS-assisted RS-NOMA networks, this invention proposes a STAR-RIS-based RS-NOMA network covert communication optimization method while ensuring the probability of security interruption. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network, in order to solve the problems mentioned in the background art.

[0006] To achieve the above-mentioned objectives, the present invention adopts the following technical solution: a method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network, comprising the following steps:

[0007] S1: Construct a covert communication system model based on STAR-RIS assisted NOMA. The model includes a sender Alice, a STAR-RIS, two covert users Bob-r and Bob-t, and two eavesdroppers Willie-r and Willie-t. Bob-r and Willie-r are located in the reflection area of ​​STAR-RIS, while Bob-t and Willie-t are located in the transmission area of ​​STAR-RIS.

[0008] S2: Introduce a rate division strategy at the sender Alice to divide the signals sent to Bob-r and Bob-t into public signals and private signals respectively;

[0009] S3: Based on the binary hypothesis test, the detection error probabilities of the eavesdroppers Willie-r and Willie-t are obtained respectively;

[0010] S4: Obtain the corresponding signal transmission rates of the covert users Bob-r and Bob-t based on their received signal expressions;

[0011] S5: Considering physical layer security, obtain the security interruption probability of hidden users on both sides of STAR-RIS respectively;

[0012] S6: Construct an optimization problem with the goal of maximizing the system's covert transmission rate, constrained by the probability of detection errors of illegal users and the probability of security interruption;

[0013] S7: Based on the adaptive chaotic particle swarm optimization algorithm, the power allocation coefficient, rate allocation coefficient and STAR-RIS energy division coefficient of the transmitter are jointly optimized to maximize the system's covert transmission rate.

[0014] Furthermore, as a preferred embodiment of the present invention, in step S2, a rate segmentation strategy is introduced at the sender Alice, and the message of user Bob-r is segmented. and M r The messages from user Bob-t were divided into and M t , will message and Merged into a common information flow co Message M r and M t They are respectively encoded into private information streams s r and s tThe superimposed information stream transmitted by Alice is obtained as follows: Where P represents Alice's transmission power, a r a t and a co They represent s respectively r s t and s co The power allocation factor.

[0015] Further, as a preferred embodiment of the present invention, step S3 includes: step S3.1, performing a binary hypothesis test on Willie-r and Willie-t, wherein the null hypothesis H0 indicates that Alice did not transmit information to Bob-r and Bob-t, and the alternative hypothesis H1 indicates that Alice transmitted information to Bob-r and Bob-t. According to the binary hypothesis, the signal received by the eavesdropper Willie-r is specifically represented as follows:

[0016] The signal received by the eavesdropper Willie-t is specifically represented as follows:

[0017]

[0018] in h AS Θ represents the channel coefficient vectors between STAR-RIS and Willie-r, between STAR-RIS and Willie-t, and between Alice and STAR-RIS, respectively. r and Θ t These represent the transmission coefficient matrix and reflection coefficient matrix of STAR-RIS, respectively. and α AS These are the path loss exponents of the links between STAR-RIS and Willie-r, and between STAR-RIS and Willie-t, respectively. AS It is the distance from Alice to STAR-RIS. It is the distance from Alice to Willie-r. It is the distance from Alice to Willie-t. and These represent the additive white Gaussian noise at Willie-r and Willie-t, respectively.

[0019] Steps S3.2, Willie-r, and Willie-t are detected using an energy detector. Based on the Neyman-Pearson criterion, the decision rules are as follows: and Where, τ r and τ trepresent the pre-set energy detection thresholds for Willie-r and Willie-t, respectively, and D1 and D0 represent the decisions that support the binary hypotheses H1 and H0, respectively;

[0020] Step S3.3: Based on the binary assumptions and decision criteria, the false alarm probability of Willie-r is expressed as:

[0021]

[0022] The probability of a missed detection is expressed as:

[0023]

[0024] in, Γ(·) is the Gamma function, and γ(·,·) is the incomplete Gamma function. N is the number of elements in STAR-RIS. Let be the expected function. It is the variance function. 1F1(·,·;·) is a first-class merging hypergeometric function;

[0025] Step S3.4: Based on the binary assumptions and decision criteria, the false alarm probability of Willie-t is expressed as...

