IRS-NOMA communication resource optimal allocation method, system and device capable of resisting internal and external eavesdropping and medium

Abstract

The invention discloses an IRS-NOMA communication resource optimal allocation method, system, equipment and medium for resisting internal and external eavesdropping, and the method comprises the following steps: a base station firstly sends a signal, the signal is received by a legal user through a direct link and an indirect link, and the transmission rate of the signal of the legal user is calculated; the method comprises the following steps of: firstly, acquiring an IRS-assisted NOMA secure communication system, then calculating an eavesdropping rate for eavesdropping and receiving a signal by an external eavesdropper or an internal eavesdropper in the IRS-assisted NOMA secure communication system, then determining a resource optimization target of the IRS-assisted NOMA secure communication system by analyzing and deducing a secrecy rate formula obtained in a communication process, and finally, aiming at the resource optimization target, determining a secrecy rate of the IRS-assisted NOMA secure communication system. Performing performance optimization on the beamforming, the IRS phase shift matrix and the power distribution factor through an alternating optimization method to obtain an optimization result; the system, the equipment and the medium are used for implementing the method. The method has the advantages of being low in communication cost, low in design complexity, comprehensive in design, simple and easy to optimize, high in optimization efficiency, high in optimization adaptability and high in secrecy rate.

Description

Technical Field

[0001] The present invention relates to the fields of wireless communication security, intelligent reflecting surface and non-orthogonal multiple access technology, and in particular to an IRS-NOMA communication resource optimization allocation method, system, device and medium that are resistant to internal and external eavesdropping. Background Art

[0002] With the advancement of science and technology, the demand for high capacity and low latency in wireless communications has increased dramatically, driving continuous technological innovation. Non-Orthogonal Multiple Access (NOMA) technology has attracted much attention in academia and industry for its efficient spectrum utilization, wide connectivity, and enhanced user fairness. However, while NOMA improves spectrum efficiency and user fairness, it also faces security threats such as eavesdropping, which challenges transmission confidentiality and integrity, and poses a severe test to the security of wireless communication systems.

[0003] To address the above challenges, physical layer security (PLS) technology has emerged. This technology uses wireless channel characteristics (such as attenuation, multipath, and interference) to enhance information confidentiality and integrity. Among them, the intelligent reflecting surface (IRS) technology is particularly critical. It can dynamically adjust the signal phase, enhance the signal received by legitimate users through optimized configuration, and weaken the interception ability of eavesdroppers, opening up a new way to improve the transmission performance and security of NOMA networks.

[0004] IRS technology has led a new wave of wireless network research with its significant advantages. Combining NOMA and IRS technology can build an efficient and secure wireless communication system that not only meets the needs of high capacity and low latency, but also effectively resists eavesdropping and ensures information confidentiality and integrity. Therefore, it is very necessary to jointly optimize the resources of the IRS-assisted NOMA network system to achieve secure communication.

[0005] However, since NOMA allows users to share resources, the reflection beam of IRS also needs to be adjusted according to the changes of users. Therefore, it is necessary to design an efficient and simple resource joint optimization algorithm to ensure fairness and efficiency and reduce system complexity and scheduling difficulty. The resource joint optimization process often involves complex non-convex optimization problems. Existing optimization methods often need to use some complex algorithms to convert non-convex problems into convex problems for solution, which is relatively complex and computationally difficult. At the same time, in order to achieve the security of IRS-assisted NOMA network systems, existing methods often need to use additional system overhead, such as artificial noise, which will increase system costs and increase system complexity.

[0006] The Chinese patent application with publication number CN115002802A discloses a method for maximizing the security rate of an IRS-assisted NOMA drone network. However, since the patent application only considers external eavesdroppers and the method used consumes a lot of resources, it has the disadvantages of incomplete system consideration and low optimization efficiency.

[0007] Wei Wang et al. published Beamforming and Jamming Optimization for IRS-Aided Secure NOMA Networks (W. Wang et al., "Beamforming and Jamming Optimization for IRS-Aided Secure NOMA Networks," in IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1557-1569, March 2022, doi: 10.1109 / TWC.2021.3104856.). The paper proposes to enhance the secure communication of the IRS-assisted NOMA system with the help of artificial noise. The base station needs to combine the NOMA signal with the artificial noise and send it to the user together. The user can separate the artificial noise to obtain the NOMA signal, while the eavesdropper is interfered by the artificial noise and cannot obtain the NOMA signal. However, since this design requires more communication resources, it has the disadvantages of high complexity and high communication cost. Summary of the invention

[0008] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide an IRS-NOMA communication resource optimization allocation method, system, device and medium that are resistant to internal and external eavesdropping. The method jointly optimizes the power allocation factor, beamforming and IRS phase shift matrix of the IRS-assisted NOMA network system through simple alternating optimization, and can obtain optimized resources and maximized security rate more quickly and simply, thereby achieving system performance with both security and efficiency. It has the characteristics of low communication cost, low design complexity, comprehensive design, simple and easy optimization, high optimization efficiency, strong optimization adaptability and high confidentiality rate.

[0009] In order to achieve the above object, the technical solution adopted by the present invention is:

[0010] The IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping includes the following steps:

[0011] Step 1: The base station sends the signal to be transmitted, and the signal is received by the legitimate user through the direct link and the indirect link, and the transmission rate of the legitimate user signal is calculated. The direct link is the base station directly broadcasting to the user, and the indirect link is the base station transmitting to the IRS, which is decoded by the IRS and then broadcast to the user;

[0012] Step 2: Calculate the eavesdropping rate of the signal broadcast in step 1 by an external eavesdropper or an internal eavesdropper in the IRS-assisted NOMA secure communication system;

[0013] Step 3: By analyzing and deriving the confidentiality rate formula obtained from the communication process of steps 1 to 2, the resource optimization goal of the IRS-assisted NOMA secure communication system is clarified;

[0014] Step 4: Based on the resource optimization target analyzed and derived in step 3, the performance of beamforming, IRS phase shift matrix and power allocation factor is optimized through the alternating optimization method to obtain the optimization result.

