NOMA network communication method and system based on dual-active RIS assistance

Abstract

The invention discloses an NOMA network communication method and system based on dual-active RIS assistance. The method comprises the following steps: constructing an NOMA secure communication network model based on dual-active RIS communication; according to an NOMA secure communication network model, establishing an optimization problem of maximizing the secrecy rate of legal users under the condition of ensuring that all communicators reach the minimum communication threshold rate and NOMA system decoding sequence constraint by a strategy of dynamically amplifying the signal strength in the target direction and optimizing beam forming; the optimization problem is decomposed, sub-problems obtained through decomposition are solved alternately until the objective function converges, and a corresponding optimal NOMA secure communication network model is obtained; and performing network communication secure transmission according to the optimal NOMA secure communication network model. According to the invention, the dual-active RIS and NOMA technologies are combined, the problem of spectrum resource shortage can be effectively solved, and safer and more reliable connection is provided for legal users.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically to a NOMA network communication method and system based on dual active RIS assistance. Background Technology

[0002] With the rapid development of wireless communication technology, the advent of the 6th Generation Mobile Communication System (6G) and the era of big data, massive amounts of data will be transmitted by wireless communication systems, requiring more spectrum resources. Furthermore, the openness of wireless channels poses challenges to the secure transmission of this data, making anti-eavesdropping technology a current research hotspot.

[0003] In recent years, Reconfigurable Intelligent Surfaces (RIS) have emerged as a novel wireless communication technology. RIS consists of a two-dimensional planar array of multiple reflective elements, each of which can be dynamically configured to control the phase and amplitude of the incident electromagnetic signal. This allows for the clever manipulation of the signal propagation path, enabling communication signals to be received by the intended receiver in a specific area while remaining difficult to detect in other areas, thus enhancing communication security. However, wireless links that rely solely on a single RIS to enhance signal reflection will have limited performance when the propagation link is obstructed, failing to provide secure and reliable connections. Furthermore, next-generation communication technologies require significantly more spectrum resources for massive data transmission. Summary of the Invention

[0004] This invention provides a NOMA network communication method and system based on dual active RIS assistance to solve the technical problems of spectrum resource shortage and inability to provide secure and reliable connections for legitimate users in current wireless communication methods.

[0005] To address the aforementioned technical problems, according to one aspect of the present invention, a NOMA network communication method based on dual active RIS assistance is provided, comprising:

[0006] Construct a NOMA secure communication network model based on dual active RIS communication;

[0007] Based on the NOMA secure communication network model, this paper proposes an optimization problem to maximize the confidentiality rate of legitimate users by dynamically amplifying the signal strength in the target direction and optimizing beamforming, while ensuring that all communicators reach the minimum communication threshold rate and the decoding order constraint of the NOMA system.

[0008] The optimization problem is decomposed and the subproblems obtained by the decomposition are solved alternately until the objective function converges, thus obtaining the corresponding optimal NOMA secure communication network model.

[0009] Secure network communication transmission is performed based on the optimal NOMA secure communication network model.

[0010] To address the aforementioned technical problems, according to another aspect of the present invention, a NOMA network communication system based on dual active RIS assistance is provided, comprising:

[0011] The model building module is used to build a NOMA secure communication network model based on dual active RIS communication.

[0012] The optimization problem construction module is used to establish an optimization problem of maximizing the confidentiality rate of legitimate users based on the NOMA secure communication network model, using a strategy of dynamically amplifying the signal strength in the target direction and optimizing beamforming, while ensuring that all communicators reach the minimum communication threshold rate and the decoding order constraint of the NOMA system.

[0013] The algorithm solution module is used to decompose the optimization problem and solve the subproblems obtained by decomposition alternately until the objective function converges, thus obtaining the corresponding optimal NOMA secure communication network model.

[0014] The communication module is used for secure network communication transmission based on the optimal NOMA secure communication network model.

[0015] The beneficial technical effects of this invention are as follows: Compared with the prior art, this invention constructs a NOMA secure communication network model based on dual active RIS communication. It employs a strategy of dynamically amplifying the signal strength in the target direction and optimizing beamforming to establish an optimization problem that maximizes the confidentiality rate of legitimate users under the conditions of ensuring all communicators reach the minimum communication threshold rate and the NOMA system decoding order constraint. To facilitate the solution, the optimization problem is further decomposed, and the resulting sub-problems are solved alternately until the objective function converges, yielding the corresponding optimal NOMA secure communication network model. Communication is then conducted based on this optimal NOMA secure communication network model to ensure secure data transmission. It is evident that this invention, combining dual active RIS and NOMA technology, can effectively solve the problem of spectrum resource shortage. Furthermore, by dynamically adjusting the phase of the dual active RIS, the signal strength in the target direction can be dynamically amplified, and the phase of the received signal by the user at the eavesdropping node can fluctuate randomly, disrupting its signal coherence. Beamforming can also be optimized, maximizing the confidentiality rate of legitimate users by optimizing the phase shift of the base station beam and the RIS, thus providing a more secure and reliable connection for legitimate users. Attached Figure Description

[0016] Figure 1This is a flowchart illustrating a NOMA network communication method based on dual active RIS assistance according to the present invention.

