A near field NOMA security transmission method and system for preventing near-end user internal eavesdropping

Abstract

The application discloses a near-field NOMA security transmission system for preventing near-end user internal eavesdropping, and belongs to the wireless communication field. The transmission system comprises the following steps: calculating the distances from near-end users and far-end users to a base station antenna in a communication system; calculating near-field channels from the base station to the near-end users and from the base station to the far-end users according to the distances based on a near-field uniform spherical wave model; constructing a signal model for characterizing received signals of the near-end users and the far-end users according to beamforming vectors and power allocation coefficients according to the near-field channels; designing a serial interference cancellation mechanism for preventing near-end user eavesdropping according to the signal model; and maximizing the system secrecy capacity under the constraint of maximum transmission power by jointly optimizing the beamforming vectors and the power allocation coefficients in the signal model, establishing an optimization problem model, and alternately iterating the beamforming vectors and the power allocation coefficients to solve the optimization problem model.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, and more specifically to a near-field NOMA secure transmission system for preventing eavesdropping within the user's premises. Background Technology

[0002] Non-Orthogonal Multiple Access (NOMA) overcomes the orthogonal spectrum allocation limitation of Orthogonal Multiple Access (OMA) through power domain multiplexing and Successive Interference Cancellation (SIC) mechanisms. Theoretical analyses show it has the potential to improve spectrum efficiency by 40%-100% compared to OMA, particularly in ultra-dense networks and large-scale machine-type communication scenarios, supporting several times the user access capacity compared to traditional systems. However, as a key enabling technology for 5G-Advanced and 6G networks, while NOMA significantly improves spectrum utilization and system throughput, the communication security issues caused by its non-orthogonal characteristics are becoming increasingly prominent.

[0003] In NOMA networks, unauthorized third parties can launch active eavesdropping attacks through the openness of the wireless channel, leading to the leakage of sensitive information and constituting external eavesdropping. Simultaneously, NOMA users can also become eavesdroppers, maliciously intercepting other users' signals using decoding privileges, constituting internal eavesdropping. Due to the superimposed transmission characteristics of NOMA user signals, both external and internal eavesdropping pose significant challenges to the security of communication systems. Currently, researchers have conducted extensive exploration and research on the eavesdropping problem in NOMA networks. One core approach is to design multi-antenna beamforming based on Channel State Information (CSI) and optimize transmit power allocation strategies to maximize the signal-to-interference-plus-noise ratio (SIR) received by legitimate users and minimize the SIR received by eavesdroppers, thereby improving the physical layer security channel capacity. Another core approach is to inject artificial noise, causing strong interference to eavesdroppers while having a smaller impact on legitimate users, thus increasing the difficulty for eavesdroppers to obtain effective information.

[0004] The existing technology "A Secure Transmission Method and System for Uplink Non-Orthogonal Multiple Access (CN111988783A)" considers the situation where there are external eavesdroppers in the uplink NOMA link. It selects a friendly energy harvesting jammer in the network to send artificial noise, which degrades the reception quality of external eavesdroppers, thereby ensuring secure uplink NOMA communication between a single-antenna base station and the user. However, this technology is designed for single-antenna base stations and user nodes, and fails to fully utilize the beamforming capabilities of multi-antenna base stations in practice. Moreover, this technology is designed for secure transmission schemes against external eavesdroppers and cannot resist internal eavesdropping in the NOMA system. Another technology "Duan Lili, Liu Xueyu, Wang Yinghui, et al. Analysis of Physical Layer Security Strategy of NOMA System in Near-Field Scenarios [J / OL]. Telecommunications Technology, 1-9 [2025-03-25]" is designed for near-field multi-input single-output NOMA systems with internal eavesdroppers. It considers the remote user as the internal eavesdropping user and assigns two independent beams to the remote user and the near user. To maximize the safe rate for near-end users, this technical solution proposes a two-stage beamforming optimization strategy based on the semidefinite relaxation (SDR) and continuous convex approximation (SCA) methods.

