A ris-assisted zero-forcing beamforming method based on physical layer security technology

Abstract

The application discloses an RIS-assisted zero-forcing beamforming method based on a physical layer security technology, and the method comprises the following steps: constructing an RIS-assisted downlink MISO security transmission system model, constructing a passive eavesdropping user mathematical model based on random geometry, and constructing a legal user and eavesdropping user received signal-to-noise ratio expression; constructing a legal user received signal-to-noise ratio maximization optimization problem model, including determining optimization variables, an objective function and constraint conditions, and pre-designing a transmission beamforming vector based on a zero-forcing algorithm; constructing a joint optimization transmission beamforming and reflection beamforming vector algorithm, dividing the legal user received signal-to-noise ratio maximization optimization problem into two sub-problems for solving, and optimizing the reflection beamforming vector at the RIS and the transmission beamforming vector at the base station. The application jointly optimizes the transmission beamforming vector and the reflection beamforming vector, and realizes the optimization target of legal user received signal-to-noise ratio maximization.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and specifically to a RIS-assisted zero-forcing beamforming method based on physical layer security technology. Background Technology

[0002] B5G (Beyond 5G) and 6G (The sixth generation) mobile communication networks have introduced high-reliability and ultra-high-capacity wireless communication requirements. This poses a greater challenge to the secure communication of wireless communication systems. MIMO (Multiple-input Multiple-output) technology, as one of the key technologies of 5G / B5G networks, can significantly improve the aggregated capacity for users.

[0003] Physical Layer Security (PLS) is considered a key technology for protecting wireless networks. Compared to encryption methods that operate at the application layer, PLS is seen as a highly effective alternative. It does not require powerful computing capabilities or high-quality hardware implementation; instead, it relies on the inherent properties of the physical layer channel to prevent desired information from being leaked to eavesdropping users. Traditional PLS techniques include cooperative relay schemes, artificial noise (AN), and cooperative jamming. However, cooperative relay schemes have high hardware costs, and AN-assisted beamforming and cooperative jamming techniques require additional energy. Summary of the Invention

[0004] To overcome the defects and shortcomings of existing technologies, this invention provides a RIS-assisted zero-forcing beamforming method based on physical layer security technology. This invention establishes a RIS-assisted downlink MISO secure transmission system model and a mathematical model of passive eavesdropping users based on stochastic geometry. It pre-designs the transmit beamforming vector using the zero-forcing algorithm. Under the constraints of the received signal-to-noise ratio of active and passive eavesdropping users, the RIS reflection phase constraint, and the total transmit power of the base station, iterative optimization algorithms jointly optimize the transmit beamforming vector and the reflection beamforming vector to maximize the signal-to-noise ratio of legitimate users.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention provides a RIS-assisted zero-forcing beamforming method based on physical layer security technology, comprising the following steps:

[0007] A RIS-assisted downlink MISO secure transmission system model is constructed. The downlink MISO secure transmission system model is equipped with a base station with multiple antennas and a RIS with multiple reflection units. The base station transmits downlink data with a legitimate user with a single antenna. There are multiple active eavesdropping users and passive eavesdropping users around the legitimate user. Channels are established from the base station to the user, from the base station to the RIS, and from the RIS to the user.

[0008] A mathematical model of passive eavesdropping users based on stochastic geometry is constructed, and expressions for the received signal-to-noise ratio of legitimate users and eavesdropping users are constructed.

[0009] Construct an optimization problem model for maximizing the signal-to-noise ratio received by legitimate users, including determining the optimization variables, objective function, and constraints, and pre-designing the transmit beamforming vector based on the zero-forcing algorithm;

[0010] A joint optimization algorithm for transmit beamforming and reflective beamforming vectors is constructed. The optimization problem of maximizing the signal-to-noise ratio received by legitimate users is divided into two sub-problems for solution: optimizing the reflective beamforming vector at the RIS and optimizing the transmit beamforming vector at the base station.