[0017] Figure 2 This is a schematic diagram of a sub-process of a NOMA network communication method based on dual active RIS assistance according to the present invention.

[0018] Figure 3 yes Figure 2 The diagram illustrates the application of a NOMA secure communication network model based on dual active RIS communication constructed using the method shown.

[0019] Figure 4 yes Figure 3 The diagram shows the location of each component in the application scenario.

[0020] Figure 5 yes Figure 1 The diagram illustrates the relationship between the total number of reflection units and the model's security rate in a simulation example of the method shown.

[0021] Figure 6 yes Figure 1 The diagram illustrates the relationship between the number of different reflection units and the convergence rate of the model's objective function in a simulation example of the method shown.

[0022] Figure 7 yes Figure 1 The diagram illustrates the relationship between the total maximum amplification power budget and the model security rate in a simulation example of the method shown.

[0023] Figure 8 yes Figure 1 The simulation example of the method shown illustrates the relationship between the maximum amplitude of the active element and the model's security rate.

[0024] Figure 9 This is a schematic diagram of the structure of a NOMA network communication system based on dual active RIS assistance according to the present invention. Detailed Implementation

[0025] To enable those skilled in the art to more clearly understand the purpose, technical solution, and advantages of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0026] Reference Figure 1 , Figure 1 This is a flowchart illustrating the NOMA network communication method based on dual active RIS assistance according to the present invention. In the embodiment shown in the figure, the NOMA network communication method based on dual active RIS assistance includes:

[0027] S101. Construct a NOMA secure communication network model based on dual active RIS communication.

[0028] In this invention, dual active RIS and NOMA technologies are combined, with dual active RIS assisting NOMA communication to provide more spectrum resources and secure and reliable connections for legitimate users.

[0029] like Figure 2 As shown, this step specifically includes steps S1011-S1015:

[0030] S1011. The NOMA secure communication network model based on dual active RIS communication is defined as including a base station, active RIS1, active RIS2, a single-antenna legitimate user, and a single-antenna eavesdropping node; wherein, the base station is equipped with M antennas, active RIS1 is equipped with N1 reflective elements, and active RIS2 is equipped with N2 reflective elements.

[0031] In this step, a virtual visual link is constructed through the cooperative reflection of two active RIS equipped with reflective elements to propagate signals between the base station and the user. Preferably, in this embodiment, M > 1, and N1 ≥ 1 and N2 ≥ 1.

[0032] S1012, Obtain the channel gain from the base station to the active RIS1, from the active RIS1 to the legitimate user, from the active RIS1 to the eavesdropping node, from the active RIS1 to the active RIS2, from the active RIS2 to the legitimate user, and from the active RIS2 to the eavesdropping node.

[0033] S1013. Based on the channel gain, determine the decoding order of legitimate users and eavesdropping nodes, as well as the signal-to-noise ratio during decoding.

[0034] S1014. Based on the decoding order and the signal interference-to-noise ratio, determine the signals received by the legitimate user and the eavesdropping node, as well as the achievable rate of the legitimate user and the internal eavesdropping rate of the model, and obtain the model security rate.

[0035] S1015. Construct the NOMA secure communication network model based on dual active RIS communication according to the signals received at the legitimate user and the eavesdropping node and the model confidentiality rate.

[0036] In this embodiment, the active RIS1 and active RIS2 can fix the amplification power of the unit and use a separate power supply to connect to each of the reflection units to amplify the signal. Assuming that the Channel State Information (CSI) of all links is known, the base station sends the superimposed signal of all users, and the receiver can separate the required information from the superimposed signal according to the SIC decoding rules.

[0037] The expression for the constructed NOMA secure communication network model based on dual active RIS communication is shown below:

[0038] t = f1s1 + f2s2(a)

[0039]

[0040]

[0041] R sec =[R b -R q ] + (i)

[0042] Where t is the superimposed signal transmitted by the base station, s1 and s2 represent the valid information transmitted by the base station to the eavesdropping node and the legitimate user, respectively, f1 and f2 represent the beamforming vectors transmitted by the base station to the eavesdropping node and the legitimate user, respectively, and U e and U b These represent the signals received by the eavesdropping node and the legitimate user, respectively. and Let Φ1 and Φ2 represent the equivalent baseband channel matrices from the base station to active RIS1, from active RIS1 to active RIS2, from active RIS2 to the eavesdropping node, and from active RIS2 to the legitimate user, respectively. Let Φ1 and Φ2 represent the phase shift matrices of active RIS1 and active RIS2, respectively, i.e., the reflection coefficients of the reflection matrix of the active RIS1. This indicates the thermal noise introduced by the active RIS. This represents additive white Gaussian noise at the eavesdropping node and the legitimate user. This indicates the signal-to-interference-plus-noise ratio (SNR) of the decoded information s1 from the eavesdropping node. and R represents the signal-to-interference-plus-noise ratio (SNR) and signal-to-noise ratio (SNR) when a legitimate user decodes information s1 and s2, respectively. b and R q R represents the achievable rate for legitimate users (i.e., the transmission rate of the signal to the legitimate user) and the internal eavesdropping rate of the model, respectively. sec This represents the model's security rate.