[0005] The main shortcomings of existing technologies are: 1) Most existing physical layer anti-eavesdropping security transmission schemes are designed based on far-field channel models. Directly transplanting them to the near field will severely reduce the security rate due to channel characteristic adaptation. Although a few studies have attempted to introduce near-field models, they use independent beams to serve power domain multiplexing for both near and far-end users, failing to fully utilize the non-orthogonal advantages of NOMA; 2) Existing schemes mainly focus on external eavesdropping scenarios. However, the internal eavesdropping threat unique to NOMA systems poses a more serious security risk than external eavesdropping. Although some schemes have studied internal eavesdropping scenarios, they assume the eavesdropper is a far-end user. The situation where the eavesdropper is a near-end user is actually more serious, but there is still a lack of effective solutions for the near-end user eavesdropping problem in near-field NOMA networks. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a method to achieve its objective through the following technical solution:

[0007] A first aspect of the present invention relates to a near-field NOMA secure transmission method for preventing eavesdropping within a near-end user's premises, comprising the following steps:

[0008] Calculate the distance from near-end users and far-end users to the base station antenna in a communication system;

[0009] Based on the near-field uniform spherical wave model, the near-field channels from the base station to the near-end user and from the base station to the far-end user are calculated according to the distance.

[0010] Based on the near-field channel, a signal model is constructed that characterizes the received signals of near-end users and far-end users according to the beamforming vector and power allocation coefficient;

[0011] By jointly optimizing the beamforming vector and power allocation coefficient in the signal model, the system's security capacity is maximized under the constraint of maximum transmit power, and an optimization problem model is established.

[0012] The optimization problem model is solved by iteratively applying beamforming vectors and power allocation coefficients.

[0013] Optionally, the remote user performs the SIC mechanism to decode the signal from the near-end user and remove it from the received signal; the near-end user treats the signal from the remote user as interference and directly decodes the signal from the near-end user.

[0014] Optionally, during the execution of the SIC mechanism at the remote user, the signal-to-interference-plus-noise ratio (SIR) of the decoded s1 at Eve is obtained by calculating the ratio of the target signal power to the interference plus noise power:

[0015]

[0016] The SINR of s1 decoded at the remote user is:

[0017]

[0018] Where x H It is the conjugate transpose of x;

[0019] As an internal eavesdropping user, after successfully decoding the information s1 sent to them by the base station, the near-end user will attempt to decode the confidential information sent by the base station to the remote user; at this time, the SINR of the near-end user decoding s2 is calculated as follows:

[0020]

[0021] After the remote user successfully decodes s1, it can discard that signal and then decode its own signal s2. At this point, the SINR of the decoded s2 at the remote user is:

[0022]

[0023] To ensure successful SIC decoding at the remote user, the data transmission rate for the near user should be:

[0024]

[0025] Optionally, the near-field channel is calculated by the following steps:

[0026] Let b(r1,θ1) and b(r2,θ2) represent the array response vectors of the base station at the near-end user and far-end user locations, respectively. Based on the propagation characteristics of near-field spherical waves, we can obtain:

[0027]

[0028] Where x T Indicates the transpose of x;

[0029] Considering that both near-end and far-end users are located within the near-field range of the base station, a near-field uniform spherical wave model is used to model the near-field channel;

[0030] Assuming channel gain in, λ represents the wavelength, and r represents the distance between the transmitting and receiving ends; the near-field channel between the base station and the near-end user is:

[0031]

[0032] Similarly, the near-field channel from the base station to the remote user is:

[0033]

[0034] Optionally, the method for constructing the signal model includes the following steps:

[0035] Let s1, s2 ~ CN(0,1) be the symbols transmitted by the base station to the near-end user and the far-end user, respectively, where CN(0,1) represents a complex Gaussian distribution with mean 0 and variance 1; let the transmit power allocation coefficients for the near-end user and the far-end user be α1 and α2, respectively. Then the signal transmitted by the base station is:

[0036]

[0037] Where w represents the beamforming vector of the base station; the signal transmitted by the base station is transmitted through the channel, and the received signals to the near-end user and the far-end user are respectively:

[0038]

[0039] Where x H It is the conjugate transpose of x. and These are the additive white Gaussian noises at the near-end user and far-end user receivers, respectively.

