IRS-NOMA communication resource optimization allocation method, system, device and medium for resisting internal and external eavesdropping

Abstract

This invention discloses a method, system, device, and medium for optimizing the allocation of IRS-NOMA communication resources to resist internal and external eavesdropping. The method includes: a base station first transmits a signal, which is received by a legitimate user through direct and indirect links, and the transmission rate of the legitimate user's signal is calculated. Then, the eavesdropping rate of external or internal eavesdroppers in the IRS-assisted NOMA secure communication system is calculated. Then, by analyzing and deriving the confidentiality rate formula obtained in the communication process, the resource optimization target of the IRS-assisted NOMA secure communication system is clarified. Finally, based on the resource optimization target, the beamforming, IRS phase shift matrix, and power allocation factor are optimized using an alternating optimization method to obtain the optimization result. The system, device, and medium are used to implement this method. This invention has the advantages of low communication cost, low design complexity, comprehensive design, simple and easy optimization, high optimization efficiency, strong optimization adaptability, and high confidentiality rate.

Description

Technical Field

[0001] This invention relates to the fields of wireless communication security, intelligent reflectors, and non-orthogonal multiple access technologies, specifically to an IRS-NOMA communication resource optimization allocation method, system, device, and medium that resists internal and external eavesdropping. Background Technology

[0002] With technological advancements, the demand for high-capacity, low-latency wireless communication has surged, driving continuous technological innovation. Non-Orthogonal Multiple Access (NOMA) technology has garnered significant attention in both academia and industry due to its efficient spectrum utilization, widespread connectivity, and enhanced user fairness. However, while improving spectrum efficiency and user fairness, NOMA also faces security threats such as eavesdropping, challenging the confidentiality and integrity of transmissions and posing a severe test to the security of wireless communication systems.

[0003] To address these challenges, Physical Layer Security (PLS) has emerged, leveraging wireless channel characteristics (such as attenuation, multipath, and interference) to enhance information confidentiality and integrity. Among these technologies, Intelligent Reflecting Surface (IRS) is particularly crucial. It dynamically adjusts signal phase, enhancing the signal received by legitimate users through optimized configuration, while simultaneously weakening the interception capabilities of eavesdroppers. This opens up new avenues for improving the transmission performance and security of NOMA networks.

[0004] IRS technology, with its significant advantages, is leading a new wave of research in wireless networks. Combining NOMA and IRS technologies can construct efficient and secure wireless communication systems that meet high-capacity, low-latency requirements while effectively resisting eavesdropping and ensuring information confidentiality and integrity. Therefore, joint resource optimization of IRS-assisted NOMA network systems to achieve secure communication is essential.

[0005] However, since NOMA allows users to share resources, the IRS reflection beam also needs to be adjusted according to changes in users. Therefore, it is necessary to design efficient and simple joint resource optimization algorithms to ensure fairness and efficiency, and reduce system complexity and scheduling difficulty. The joint resource optimization process often involves complex non-convex optimization problems. Existing optimization methods often require complex algorithms to transform the non-convex problem into a convex problem before solving it, which is complex and computationally difficult. Furthermore, to achieve security in IRS-assisted NOMA network systems, existing methods often require additional system overhead, such as artificial noise, which increases system cost and complexity.

[0006] Chinese patent application CN115002802A discloses an IRS-assisted method for maximizing the security rate of NOMA drone networks. However, this patent application only considers external eavesdroppers and the method used is resource-intensive, resulting in incomplete system considerations 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.). This paper proposes to enhance the secure communication of IRS-aided NOMA systems with artificial noise. The base station needs to combine the NOMA signal with artificial noise and send them together to the user. The user can separate the artificial noise to obtain the NOMA signal, while the eavesdropper cannot obtain the NOMA signal due to the interference of the artificial noise. However, this design requires a lot of communication resources and has the disadvantages of high complexity and high communication cost. Summary of the Invention

[0008] To overcome the shortcomings of the prior art, the present invention aims to provide an IRS-NOMA communication resource optimization allocation method, system, device, and medium resistant to internal and external eavesdropping. This method, through simple alternating optimization, jointly optimizes the power allocation factor, beamforming, and IRS phase shift matrix of the IRS-assisted NOMA network system. It can quickly and easily obtain optimized resources and maximized security rate, achieving system performance that combines security and efficiency. It features low communication cost, low design complexity, comprehensive design, simple and easy optimization, high optimization efficiency, strong optimization adaptability, and high security rate.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

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

[0011] Step 1: The base station sends the signal to be transmitted. The signal is received by the legitimate user through the direct link and the indirect link. The transmission rate of the legitimate user's signal is calculated. The direct link is when the base station broadcasts directly to the user. The indirect link is when the base station transmits to the IRS. The IRS decodes the signal and then broadcasts it to the user.