[0015] The step 1 specifically includes:

[0016] All channel coefficients are set to exhibit small-scale fading with path loss, following the Rayleigh fading model. The coefficient of the channel from the base station to the IRS is The coefficient of the channel from the base station to user k is The coefficient of the channel from the base station to the external eavesdropper is The coefficient of the channel from the base station to the internal eavesdropper is The coefficient of the channel from IRS to user k is The coefficient of the channel from IRS to the external eavesdropper is The coefficient of the channel from IRS to the internal eavesdropper is set up is the signal to be transmitted, then among all legal users, the signal that the kth user can receive is:

[0017]

[0018] Among them, K is the number of users, k is the user, T is the matrix transpose operation, and w k is the beamforming matrix, a k is the NOMA power allocation factor, x k is the signal to be transmitted, P t is the transmission power of the base station, Φ=diag{φ1,…,φ n ,…,φ N}, is the IRS phase shift matrix, where φ n is the nth reflected element of the phase shift matrix, is the nth reflection coefficient of the phase shift matrix, nk is Gaussian white noise;

[0019] In the IRS-assisted NOMA secure communication system, users use the Successive Interference Cancellation (SIC) technology to decode signals. The demodulation order in SIC is determined according to the equivalent channel gain of the combined channel. The user with the strongest channel gain demodulates first, while the user with the weaker channel gain directly receives the information. The order of user demodulation is ||h1|| 2 ≥||h2|| 2 ≥…≥||h K || 2 , where the kth ranked user corresponds to the kth strongest channel. The kth strongest user decodes the signal by treating the signals of other weaker users as interference. Therefore, the signal-to-interference-plus-noise ratio (SINR) of the kth user when decoding the signal is expressed as:

[0020]

[0021] Among them, a k is the NOMA power allocation factor, w k is the beamforming matrix, σ k is the noise at user k;

[0022] Then the transmission rate of the kth user is expressed as:

[0023] R k =log2(1+γ k ) (1-3)

[0024] Among them, R k is the signal-to-interference-noise ratio, γ k is the beamforming matrix.

[0025] The step 2 specifically includes:

[0026] The signal received by the external eavesdropper is derived as:

[0027]

[0028] Where e is an external eavesdropper, K is the number of users, k is a user, T is a matrix transpose operation, and h is b,e is the coefficient of the channel from the base station to the external eavesdropper, h r,e is the coefficient of the channel from IRS to the external eavesdropper, G is the coefficient of the channel from the base station to IRS, Φ=diag{φ1,…,φ n ,…,φN}, is the IRS phase shift matrix, where φ n is the nth reflected element of the phase shift matrix, is the nth reflection coefficient of the phase shift matrix, w k is the beamforming matrix, P t is the transmission power of the base station, a k is the NOMA power allocation factor, x k is the signal to be transmitted, n e is the Gaussian white noise at the eavesdropper;

[0029] The SINR of the k-th user signal eavesdropped by an external eavesdropper is expressed as:

[0030]

[0031] Among them, a k is the NOMA power allocation factor, w k is the beamforming matrix, σ k is the noise at user k;

[0032] The eavesdropping rate of all eavesdroppers is expressed as:

[0033] R e→k =log2(1+γ e→k ) (2-3)

[0034] Among them, γ e→k The SINR of the k-th user signal eavesdropped by an external eavesdropper;

[0035] The signal received by the internal eavesdropper is expressed as:

[0036]

[0037] Among them, f is the internal eavesdropper, n f is the Gaussian white noise at the eavesdropper, h b,f is the coefficient of the channel from the base station to the internal eavesdropper, h r,k is the coefficient of the channel from IRS to user k;

[0038] The SINR of the internal eavesdropper eavesdropping on the kth user is expressed as:

[0039]

[0040] Among them, w f is the beamforming at the internal eavesdropper f, σ f is the noise at the eavesdropper's location;

[0041] Then the eavesdropping rate of the external eavesdropper can be expressed as:

[0042] R f→k =log2(1+γ f→k ) (2-6)

[0043] The step 3 specifically includes:

[0044] The formula for calculating the confidentiality rate of the IRS-assisted NOMA secure communication system is expressed as:

[0045]

[0046] Among them, R k is the transmission rate of the kth user, R δ→k is the eavesdropping rate of the eavesdropper eavesdropping on user k, e is the external eavesdropper, and f is the internal eavesdropper;

[0047] By optimizing the beamforming, IRS phase shift matrix and power allocation factor in sequence through alternating optimization, the resource optimization objective function of the IRS-assisted NOMA secure communication system is written as:

[0048]

[0049] Among them, a and a k is the NOMA power allocation factor, w and w k is the beamforming matrix, Φ is the IRS phase shift matrix, K is the number of users, k is the number of users, h bk is the coefficient of the channel from the base station to user k, h rk is the coefficient of the channel from IRS to user k, P t is the transmission power of the base station, is the element in the IRS phase shift matrix, where is the nth reflection coefficient of the phase shift matrix.