[0040] Optionally, the constraints of the optimization problem model also include that the transmission rate of the near-end user is greater than or equal to the minimum rate threshold of the near-end user.

[0041] Optionally, the alternating iteration steps include: optimizing the beamforming vector given the power allocation coefficients; then updating the power allocation coefficients using the obtained optimized beamforming vectors; and iteratively repeating the same process until convergence.

[0042] A second aspect of the present invention relates to a near-field NOMA secure transmission system for preventing eavesdropping within a near-end user's premises, comprising:

[0043] Communication distance calculation module: capable of calculating the distance from near-end users and far-end users to the base station antenna in a communication system;

[0044] Near-field channel calculation module: Based on the near-field uniform spherical wave model, it can calculate the near-field channel from the base station to the near-end user and from the base station to the far-end user according to the distance;

[0045] The signal model construction module is able to construct a signal model based on the near-field channel, which characterizes the signals received by near-end users and far-end users according to the beamforming vector and power allocation coefficient.

[0046] Optimization Problem Construction Module: By jointly optimizing the beamforming vector and power allocation coefficient in the signal model, the system's security capacity is maximized under the constraint of maximum transmit power, thus establishing an optimization problem model;

[0047] In addition, the solution module: alternately iterates the beamforming vector and power allocation coefficient to solve the optimization problem model.

[0048] A third aspect of the present invention relates to a computer-readable storage medium storing instructions that, when executed, implement the aforementioned near-field NOMA secure transmission method for preventing eavesdropping within the user's premises.

[0049] A fourth aspect of the present invention relates to a communication device comprising: the computer-readable storage medium described above.

[0050] The beneficial effects of this invention are:

[0051] (1) By using a near-field uniform spherical wave model to describe the base station-user channel, the nonlinear effect of the array antenna on the signal phase is accurately reflected. Compared with the far-field plane wave model currently used, it can more accurately describe the signal propagation characteristics in the high-frequency ultra-large-scale antenna array scenario.

[0052] (2) Consider the worst-case scenario of secure transmission, where the eavesdropping user Eve is closer to the base station. By migrating the SIC mechanism to the remote legitimate user Bob, the remote user first decodes the signal of the near-end eavesdropping user Eve and removes interference. The near-end user can only decode its own signal first and then try to decode the secure information, thus blocking the way for the internal eavesdropping user Eve to directly obtain secure information using the traditional SIC mechanism.

[0053] (3) Taking full advantage of the non-orthogonal characteristics of NOMA, two NOMA users share the same beam, and then establish a joint optimization problem of beamforming vector and power allocation coefficient. Through subproblem decomposition, auxiliary variables and continuous convex approximation techniques, an alternating iterative solution algorithm for the optimization problem is proposed, which can ensure the service quality of all users while saving beam resources and effectively improve the security rate. Attached Figure Description

[0054] The invention will now be further described with reference to the accompanying drawings.

[0055] Figure 1 This is a model diagram of the NOMA system in the embodiments of this application;

[0056] Figure 2 This illustrates the relationship between simulated user distance and security rate in the embodiments of this application.

[0057] Figure 3 This is a flowchart illustrating the design of a near-field NOMA secure transmission system for preventing eavesdropping by nearby users, as described in this application. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Step 1: Establish a system model

[0060] This invention provides a near-field NOMA secure transmission system to prevent eavesdropping within the user's premises. For example... Figure 1 As shown, the system consists of a base station (BS), a near-end user (Eve), and a far-end user (Bob). Both Bob and Eve are located in the near-field region of the BS, with Eve being closer to the BS than Bob. The near-end user, Eve, attempts to eavesdrop on signals transmitted from the BS to the far-end user, Bob, posing a threat to Bob's information security. Both Bob and Eve are equipped with a single antenna, while the BS is equipped with M antennas and operates in half-duplex mode.