[0011] As a preferred technical solution, the effective channel from the base station to the user via RIS is represented as follows:

[0012]

[0013]

[0014] The channel model between the base station and the user is represented as follows:

[0015]

[0016]

[0017] Among them, f k The channel from RIS to the user is represented by G, and the channel from the base station to the user is represented by h. k This indicates the channel from the base station to the RIS. β represents the reflected beamforming vector. N and θ N The amplitude and phase of the Nth reflecting unit are represented by L0, and the path loss at a reference distance of 1m is represented by d. bk α is the distance between the base station and the user. bk κ is the path loss factor between the base station and the user. bk Rice factor, For the direct path component, This represents the Rayleigh fading component.

[0018] As a preferred technical solution, the signal received at the user's location is represented as follows:

[0019]

[0020]

[0021] in, This represents Gaussian white noise at user k. Indicates noise power. Represents a set of users, where UE represents a legitimate user and AE represents a user. p This indicates known CSI eavesdropping users and PEs. q This indicates that the user being monitored by the unknown CSI is unknown. β represents the reflected beamforming vector. N and θ N Let f represent the amplitude and phase of the Nth reflecting element, ω represent the base station transmit beamforming vector, s represent the desired signal of the legitimate user, and f k The channel from RIS to the user is represented by G, and the channel from the base station to the user is represented by h. k This indicates the channel from the base station to the RIS;

[0022] The general formula for the received signal-to-noise ratio at user k is:

[0023]

[0024] For passively eavesdropping users with unknown CSI, a homogeneous Poisson process is used for modeling. The average signal-to-noise ratio is introduced to examine the eavesdropping behavior of passively eavesdropping users. The combined channel vector at user k is denoted as... The average signal-to-noise ratio at the passive eavesdropping user is:

[0025]

[0026] in, PE represents the average effective channel from the base station to the passively eavesdropping user. q This indicates an unknown CSI eavesdropping user.

[0027] As a preferred technical solution, an optimization problem model for maximizing the signal-to-noise ratio received by legitimate users is constructed, specifically including:

[0028]

[0029]

[0030]

[0031] |υ n |≤1

[0032] ||ω|| 2 ≤P B

[0033] Among them, SNR UE Indicates the signal-to-noise ratio received by legitimate users. ξ represents the received signal-to-noise ratio of the passively eavesdropping user, and ξ represents the set threshold for the received signal-to-noise ratio of the passively eavesdropping user. Indicates the received signal-to-noise ratio of an actively eavesdropping user, |υ n | represents the reflection phase of the RIS, ω represents the base station transmit beamforming vector, P B This indicates the set threshold for the transmit power at the base station.

[0034] As a preferred technical solution, the transmit beamforming vector is pre-designed based on the null-forcing algorithm, specifically including:

[0035] Assuming a RIS-assisted downlink MISO secure transmission system model has an active eavesdropping user, the designed zero-forcing beamforming vector is represented by projecting the legitimate user's channel onto the active eavesdropping user's null space:

[0036]

[0037] Among them, matrix The M-1 columns constitute the basis vectors of the active eavesdropping user, H UE This indicates the channel from the base station to the legitimate user.

[0038] As a preferred technical solution, optimizing the reflected beamforming vector at RIS specifically includes:

[0039] By fixing the transmit beamforming vector ω, the optimization problem model for maximizing the signal-to-noise ratio (SNR) of legitimate users with RIS assistance is transformed into:

[0040]

[0041]

[0042]

[0043] Introducing an auxiliary variable t, the optimization problem model for maximizing the signal-to-noise ratio received by legitimate users with RIS assistance is transformed into:

[0044]

[0045]

[0046]

[0047]

[0048] in, β represents the reflected beamforming vector. N and θ N This represents the amplitude and phase of the Nth reflecting unit. G represents the channel from the base station to the user, f UE f represents the channel from RIS to the legitimate user. PE This represents the channel from RIS to the unknown CSI eavesdropping user, where ω represents the base station transmit beamforming vector. Let ξ represent the noise power, and ξ represent the set threshold for the received signal-to-noise ratio of the passive eavesdropping user. n | indicates the reflection phase of RIS;