[0026] The probability of a missed detection is expressed as:

[0027] Γ(·) is the Gamma function, and γ(·,·) is the incomplete Gamma function.

[0028] N is the number of elements in STAR-RIS. Let be the expected function. It is the variance function.

[0029] 1F1(·,·;·) is a first-class merging hypergeometric function;

[0030] Step S3.5: Define the detection error probability according to Willie-r and Willie-t. and Willie-r's detection error probability can be expressed as Willie-t's detection error probability can be expressed as

[0031] Furthermore, as a preferred embodiment of the present invention, step S4 includes:

[0032] Step S4.1, Bob-r's received signal is in, This is the channel coefficient vector between STAR-RIS and Bob-r. It is the path loss index of the link between STAR-RIS and Bob-r. It is the distance from Alice to Bob-r. This represents additive white Gaussian noise at Bob-r.

[0033] Step S4.2, Bob-t's received signal is in, This is the channel coefficient vector between STAR-RIS and Bob-t. It is the path loss index of the link between STAR-RIS and Bob-t. It is the distance from Alice to Bob-t. This represents additive white Gaussian noise at Bob-t.

[0034] Step S4.3: Based on the NOMA principle, rate segmentation strategy, and the definition of covert transmission rate, the covert transmission rates of Bob-r and Bob-t are respectively... and in, For public information transmission rate, For Bob-r's public information transmission rate,

[0035] For Bob-t's public information transmission rate, For Bob-r's private message transmission rate, For Bob-t's private information transmission rate,

[0036] Step S4.4, the covert transmission rate of the STAR-RIS-assisted RS-NOMA communication system is expressed as:

[0037] Furthermore, as a preferred embodiment of the present invention, in step S5, based on the NOMA principle, rate partitioning strategy, and the definition of security interruption probability, the security interruption probability of the STAR-RIS assisted RS-NOMA system is expressed as follows:

[0038] in The approximate formula for calculating the safe interruption probability of Bob-r is: The approximate formula for calculating the safe interruption probability of Bob-t is as follows: R th The target speed of the system.

[0039] Furthermore, as a preferred embodiment of the present invention, in step S6, the optimization problem with maximizing the system's covert transmission rate as the optimization objective and constraining the probability of detection errors by unauthorized users and the probability of security interruption as the constraints is expressed as follows:

[0040]

[0041] sta r +a t +a co =1,

[0042] 0 < a r ,a t ,a co <1,a r <a t ,

[0043]

[0044] β r +β t =1, 0 < β t <β r <1,

[0045] λ r +λ t =1, 0 < λ r ,λ t <1,

[0046] ξ k >ε k ,k∈{r,t},

[0047]

[0048] Among them, a r a t and a co These represent Bob-r's private information flow s, respectively. r Bob-t private information streams t and public information flow co The power allocation factor, β r and β t Let λ represent the reflectance and transmittance of STAR-RIS, respectively. r and λ tLet represent the rate allocation coefficients for Bob-r and Bob-t, respectively, and let P represent Alice's transmit power. h AS Θ represents the channel coefficient vectors between STAR-RIS and Bob-r, between STAR-RIS and Bob-t, and between Alice and STAR-RIS, respectively. r and Θ t These represent the transmission coefficient matrix and reflection coefficient matrix of STAR-RIS, respectively. and α AS These are the path loss exponents of the links between STAR-RIS and Bob-r, and between STAR-RIS and Bob-t, respectively. AS It is the distance from Alice to STAR-RIS. It is the distance from Alice to Bob-r. ξ is the distance from Alice to Bob-t. k This is the detection error probability of Willie-k, ε k It is the detection error probability threshold, k∈{r,t}, P SOP It is the probability of a safe interruption in a STAR-RIS-assisted RS-NOMA system. It is the threshold for the probability of a security interruption.