[0043] S102. Based on the NOMA secure communication network model, and using a strategy of dynamically amplifying the signal strength in the target direction and optimizing beamforming, establish an optimization problem to maximize the confidentiality rate of legitimate users while ensuring that all communicators reach the minimum communication threshold rate and the NOMA system decoding order constraint.

[0044] This step specifically includes: based on the NOMA secure communication network model, establishing an optimization problem for maximizing the confidentiality rate of legitimate users according to the following formula:

[0045]

[0046] stR e≥R th,1 R b ≥R th,2 (1a)

[0047]

[0048] in, Indicates the maximum transmission power of the base station. and R represents the maximum amplification power budget of the active components in active RIS1 and active RIS2, respectively; e This represents the signal transmission rate at the eavesdropping node. Equation (1a) represents the transmission constraint, R th The minimum communication rate limit is given by equation (1b), which represents the base station transmit power constraint. Equations (1c) and (1d) represent the maximum amplification power budget constraints of the amplification factor in active RIS1 and active RIS2, respectively. max This indicates that the active element amplifies the amplitude, and formula (1f) represents the model decoding order constraint, which first decodes the information s1.

[0049] S103. Decompose the optimization problem and solve the subproblems obtained by decomposition alternately until the objective function converges, and obtain the corresponding optimal NOMA secure communication network model.

[0050] In this embodiment, an alternating optimization method is used to decompose a complex optimization problem into two subproblems for solution. Specifically, this step includes: decomposing the optimization problem to obtain a first subproblem and a second subproblem; alternately using the first-order Taylor expansion method based on the SDR and SCA algorithms to perform convex transformation on the objective functions and constraints of the first and second subproblems; and using the relaxed rank-1 constraints, constructing the corresponding convex optimization problems of the first and second subproblems using an iterative algorithm based on the penalty function and SCA, and iteratively optimizing to obtain local optima.

[0051] In some embodiments, if the first subproblem is to optimize the base station beamforming vectors f1 and f2 given the phase shift matrices Φ1 and Φ2 of active RIS1 and active RIS2, the first step can be to let And satisfy F1≥0, F2≥0, and rank(F1)=rank(F2)=1; then perform a trace transformation on the first subproblem. During the transformation, to simplify the calculation, define... but and The transformed problem remains a non-convex optimization problem that is difficult to solve, so we continue to apply the SCA algorithm:

[0052]

[0053] in tr(·) represents the solution obtained in the iteration, and tr(·) represents the trace operation on the matrix. To facilitate the solution, an auxiliary variable q1 is introduced to replace the transmission rate R at the eavesdropping node. e Then replace the non-convex terms. Considering that the constraint "rank(F1)=rank(F2)=1" is still non-convex, relax the rank 1 constraint. The rank 1 constraint is equivalent to "tr(F)-λ max (F) = 0”, where λ max (F) is the largest eigenvalue of matrix F; then, a penalty term "tr(F)-λ" is introduced into the objective function. max (F)”, the first subproblem can be transformed into a penalty problem: Where ρ k This is the penalty coefficient, which needs to be continuously updated iteratively. The penalty term contains "λ". max (F k The ')' is convex, so the SCA algorithm is applied for transformation at feasible points. The first-order Taylor expansion at point can be expressed as:

[0054]

[0055] in, express The corresponding unit norm eigenvectors are ultimately transformed into an SCA convex optimization iterative problem based on the penalty function, which can then be solved using convex optimization tools (such as CVX).

[0056] Specifically, the first subproblem's objective function and constraints are transformed into a convex form using the first-order Taylor expansion method based on SDR and SCA algorithms. Then, with the relaxed rank-1 constraints, an iterative algorithm based on the penalty function and SCA is used to construct the corresponding convex optimization problem. Iterative optimization yields a local optimum, including:

[0057] According to the following formula:

[0058]

[0059] q1≥R th,1 log2(1+r2tr(G2F2))≥R th,2 (2a)

[0060]

[0061] tr(G1F2)≥tr(G2F2)(2f)

[0062] A convex optimization problem corresponding to the first subproblem is constructed and solved using a convex optimization toolkit. For the solved F1 and F2, singular value decomposition is used to obtain the optimized base station beamforming vectors f1 and f2; where... And satisfy F1≥0, F2≥0, rank(F1)=rank(F2)=1; Formula (2) represents the SCA iterative objective function based on the penalty function for optimizing f1 and f2, ρ k It is the penalty coefficient. To be at the feasible point First-order Taylor expansion at the point; And q1 represents the signal transmission rate R at the eavesdropping node. e .