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

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

[0014] Step 4: Based on the resource optimization objectives analyzed and derived in Step 3, the beamforming, IRS phase shift matrix, and power allocation factor are optimized using an alternating optimization method to obtain the optimization results.

[0015] Step 1 specifically includes:

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

[0017]

[0018] Where K is the number of users, k is the number of users, T is the matrix transpose operation, and w k For the beamforming matrix, a k x is the NOMA power allocation factor. k For the signal to be transmitted, P t Let Φ be the base station's transmit power, and Φ = diag{φ1,…,φ n ,…,φ N}, Let φ be the phase shift matrix of the IRS, where φ n It is the nth reflection element of the phase shift matrix. Let n be the nth reflection coefficient of the phase shift matrix.k It is Gaussian white noise;

[0019] In an IRS-assisted NOMA secure communication system, users employ Successive Interference Cancellation (SIC) technology for signal decoding. The demodulation order in SIC is determined based on the equivalent channel gain of the combined channels. Users with the strongest channel gain are demodulated first, while users with weaker channel gains receive information directly. The user demodulation order is ||h1|| 2 ≥||h2|| 2 ≥…≥||h K || 2 The k-th ranked user corresponds to the k-th strongest channel. The k-th 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 k-th user when decoding the signal is expressed as:

[0020]

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

[0022] The transmission rate of the k-th user is then expressed as:

[0023] R k =log2(1+γ) k (1-3)

[0024] Among them, R k For signal-to-interference-plus-noise ratio, γ k Beamforming matrix.

[0025] Step 2 specifically includes:

[0026] The signal received by the external eavesdropper can be deduced as follows:

[0027]

[0028] Where e represents the external eavesdropper, K represents the number of users, k represents the number of users, T represents the matrix transpose operation, and h represents the number of users. b,e h is the coefficient of the channel from the base station to the external eavesdropper. r,e Let Φ be the coefficient of the channel from the IRS to the external eavesdropper, and G be the coefficient of the channel from the base station to the IRS. Φ = diag{φ1,…,φ n ,…,φN}, Let φ be the phase shift matrix of the IRS, where φ n It is the nth reflection element of the phase shift matrix. w is the nth reflection coefficient of the phase shift matrix. k For the beamforming matrix, P t For the base station's transmission power, a k x is the NOMA power allocation factor. k For the signal to be transmitted, n e Gaussian white noise at the eavesdropper's location;

[0029] The SINR of an external eavesdropper intercepting the k-th user's signal is expressed as:

[0030]

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

[0032] The eavesdropping rate of the eavesdropper is expressed as:

[0033] R e→k =log2(1+γ) e→k (2-3)

[0034] Where, γ e→k SINR for external eavesdroppers to eavesdrop on the k-th user's signal;

[0035] The signal received by the inside eavesdropper is represented as:

[0036]

[0037] Where f is the insider eavesdropper, and n f For the eavesdropper, the Gaussian white noise is h. b,f h is the coefficient of the channel from the base station to the inside eavesdropper. r,k The coefficients of the channel from the IRS to user k;

[0038] The SINR of an insider eavesdropping on the k-th user is represented as:

[0039]

[0040] Among them, w f Beamforming at point f for the internal eavesdropper, σ f Noise at the location of the eavesdropper;

[0041] The eavesdropping rate of an external eavesdropper can then be expressed as:

[0042] R f→k =log2(1+γ) f→k (2-6)

[0043] Step 3 specifically includes:

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

[0045]

[0046] Among them, R k R is the transmission rate for the k-th user. δ→k Let e ​​be the eavesdropping rate at which the eavesdropper eavesdrops on user k, e be the external eavesdropper, and f be the internal eavesdropper.