[0050] The step 4 specifically includes:

[0051] Step 4.1: Beamforming optimization;

[0052] When the IRS phase shift matrix is ​​fixed, the optimization problem of beamforming degenerates into the traditional multi-user system confidentiality rate maximization problem. The beamforming is optimized by the WMMSE algorithm to obtain the following formula:

[0053] h k =h b,k +GΦh r,k (4-1)

[0054]

[0055]

[0056] Among them, K is the number of legal users, k is the kth user serial number; the coefficient of the channel from the base station to IRS is The coefficient of the channel from the base station to user k is The coefficient of the channel from IRS to user k is h k is the comprehensive channel condition of user k; T is the matrix transposition operation; Φ is the IRS phase shift matrix; a k is the NOMA power allocation factor, σ k is the noise at user k; I M is the M×M identity matrix; i and x k is the precoding matrix of user k; κ i and κ k is the intermediate matrix; w i and k is the beamforming matrix;

[0057] Step 4.2: IRS phase shift matrix optimization;

[0058] By combining the optimized beamforming in step 4.1, the direct link legal channel matrix and the indirect link legal channel matrix are redefined as:

[0059] a i,k =h r,k w i (4-5)

[0060] b i,k =h b,k w i (4-6)

[0061] in, is the channel coefficient from the base station to user k; is the channel coefficient from IRS to user k; w i is the beamforming matrix; a i,k and b i,k are the indirect link channel matrix and the direct link channel matrix respectively;

[0062] The direct link eavesdropping channel matrix and the indirect link eavesdropping channel matrix are redefined as:

[0063] a i,δ =h r,δ w i (4-7)

[0064] b i,δ =h b,δ w i (4-8)

[0065] in, is the channel coefficient from the base station to the eavesdropper δ; is the channel coefficient from IRS to eavesdropper δ; a i,δ and b i,δ They are the indirect link eavesdropping channel matrix and the direct link eavesdropping channel matrix respectively;

[0066] Then the original optimization problem is transformed into convex optimization, and the optimization subproblem is written as:

[0067]

[0068] Where P(C) represents the optimization subproblem, n and N are the IRS element subscripts and the number of IRS elements, respectively, Φ is the IRS phase shift matrix, φ n is the element in the IRS phase shift matrix, f C (Φ) is the expression of the optimization subproblem, which can be specifically expressed as:

[0069]

[0070] Among them, K is the number of legal users, k is the kth user serial number, a k,k 、a i,k and b k,k 、b i,k are the indirect link channel matrix and the direct link channel matrix respectively, a k,δ 、a i,δ and b k,δ 、b i,δ are the indirect link eavesdropping channel matrix and the direct link eavesdropping channel matrix, respectively, k and η i is the product of the power allocation factor and the power, σ k With σ δ are the noise at user k and eavesdropper k respectively;

[0071] Because f C (Φ) is continuously differentiable, and the constraint set of Φ forms a complex circle. Therefore, the solution to P(C) is obtained through the Riemannian conjugate gradient (RCG). The iteration of RCG mainly includes three stages. The first stage is to calculate the Riemann gradient, which is the orthogonal projection of the Euclidean gradient onto the complex circle:

[0072]

[0073] Among them, the Euclidean gradient ▽f C for:

[0074]

[0075] Among them, A k Written as:

[0076]

[0077] Choose the tangent vector conjugate to the gradient direction as the search direction:

[0078]

[0079] Among them, d is the search direction, gradf C is the Riemann gradient, τ1 is the conjugate gradient update parameter, is the original search direction, and defines the vector transfer function for:

[0080]

[0081] Finally, the updated indentation length is determined by projecting the tangent vector onto the complex circle:

[0082]

[0083] Where τ2 is the Armijo step length, φ n is the element in the IRS phase shift matrix;

[0084] Step 4.3: Optimization of power allocation factors;

[0085] After the optimization of beamforming and IRS dependency matrix in steps 4.1 and 4.2 is completed, the power allocation factor is optimized by the traditional optimization algorithm. Here, the genetic algorithm is used:

[0086] Step 4.3.1: Select the objective function And set the constraints as Among them, K is the number of legal users, k is the kth user serial number, a k is the power allocation factor at user k;

[0087] Step 4.3.2: Using the objective function and constraints selected in step 4.3.1, call the genetic algorithm GA to solve and obtain the optimized power allocation factor.

[0088] The IRS-NOMA communication resource optimization allocation system that resists internal and external eavesdropping includes:

[0089] Security model module: The base station sends the signal to be transmitted, and the signal is received by the legitimate user through the direct link and the indirect link, and the transmission rate of the legitimate user signal is calculated, and the eavesdropping rate of the signal eavesdropped by the external eavesdropper or the internal eavesdropper in the IRS-assisted NOMA secure communication system is calculated. Among them, the direct link is the base station directly broadcasting to the user, and the indirect link is the base station transmitting to the IRS, and the IRS decodes and then broadcasts to the user;

[0090] Optimization target confirmation module: derives the confidentiality rate formula by analyzing and deducing the communication process, and then clarifies the optimization target of the IRS-assisted NOMA secure communication system;

[0091] Resource optimization module: Through alternating optimization, the beamforming, IRS phase shift matrix and power allocation factor of the base station are optimized and configured in turn according to the optimization target to obtain the optimization result.

[0092] IRS-NOMA communication resource optimization equipment that resists internal and external eavesdropping, including:

[0093] Memory: used to store a computer program for implementing the IRS-NOMA communication resource optimization allocation method for resisting internal and external eavesdropping;

[0094] Processor: used to implement the IRS-NOMA communication resource optimization allocation method that is resistant to internal and external eavesdropping when executing the computer program.

[0095] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the IRS-NOMA communication resource optimization allocation method that is resistant to internal and external eavesdropping.