[0063] In some embodiments, if the second sub-problem includes optimizing the phase shift matrices Φ1 and Φ2 of active RIS1 and active RIS2 given base station beamforming vectors f1 and f2, when optimizing the reflection phase shift matrix Φ1, it can first be set as follows: To simplify calculations during the conversion, the following definition is used: as well as definition E 11 =U 12 +D1, E 21 =U 22 +D2, and satisfying V1≥0 and rank(V1)=1, by applying the SCA algorithm, the objective function confidentiality rate and the non-convex constraint first-order Taylor expansion are obtained as follows:

[0064]

[0065] Then, a penalty term "tr(V1)-λ" is introduced into the objective function. max (V1)”, λ max (V1) is the largest eigenvalue of V1, λ max (V1) is convex, as obtained by applying the SCA algorithm: in, express The corresponding unit norm eigenvectors are ultimately transformed into an SCA convex optimization iterative problem based on the penalty function. Similarly, the reflection phase shift matrix Φ2 can be optimized in the same way, and finally solved with the help of convex optimization tools (such as CVX).

[0066] Specifically, the objective function and constraints of the second subproblem are convexly transformed using the first-order Taylor expansion method based on the SDR and SCA algorithms. Then, using the relaxed rank-1 constraints, an iterative algorithm based on the penalty function and SCA is used to construct the corresponding convex optimization problem for the second subproblem. Iterative optimization yields a local optimum, including:

[0067] According to the following formula:

[0068]

[0069] stq2≥R th,1 (3a)

[0070]

[0071] tr(U 11 V1)≥tr(U 22 V1)(3g)

[0072] Construct the convex optimization problem corresponding to Φ1 in the second subproblem, where in This represents the thermal noise introduced by the active RIS1, and satisfies rank(V1)=1, ρ a It is the penalty coefficient, and q2 represents the transmission rate R at the eavesdropping node. e ;

[0073] And according to the following formula:

[0074]

[0075] stq3≥R th,1 (4a)

[0076]

[0077] tr(Y 11 V2)≥tr(Y 22 V2)(4f)

[0078] Introducing auxiliary variables Construct the convex optimization problem corresponding to Φ2 in the second subproblem, where, And it satisfies rank(V2)=1, ρ b It is the penalty coefficient, and q3 represents the transmission rate R at the eavesdropping node. e ,and Y 11 =(diag(g1)HΦ1Gf1)(diag(g1)HΦ1Gf1) H Y 22 =(diag(g2)HΦ1Gf2)(diag(g2)HΦ1Gf2) H ,in This indicates the thermal noise introduced by the active RIS2.

[0079] Then, the convex optimization toolkit is used to solve the problem. The solved V1 and V2 can be decomposed by singular value decomposition to obtain the optimized active RIS1 reflection phase shift matrix Φ1 and active RIS2 reflection phase shift matrix Φ2.

[0080] S103. Perform secure network communication transmission based on the optimal NOMA secure communication network model.

[0081] Understandably, in order to verify the beneficial effects of the present invention, simulation experiments were conducted on a computer using the MATLAB tool, and the model diagram is shown below. Figure 3 As shown in the simulation, the channels are assumed to be independent of each other, and the channel state information remains constant in the relevant time blocks. The positions of the base station, active RIS1, active RIS2, a single-antenna legitimate user, and a single-antenna eavesdropping node (i.e., the eavesdropping user) are as follows. Figure 4 As shown, different security rates can be obtained by adjusting the number of reflection units, the maximum amplification power budget, and the maximum amplitude of the active components in the simulation.

[0082] like Figure 5 It is known that when the number of reflection units is the same, the model security rate of the NOMA network communication method based on dual active RIS assistance proposed in this invention is greater than that of the comparison scheme with a single active RIS or traditional passive RIS assistance. Furthermore, as the total number of reflection units increases, the difference in security rate also increases. When it gradually increases to a certain value, the security performance of the single active RIS scheme is close to that of the present invention. This is because the total power budget is limited, and the power allocated to the two active RIS is limited, resulting in a smaller improvement in security performance. However, the security performance of the present invention is always better than that of the single active RIS comparison scheme.

[0083] Figure 6 The convergence speed of the objective function of the present invention under different numbers of reflection units is also shown to verify the convergence of the algorithm. As can be seen from the figure, although the total number of reflection units is different, the alternating optimization algorithm is non-decreasing as the number of iterations increases, and can achieve convergence. It has good convergence. Moreover, as the number of iterations increases, the model security rate continuously increases and reaches the upper limit, and the more total reflection units there are, the higher the upper limit.