[0049] Furthermore, as a preferred embodiment of the present invention, step S7 includes:

[0050] Step S7.1: Set the particle population size to Q and the initial acceleration factor to... and The maximum number of iterations is L max ;

[0051] Step S7.2: Using the Logistic chaotic function, generate a chaotic sequence based on the power allocation coefficient, rate allocation coefficient, and STAR-RIS energy partitioning coefficient at the transmitting end to obtain the initial particle positions. Randomly initialize particle velocities

[0052]

[0053] Step S7.3: Set the optimal position for the individual. Calculate the global optimal position Where the fitness function is and These are the penalty factors for the detection error probability of Willie-k and the safety outage probability of the STAR-RIS-assisted RS-NOMA system, respectively, k∈{r,t};

[0054] Step S7.4: Set the iteration count to start from 1, according to... Update particle velocity, according to Update particle position, where l is the current iteration number. It is the inertia weighting factor. and Let be a random number located in (0,1). and An acceleration factor is used to control the convergence direction of the algorithm. After the update, it is necessary to determine whether the constraints are met. If not, normalization is required to update the individual's optimal position. but otherwise according to Update the global optimal position;

[0055] Step S7.5, repeat step S7.4 until the maximum number of iterations L. max ;

[0056] Step S7.6, according to Output the optimal solution, including the power allocation coefficient, rate allocation coefficient, and STAR-RIS energy division coefficient of the optimal transmitter.

[0057] The STAR-RIS-assisted RS-NOMA network covert communication optimization method described in this invention has the following technical advantages compared with existing technologies:

[0058] (1) The present invention utilizes a rate segmentation strategy to process the signal at the transmitting end in a split-through manner, thereby increasing the detection error probability of the monitor and enhancing the concealment rate of the system.

[0059] (2) This invention utilizes an adaptive chaotic particle swarm optimization algorithm, employs a chaotic sequence to improve the initial diversity of particles, and enhances the search capability of the algorithm through an adaptive inertia weight factor. The algorithm performance is superior to the traditional PSO algorithm.

[0060] (3) This invention utilizes adjustments to the transmit power allocation coefficient, STAR-RIS energy splitting coefficient, and rate allocation coefficient to effectively increase the system's stealth rate while maintaining system reliability (low detection probability and low security interruption probability). Compared to traditional solutions, it has greater potential in improving the performance of joint PLS and covert communication. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of a communication system in the STAR-RIS assisted RS-NOMA network of the present invention, where there are two non-colluding illegal users.

[0062] Figure 2 This is a flowchart of the steps of the STAR-RIS-assisted RS-NOMA network covert communication optimization method proposed in this invention;

[0063] In the attached diagram, 101-Alice; 102-STAR-RIS; 103-Bob-r; 104-Bob-t; 105-Willie-r; 106-Willie-t; 107-Obstacle; 108-Signal; 109-First Link; 110-Second Link; 111-Third Link; 112-Fourth Link. Detailed Implementation

[0064] The present invention will be further explained in detail below with reference to the accompanying drawings, so that those skilled in the art can better understand and implement the present invention. However, the following examples are only used to explain the present invention and are not intended to limit the present invention.

[0065] Reference Figure 1This invention provides a communication system for a STAR-RIS-assisted RS-NOMA network with two non-colluding illegal users. The system consists of a fixed single-antenna user Alice 101, a fixed STAR-RIS 102, two fixed covert users Bob-r 103 and Bob-t 104, and two non-colluding illegal users Willie-r 105 and Willie-t 106. STAR-RIS 102 divides the entire space into a reflection subspace and a transmission subspace. Bob-r 103 and Willie-r 105 are located in the reflection subspace, while Bob-t 104 and Willie-t 106 are located in the transmission subspace. Alice 101's objective is to secretly transmit information to the two covert users Bob-r 103 and Bob-t 104 with the assistance of STAR-RIS 102, in order to counter the two non-colluding illegal users Willie-r 105 and Willie-t 106. The superimposed signal 108 of Bob-r103 and Bob-t104 is sent to STAR-RIS102 via the first link 109 and power is allocated. Then, STAR-RIS102 transmits the signal 108 to Bob-r103 via the first link 109 and to Bob-t104 via the second link 110 through the power allocation protocol. At the same time, Willie-r105 obtains the signal 108 through the third link 111 and Willie-t106 obtains the signal 108 through the fourth link 112. According to the NOMA and rate division strategy principle, Bob-r103 is set as a strong user and needs to be processed by continuous interference cancellation. Due to the existence of obstacle 107, there is no direct link between Alice101 and Bob-r103 and Willie-r105, and there is STAR-RIS102 between Bob-t104 and Willie-t106 and Alice101, so there is no direct link between them either.