[0096] Compared with the prior art, the beneficial effects of the present invention are:

[0097] 1. By analyzing the behaviors of legitimate users, external eavesdroppers and internal eavesdroppers, the present invention realizes the design of IRS-assisted NOMA secure communication system in the presence of eavesdroppers. It not only provides a comprehensive and systematic perspective to facilitate analysis and design, but also has low communication cost and design complexity.

[0098] 2. The present invention realizes the optimization of multiple resources of the IRS-assisted NOMA system by using alternating optimization, which is simple to implement and has high optimization efficiency.

[0099] 3. The present invention achieves the maximization of the total confidentiality rate by establishing the optimization target of the IRS-assisted NOMA system and performing step-by-step optimization with the help of alternating optimization, and can provide a resource optimization performance analysis solution with a high confidentiality rate.

[0100] In summary, the present invention has the effects of low communication cost, low design complexity, comprehensive design, simple and easy optimization, high optimization efficiency, strong optimization adaptability and high confidentiality rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 This is a model diagram of the IRS-assisted NOMA secure communication system based on alternating optimization.

[0102] Figure 2 This is a simulation experiment result diagram of the relationship between the total confidentiality rate and the transmission power in the internal eavesdropping scenario in this embodiment.

[0103] Figure 3 This is a simulation experiment result diagram of the relationship between the total confidentiality rate and the transmission power in the external eavesdropping scenario in this embodiment. DETAILED DESCRIPTION

[0104] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0105] like Figure 1 As shown, the IRS-NOMA communication resource optimization allocation method for resisting internal and external eavesdropping is applied to communication in a wireless network scenario. The communication adopts an IRS-assisted NOMA secure communication system. The system includes a base station, an IRS, K users, and two types of eavesdroppers: a malicious external eavesdropper or a "rebellious" internal eavesdropper. The base station is equipped with M antennas, the IRS is equipped with N reflective elements, and the user and the eavesdropper are equipped with a single antenna, including the following steps:

[0106] Step 1: The base station sends the signal to be transmitted, and the signal is received by the legitimate user through the direct link and the indirect link, and the transmission rate of the legitimate user signal is calculated. The direct link is the base station directly broadcasting to the user, and the indirect link is the base station transmitting to the IRS, which is decoded by the IRS and then broadcast to the user;

[0107] Step 2: Calculate the eavesdropping rate of the signal broadcast in step 1 by an external eavesdropper or an internal eavesdropper in the IRS-assisted NOMA secure communication system;

[0108] Step 3: By analyzing and deriving the confidentiality rate formula obtained from the communication process of steps 1 to 2, the resource optimization goal of the IRS-assisted NOMA secure communication system is clarified;

[0109] Step 4: Based on the resource optimization target analyzed and derived in step 3, the performance of beamforming, IRS phase shift matrix and power allocation factor is optimized through the alternating optimization method to obtain the optimization result.

[0110] The step 1 specifically includes:

[0111] All channel coefficients are set to exhibit small-scale fading with path loss, following the Rayleigh fading model. The coefficient of the channel from the base station to the IRS is The coefficient of the channel from the base station to user k is The coefficient of the channel from the base station to the external eavesdropper is The coefficient of the channel from the base station to the internal eavesdropper is The coefficient of the channel from IRS to user k is The coefficient of the channel from IRS to the external eavesdropper is The coefficient of the channel from IRS to the internal eavesdropper is set up is the signal to be transmitted, then among all legal users, the signal that the kth user can receive is:

[0112]

[0113] Among them, K is the number of users, k is the user, T is the matrix transpose operation, and w k is the beamforming matrix, a k is the NOMA power allocation factor, x k is the signal to be transmitted, P t is the transmission power of the base station, Φ=diag{φ1,…,φ n ,…,φ N}, is the IRS phase shift matrix, where φ n is the nth reflected element of the phase shift matrix, is the nth reflection coefficient of the phase shift matrix, n k is Gaussian white noise;

[0114] In the IRS-assisted NOMA secure communication system, users use the Successive Interference Cancellation (SIC) technology to decode signals. The demodulation order in SIC is determined according to the equivalent channel gain of the combined channel. The user with the strongest channel gain demodulates first, while the user with the weaker channel gain directly receives the information. The order of user demodulation is ||h1|| 2 ≥||h2|| 2 ≥…≥||h K || 2 , where the kth ranked user corresponds to the kth strongest channel. The kth strongest user decodes the signal by treating the signals of other weaker users as interference. Therefore, the signal-to-interference-plus-noise ratio (SINR) of the kth user when decoding the signal is expressed as:

[0115]

[0116] Among them, a k is the NOMA power allocation factor, w k is the beamforming matrix, σ k is the noise at user k;

[0117] Then the transmission rate of the kth user is expressed as:

[0118] R k =log2(1+γ k ) (1-3)

[0119] Among them, R k is the signal-to-interference-noise ratio, γ k is the beamforming matrix.

[0120] The step 2 specifically includes:

[0121] Considering the adverse effects of complex electromagnetic environment on secure communications through exposed radio signals, external and internal eavesdropping scenarios are set up to analyze the security and confidentiality performance of the IRS-assisted NOMA secure communication system.