[0084] like Figure 7As shown, under the same power budget, i.e., the same maximum amplification power budget, the model security rate of the NOMA network communication method based on dual active RIS assistance proposed in this invention is greater than that of the comparison scheme with a single active RIS or traditional passive RIS assistance. Furthermore, as the maximum amplification power budget increases, the security rate of all schemes improves, especially the security performance of the method proposed in this invention. The dual active RIS assistance scheme directly improves the signal-to-noise ratio of legitimate users. As can be seen from the figure, when the power budget is 40dBm, the security rate of the NOMA network communication method based on dual active RIS assistance proposed in this invention is about 50% higher than that of the dual passive RIS comparison scheme, verifying the significant advantages of the proposed scheme in communication security.

[0085] like Figure 8 As shown, under the same maximum amplitude conditions, the model security rate of the NOMA network communication method based on dual active RIS assistance proposed in this invention is greater than that of the comparison scheme with a single active RIS or traditional passive RIS assistance. The security performance of passive RIS is independent of the amplitude of the incident signal and can only adjust the signal phase. As the maximum amplitude increases, the dual active RIS assistance scheme of this invention has better security performance than the single active RIS assistance scheme. That is, the larger the maximum amplitude, the stronger the energy of the reflected signal of dual active RIS, the signal-to-noise ratio of legitimate users is significantly improved, and it can also interfere with the management to suppress the eavesdropping performance of eavesdropping users at the eavesdropping node. The larger the amplitude, the more significant the randomization effect, the more the bit error rate of eavesdropping users increases sharply, and the higher the model security rate.

[0086] As can be seen from the above, the present invention, by combining dual active RIS and NOMA, can effectively solve the problem of spectrum resource shortage and provide a secure and reliable connection for legitimate users.

[0087] Reference Figure 9 , Figure 9 This is a schematic block diagram of a NOMA network communication system based on dual active RIS assistance according to the present invention. In the embodiment shown in the figure, the NOMA network communication system based on dual active RIS assistance includes a model building module 110, an optimization problem building module 120, an algorithm solving module 130, and a communication module 140.

[0088] The model building module 110 is used to build a NOMA secure communication network model based on dual active RIS communication.

[0089] The optimization problem construction module 120 is used to establish an optimization problem of maximizing the confidentiality rate of legitimate users based on the NOMA secure communication network model, using a strategy of dynamically amplifying the signal strength in the target direction and optimizing beamforming, while ensuring that all communicators reach the minimum communication threshold rate and the NOMA system decoding order constraint.

[0090] The algorithm solving module 130 is used to decompose the optimization problem and solve the sub-problems obtained by decomposition alternately until the objective function converges, thereby obtaining the corresponding optimal NOMA secure communication network model.

[0091] The communication module 140 is used for secure network communication transmission based on the optimal NOMA secure communication network model.

[0092] In some embodiments, the model building module 110 is specifically used for:

[0093] The NOMA secure communication network model based on dual active RIS communication is defined as including a base station, active RIS1, active RIS2, a single-antenna legitimate user, and a single-antenna eavesdropping node; wherein, the base station is equipped with M antennas, active RIS1 is equipped with N1 reflective elements, and active RIS2 is equipped with N2 reflective elements.

[0094] Obtain the channel gain from the base station to the active RIS1, from the active RIS1 to the legitimate user, from the active RIS1 to the eavesdropping node, from the active RIS1 to the active RIS2, from the active RIS2 to the legitimate user, and from the active RIS2 to the eavesdropping node;

[0095] Based on the channel gain, determine the decoding order of legitimate users and eavesdropping nodes, as well as the signal-to-noise ratio during decoding;

[0096] Based on the decoding order and the signal-to-noise ratio, determine the signals received by the legitimate user and the eavesdropping node, as well as the achievable rate of the legitimate user and the internal eavesdropping rate of the model, and obtain the model security rate;

[0097] The NOMA secure communication network model based on dual active RIS communication is constructed based on the signals received at the legitimate user and the eavesdropping node and the model's security rate; wherein, the expression of the NOMA secure communication network model based on dual active RIS communication is as follows:

[0098] t = f1s1 + f2s2(a)

[0099]

[0100] R sec =[R b -R q ] + (i)

[0101] Where t is the superimposed signal transmitted by the base station, s1 and s2 represent the valid information transmitted by the base station to the eavesdropping node and the legitimate user, respectively, f1 and f2 represent the beamforming vectors transmitted by the base station to the eavesdropping node and the legitimate user, respectively, and U e and Ub These represent the signals received by the eavesdropping node and the legitimate user, respectively. and Let Φ1 and Φ2 represent the equivalent baseband channel matrices from the base station to active RIS1, from active RIS1 to active RIS2, from active RIS2 to the eavesdropping node, and from active RIS2 to the legitimate user, respectively. Let Φ1 and Φ2 represent the phase shift matrices of active RIS1 and active RIS2, respectively. This indicates the thermal noise introduced by the active RIS. This represents additive white Gaussian noise at the eavesdropping node and the legitimate user. This indicates the signal-to-interference-plus-noise ratio (SNR) of the decoded information s1 from the eavesdropping node. and R represents the signal-to-interference-plus-noise ratio (SNR) and signal-to-noise ratio (SNR) when a legitimate user decodes information s1 and s2, respectively. b and R q R represents the achievable rate for legitimate users and the internal eavesdropping rate of the model, respectively. sec This represents the model's security rate.