[0061] (1) Communication distance

[0062] For convenience, assume that the base station (BS) antenna array is deployed on the x-axis of the coordinate system, with the center of the array located at the origin, and the distance between the centers of two adjacent antennas along the x-axis is d. Then, the position coordinate vector of the m-th antenna can be expressed as:

[0063] r m =(dδ (m) ,0)

[0064] in

[0065] Assuming d1 represents the distance from Eve to the center of the antenna array, and θ1 is the azimuth angle of Eve relative to the center of the antenna array, then the position coordinate vector of Eve is expressed as:

[0066] r1=(d1sinθ1,d1cosθ1)

[0067] Assuming d2 represents the distance from Bob to the center of the antenna array, and θ2 is the azimuth angle of Bob relative to the center of the antenna array, then Bob's position coordinate vector is represented as:

[0068] r2=(d2sinθ2,d2cosθ2)

[0069] Let r1 (m) r2 (m) Let $\mathbf$ and $\mathbf$ represent the distances from Eve and Bob to the m-th antenna of BS, respectively. These distances can be calculated using the law of cosines.

[0070]

[0071] (2) BS-Eve / Bob near-field channel model

[0072] Let b(r1,θ1) and b(r2,θ2) represent the array response vectors of BS at the Eve and Bob positions, respectively. Based on the near-field spherical wave propagation characteristics, we can obtain:

[0073]

[0074] Where x T This represents the transpose of x.

[0075] Considering that Eve and Bob are located in the near field range of the base station BS, the near field channel is modeled using the Uniform Spherical Wave (USW) model.

[0076] In the USW model, when the propagation distance r is greater than a certain threshold, i.e., the uniform power distance, the receiver power can be considered to remain relatively stable, and will not fluctuate excessively due to small changes in distance. Therefore, the channel gain can be assumed. Where a0 is mainly determined by the free space path loss, its expression is: Where λ represents the wavelength and r represents the distance between the transmitting and receiving ends. The near-field channel model between BS and Eve is given by the following formula:

[0077]

[0078] Similarly, the near-field channel from BS to Bob is:

[0079]

[0080] (3) Signal Model

[0081] Let s1, s2 ~ CN(0,1) be the symbols sent by BS to Eve and Bob, respectively, where CN(0,1) represents a complex Gaussian distribution with mean 0 and variance 1. Let the transmit power allocation coefficients for Eve and Bob be α1 and α2, respectively. Then the signal transmitted by BS can be expressed as:

[0082]

[0083] Where w represents the BS beamforming vector. The signal transmitted by the BS is transmitted through the channel, and the received signals arriving at Eve and Bob are as follows:

[0084]

[0085] Where x H It is the conjugate transpose of x. and These are additive white Gaussian noise at Eve's and Bob's receivers, respectively.

[0086] Step 2: Design a serial interference cancellation mechanism to prevent eavesdropping.

[0087] Typically, in NOMA systems, near-end users with better channel conditions use SIC (Search Injection) technology to decode the signals of far-end users and remove them from the received signal to reduce interference. However, when the near-end user is also an eavesdropper, this SIC mechanism is equivalent to completely exposing confidential information to the eavesdropper.

[0088] Therefore, this invention designs a SIC mechanism different from traditional NOMA systems: specifically, SIC is performed at the remote user Bob, meaning Bob first decodes the signal from the near-end user and removes it from the received signal, while the near-end user Eve treats Bob's signal as interference and directly decodes her own signal. By calculating the ratio of the target signal power to the interference plus noise power, the signal-to-interference-plus-noise ratio (SINR) of Eve's decoding s1 is obtained as follows:

[0089]

[0090] The SINR of s1 decoded at Bob is:

[0091]

[0092] Where x H It is the conjugate transpose of x.