[0066] Reference Figure 2 A method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network includes the following steps:

[0067] S1: Construct a covert communication system model based on STAR-RIS assisted NOMA. The model includes a sender Alice, a STAR-RIS, two covert users Bob-r and Bob-t, and two eavesdroppers Willie-r and Willie-t. Bob-r and Willie-r are located in the reflection area of ​​STAR-RIS, while Bob-t and Willie-t are located in the transmission area of ​​STAR-RIS.

[0068] S2: Introduce a rate division strategy at the sender Alice to divide the signals sent to Bob-r and Bob-t into public signals and private signals respectively;

[0069] S3: Based on the binary hypothesis test, the detection error probabilities of the eavesdroppers Willie-r and Willie-t are obtained respectively;

[0070] S4: Obtain the corresponding signal transmission rates of the covert users Bob-r and Bob-t based on their received signal expressions;

[0071] S5: Considering physical layer security, obtain the security interruption probability of hidden users on both sides of STAR-RIS respectively;

[0072] S6: Construct an optimization problem with the goal of maximizing the system's covert transmission rate, constrained by the probability of detection errors of illegal users and the probability of security interruption;

[0073] S7: Based on the adaptive chaotic particle swarm optimization algorithm, the power allocation coefficient, rate allocation coefficient and STAR-RIS energy division coefficient of the transmitter are jointly optimized to maximize the system's covert transmission rate.

[0074] Specifically, in step S2, a rate-segmentation strategy is introduced at the sender Alice, and user Bob-r's message is divided into... and M r The messages from user Bob-t were divided into and M t , will message and Merged into a common information flow co Message M r and M t They are respectively encoded into private information streams s r and s t The superimposed information stream transmitted by Alice is obtained as follows: Where P represents Alice's transmission power, a r a t and a co They represent s respectively r s t and s co The power allocation factor.

[0075] Specifically, step S3 includes:

[0076] Step S3.1: Perform a binary hypothesis test on Willie-r and Willie-t, where the null hypothesis H0 indicates that Alice did not transmit information to Bob-r and Bob-t, and the alternative hypothesis H1 indicates that Alice transmitted information to Bob-r and Bob-t. According to the binary hypothesis, the signal received by the eavesdropper Willie-r is specifically represented as follows: The signal received by the eavesdropper Willie-t is specifically represented as follows: in h AS Θ represents the channel coefficient vectors between STAR-RIS and Willie-r, between STAR-RIS and Willie-t, and between Alice and STAR-RIS, respectively. r and Θ t These represent the transmission coefficient matrix and reflection coefficient matrix of STAR-RIS, respectively. and α AS These are the path loss exponents of the links between STAR-RIS and Willie-r, and between STAR-RIS and Willie-t, respectively. AS It is the distance from Alice to STAR-RIS. It is the distance from Alice to Willie-r. It is the distance from Alice to Willie-t. and These represent the additive white Gaussian noise at Willie-r and Willie-t, respectively.

[0077] Steps S3.2, Willie-r, and Willie-t are detected using an energy detector. Based on the Neyman-Pearson criterion, the decision rules are as follows: and Where, τ r and τ t represent the pre-set energy detection thresholds for Willie-r and Willie-t, respectively, and D1 and D0 represent the decisions that support the binary hypotheses H1 and H0, respectively;

[0078] Step S3.3: Based on the binary assumptions and decision criteria, the false alarm probability of Willie-r is expressed as: The probability of a missed detection is expressed as in, Γ(·) is the Gamma function, and γ(·,·) is the incomplete Gamma function. N is the number of elements in STAR-RIS. Let be the expected function. It is the variance function. 1F1(·,·;·) is a first-class merging hypergeometric function;

[0079] Step S3.4: Based on the binary assumptions and decision criteria, the false alarm probability of Willie-t is expressed as... The probability of a missed detection is expressed as Γ(·) is the Gamma function, and γ(·,·) is the incomplete Gamma function. N is the number of elements in STAR-RIS. Let be the expected function. It is the variance function. 1F1(·,·;·) is a first-class merging hypergeometric function;

[0080] Step S3.5: Define the detection error probability according to Willie-r and Willie-t. and Willie-r's detection error probability can be expressed as Willie-t's detection error probability can be expressed as

[0081] Specifically, step S4 includes:

[0082] Step S4.1, Bob-r's received signal is in, This is the channel coefficient vector between STAR-RIS and Bob-r. It is the path loss index of the link between STAR-RIS and Bob-r. It is the distance from Alice to Bob-r. This represents additive white Gaussian noise at Bob-r.