[0102] In some embodiments, the optimization problem construction module 120 is specifically used for:

[0103] Based on the NOMA secure communication network model, an optimization problem for maximizing the confidentiality rate of legitimate users is established according to the following formula:

[0104]

[0105] stR e ≥R th,1 R b ≥R th,2 (1a)

[0106]

[0107] in, Indicates the maximum transmission power of the base station. and R represents the maximum amplification power budget for the amplification factor of active RIS1 and active RIS2, respectively; e Indicates the signal transmission rate at the eavesdropping node and R th The minimum communication rate limit is given by equation (1b), which represents the base station transmit power constraint. Equations (1c) and (1d) represent the maximum amplification power budget constraints of the amplification factor in active RIS1 and active RIS2, respectively. max This indicates that the active element amplifies the amplitude, and formula (1f) represents the model decoding order constraint, which first decodes the information s1.

[0108] In some embodiments, the algorithm solving module 130 is specifically used for:

[0109] The optimization problem is decomposed into a first subproblem and a second subproblem.

[0110] The objective functions and constraints of the first and second subproblems are transformed into convex values ​​by alternating between the first-order Taylor expansion method based on SDR and SCA algorithms. With the relaxed rank-1 constraint, the corresponding convex optimization problems of the first and second subproblems are constructed by an iterative algorithm based on the penalty function and SCA. The local optimum is obtained by iterative optimization.

[0111] Understandably, the specific limitations regarding the NOMA network communication system based on dual active RIS assistance can be found in the limitations of the NOMA network communication method based on dual active RIS assistance described above, and will not be repeated here. Each module in the aforementioned NOMA network communication system based on dual active RIS assistance can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the corresponding operations of each module.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Those skilled in the art can make various equivalent changes and improvements based on the above embodiments, and all equivalent variations or modifications made within the scope of the claims should fall within the protection scope of the present invention.

Claims

1. A method for network communication based on dual-active RIS assistance, characterized in that, include: Construct a NOMA secure communication network model based on dual active RIS communication; Based on the NOMA secure communication network model, this paper proposes an optimization problem to maximize the confidentiality rate of legitimate users by dynamically amplifying the signal strength in the target direction and optimizing beamforming, while ensuring that all communicators reach the minimum communication threshold rate and the decoding order constraint of the NOMA system. The optimization problem is decomposed and the subproblems obtained by the decomposition are solved alternately until the objective function converges, thus obtaining the corresponding optimal NOMA secure communication network model. Secure network communication transmission is performed based on the optimal NOMA secure communication network model.

2. The dual-active RIS-assisted based NOMA network communication method of claim 1, wherein, The construction of the NOMA secure communication network model based on dual active RIS communication specifically includes: The NOMA secure communication network model based on dual active RIS communication is defined as including a base station, active RIS1, active RIS2, a single-antenna legitimate user, and a single-antenna eavesdropping node; wherein, the base station is equipped with M antennas, active RIS1 is equipped with N1 reflective elements, and active RIS2 is equipped with N2 reflective elements. Obtain the channel gain from the base station to the active RIS1, from the active RIS1 to the legitimate user, from the active RIS1 to the eavesdropping node, from the active RIS1 to the active RIS2, from the active RIS2 to the legitimate user, and from the active RIS2 to the eavesdropping node; Based on the channel gain, determine the decoding order of legitimate users and eavesdropping nodes, as well as the signal-to-noise ratio during decoding; Based on the decoding order and the signal-to-noise ratio, determine the signals received by the legitimate user and the eavesdropping node, as well as the achievable rate of the legitimate user and the internal eavesdropping rate of the model, and obtain the model security rate; The NOMA secure communication network model based on dual active RIS communication is constructed based on the signals received at the legitimate user and the eavesdropping node and the model's confidentiality rate. 3.The dual-active RIS-assisted based NOMA network communication method of claim 2, wherein, The expression for the NOMA secure communication network model based on dual active RIS communication is as follows: t = f1s1 + f2s2(a) R sec = [R b -R q ] + (i) where t is the superposed signal transmitted by the base station, s1 and s2 represent the effective information transmitted by the base station to the eavesdropping node and the legitimate user respectively, f1 and f2 represent the beamforming vectors of the base station to the eavesdropping node and the legitimate user respectively, U e and U b represent the signals received at the eavesdropping node and the legitimate user respectively, and represent the equivalent baseband channel matrices from the base station to the active RIS1, from the active RIS1 to the active RIS2, from the active RIS2 to the eavesdropping node and from the active RIS2 to the legitimate user respectively, Φ1 and Φ2 represent the phase shift matrices of the active RIS1 and the active RIS2 respectively, represents the thermal noise introduced by the active RIS, represents the additive white Gaussian noise at the eavesdropping node and the legitimate user, represents the signal-to-jamming noise ratio of the eavesdropping node decoding information s1, and represent the signal-to-jamming noise ratio and the signal-to-noise ratio when the legitimate user decodes information s1 and information s2 respectively, R b and R q represent the achievable rate of the legitimate user and the internal eavesdropping rate of the model respectively, R sec represents the model secrecy rate.