[0122] The external eavesdropper is an unauthorized attacker who is not a member of the NOMA pairing. The broadcast characteristics of the wireless network enable it to eavesdrop on the signal broadcast by the IRS base station. Therefore, the signal received by the external eavesdropper is derived as follows:

[0123]

[0124] Where e is an external eavesdropper, K is the number of users, k is a user, T is a matrix transpose operation, and h is b,e is the coefficient of the channel from the base station to the external eavesdropper, h r,e is the coefficient of the channel from IRS to the external eavesdropper, G is the coefficient of the channel from the base station to IRS, Φ=diag{φ1,…,φ n ,…,φ N}, is the IRS phase shift matrix, where φ n is the nth reflected element of the phase shift matrix, is the nth reflection coefficient of the phase shift matrix, w k is the beamforming matrix, P t is the transmission power of the base station, a k is the NOMA power allocation factor, x k is the signal to be transmitted, n e is the Gaussian white noise at the eavesdropper;

[0125] The external eavesdropper decodes the information of the kth user by applying serial interference cancellation. Therefore, the SINR of the kth user signal eavesdropped by the external eavesdropper is expressed as:

[0126]

[0127] Among them, a k is the NOMA power allocation factor, w k is the beamforming matrix, σ k is the noise at user k;

[0128] The eavesdropping rate of the external eavesdropper is expressed as:

[0129] R e→k =log2(1+γ e→k ) (2-3)

[0130] Among them, γ e→k The SINR of the k-th user signal eavesdropped by an external eavesdropper;

[0131] The internal eavesdropper is a legitimate user that can perform NOMA pairing, but because its channel condition is relatively weak compared to other users, it can act as an internal eavesdropper to eavesdrop on other legitimate users. The signal received by the internal eavesdropper is expressed as:

[0132]

[0133] Among them, f is the internal eavesdropper, n f is the Gaussian white noise at the eavesdropper, h b,f is the coefficient of the channel from the base station to the internal eavesdropper, h r,k is the coefficient of the channel from IRS to user k;

[0134] The SINR of the internal eavesdropper eavesdropping on the kth user is expressed as:

[0135]

[0136] Among them, w f is the beamforming at the internal eavesdropper f, σ f is the noise at the eavesdropper's location;

[0137] Then the eavesdropping rate of the external eavesdropper can be expressed as:

[0138] R f→k =log2(1+γ f→k ) (2-6)

[0139] Among them, γ f→k is the SINR of the kth user signal eavesdropped by the internal eavesdropper.

[0140] The step 3 specifically includes:

[0141] The goal of the present invention is to maximize the total confidentiality rate of the IRS-assisted NOMA secure communication system by means of the joint optimization of the base station beamforming w and the power allocation factor a, and the IRS phase shift matrix Φ in the IRS-assisted NOMA secure communication model, and obtain the corresponding performance analysis. By alternately optimizing the beamforming, phase shift matrix and power allocation factor in sequence, the optimized transmission strategy and the optimal total confidentiality rate and other performances are obtained. The formula for calculating the confidentiality rate of the IRS-assisted NOMA secure communication system is expressed as:

[0142]

[0143] Among them, R k is the transmission rate of the kth user, R δ→k is the eavesdropping rate of the eavesdropper eavesdropping on user k, e is the external eavesdropper, and f is the internal eavesdropper;

[0144] By optimizing the beamforming, IRS phase shift matrix and power allocation factor in sequence through alternating optimization, the resource optimization objective function of the IRS-assisted NOMA secure communication system is written as:

[0145]

[0146] Among them, a and a k is the NOMA power allocation factor, w and w k is the beamforming matrix, Φ is the IRS phase shift matrix, K is the number of users, k is the number of users, h bk is the coefficient of the channel from the base station to user k, h rk is the coefficient of the channel from IRS to user k, P t is the transmission power of the base station, is the element in the IRS phase shift matrix, where is the nth reflection coefficient of the phase shift matrix.

[0147] By optimizing the beamforming, phase shift matrix and power allocation factor in sequence through alternating optimization, the optimized transmission strategy and the best total confidentiality rate and other performances can be obtained.

[0148] The step 4 specifically includes:

[0149] Step 4.1: Beamforming optimization;

[0150] When the IRS phase shift matrix is ​​fixed, the optimization problem of beamforming degenerates into the traditional multi-user system confidentiality rate maximization problem. The beamforming is optimized by the WMMSE algorithm to obtain the following formula:

[0151] hk =h b,k +GΦh r,k (4-1)

[0152]

[0153] Among them, K is the number of legal users, k is the kth user serial number; the coefficient of the channel from the base station to IRS is The coefficient of the channel from the base station to user k is The coefficient of the channel from IRS to user k is h k is the comprehensive channel condition of user k; T is the matrix transposition operation; Φ is the IRS phase shift matrix; a k is the NOMA power allocation factor, σ k is the noise at user k; I M is the M×M identity matrix; i and x k is the precoding matrix of user k; κ i and κ k is the intermediate matrix; w i and k is the beamforming matrix;

[0154] Step 4.2: IRS phase shift matrix optimization;

[0155] After optimizing the beamforming, the IRS phase shift matrix can be optimized. By combining the optimized beamforming in step 4.1, the direct link legal channel matrix and the indirect link legal channel matrix are redefined as:

[0156] a i,k =h r,k w i (4-5)

[0157] b i,k =h b,k w i (4-6)

[0158] in, is the channel coefficient from the base station to user k; is the channel coefficient from IRS to user k; w i is the beamforming matrix; a i,k and b i,k are the indirect link channel matrix and the direct link channel matrix respectively;

[0159] The direct link eavesdropping channel matrix and the indirect link eavesdropping channel matrix are redefined as:

[0160] a i,δ =h r,δ wi (4-7)

[0161] b i,δ =h b,δ w i (4-8)

[0162] in, is the channel coefficient from the base station to the eavesdropper δ; is the channel coefficient from IRS to eavesdropper δ; a i,δ and b i,δ They are the indirect link eavesdropping channel matrix and the direct link eavesdropping channel matrix respectively;

[0163] Then the original optimization problem is transformed into convex optimization, and the optimization subproblem is written as:

[0164]

[0165] Where P(C) represents the optimization subproblem, n and N are the IRS element subscripts and the number of IRS elements, respectively, Φ is the IRS phase shift matrix, φ n is the element in the IRS phase shift matrix, f C (Φ) is the expression of the optimization subproblem, which can be specifically expressed as:

[0166]

[0167] Where K is the number of legal users, k is the kth user serial number, η k and η i is the product of the power allocation factor and the power, σ k With σ δ are the noise at user k and eavesdropper k respectively;

[0168] Because f C (Φ) is continuously differentiable, and the constraint set of Φ forms a complex circle. Therefore, the solution to P(C) is obtained through the Riemannian conjugate gradient (RCG). The iteration of RCG mainly includes three stages. The first stage is to calculate the Riemannian gradient, which is the orthogonal projection of the Euclidean gradient onto the complex circle:

[0169]

[0170] Among them, the Euclidean gradient ▽f C for:

[0171]

[0172] Among them, A k Written as:

[0173]

[0174] Choose the tangent vector conjugate to the gradient direction as the search direction:

[0175]

[0176] Among them, d is the search direction, gradf C is the Riemann gradient, τ1 is the conjugate gradient update parameter, is the original search direction, and defines the vector transfer function for:

[0177]

[0178] Finally, the updated indentation length is determined by projecting the tangent vector onto the complex circle:

[0179]

[0180] Where τ2 is the Armijo step length, φ n is the element in the IRS phase shift matrix;

[0181] Step 4.3: Optimization of power allocation factors;

[0182] After the optimization of beamforming and IRS dependency matrix in steps 4.1 and 4.2 is completed, the power allocation factor is optimized by the traditional optimization algorithm. Here, the genetic algorithm is used:

[0183] Step 4.3.1: Select the objective function And set the constraints as Among them, K is the number of legal users, k is the kth user serial number, a k is the power allocation factor at user k;

[0184] Step 4.3.2: Using the objective function and constraints selected in step 4.3.1, call the genetic algorithm GA to solve and obtain the optimized power allocation factor.

[0185] The resource optimization system of the IRS-assisted NOMA secure communication system based on alternating optimization includes:

[0186] Security model module: The base station sends the signal to be transmitted, and the signal is received by the legitimate user through the direct link and the indirect link, and the transmission rate of the legitimate user signal is calculated, and the eavesdropping rate of the signal eavesdropped by the external eavesdropper or the internal eavesdropper in the IRS-assisted NOMA secure communication system is calculated. Among them, the direct link is the base station directly broadcasting to the user, and the indirect link is the base station transmitting to the IRS, and the IRS decodes and then broadcasts to the user, which is used to implement steps 1 to 2 of the IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping;

[0187] Optimization target confirmation module: By analyzing and deducing the communication process, the confidentiality rate formula is obtained, and then the optimization target of the IRS-assisted NOMA secure communication system is clarified, which is used to implement step 3 of the IRS-NOMA communication resource optimization allocation method that is resistant to internal and external eavesdropping;

[0188] Resource optimization module: Through alternating optimization, the beamforming, IRS phase shift matrix and power allocation factor of the base station are optimized and configured in turn according to the optimization target, and the optimization result is obtained, which is used to implement step 4 of the IRS-NOMA communication resource optimization allocation method that is resistant to internal and external eavesdropping.

[0189] IRS-NOMA communication resource optimization equipment that resists internal and external eavesdropping, including:

[0190] Memory: used to store a computer program for implementing the IRS-NOMA communication resource optimization allocation method for resisting internal and external eavesdropping;

[0191] Processor: used to implement the IRS-NOMA communication resource optimization allocation method that is resistant to internal and external eavesdropping when executing the computer program.

[0192] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the IRS-NOMA communication resource optimization allocation method that is resistant to internal and external eavesdropping.

[0193] Simulation analysis:

[0194] Figure 2 The relationship between the total confidentiality rate and the transmission power under the scenario of the presence of internal eavesdroppers is described. The specific simulation conditions are as follows: the number of antennas equipped by the base station is M = {2, 4, 6, 8, 10}, the number of reflective elements equipped by the IRS is N = 100, and the number of legal users is K = 2. As can be seen from the figure, as the transmission power increases, the total confidentiality rate continues to increase; as the number of antennas equipped by the base station increases, the total confidentiality rate also continues to increase. Therefore, the greater the transmission power of the base station and the more antennas the base station is equipped with, the stronger the security of the system.

[0195] Figure 3 The relationship between the total confidentiality rate and the transmission power when the number of reflection elements equipped with the IRS is different in the scenario where an external eavesdropper exists is described. The specific simulation conditions are as follows: the number of antennas equipped by the base station is M=5, the number of reflection elements equipped by the IRS is N={50,100,150,200}, and the number of legal users is K=2. As can be seen from the figure, as the transmission power continues to increase, the total confidentiality rate continues to increase; as the number of reflection elements equipped with the IRS increases, the total confidentiality rate also continues to increase and eventually stabilizes. Figure 2 In comparison, the total confidentiality rate in the presence of external eavesdropping is generally lower than the total confidentiality rate in the internal eavesdropping scenario.

[0196] It can be seen from the simulation results that, compared with the prior art, the present invention comprehensively considers the internal eavesdropper scenario and the external eavesdropper scenario, and achieves a higher total confidentiality rate through alternating optimization: the total confidentiality rate can reach 7.61bps / Hz in the scenario where an internal eavesdropper exists, and the total confidentiality rate can reach 2.83bps / Hz in the scenario where an external eavesdropper exists. At the same time, the present invention has the advantages of simple and easy optimization, high optimization efficiency, strong optimization adaptability, and high confidentiality rate.