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

[0048]

[0049] Among them, a and a k For NOMA power allocation factors, w and w k Let Φ be the beamforming matrix, Φ be the IRS phase shift matrix, K be the number of users, k be the number of users, and h be the number of users. bk h is the coefficient of the channel from the base station to user k. rk P is the coefficient of the channel from IRS to user k. t This refers to the base station's transmission power. The elements are in the IRS phase shift matrix, where It is the nth reflection coefficient of the phase shift matrix.

[0050] Step 4 specifically includes:

[0051] Step 4.1: Beamforming optimization;

[0052] When the IRS phase shift matrix is ​​fixed, the beamforming optimization problem degenerates into the traditional problem of maximizing the secure rate of a multi-user system. The following formula is obtained by optimizing beamforming using the WMMSE algorithm:

[0053] h k =h b,k +GΦh r,k (4-1)

[0054]

[0055]

[0056] Where K is the number of legitimate users, and k is the sequence number of the kth user; the coefficient of the channel from the base station to the IRS is... The coefficients of the channel from the base station to user k are: The coefficients of the channel from IRS to user k are: h k Here is the integrated channel condition for user k; T is the matrix transpose operation; Φ is the IRS phase shift matrix; a k σ is the NOMA power allocation factor. k The noise at user k; I M It is an M×M identity matrix; χ i and χ k κ is the precoding matrix for user k; i and κ k For the intermediate matrix; w i and w k Beamforming matrix;

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

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

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

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

[0061] in, Here are the channel coefficients from the base station to user k; The channel coefficients from the IRS to user k; w i Beamforming matrix; a i,k and b i,k These 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 follows:

[0063] a i,δ =h r,δ w i (4-7)

[0064] b i,δ =h b,δ w i (4-8)

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

[0066] The original optimization problem is then transformed into a convex optimization problem, and this subproblem can be written as:

[0067]

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

[0069]

[0070] Where K is the number of valid users, k is the sequence number of the kth user, and a k,k a i,k and b k,k b i,k These are the indirect link channel matrix and the direct link channel matrix, respectively. k,δ a i,δ and b k,δ b i,δ These are the indirect link eavesdropping channel matrix and the direct link eavesdropping channel matrix, respectively, η k and η i σ is the product of the power allocation factor and the power. k With σ δ The noise at user k and eavesdropper k are respectively;

[0071] Because f C Since (Φ) is continuously differentiable and the constraint set of Φ forms a complex circle, the solution for P(C) is obtained through the Riemannian conjugate gradient (RCG). The iteration of the Riemannian conjugate gradient (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:

[0072]

[0073] Wherein, 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] Where d is the search direction, gradf C This is the Riemann gradient, and τ1 is the conjugate gradient update parameter. This follows 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 Amiho step size, φ n These are elements in the IRS phase shift matrix;

[0084] Step 4.3: Power allocation factor optimization;

[0085] With the beamforming and IRS dependency matrix optimization completed in steps 4.1 to 4.2, the power allocation factor is optimized using traditional optimization algorithms. Here, a genetic algorithm is employed:

[0086] Step 4.3.1: Select the objective function And set the restrictions as follows Where K is the number of valid users, k is the sequence number of the kth user, and a k 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 the problem and obtain the optimized power allocation factor.

[0088] An IRS-NOMA communication resource optimization allocation system resistant to internal and external eavesdropping includes:

[0089] Security Model Module: The base station sends the signal to be transmitted. The signal is received by the legitimate user through direct and indirect links. The module calculates the transmission rate of the legitimate user's signal and the eavesdropping rate of the signal received by external or internal eavesdroppers in the IRS-assisted NOMA secure communication system. The direct link is the base station broadcasting directly to the user, and the indirect link is the base station transmitting to the IRS, which decodes the signal and then broadcasts it to the user.

[0090] Optimization Target Confirmation Module: By analyzing and deriving the confidentiality rate formula through the communication process, the optimization target of the IRS-assisted NOMA secure communication system is clarified;

[0091] Resource optimization module: Through alternating optimization, the base station's beamforming, IRS phase shift matrix, and power allocation factor are configured sequentially to achieve the optimization target, and the optimization results are obtained.

[0092] IRS-NOMA communication resource optimization equipment resistant to internal and external eavesdropping includes:

[0093] Memory: Used to store the 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 for resisting internal and external eavesdropping when executing the computer program.