[0093] As an insider eavesdropping user, after successfully decoding message s1 sent to her by BS, Eve will attempt to decode the confidential message BS sent to Bob. At this point, Eve's SINR calculation for decoding s2 is:

[0094]

[0095] After Bob successfully decodes s1, he can discard that signal and then decode his own signal s2. At this point, the SINR of Bob's decoded s2 is:

[0096]

[0097] To ensure successful SIC decoding at Bob's location, Eve's data transfer rate should be:

[0098]

[0099] Step 3: Modeling the near-field secure transmission optimization problem:

[0100] Let S2 be Bob's security rate. By calculating the difference between the rate at which Bob decodes s2 and the rate at which Eve decodes s2, and taking the non-negative value, we can obtain:

[0101]

[0102] To improve secure transmission performance, targeting Figure 1The system shown establishes an optimization problem model, following the following two principles: (1) Ensure the service quality of the internal eavesdropping user Eve is guaranteed; (2) The transmission power cannot exceed the base station's maximum power budget. The first principle requires R1≥r1, where r1 is Eve's minimum rate threshold. The second principle requires α1+α2=1, α i ∈(0,1) and Where P is the maximum power budget.

[0103] This invention aims to maximize the system's security capacity under the constraint of maximum transmit power by jointly optimizing the beamforming vector and power allocation coefficient. Based on this, the following optimization problem is established:

[0104]

[0105] stC1:R1≥r1

[0106] C2: α1 + α2 = 1, α i ∈(0,1)

[0107]

[0108] In problem P1, C1 is the constraint to ensure the quality of service for the internal eavesdropping user Eve, C2 is the constraint of the power allocation coefficient, and C3 is the constraint of the maximum transmit power of the BS.

[0109] Due to the non-convexity of the objective function and constraints, P1 is a non-convex problem. To make the optimization problem easier to handle, problem P1 is transformed as follows:

[0110]

[0111] stC2:α1+α2=1,α i ∈(0,1)

[0112]

[0113] The objective function in P2 is restated as follows:

[0114]

[0115] Step 4: Design of a solution algorithm based on alternating iteration

[0116] In problem P2, the variable w to be optimized is coupled with α1 and α2, making it difficult to solve directly. Therefore, this invention proposes a solution algorithm based on alternating iteration: First, given the power allocation coefficients α1 and α2, optimize the BS beamforming vector w; then, update the power allocation coefficients α1 and α2 using the obtained w. This process is repeated iteratively until convergence.

[0117] (1) With α1 and α2 fixed, optimize the beamforming vector w. The beamforming optimization subproblem can then be written as:

[0118]

[0119] stC3,C4,C5

[0120] The objective function in P3 is expressed as follows:

[0121]

[0122] First, two auxiliary variables a and b are introduced, satisfying the following constraints:

[0123]

[0124] The problem can then be transformed into:

[0125]

[0126] stC3,C4,C5,C6,C7

[0127] Among them, C4 to C7 are non-convex constraints. Next, we will perform convex approximation on these non-convex constraints.

[0128] Convex approximation of C4: By introducing auxiliary variables c and d, C4 can be equivalently transformed into the following constraints:

[0129]

[0130] C9 and C10 are convex, but C8 remains non-convex. To approximate C8 as a convex constraint, at the known point w... (0) Applying a first-order Taylor expansion to the left-hand side of the inequality in C8, we obtain:

[0131]

[0132] Where Re{·} denotes the operation of finding the real part.

[0133] Convex approximation of C5: Introducing auxiliary variables e and f, satisfying the following constraints:

[0134]

[0135] C12 and C13 are convex, but C11 is still non-convex. To approximate C11 as a convex constraint, at the known point w... (0) Applying a first-order Taylor expansion to the left-hand side of the inequality in C11, we obtain:

[0136]

[0137] Convex approximation of C6: By introducing an auxiliary variable g, C6 can be equivalently transformed into the following constraint:

[0138]

[0139] C14 is convex, while C15 remains non-convex. To approximate C15 as a convex constraint, at the known point w... (0) Applying a first-order Taylor expansion to the left-hand side of the inequality in C15, we obtain:

[0140]

[0141] Convex approximation of C7: By introducing an auxiliary variable h, C7 can be equivalently transformed into the following constraint:

[0142]

[0143] C17 is convex, while C16 remains non-convex. To approximate C16 as a convex constraint, at the known point h... (0) Applying a first-order Taylor expansion to the terms on the right-hand side of the inequality in C16, we obtain:

[0144]

[0145] Substituting all the constraints obtained from the convex approximation into problem P4, the optimization problem can now be reformulated as:

[0146]

[0147] stC3,C8′,C9,C10,C11′,C12,C13,C14,C15′,C16′,C17

[0148] This problem is convex and can be solved efficiently using the CVX toolkit in Matlab.