Claims

1. The IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping is characterized by: The following steps are involved: Step 1: The base station sends the signal to be transmitted, and the signal is received by the legitimate user through the direct link and the indirect link, and the transmission rate of the legitimate user signal is calculated. The direct link is the base station directly broadcasting to the user, and the indirect link is the base station transmitting to the IRS, which is decoded by the IRS and then broadcast to the user; Step 2: Calculate the eavesdropping rate of the signal broadcast in step 1 by an external eavesdropper or an internal eavesdropper in the IRS-assisted NOMA secure communication system; Step 3: By analyzing and deriving the confidentiality rate formula obtained from the communication process of steps 1 to 2, the resource optimization goal of the IRS-assisted NOMA secure communication system is clarified; Step 4: Based on the resource optimization target analyzed and derived in step 3, the performance of beamforming, IRS phase shift matrix and power allocation factor is optimized through the alternating optimization method to obtain the optimization result.

2. The IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping according to claim 1 is characterized in that: The step 1 specifically includes: All channel coefficients are set to exhibit small-scale fading with path loss, following the Rayleigh fading model. The coefficient of the channel from the base station to the IRS is The coefficient of the channel from the base station to user k is The coefficient of the channel from the base station to the external eavesdropper is The coefficient of the channel from the base station to the internal eavesdropper is The coefficient of the channel from IRS to user k is The coefficient of the channel from IRS to the external eavesdropper is The coefficient of the channel from IRS to the internal eavesdropper is set up is the signal to be transmitted, then among all legal users, the signal that the kth user can receive is: Among them, K is the number of users, k is the user, T is the matrix transpose operation, and w k is the beamforming matrix, a k is the NOMA power allocation factor, x k is the signal to be transmitted, P t is the transmission power of the base station, Φ=diag{φ1,…,φ n ,…,φ N }, is the IRS phase shift matrix, where φ n is the nth reflected element of the phase shift matrix, is the nth reflection coefficient of the phase shift matrix, n k is Gaussian white noise; In the IRS-assisted NOMA secure communication system, users use the Successive Interference Cancellation (SIC) technology to decode signals. The demodulation order in SIC is determined according to the equivalent channel gain of the combined channel. The user with the strongest channel gain demodulates first, while the user with the weaker channel gain directly receives the information. The order of user demodulation is ||h1|| 2 ≥||h2|| 2 ≥…≥||h K || 2 , where the kth ranked user corresponds to the kth strongest channel. The kth strongest user decodes the signal by treating the signals of other weaker users as interference. Therefore, the signal-to-interference-plus-noise ratio (SINR) of the kth user when decoding the signal is expressed as: Among them, a k is the NOMA power allocation factor, w k is the beamforming matrix, σ k is the noise at user k; Then the transmission rate of the kth user is expressed as: R k =log2(1+γ k ) (1-3) Among them, R k is the signal-to-interference-noise ratio, γ k is the beamforming matrix.

3. The IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping according to claim 1 is characterized in that: The step 2 specifically includes: The signal received by the external eavesdropper is derived as: Where e is an external eavesdropper, K is the number of users, k is a user, T is a matrix transpose operation, and h is b,e is the coefficient of the channel from the base station to the external eavesdropper, h r,e is the coefficient of the channel from IRS to the external eavesdropper, G is the coefficient of the channel from the base station to IRS, Φ=diag{φ1,…,φ n ,…,φ N }, is the IRS phase shift matrix, where φ n is the nth reflected element of the phase shift matrix, is the nth reflection coefficient of the phase shift matrix, w k is the beamforming matrix, P t is the transmission power of the base station, a k is the NOMA power allocation factor, x k is the signal to be transmitted, n e is the Gaussian white noise at the eavesdropper; The SINR of the k-th user signal eavesdropped by an external eavesdropper is expressed as: Among them, a k is the NOMA power allocation factor, w k is the beamforming matrix, σ k is the noise at user k; Then the eavesdropping rate of the external eavesdropper is R e→k It is expressed as: R e→k =log2(1+γ e→k ) (2-3) Among them, γ e→k The SINR of the k-th user signal eavesdropped by an external eavesdropper; The signal received by the internal eavesdropper is expressed as: Among them, f is the internal eavesdropper, n f is the Gaussian white noise at the eavesdropper, h b,f is the coefficient of the channel from the base station to the internal eavesdropper, h r,k is the coefficient of the channel from IRS to user k; The SINR of the internal eavesdropper eavesdropping on the kth user is expressed as: Among them, w f is the beamforming at the internal eavesdropper f, σ f is the noise at the eavesdropper's location; Then the eavesdropping rate of the external eavesdropper can be expressed as: R f→k =log2(1+γ f→k ) (2-6) Among them, γ f→k is the SINR of the kth user signal eavesdropped by the internal eavesdropper.

4. The IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping according to claim 1 is characterized in that: The step 3 specifically includes: The formula for calculating the confidentiality rate of the IRS-assisted NOMA secure communication system is expressed as: Among them, R k is the transmission rate of the kth user, R δ→k is the eavesdropping rate of the eavesdropper eavesdropping on user k, e is the external eavesdropper, and f is the internal eavesdropper; By optimizing the beamforming, IRS phase shift matrix and power allocation factor in sequence through alternating optimization, the resource optimization objective function of the IRS-assisted NOMA secure communication system is written as: Among them, a and a k is the NOMA power allocation factor, w and w k is the beamforming matrix, Φ is the IRS phase shift matrix, K is the number of users, k is the number of users, h bk is the coefficient of the channel from the base station to user k, h rk is the coefficient of the channel from IRS to user k, P t is the transmission power of the base station, is the element in the IRS phase shift matrix, where is the nth reflection coefficient of the phase shift matrix.