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

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

[0097] 1. This invention, by analyzing the behavior of legitimate users, external eavesdroppers, and internal eavesdroppers, realizes the design of an IRS-assisted NOMA secure communication system with eavesdroppers present. It not only provides a comprehensive and systematic perspective for analysis and design, but also has low communication costs and low design complexity.

[0098] 2. This invention achieves various resource optimizations for IRS-assisted NOMA systems by using alternating optimization, and has the advantages of being simple to implement and having high optimization efficiency.

[0099] 3. This invention maximizes the overall security rate by establishing an optimization objective for an IRS-assisted NOMA system and performing step-by-step optimization through alternating optimization, and can provide a resource optimization performance analysis scheme with high security rate.

[0100] In summary, this invention has the advantages of low communication cost, low design complexity, comprehensive design, simple and easy optimization, high optimization efficiency, strong adaptability, and high security rate. Attached Figure Description

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

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

[0103] Figure 3 This is a simulation result diagram showing the relationship between total security rate and transmission power in the external eavesdropping scenario of this embodiment. Detailed Implementation

[0104] The present invention will now be described in detail with reference to the accompanying drawings.

[0105] like Figure 1 As shown, an IRS-NOMA communication resource optimization allocation method to resist internal and external eavesdropping is applied to communication in a wireless network scenario. The communication adopts an IRS-assisted NOMA secure communication system, which 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 users and eavesdroppers are equipped with a single antenna. The method includes the following steps:

[0106] Step 1: The base station sends the signal to be transmitted. The signal is received by the legitimate user through the direct link and the indirect link. The transmission rate of the legitimate user's signal is calculated. The direct link is when the base station broadcasts directly to the user. The indirect link is when the base station transmits to the IRS. The IRS decodes the signal and then broadcasts it to the user.

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

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

[0109] Step 4: Based on the resource optimization objectives analyzed and derived in Step 3, the beamforming, IRS phase shift matrix, and power allocation factor are optimized using an alternating optimization method to obtain the optimization results.

[0110] Step 1 specifically includes:

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

[0112]

[0113] Where K is the number of users, k is the number of users, T is the matrix transpose operation, and w k For the beamforming matrix, a k x is the NOMA power allocation factor. k For the signal to be transmitted, P t Let Φ be the base station's transmit power, and Φ = diag{φ1,…,φ n ,…,φ N}, Let φ be the phase shift matrix of the IRS, where φ n It is the nth reflection element of the phase shift matrix. Let n be the nth reflection coefficient of the phase shift matrix. k It is Gaussian white noise;

[0114] In an IRS-assisted NOMA secure communication system, users employ Successive Interference Cancellation (SIC) technology for signal decoding. The demodulation order in SIC is determined based on the equivalent channel gain of the combined channels. Users with the strongest channel gain are demodulated first, while users with weaker channel gains receive information directly. The user demodulation order is ||h1|| 2 ≥||h2|| 2 ≥…≥||h K || 2 The k-th ranked user corresponds to the k-th strongest channel. The k-th 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 k-th user when decoding the signal is expressed as:

[0115]

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

[0117] The transmission rate of the k-th user is then expressed as:

[0118] R k =log2(1+γ) k (1-3)

[0119] Among them, R k For signal-to-interference-plus-noise ratio, γ k Beamforming matrix.

[0120] Step 2 specifically includes:

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

[0122] The external eavesdropper is unauthorized and not part of the NOMA pairing attacker. The broadcast nature of the wireless network allows them to eavesdrop on signals broadcast by the IRS base station. Therefore, the signal received by the external eavesdropper can be deduced as follows:

[0123]

[0124] Where e represents the external eavesdropper, K represents the number of users, k represents the number of users, T represents the matrix transpose operation, and h represents the number of users. b,e h is the coefficient of the channel from the base station to the external eavesdropper. r,e Let Φ be the coefficient of the channel from the IRS to the external eavesdropper, and G be the coefficient of the channel from the base station to the IRS. Φ = diag{φ1,…,φ n ,…,φ N}, Let φ be the phase shift matrix of the IRS, where φ n It is the nth reflection element of the phase shift matrix. w is the nth reflection coefficient of the phase shift matrix. k For the beamforming matrix, P t For the base station's transmission power, a k x is the NOMA power allocation factor. k For the signal to be transmitted, n e Gaussian white noise at the eavesdropper's location;