[0149] (2) With the beamforming vector w fixed, optimize the power allocation coefficients α1 and α2. The power allocation optimization subproblem can then be written as:

[0150]

[0151] stC2,C4,C5

[0152] The objective function in P6 is expressed as follows:

[0153]

[0154] Since α1 = 1 - α2, problem P6 can be equivalently transformed into:

[0155]

[0156] stC2′:α2∈(0,1)

[0157]

[0158] In problem P7, all constraints are convex, while the objective function is non-convex. At the given point... Performing a first-order Taylor expansion on the second term of the objective function, we obtain its convex approximate lower bound as:

[0159]

[0160] At this point, problem P7 can be approximately transformed into...

[0161]

[0162] stC2′,C4′,C5′

[0163] This problem is convex and can be solved efficiently using the CVX toolkit in Matlab.

[0164] Step 5: Implementation of the optimization algorithm

[0165] 1. The specific steps of the algorithm for solving problem P1 are as follows:

[0166] S10: Initialize the number of iterations n = 1, and the maximum number of iterations n max Initialize the optimization variable as w (1) and α2 (1) Set the iteration precision to ε, and calculate the objective function C in problem P2. S (w (1) ,α2 (1) );

[0167] S20: Given w is obtained by solving problem P3. (n+1) ;

[0168] S30: Given w (n+1) α2 is obtained by solving problem P6. (n+1) ;

[0169] S40: Calculate the confidentiality capacity C S (w (n+1) ,α2 (n+1) );

[0170] S60: If |C S (w (n+1) ,α2 (n+1) )-C S (w (n) ,α2 (n ))|≤ε holds true or n=n maxIf the condition is met, the external iteration is considered to have converged, and the external iteration is terminated; otherwise, n+1→n is set and the process returns to S20.

[0171] 2. The specific steps of the algorithm for solving problem P3 are as follows:

[0172] S10: Let t be the maximum number of iterations within the internal iteration. max And precision ε. Initialize parameter w (1) h (1) Set the iteration number t = 1, and calculate the objective function value f(w) in problem P3. (t) );

[0173] S20: Given w (t) and h (t) w is obtained by solving problem P5. (t+1) and h (t+1) ;

[0174] S30: Calculate the objective function f(w) (t+1) );

[0175] S40: If |f(w (t+1) )-f(w (t) )|≤ε holds true or t=t max If the condition is met, the internal iteration is considered converged, and the internal iteration is terminated; otherwise, set t←t+1 and update w in problem P5. (t) and h (t) Proceed to step S20.

[0176] 3. The specific steps of the algorithm for solving problem P6 are as follows:

[0177] S10: Let l be the maximum number of iterations within the internal iteration. max And precision ε. Initialization parameters Set the iteration count l = 1, and calculate the objective function value in problem P6.

[0178] S20: Given By solving problem P8, we obtain...

[0179] S30: Calculate the objective function f2(α) (t+1) );

[0180] S40: If |f2(α) (t+1) )-f2(α (t) )|≤ε holds true or l=l max If the condition is met, the internal iteration is considered converged, and the internal iteration is terminated; otherwise, set l←l+1 and update the value in problem P8. Proceed to step S20.

[0181] In other embodiments of the present invention, in order to further verify the technical effect of the near-field NOMA secure transmission system of the above embodiments, simulation experiments were conducted using the Matlab platform to evaluate the performance of the near-field NOMA secure transmission system of this embodiment.