[0049] The optimal reflected beamforming vector is obtained by solving a convex optimization problem using a semidefinite relaxation method, and a rank-one υ is generated through a Gaussian randomization process. * ;

[0050] Perform a binary search on the auxiliary variable t to obtain the optimal solution set {V} for the above optimization problem. * ,t *}, for the optimal solution set {V * ,t * A rank-one V is generated through a Gaussian random process. * And obtain the optimal reflected beamforming vector υ from it. * .

[0051] As a preferred technical solution, the optimal reflected beamforming vector is obtained by solving a convex optimization problem using a semi-definite relaxation method, specifically including:

[0052] The semidefinite relaxation method is used to solve the convex optimization problem, which is expressed as:

[0053]

[0054]

[0055]

[0056]

[0057] Among them, P B This indicates the set threshold for the transmit power at the base station.

[0058] Depend on remember The optimization problem then transforms into the following problem:

[0059]

[0060]

[0061]

[0062] |V n,n |≤1

[0063] V N+1,N+1 =1

[0064]

[0065] rank(V)≤1

[0066] By relaxing the formula, the optimization problem is transformed into a feasibility problem for solution.

[0067] As a preferred technical solution, optimizing the transmit beamforming vector at the base station specifically includes:

[0068] Based on the obtained optimal reflection beamforming vector, the transmit beamforming weighting factor is constructed by maximizing the received signal-to-noise ratio at the legitimate user by optimizing the transmit beamforming vector ω. The relationship with the transmitted beamforming vector ω is expressed as:

[0069]

[0070] in, MN A The columns form the basis vectors for actively eavesdropping on users, leading to the following optimization problem:

[0071]

[0072]

[0073]

[0074] in, This represents the equivalent channel between the base station and the legitimate user. This represents the equivalent channel between the base station and the unknown CSI eavesdropping user. P represents noise power. B This indicates the set threshold for the transmit power at the base station.

[0075] Based on the CCCP architecture, the optimal transmit beamforming weight vector a is obtained by using the SDP method. * And obtain the optimal transmit beamforming vector ω. * .

[0076] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0077] (1) Compared with traditional physical layer security technologies, this invention introduces RIS technology, which maximizes the security performance of the communication system without using AN technology or cooperative interference technology that require additional transmission power when the transmission power is limited. By jointly optimizing the transmit beamforming vector and the reflective beamforming vector, the received signal-to-noise ratio of the desired signal at the active eavesdropping user and the passive eavesdropping user is suppressed, thereby maximizing the received signal-to-noise ratio of the legitimate user.

[0078] (2) This invention models passive eavesdroppers with unknown CSI based on geometric graphics. It performs mathematical modeling of eavesdroppers in the case of unknown channel state information (specific number of eavesdroppers, accurate location of eavesdroppers, etc.), providing a feasible solution for further establishing a signal-to-noise ratio model and optimizing system security performance. Attached Figure Description

[0079] Figure 1 This is a flowchart illustrating the RIS-assisted zero-forcing beamforming method based on physical layer security technology of the present invention.

[0080] Figure 2 This is a schematic diagram of the RIS-assisted downlink MISO secure transmission system model of the present invention;

[0081] Figure 3 This is a schematic diagram illustrating the change in signal-to-noise ratio (SNR) received by legitimate users under different base station transmit power budgets according to the present invention. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0083] like Figure 1 As shown, this embodiment provides a RIS-assisted zero-forcing beamforming method based on physical layer security technology, including the following steps:

[0084] Step 1: As Figure 2 As shown, a RIS-assisted downlink MISO secure transmission system model is established;