[0125] An external eavesdropper decodes the information of the k-th user by applying serial interference cancellation. Therefore, the SINR of the external eavesdropper eavesdropping on the k-th user's signal is expressed as:

[0126]

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

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

[0129] R e→k =log2(1+γ) e→k (2-3)

[0130] Where, γ e→k SINR for external eavesdroppers to eavesdrop on the k-th user's signal;

[0131] An insider eavesdropper is a legitimate user capable of NOMA pairing, but due to relatively weaker channel conditions compared to other users, it can act as an insider eavesdropper to listen to other legitimate users. The signal received by the insider eavesdropper is represented as follows:

[0132]

[0133] Where f is the insider eavesdropper, and n f For the eavesdropper, the Gaussian white noise is h. b,f h is the coefficient of the channel from the base station to the inside eavesdropper. r,k The coefficients of the channel from the IRS to user k;

[0134] The SINR of an insider eavesdropping on the k-th user is represented as:

[0135]

[0136] Among them, w f Beamforming at point f for the internal eavesdropper, σ f Noise at the location of the eavesdropper;

[0137] The eavesdropping rate of an external eavesdropper can then be expressed as:

[0138] R f→k =log2(1+γ) f→k (2-6)

[0139] Where, γ f→k SINR for an insider eavesdropping on the k-th user's signal.

[0140] Step 3 specifically includes:

[0141] The objective of this invention is to maximize the overall security rate of an IRS-assisted NOMA secure communication system by jointly optimizing the base station beamforming w, the power allocation factor a, and the IRS phase shift matrix Φ, and to obtain corresponding performance analysis. By alternately optimizing beamforming, the phase shift matrix, and the power allocation factor in sequence, an optimized transmission strategy and the best overall security rate are obtained. The formula for calculating the security rate of an IRS-assisted NOMA secure communication system is expressed as:

[0142]

[0143] Among them, R k R is the transmission rate for the k-th user. δ→k Let e ​​be the eavesdropping rate at which the eavesdropper eavesdrops on user k, e be the external eavesdropper, and f be the internal eavesdropper.

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

[0145]

[0146] Among them, a and a k For NOMA power allocation factors, w and w k Let Φ be the beamforming matrix, Φ be the IRS phase shift matrix, K be the number of users, k be the number of users, and h be the number of users. bk h is the coefficient of the channel from the base station to user k. rk P is the coefficient of the channel from IRS to user k. t This refers to the base station's transmission power. The elements are in the IRS phase shift matrix, where It is the nth reflection coefficient of the phase shift matrix.

[0147] By alternately optimizing beamforming, phase shift matrix, and power allocation factor in sequence, an optimized transmission strategy and the best overall security rate can be obtained.

[0148] Step 4 specifically includes:

[0149] Step 4.1: Beamforming optimization;

[0150] When the IRS phase shift matrix is ​​fixed, the beamforming optimization problem degenerates into the traditional problem of maximizing the secure rate of a multi-user system. The following formula is obtained by optimizing beamforming using the WMMSE algorithm:

[0151] hk =h b,k +GΦh r,k (4-1)

[0152]

[0153] Where K is the number of legitimate users, and k is the sequence number of the kth user; the coefficient of the channel from the base station to the IRS is... The coefficients of the channel from the base station to user k are: The coefficients of the channel from IRS to user k are: h k Here is the integrated channel condition for user k; T is the matrix transpose operation; Φ is the IRS phase shift matrix; a k σ is the NOMA power allocation factor. k The noise at user k; I M It is an M×M identity matrix; χ i and χ k κ is the precoding matrix for user k; i and κ k For the intermediate matrix; w i and w k 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 from step 4.1, the direct link legal channel matrix and the indirect link legal channel matrix are redefined as follows:

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

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

[0158] in, Here are the channel coefficients from the base station to user k; The channel coefficients from the IRS to user k; w i Beamforming matrix; a i,k and b i,k These 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 follows:

[0160] a i,δ =h r,δ wi (4-7)

[0161] b i,δ =h b,δ w i (4-8)

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

[0163] The original optimization problem is then transformed into a convex optimization problem, and this subproblem can be written as:

[0164]

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

[0166]

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

[0168] Because f C Since (Φ) is continuously differentiable and the constraint set of Φ forms a complex circle, the solution for P(C) is obtained through the Riemannian conjugate gradient (RCG). The iteration of the Riemannian conjugate gradient (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] Wherein, 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] Where d is the search direction, gradf C This is the Riemann gradient, and τ1 is the conjugate gradient update parameter. This follows 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 Amiho step size, φ n These are elements in the IRS phase shift matrix;

[0181] Step 4.3: Power allocation factor optimization;

[0182] With the beamforming and IRS dependency matrix optimization completed in steps 4.1 to 4.2, the power allocation factor is optimized using traditional optimization algorithms. Here, a genetic algorithm is employed:

[0183] Step 4.3.1: Select the objective function And set the restrictions as follows Where K is the number of valid users, k is the sequence number of the kth user, and a k 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 the problem and obtain the optimized power allocation factor.

[0185] A resource optimization system for an IRS-assisted NOMA secure communication system based on alternating optimization includes:

[0186] Security Model Module: The base station sends the signal to be transmitted. The signal is received by legitimate users through direct and indirect links. The transmission rate of the legitimate user signal is calculated. The eavesdropping rate of external or internal eavesdroppers in the IRS-assisted NOMA secure communication system is calculated. The direct link is the base station broadcasting directly to the user. The indirect link is the base station transmitting to the IRS, which decodes and then broadcasts to the user. This is used to implement steps 1 to 2 of the IRS-NOMA communication resource optimization allocation method to resist internal and external eavesdropping.

[0187] Optimization Target Confirmation Module: By analyzing and deriving the communication process, the confidentiality rate formula is obtained, thereby clarifying the optimization target of the IRS-assisted NOMA secure communication system, which is used to realize the step 3 of the IRS-NOMA communication resource optimization allocation method to resist internal and external eavesdropping;

[0188] Resource optimization module: Through alternating optimization, the base station's beamforming, IRS phase shift matrix, and power allocation factor are configured sequentially to achieve the optimization target, and the optimization results are obtained. This is 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 resistant to internal and external eavesdropping includes:

[0190] Memory: Used to store the 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 for resisting internal and external eavesdropping when executing the computer program.

[0192] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the described IRS-NOMA communication resource optimization allocation method for resisting internal and external eavesdropping.

[0193] Simulation analysis:

[0194] Figure 2 This paper describes the relationship between the overall security rate and transmission power under different numbers of antennas equipped at base stations in the presence of an internal eavesdropper. The specific simulation conditions are as follows: the number of antennas equipped at the base station is M = {2, 4, 6, 8, 10}, the number of reflectors in the IRS is N = 100, and the number of legitimate users is K = 2. As can be seen from the figure, the overall security rate increases with increasing transmission power; similarly, the overall security rate also increases with increasing number of antennas equipped at the base station. Therefore, the higher the transmission power of the base station and the more antennas it has, the stronger the system security.

[0195] Figure 3 This paper describes the relationship between the overall security rate and the transmit power under different numbers of reflectors equipped in the IRS in the presence of external eavesdroppers. The specific simulation conditions are as follows: the number of antennas equipped in the base station is M=5, the number of reflectors equipped in the IRS is N={50,100,150,200}, and the number of legitimate users is K=2. As shown in the figure, the overall security rate increases with increasing transmit power; the overall security rate also increases with increasing number of reflectors equipped in the IRS, eventually stabilizing. Figure 2 In comparison, the overall security rate under external eavesdropping conditions is lower than that under internal eavesdropping conditions.

[0196] Simulation results show that, compared with existing technologies, this invention comprehensively considers both internal and external eavesdropping scenarios, and achieves a higher overall security rate through alternating optimization: the overall security rate can reach 7.61 bps / Hz in the scenario with internal eavesdroppers, and 2.83 bps / Hz in the scenario with external eavesdroppers. Furthermore, this invention has the advantages of simple and easy optimization, high optimization efficiency, strong adaptability, and high security rate.