5. The IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping according to claim 1 is characterized in that: The step 4 specifically includes: Step 4.1: Beamforming optimization; When the IRS phase shift matrix is ​​fixed, the optimization problem of beamforming degenerates into the traditional multi-user system confidentiality rate maximization problem. The beamforming is optimized by the WMMSE algorithm to obtain the following formula: h k =h b,k +GΦh r,k (4-1) Among them, K is the number of legal users, k is the kth user serial number; the coefficient of the channel from the base station to IRS is The coefficient of the channel from the base station to user k is The coefficient of the channel from IRS to user k is h k is the comprehensive channel condition of user k; T is the matrix transposition operation; Φ is the IRS phase shift matrix; a k is the NOMA power allocation factor, σ k is the noise at user k; I M is the M×M identity matrix; i and x k is the precoding matrix of user k; κ i and κ k is the intermediate matrix; w i and k is the beamforming matrix; Step 4.2: IRS phase shift matrix optimization; By combining the optimized beamforming in step 4.1, the direct link legal channel matrix and the indirect link legal channel matrix are redefined as: a i,k =h r,k w i (4-5) b i,k =h b,k w i (4-6) in, is the channel coefficient from the base station to user k; is the channel coefficient from IRS to user k; w i is the beamforming matrix; a i,k and b i,k are the indirect link channel matrix and the direct link channel matrix respectively; The direct link eavesdropping channel matrix and the indirect link eavesdropping channel matrix are redefined as: a i,δ =h r,δ w i (4-7) b i,δ =h b,δ w i (4-8) in, is the channel coefficient from the base station to the eavesdropper δ; is the channel coefficient from IRS to eavesdropper δ; a i,δ and b i,δ They are the indirect link eavesdropping channel matrix and the direct link eavesdropping channel matrix respectively; Then the original optimization problem is transformed into convex optimization, and the optimization subproblem is written as: Where P(C) represents the optimization subproblem, n and N are the IRS element subscripts and the number of IRS elements, respectively, Φ is the IRS phase shift matrix, φ n is the element in the IRS phase shift matrix, f C (Φ) is the expression of the optimization subproblem, which can be specifically expressed as: Among them, K is the number of legal users, k is the kth user serial number, a k,k 、a i,k and b k,k 、b i,k are the indirect link channel matrix and the direct link channel matrix respectively, a k,δ 、a i,δ and b k,δ 、b i,δ are the indirect link eavesdropping channel matrix and the direct link eavesdropping channel matrix, respectively, k and η i is the product of the power allocation factor and the power, σ k With σ δ are the noise at user k and eavesdropper k respectively; Because f C (Φ) is continuously differentiable, and the constraint set of Φ forms a complex circle. Therefore, the solution to P(C) is obtained through the Riemannian conjugate gradient (RCG). The iteration of RCG mainly includes three stages. The first stage is to calculate the Riemannian gradient, which is the orthogonal projection of the Euclidean gradient onto the complex circle: Among them, the Euclidean gradient ▽f C for: Among them, K is the number of legal users, k is the kth user serial number, A k Written as: Choose the tangent vector conjugate to the gradient direction as the search direction: Among them, d is the search direction, gradf C is the Riemann gradient, τ1 is the conjugate gradient update parameter, is the original search direction, and defines the vector transfer function for: Finally, the updated indentation length is determined by projecting the tangent vector onto the complex circle: Where τ2 is the Armijo step length, φ n is the element in the IRS phase shift matrix; Step 4.3: Optimization of power allocation factors; After the optimization of beamforming and IRS dependency matrix in steps 4.1 and 4.2 is completed, the power allocation factor is optimized by the traditional optimization algorithm. Here, the genetic algorithm is used: Step 4.3.1: Select the objective function And set the constraints as Among them, K is the number of legal users, k is the kth user serial number, a k is the power allocation factor at user k; Step 4.3.2: Using the objective function and constraints selected in step 4.3.1, call the genetic algorithm GA to solve and obtain the optimized power allocation factor.

6. A system according to any one of claims 1 to 5, wherein the method for optimizing allocation of IRS-NOMA communication resources against internal and external eavesdropping is characterized in that: include: Security model module: The base station sends the signal to be transmitted, and the signal is received by the legitimate user through the direct link and the indirect link, and the transmission rate of the legitimate user signal is calculated, and the eavesdropping rate of the signal eavesdropped by the external eavesdropper or the internal eavesdropper in the IRS-assisted NOMA secure communication system is calculated. Among them, the direct link is the base station directly broadcasting to the user, and the indirect link is the base station transmitting to the IRS, and the IRS decodes and then broadcasts to the user; Optimization target confirmation module: derives the confidentiality rate formula by analyzing and deducing the communication process, and then clarifies the optimization target of the IRS-assisted NOMA secure communication system; Resource optimization module: Through alternating optimization, the beamforming, IRS phase shift matrix and power allocation factor of the base station are optimized and configured in turn according to the optimization target to obtain the optimization result.

7. IRS-NOMA communication resource optimization allocation equipment that resists internal and external eavesdropping, characterized in that: include: Memory: used to store a computer program for implementing the IRS-NOMA communication resource optimization allocation method for resisting internal and external eavesdropping as described in any one of claims 1 to 5; Processor: used to implement the IRS-NOMA communication resource optimization allocation method for resisting internal and external eavesdropping as described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the IRS-NOMA communication resource optimization allocation method that is resistant to internal and external eavesdropping are implemented as described in any one of claims 1 to 5.

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