[0182] Figure 2 This paper compares the near-field NOMA secure transmission system of this embodiment with existing schemes based on far-field channel models. The number of antenna arrays is set to 60, the distance of the legitimate user (Bob) from the center of the base station antenna array is set to 10m, and the distance of the eavesdropping user (Eve) from the center of the base station antenna array is set to 4m, 5m, 6m, 7m, 8m, and 9m. It is assumed that Eve and Bob are at the same azimuth angle relative to the center of the base station antenna array. Numerical results show that, under the different parameter settings considered, the scheme based on the near-field channel model can achieve good security rates, while the scheme based on the far-field channel model cannot keep Bob's information confidential. Therefore, in NOMA near-field communication scenarios with near-end user eavesdropping, if the traditional plane wave-based far-field channel model is still used, the secure communication performance will be significantly reduced, while the scheme proposed in this invention can achieve effective security in near-field communication scenarios.

[0183] In summary, this invention proposes a near-field NOMA secure transmission model to prevent eavesdropping within near-end users, in which near-end and far-end users share the same beam, making full use of the non-orthogonal characteristics of NOMA.

[0184] A near-field uniform spherical wave model is used to describe the near-field channel between the base station and the user. It takes into account the nonlinear effect of the base station antenna array on the signal phase. Compared with the traditional plane wave-based model, it can more accurately describe the propagation characteristics of near-field communication signals.

[0185] A SIC mechanism was designed to prevent eavesdropping by nearby users. By adjusting the SIC execution rules, the eavesdropping ability of nearby users was weakened, and the risk of leakage of confidential information was reduced.

[0186] This invention constructs a joint optimization framework based on the differentiated service requirements of dual users. The optimization objective is the achievable confidentiality rate for legitimate and secure users, while simultaneously introducing a service quality threshold constraint for internal eavesdropping users. A joint optimization problem of beamforming and power allocation coefficients is established. To address this problem, this invention proposes an alternating iterative algorithm. By introducing auxiliary variables to reformulate the objective function, the problem of maximizing confidentiality capacity is transformed into two alternating sub-problems: beamforming and power allocation coefficient optimization. Separate solution algorithms are designed for these two sub-problems, and the proposed algorithm achieves a high confidentiality rate.

[0187] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0188] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A near-field NOMA secure transmission method for preventing eavesdropping within the user's premises, characterized in that, Includes the following steps: Calculate the distance from near-end users and far-end users to the base station antenna in a communication system; Based on the near-field uniform spherical wave model, the near-field channels from the base station to the near-end user and from the base station to the far-end user are calculated according to the distance. Based on the near-field channel, a signal model is constructed that characterizes the received signals of near-end users and far-end users according to the beamforming vector and power allocation coefficient; By jointly optimizing the beamforming vector and power allocation coefficient in the signal model, the system's security capacity is maximized under the constraint of maximum transmit power, and an optimization problem model is established. The optimization problem model is solved by iteratively applying beamforming vectors and power allocation coefficients.

2. The near-field NOMA secure transmission method for preventing eavesdropping within the user's premises according to claim 1, characterized in that, At the remote user, a serial interference cancellation mechanism is implemented to decode the signal from the near-end user and remove it from the received signal; the near-end user treats the signal from the remote user as interference and directly decodes the signal from the near-end user.

3. The near-field NOMA secure transmission method for preventing eavesdropping within the user's premises according to claim 2, characterized in that, During the execution of the serial interference cancellation mechanism at the remote user end, the ratio of the target signal power to the interference plus noise power is calculated to obtain the decoding result at the near-end user end. The signal-to-interference-plus-noise ratio is: Decoding at remote user The SINR is: in yes The conjugate transpose of; The near-end user, acting as an internal eavesdropper, successfully decodes the information sent to them by the base station. Next, it will attempt to decode the confidential information sent by the base station to the remote user; at this time, the near-end user decodes... of SINR The calculation is as follows: Remote user successfully decoded Then, the signal can be removed, and then your own signal can be decoded. At this time, the remote user decodes. The SINR is: To ensure successful serial interference cancellation and decoding at the remote user, the data transmission rate for the near-end user should be: in, Represents the beamforming vector of the base station; and This represents the transmit power allocation coefficients for near-end users and far-end users; These are the symbols sent by the base station to the near-end user and the far-end user, respectively. This represents a complex Gaussian distribution with a mean of 0 and a variance of 1. g1 is the near-field channel between the base station and the near-end user, and g2 is the near-field channel between the base station and the far-end user; R 1 represents the data transmission rate for near-end users.