Claims

1. A method for optimizing the allocation of communication resources in an intelligent reflective surface-non-orthogonal multiple access (IRS-NOMA) system resistant to internal and external eavesdropping, characterized in that: The following steps are involved: Step 1: The base station sends the signal to be transmitted. The signal is received by the legitimate user through the direct link and the indirect link. The transmission rate of the legitimate user's signal is calculated. The direct link is when the base station broadcasts directly to the user. The indirect link is when the base station transmits to the IRS. The IRS decodes the signal and then broadcasts it to the user. Step 1 specifically includes: setting all channel coefficients to exhibit small-scale fading with path loss, following the Rayleigh fading model, with the coefficients of the channel from the base station to the IRS being... The coefficients of the channel from the base station to user k are: 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 coefficients of the channel from IRS to user k are: The coefficient of the IRS to the external eavesdropper is The coefficient of the channel from the IRS to the insider is Step 2: Calculate the eavesdropping rate of an external or internal eavesdropper in the IRS-assisted NOMA secure communication system for eavesdropping on the signal broadcast in Step 1. Step 3: By analyzing and deriving the confidentiality rate formula obtained in Steps 1 and 2 of the communication process, clarify the resource optimization objective of the IRS-assisted NOMA secure communication system. Step 3 specifically includes: The formula for calculating the confidentiality rate in an IRS-assisted NOMA secure communication system is expressed as follows: Among them, R k R is the transmission rate for the k-th user. δ→k Let e ​​be the eavesdropping rate at which the eavesdropper eavesdrops on user k, e be the external eavesdropper, and f be the internal eavesdropper. By alternately optimizing beamforming, the IRS phase shift matrix, and the power allocation factor in sequence, the resource optimization objective function of the IRS-assisted NOMA secure communication system can be written as: Among them, a and a k For NOMA power allocation factors, w and w k Let Φ be the beamforming matrix, Φ be the IRS phase shift matrix, K be the number of users, k be the number of users, and h be the number of users. bk h is the coefficient of the channel from the base station to user k. rk P is the coefficient of the channel from IRS to user k. t This refers to the base station's transmission power. The elements are in the IRS phase shift matrix, where It is the nth reflection coefficient of the phase shift matrix; Step 4: Based on the resource optimization objectives analyzed and derived in Step 3, the beamforming, IRS phase shift matrix, and power allocation factor are optimized using an alternating optimization method to obtain the optimization results. 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 problem of maximizing the secure rate of a multi-user system. The following formula is obtained by optimizing beamforming using the WMMSE algorithm: h k =h b,k +GΦh r,k (4-1) Where K is the number of legitimate users, and k is the sequence number of the kth user; the coefficient of the channel from the base station to the IRS is... The coefficients of the channel from the base station to user k are: The coefficients of the channel from IRS to user k are: h k Here is the integrated channel condition for user k; T is the matrix transpose operation; Φ is the IRS phase shift matrix; a k σ is the NOMA power allocation factor. k The noise at user k; I M It is an M×M identity matrix; χ i and χ k κ is the precoding matrix for user k; i and κ k For the intermediate matrix; w i and w k Beamforming matrix; Step 4.2: IRS phase shift matrix optimization; By combining the optimized beamforming from step 4.1, the direct link legal channel matrix and the indirect link legal channel matrix are redefined as follows: a i,k =h r,k w i (4-5) b i,k =h b,k w i (4-6) in, Here are the channel coefficients from the base station to user k; The channel coefficients from the IRS to user k; w i Beamforming matrix; a i,k and b i,k These 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 follows: a i,δ =h r,δ w i (4-7) b i,δ =h b,δ w i (4-8) in, Let δ be the channel coefficient from the base station to the eavesdropper; Here is the channel coefficient from the IRS to the eavesdropper δ; a i,δ and b i,δ These are the indirect link eavesdropping channel matrix and the direct link eavesdropping channel matrix, respectively. The original optimization problem is then transformed into a convex optimization problem, and the optimization subproblems are written as: Where P(C) represents the optimization subproblem, n and N are the index of the IRS element and the number of IRS elements, respectively, Φ is the IRS phase shift matrix, and φ n f is an element in the IRS phase shift matrix. C (Φ) is the expression for the optimization subproblem, which can be specifically represented as: Where K is the number of valid users, k is the sequence number of the kth user, and a k,k a i,k For the indirect link channel matrix, b k,k b i,k For the direct link channel matrix, a k,δ a i,δ For the indirect link eavesdropping channel matrix, b k,δ b i,δ For the direct link eavesdropping channel matrix, η k and η i σ is the product of the power allocation factor and the power. k With σ δ The noise at user k and eavesdropper δ are respectively; Because f C Since (Φ) is continuously differentiable and the constraint set of Φ forms a complex circle, the solution for P(C) is obtained through the Riemannian conjugate gradient (RCG). The first stage of the Riemannian conjugate gradient (RCG) iteration is to calculate the Riemannian gradient, which is the orthogonal projection of the Euclidean gradient onto the complex circle. Among them, the Euclidean gradient for: Where K is the number of valid users, k is the sequence number of the kth user, and A k Written as: Choose the tangent vector conjugate to the gradient direction as the search direction: Where d is the search direction, gradf C This is the Riemann gradient, and τ1 is the conjugate gradient update parameter. This follows 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 Amiho step size, φ n These are elements in the IRS phase shift matrix; Step 4.3: Power allocation factor optimization; With the beamforming and IRS phase shift matrix optimization completed in steps 4.1 to 4.2, the power allocation factor is optimized using an optimization algorithm, specifically a genetic algorithm. Step 4.3.1: Select the objective function And set the restrictions as follows Where K is the number of valid users, k is the sequence number of the kth user, and a k NOMA power allocation factor; Step 4.3.2: Using the objective function and constraints selected in Step 4.3.1, call the Genetic Algorithm (GA) to solve the problem and obtain the optimized power allocation factor.