4. The dual-active RIS-assisted based NOMA network communication method of claim 3, wherein, The aforementioned optimization problem, based on the NOMA secure communication network model, uses a strategy of dynamically amplifying the signal strength in the target direction and optimizing beamforming to maximize the confidentiality rate of legitimate users while ensuring that all communicators reach the minimum communication threshold rate and the NOMA system decoding order constraint. Specifically, it includes: Based on the NOMA secure communication network model, an optimization problem for maximizing the confidentiality rate of legitimate users is established according to the following formula: s.t.R e ≥R th,1 , R b ≥R th,2 (1a) wherein denotes the maximum transmit power of the base station, and denotes the maximum amplification power budget of the amplification factors of the active RIS1 and the active RIS2, respectively; R e denotes the signal transmission rate at the eavesdropping node and R th denotes the minimum limit of the communication rate, formula (1b) denotes the base station transmit power constraint, formulas (1c) and (1d) denote the maximum amplification power budget constraints of the amplification factors in the active RIS1 and the active RIS2, respectively, β max denotes the amplification amplitude of the active element, and formula (1f) denotes the model decoding order constraint, decoding the information s1 first.

5. The NOMA network communication method based on dual active RIS assistance as described in claim 4, characterized in that, The process of decomposing the optimization problem and alternately solving the resulting subproblems until the objective function converges includes: The optimization problem is decomposed into a first subproblem and a second subproblem. The objective functions and constraints of the first and second subproblems are transformed into convex values ​​by alternating between the first-order Taylor expansion method based on SDR and SCA algorithms. With the relaxed rank-1 constraint, the corresponding convex optimization problems of the first and second subproblems are constructed by an iterative algorithm based on the penalty function and SCA. The local optimum is obtained by iterative optimization.

6. The NOMA network communication method based on dual active RIS assistance as described in claim 5, characterized in that, The first subproblem involves optimizing the base station beamforming vectors f1 and f2 given the phase shift matrices Φ1 and Φ2 of active RIS1 and active RIS2. Specifically, the objective function and constraints of the first subproblem are transformed into a convex form using a first-order Taylor expansion method based on SDR and SCA algorithms. Then, with the relaxed rank-1 constraints, an iterative algorithm based on a penalty function and SCA is used to construct the corresponding convex optimization problem for the first subproblem. Iterative optimization yields a local optimum solution, specifically including: According to the following formula: q1≥ R th,1 , log2(l + r2tr(G2F2)) ≥ R th,2 (2a) tr(G1F2)≥tr(G2F2)(2f) A convex optimization problem corresponding to the first subproblem is constructed and solved using a convex optimization toolkit to obtain the optimized base station beamforming vectors f1 and f2; where... And satisfy F1≥0, F2≥0, rank(F1)=rank(F2)=1; Formula (2) represents the SCA iterative objective function based on the penalty function for optimizing f1 and f2, ρ k It is the penalty coefficient. To be at the feasible point First-order Taylor expansion at the point; And q1 represents the signal transmission rate R at the eavesdropping node. e .

7. The NOMA network communication method based on dual active RIS assistance as described in claim 5, characterized in that, The second subproblem involves optimizing the phase shift matrices Φ1 and Φ2 of active RIS1 and active RIS2, given base station beamforming vectors f1 and f2. Specifically, the objective function and constraints of the second subproblem are transformed into a convex form using a first-order Taylor expansion method based on SDR and SCA algorithms. Then, with the relaxed rank-1 constraints, an iterative algorithm based on a penalty function and SCA is used to construct the corresponding convex optimization problem for the second subproblem. Iterative optimization yields a local optimum, specifically including: According to the following formula: s.t. q2≥ R th,1 (3a) tr(U 11 V1)≥tr(U 22 V1) (3g) Construct the convex optimization problem corresponding to Φ1 in the second subproblem, where And satisfy rank(V1)=1, ρ a It is the penalty coefficient, and q2 represents the transmission rate R at the eavesdropping node. e ; And according to the following formula: s.t.q3≥R th,1 (4a) tr(Y 11 V2)≥tr(Y 22 V2) (4f) Construct the convex optimization problem corresponding to Φ2 in the second subproblem, where And it satisfies rank(V2)=1, ρ b It is the penalty coefficient, and q3 represents the transmission rate R at the eavesdropping node. e ; The optimized active RIS1 reflection phase shift matrix Φ1 and active RIS2 reflection phase shift matrix Φ2 were obtained by using the convex optimization toolkit.