[0083] Step S4.2, Bob-t's received signal is in, This is the channel coefficient vector between STAR-RIS and Bob-t. It is the path loss index of the link between STAR-RIS and Bob-t. It is the distance from Alice to Bob-t. This represents additive white Gaussian noise at Bob-t.

[0084] Step S4.3: Based on the NOMA principle, rate segmentation strategy, and the definition of covert transmission rate, the covert transmission rates of Bob-r and Bob-t are respectively... and in, For public information transmission rate, For Bob-r's public information transmission rate,

[0085] For Bob-t's public information transmission rate, For Bob-r's private message transmission rate, For Bob-t's private information transmission rate,

[0086] Step S4.4, the covert transmission rate of the STAR-RIS-assisted RS-NOMA communication system is expressed as:

[0087] Specifically, in step S5, based on the NOMA principle, rate partitioning strategy, and the definition of safe interruption probability, the safe interruption probability of the STAR-RIS-assisted RS-NOMA system is expressed as follows: in The approximate formula for calculating the safe interruption probability of Bob-r is as follows: The approximate formula for calculating the safe interruption probability of Bob-t is as follows: R th The target speed of the system.

[0088] Specifically, in step S6, the optimization problem, with the goal of maximizing the system's covert transmission rate and the constraints of the probability of detection errors by unauthorized users and the probability of security interruption, is expressed as follows:

[0089] Among them, a r a t and a co These represent Bob-r's private information flow s, respectively. r Bob-t private information streams t and public information flow co The power allocation factor, β r and β t Let λ represent the reflectance and transmittance of STAR-RIS, respectively. r and λ t Let represent the rate allocation coefficients for Bob-r and Bob-t, respectively, and let P represent Alice's transmit power. h ASΘ represents the channel coefficient vectors between STAR-RIS and Bob-r, between STAR-RIS and Bob-t, and between Alice and STAR-RIS, respectively. r and Θ t These represent the transmission coefficient matrix and reflection coefficient matrix of STAR-RIS, respectively. and α AS These are the path loss exponents of the links between STAR-RIS and Bob-r, and between STAR-RIS and Bob-t, respectively. AS It is the distance from Alice to STAR-RIS. It is the distance from Alice to Bob-r. ξ is the distance from Alice to Bob-t. k This is the detection error probability of Willie-k, ε k It is the detection error probability threshold, k∈{r,t}, P SOP It is the probability of a safe interruption in a STAR-RIS-assisted RS-NOMA system. It is the threshold for the probability of a security interruption.

[0090] Specifically, step S7 includes:

[0091] Step S7.1: Set the particle population size to Q and the initial acceleration factor to... and The maximum number of iterations is L max ;

[0092] Step S7.2: Using the Logistic chaotic function, generate a chaotic sequence based on the power allocation coefficient, rate allocation coefficient, and STAR-RIS energy partitioning coefficient at the transmitting end to obtain the initial particle positions. Randomly initialize particle velocities

[0093] Step S7.3: Set the optimal position for the individual. Calculate the global optimal position Where the fitness function is and These are the penalty factors for the detection error probability of Willie-k and the safety outage probability of the STAR-RIS-assisted RS-NOMA system, respectively, k∈{r,t};

[0094] Step S7.4: Set the iteration count to start from 1, according to... Update particle velocity, according to Update particle position, where l is the current iteration number. It is the inertia weighting factor. and Let be a random number located in (0,1). and An acceleration factor is used to control the convergence direction of the algorithm. After the update, it is necessary to determine whether the constraints are met. If not, normalization is required to update the individual's optimal position. but otherwise according to Update the global optimal position;

[0095] Step S7.5, repeat step S7.4 until the maximum number of iterations L. max ;

[0096] Step S7.6, according to Output the optimal solution, including the power allocation coefficient, rate allocation coefficient, and STAR-RIS energy division coefficient of the optimal transmitter.

[0097] The specific implementation schemes described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific implementation schemes of the present invention and are not intended to limit the scope of the present invention. Any equivalent changes and modifications made by those skilled in the art without departing from the concept and principles of the present invention should fall within the scope of protection of the present invention.