4. The near-field NOMA secure transmission method for preventing eavesdropping within the user's premises according to claim 1, characterized in that, The calculation of the near-field channel includes the following steps: make and These represent the array response vectors of the base station at the near-end user and far-end user locations, respectively. Based on the near-field spherical wave propagation characteristics, we can obtain: in express transpose; Considering that both near-end and far-end users are located within the near-field range of the base station, a near-field uniform spherical wave model is used to model the near-field channel; Assuming channel gain ,in, , Indicates wavelength. This represents the distance between the transmitting and receiving ends; the near-field channel between the base station and the near-end user is: Similarly, the near-field channel from the base station to the remote user is: ； M Indicates the number of base station antennas. This indicates the distance from the near-end user to the center of the antenna array. This indicates the distance from the remote user to the center of the antenna array; This refers to the azimuth angle of the near-end user relative to the center of the antenna array. r1 is the azimuth angle of the remote user relative to the center of the antenna array; r2 is the position coordinate vector of the near-end user; , These represent the distances from the near-end user and the far-end user to the base station, respectively. The distance between the antennas; g1 is the near-field channel between the base station and the near-end user, and g2 is the near-field channel between the base station and the far-end user.

5. The near-field NOMA secure transmission method for preventing eavesdropping within the user's premises according to claim 1, characterized in that, The method for constructing the signal model includes the following steps: definition These are the symbols sent by the base station to the near-end user and the far-end user, respectively. This represents a complex Gaussian distribution with a mean of 0 and a variance of 1; the transmit power allocation coefficients for near-end users and far-end users are respectively set as... and The signal transmitted by the base station is: in This represents the beamforming vector of the base station; the signals transmitted by the base station are transmitted through the channel, and the received signals to the near-end user and the far-end user are respectively: in yes The conjugate transpose of . and These are the additive white Gaussian noise at the receivers of the near-end user and the far-end user, respectively; g1 is the near-field channel between the base station and the near-end user, and g2 is the near-field channel between the base station and the far-end user.

6. The near-field NOMA secure transmission method for preventing eavesdropping within the user's premises according to claim 1, characterized in that, The constraints of the optimization problem model also include that the transmission rate of the near-end user is greater than or equal to the minimum rate threshold of the near-end user.

7. The near-field NOMA secure transmission method for preventing eavesdropping within the user's premises according to claim 1, characterized in that, The alternating iterative steps include: optimizing the beamforming vector given the power allocation coefficients; then updating the power allocation coefficients using the obtained optimized beamforming vectors; and iteratively repeating the same process until convergence.

8. A near-field NOMA secure transmission system for preventing eavesdropping within the user's premises, characterized in that, include: Communication distance calculation module: capable of calculating the distance from near-end users and far-end users to the base station antenna in a communication system; Near-field channel calculation module: Based on the near-field uniform spherical wave model, it can calculate the near-field channel from the base station to the near-end user and from the base station to the far-end user according to the distance; The signal model construction module is able to construct a signal model based on the near-field channel, which characterizes the received signals of near-end users and far-end users according to the beamforming vector and power allocation coefficient. Optimization Problem Construction Module: By jointly optimizing the beamforming vector and power allocation coefficient in the signal model, the system's security capacity is maximized under the constraint of maximum transmit power, thus establishing an optimization problem model; In addition, the solution module: alternately iterates the beamforming vector and power allocation coefficient to solve the optimization problem model.

9. A computer-readable storage medium storing instructions, characterized in that, When the instruction is executed, the near-field NOMA secure transmission method for preventing eavesdropping inside the user's premises as described in any one of claims 1 to 7 is implemented.

10. A communication device, characterized in that, include: The computer-readable storage medium of claim 9.

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