8. A NOMA network communication system based on dual active RIS assistance, characterized in that, The NOMA network communication system based on dual active RIS assistance includes: The model building module is used to build a NOMA secure communication network model based on dual active RIS communication. The optimization problem construction module is used to establish an optimization problem of maximizing the confidentiality rate of legitimate users based on the NOMA secure communication network model, using a strategy of dynamically amplifying the signal strength in the target direction and optimizing beamforming, while ensuring that all communicators reach the minimum communication threshold rate and the decoding order constraint of the NOMA system. The algorithm solution module is used to decompose the optimization problem and solve the subproblems obtained by decomposition alternately until the objective function converges, thus obtaining the corresponding optimal NOMA secure communication network model. The communication module is used for secure network communication transmission based on the optimal NOMA secure communication network model.

9. The NOMA network communication system based on dual active RIS assistance as described in claim 8, characterized in that, The model building module is specifically used for: The NOMA secure communication network model based on dual active RIS communication is defined as including a base station, active RIS1, active RIS2, a single-antenna legitimate user, and a single-antenna eavesdropping node; wherein, the base station is equipped with M antennas, active RIS1 is equipped with N1 reflective elements, and active RIS2 is equipped with N2 reflective elements. Obtain the channel gain from the base station to the active RIS1, from the active RIS1 to the legitimate user, from the active RIS1 to the eavesdropping node, from the active RIS1 to the active RIS2, from the active RIS2 to the legitimate user, and from the active RIS2 to the eavesdropping node; Based on the channel gain, determine the decoding order of legitimate users and eavesdropping nodes, as well as the signal-to-noise ratio during decoding; Based on the decoding order and the signal-to-noise ratio, determine the signals received by the legitimate user and the eavesdropping node, as well as the achievable rate of the legitimate user and the internal eavesdropping rate of the model, and obtain the model security rate; The NOMA secure communication network model based on dual active RIS communication is constructed based on the signals received at the legitimate user and the eavesdropping node and the model's security rate; wherein, the expression of the NOMA secure communication network model based on dual active RIS communication is as follows: t = f1s1 + f2s2(a) R sec = [R b -R q ] + (i) Where t is the superimposed signal transmitted by the base station, s1 and s2 represent the valid information transmitted by the base station to the eavesdropping node and the legitimate user, respectively, f1 and f2 represent the beamforming vectors transmitted by the base station to the eavesdropping node and the legitimate user, respectively, and U e and U b These represent the signals received by the eavesdropping node and the legitimate user, respectively. and Let Φ1 and Φ2 represent the equivalent baseband channel matrices from the base station to active RIS1, from active RIS1 to active RIS2, from active RIS2 to the eavesdropping node, and from active RIS2 to the legitimate user, respectively. Let Φ1 and Φ2 represent the phase shift matrices of active RIS1 and active RIS2, respectively. This indicates the thermal noise introduced by the active RIS. This represents additive white Gaussian noise at the eavesdropping node and the legitimate user. This indicates the signal-to-interference-plus-noise ratio (SNR) of the decoded information s1 from the eavesdropping node. and R represents the signal-to-interference-plus-noise ratio (SNR) and signal-to-noise ratio (SNR) when a legitimate user decodes information s1 and s2, respectively. b and R q R represents the achievable rate for legitimate users and the internal eavesdropping rate of the model, respectively. sec This represents the model's security rate.

10. The NOMA network communication system based on dual active RIS assistance as described in claim 9, characterized in that, The optimization problem construction module is specifically used for: Based on the NOMA secure communication network model, an optimization problem for maximizing the confidentiality rate of legitimate users is established according to the following formula: s.t.R e ≥R th,1 , R b ≥R th,2 (1a) in, Indicates the maximum transmission power of the base station. and R represents the maximum amplification power budget for the amplification factor of active RIS1 and active RIS2, respectively; e Indicates the signal transmission rate at the eavesdropping node and R th The minimum communication rate limit is given by equation (1b), which represents the base station transmit power constraint. Equations (1c) and (1d) represent the maximum amplification power budget constraints of the amplification factor in active RIS1 and active RIS2, respectively. max This indicates that the active element amplifies the amplitude, and formula (1f) represents the model decoding order constraint, which first decodes the information s1.

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