2. The IRS-NOMA communication resource optimization allocation method for resisting internal and external eavesdropping according to claim 1, characterized in that, Step 1 further includes: set up If the signal to be transmitted is given, then the signal that the k-th user can receive among all legal users is: Where K is the number of users, k is the number of users, T is the matrix transpose operation, and w k For the beamforming matrix, a k x is the NOMA power allocation factor. k For the signal to be transmitted, P t Let Φ be the base station's transmit power, and Φ = diag{φ1,…,φ n ,…,φ N }, Let φ be the phase shift matrix of the IRS, where φ n It is the nth reflection element of the phase shift matrix. Let n be the nth reflection coefficient of the phase shift matrix. k It is Gaussian white noise; In an IRS-assisted NOMA secure communication system, users employ Successive Interference Cancellation (SIC) technology for signal decoding. The demodulation order in SIC is determined based on the equivalent channel gain of the combined channel. Users with the strongest channel gain are demodulated first, while users with weakest channel gain receive information directly. The user demodulation order is ||h1|| 2 ≥||h2|| 2 ≥…≥||h K || 2 The k-th ranked user corresponds to the k-th strongest channel, and the signal-to-interference-plus-noise ratio (SINR) of the k-th user when decoding the signal is expressed as: Among them, a k For NOMA power allocation factor, w k For the beamforming matrix, σ k The noise at user k; The transmission rate of the k-th user is then expressed as: R k =log2(1+γ k (1-3).

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

4. The system of the IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping according to any one of claims 1 to 3, characterized in that, include: Security Model Module: The base station sends the signal to be transmitted. The signal is received by the legitimate user through direct and indirect links. The module calculates the transmission rate of the legitimate user's signal and the eavesdropping rate of the signal received by external or internal eavesdroppers in the IRS-assisted NOMA secure communication system. The direct link is the base station broadcasting directly to the user, and the indirect link is the base station transmitting to the IRS, which decodes the signal and then broadcasts it to the user. Optimization Target Confirmation Module: By analyzing and deriving the confidentiality rate formula through the communication process, the optimization target of the IRS-assisted NOMA secure communication system is clarified; Resource optimization module: Through alternating optimization, the base station's beamforming, IRS phase shift matrix, and power allocation factor are configured sequentially to achieve the optimization target, and the optimization results are obtained.

5. An IRS-NOMA communication resource optimization and allocation device resistant to internal and external eavesdropping, characterized in that, include: Memory: for storing a computer program that implements the IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping as described in any one of claims 1 to 3; Processor: Used to implement the IRS-NOMA communication resource optimization allocation method against internal and external eavesdropping as described in any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the IRS-NOMA communication resource optimization allocation method for resisting internal and external eavesdropping as described in any one of claims 1 to 3.

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