Claims

1. A method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network, characterized in that, Includes the following steps: S1: Construct a covert communication system model based on STAR-RIS assisted NOMA. The model includes a sender Alice, a STAR-RIS, two covert users Bob-r and Bob-t, and two eavesdroppers Willie-r and Willie-t. Bob-r and Willie-r are located in the reflection area of ​​STAR-RIS, while Bob-t and Willie-t are located in the transmission area of ​​STAR-RIS. S2: Introduce a rate division strategy at the sender Alice to divide the signals sent to Bob-r and Bob-t into public signals and private signals respectively; S3: Based on the binary hypothesis test, the detection error probabilities of the eavesdroppers Willie-r and Willie-t are obtained respectively; S4: Obtain the corresponding signal transmission rates of the covert users Bob-r and Bob-t based on their received signal expressions; S5: Considering physical layer security, obtain the security interruption probability of hidden users on both sides of STAR-RIS respectively; S6: Construct an optimization problem with the goal of maximizing the system's covert transmission rate, constrained by the probability of detection errors of illegal users and the probability of security interruption; S7: Based on the adaptive chaotic particle swarm optimization algorithm, the power allocation coefficient, rate allocation coefficient and STAR-RIS energy division coefficient of the transmitter are jointly optimized to maximize the system's covert transmission rate.

2. The method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network according to claim 1, characterized in that, In step S2, a rate segmentation strategy is introduced at the sender Alice, and user Bob-r's message is segmented. and M r The messages from user Bob-t were divided into and M t , will message and Merged into a common information flow co Message M r and M t They are respectively encoded into private information streams s r and s t The superimposed information stream transmitted by Alice is obtained as follows: Where P represents Alice's transmission power, a r a t and a co They represent s respectively r s t and s co The power allocation factor.

3. The method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network according to claim 2, characterized in that, Step S3 includes: Step S3.1, performing a binary hypothesis test on Willie-r and Willie-t, where the null hypothesis H0 indicates that Alice did not transmit information to Bob-r and Bob-t, and the alternative hypothesis H1 indicates that Alice transmitted information to Bob-r and Bob-t. According to the binary hypothesis, the signal received by the eavesdropper Willie-r is specifically represented as follows: The signal received by the eavesdropper Willie-t is specifically represented as follows: in h AS Θ represents the channel coefficient vectors between STAR-RIS and Willie-r, between STAR-RIS and Willie-t, and between Alice and STAR-RIS, respectively. r and Θ t These represent the transmission coefficient matrix and reflection coefficient matrix of STAR-RIS, respectively. and α AS These are the path loss exponents of the links between STAR-RIS and Willie-r, and between STAR-RIS and Willie-t, respectively. AS It is the distance from Alice to STAR-RIS. It is the distance from Alice to Willie-r. It is the distance from Alice to Willie-t. and These represent the additive white Gaussian noise at Willie-r and Willie-t, respectively. Steps S3.2, Willie-r, and Willie-t are detected using an energy detector. Based on the Neyman-Pearson criterion, the decision rules are as follows: and Where, τ r and τ t represent the pre-set energy detection thresholds for Willie-r and Willie-t, respectively, and D1 and D0 represent the decisions that support the binary hypotheses H1 and H0, respectively; Step S3.3: Based on the binary assumptions and decision criteria, the false alarm probability of Willie-r is expressed as: The probability of a missed detection is expressed as: in, Γ(·) is the Gamma function, and γ(·,·) is the incomplete Gamma function. N is the number of elements in STAR-RIS. Let be the expected function. It is the variance function. 1F1(·,·;·) is a first-class merging hypergeometric function; Step S3.4: Based on the binary assumptions and decision criteria, the false alarm probability of Willie-t is expressed as... The probability of a missed detection is expressed as: Γ(·) is the Gamma function, and γ(·,·) is the incomplete Gamma function. N is the number of elements in STAR-RIS. Let be the expected function. It is the variance function. 1F1(·,·;·) is the first type of merging hypergeometry function; Step S3.5, according to the definition of detection error probability of Willie-r and Willie-t. and Willie-r's detection error probability can be expressed as Willie-t's detection error probability can be expressed as 4. The method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network according to claim 3, characterized in that, Step S4 includes: Step S4.1, Bob-r's received signal is in, This is the channel coefficient vector between STAR-RIS and Bob-r. It is the path loss index of the link between STAR-RIS and Bob-r. It is the distance from Alice to Bob-r. This represents additive white Gaussian noise at Bob-r. Step S4.2, Bob-t's received signal is in, This is the channel coefficient vector between STAR-RIS and Bob-t. It is the path loss index of the link between STAR-RIS and Bob-t. It is the distance from Alice to Bob-t. This represents additive white Gaussian noise at Bob-t. Step S4.3: Based on the NOMA principle, rate segmentation strategy, and the definition of covert transmission rate, the covert transmission rates of Bob-r and Bob-t are respectively... and in, For public information transmission rate, For Bob-r's public information transmission rate, For Bob-t's public information transmission rate, For Bob-r's private message transmission rate, For Bob-t's private information transmission rate, Step S4.4, the covert transmission rate of the STAR-RIS-assisted RS-NOMA communication system is expressed as:

5. The method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network according to claim 4, characterized in that, In step S5, based on the NOMA principle, rate partitioning strategy, and the definition of security interruption probability, the security interruption probability of the STAR-RIS-assisted RS-NOMA system is expressed as follows: in The approximate formula for calculating the safe interruption probability of Bob-r is as follows: The approximate formula for calculating the safe interruption probability of Bob-t is as follows: R th The target speed of the system.

6. The method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network according to claim 5, characterized in that, In step S6, the optimization problem, which takes maximizing the system's covert transmission rate as the optimization objective and constrains the probability of unauthorized user detection errors and the probability of security interruption as the conditions, is expressed as follows: Among them, a r a t and a co These represent Bob-r's private information flow s, respectively. r Bob-t private information streams t and public information flow co The power allocation factor, β r and β t Let λ represent the reflectance and transmittance of STAR-RIS, respectively. r and λ t Let represent the rate allocation coefficients for Bob-r and Bob-t, respectively, and let P represent Alice's transmit power. h AS Θ represents the channel coefficient vectors between STAR-RIS and Bob-r, between STAR-RIS and Bob-t, and between Alice and STAR-RIS, respectively. r and Θ t These represent the transmission coefficient matrix and reflection coefficient matrix of STAR-RIS, respectively. and α AS These are the path loss exponents of the links between STAR-RIS and Bob-r, and between STAR-RIS and Bob-t, respectively. AS It is the distance from Alice to STAR-RIS. It is the distance from Alice to Bob-r. ξ is the distance from Alice to Bob-t. k This is the detection error probability of Willie-k, ε k It is the detection error probability threshold, k∈{r,t}, P SOP It is the probability of a safe interruption in a STAR-RIS-assisted RS-NOMA system. It is the threshold for the probability of a security interruption.

7. The method for optimizing covert communication in a STAR-RIS-assisted RS-NOMA network according to claim 6, characterized in that, Step S7 includes: Step S7.1: Set the particle population size to Q and the initial acceleration factor to... and The maximum number of iterations is L max ; Step S7.2: Using the Logistic chaotic function, generate a chaotic sequence based on the power allocation coefficient, rate allocation coefficient, and STAR-RIS energy partitioning coefficient at the transmitting end to obtain the initial particle positions. Randomly initialize particle velocities Step S7.3: Set the optimal position for the individual. Calculate the global optimal position Where the fitness function is and These are the penalty factors for the detection error probability of Willie-k and the safety outage probability of the STAR-RIS-assisted RS-NOMA system, respectively, k∈{r,t}; Step S7.4: Set the iteration count to start from 1, according to... Update particle velocity, according to Update particle position, where l is the current iteration number. It is the inertia weighting factor. and Let be a random number located in (0,1). and An acceleration factor is used to control the convergence direction of the algorithm. After the update, it is necessary to determine whether the constraints are met. If not, normalization is required to update the individual's optimal position. but otherwise according to Update the global optimal position q = 1, 2, ..., Q; Step S7.5, repeat step S7.4 until the maximum number of iterations L. max ; Step S7.6, according to l = 1, 2, ..., L max Output the optimal solution, including the power allocation coefficient, rate allocation coefficient, and STAR-RIS energy division coefficient of the optimal transmitter.

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