[0085] In step one, it is assumed that the RIS-assisted downlink MISO secure transmission system is deployed with a base station equipped with M antennas, transmitting downlink data with a legitimate user using a single antenna. There are N surrounding devices for the legitimate user. A There are one active eavesdropping user and an unknown number of passive eavesdropping users located in unknown areas. The user set is denoted as […]. Wherein, UE represents legitimate user, AE p This indicates known CSI eavesdropping users and PEs.q This indicates that the CSI is eavesdropping on an unknown user. Simultaneously, it... Let represent the base station transmit beamforming vector, and s represent the desired signal of a legitimate user. In addition, the channels from base station to user, base station to RIS, and RIS to user are respectively represented as follows: and Simultaneously, the reflected beamforming vector is represented as... Where β n and θ n Let represent the amplitude and phase of the nth reflecting element. Therefore, the effective channel from the base station to the user via RIS can be represented as: in

[0086] In this embodiment, it is assumed that the base station is equipped with M=10 antennas, the number of RIS reflection elements is N=20, and there are a total of N antennas in the system. A = 2 active eavesdropping users. The location coordinates of the base station, the legitimate user, and the active eavesdropping user are (5,0,10), (2,58,0), and (0,60,10), respectively. Establish the channel model between the base station and user k:

[0087]

[0088] Among them, h bk This represents the channel model between the base station and user k, where the superscript H denotes the channel conjugate transpose matrix, L0 = -30dB is the channel power gain at a reference distance of 1m, and d bk α is the distance between the base station and the user. bk g is the path loss factor between the base station and the user. bk It consists of line-of-sight paths and non-line-of-sight paths:

[0089]

[0090] Among them κ bk Rice factor, For the direct path component, This represents the Rayleigh fading component.

[0091] Step 2: Establish a mathematical model of passive eavesdropping users based on stochastic geometry, and construct the signal-to-noise ratio expressions for legitimate users and eavesdropping users;

[0092] In step two, the signal received at user k can be represented as:

[0093]

[0094] in, This represents the Gaussian white noise at user k, with noise power of... The reflected beamforming vector is represented as This represents the base station transmit beamforming vector, and s represents the desired signal of a legitimate user.

[0095] Furthermore, the general formula for the received signal-to-noise ratio at user k is:

[0096]

[0097] For passively eavesdropping users with unknown CSI, a homogeneous Poisson process is used for modeling. The average signal-to-noise ratio is considered when examining the eavesdropping behavior of the passive user. The combined channel vector at user k is denoted as... The average signal-to-noise ratio at the passive eavesdropping user is:

[0098]

[0099] in This represents the average effective channel from the base station to the passively eavesdropping user.

[0100] Step 3: Establish an optimization problem model for maximizing the signal-to-noise ratio received by legitimate users, including determining the mathematical expressions for the optimization variables, objective function, and constraints, and pre-designing the transmit beamforming vector using the zero-forcing algorithm;

[0101] By limiting the received signal-to-noise ratio (SNR) of active and passive eavesdropping users, and under the condition of limited base station transmit power, an optimization problem model for maximizing the received SNR of legitimate users with RIS assistance is established according to formulas (1)-(3):

[0102]

[0103]

[0104]

[0105] |υ n |≤1 (6d)

[0106] ||ω|| 2 ≤P B (6e)

[0107] The passive eavesdropping user's received signal-to-noise ratio is set to be less than ξ = 15dB, and the base station's transmit power is constrained to P. B =25W.

[0108] Equation (6a) represents the problem of maximizing the received signal-to-noise ratio of legitimate users; constraint equation (6b) defines the upper limit of the received signal-to-noise ratio of passive eavesdropping users with unknown CSI; constraint equation (6c) expects the received signal-to-noise ratio at active eavesdropping users to approach zero; constraint equation (6d) represents the reflection phase constraint of RIS; and equation (6e) represents the transmit power constraint at the base station.

[0109] To satisfy constraint formula (6c), the zero-forcing algorithm is used to design the transmit beamforming vector. Assuming the RIS-assisted secure transmission system has an active eavesdropping user, the designed zero-forcing beamforming vector can be obtained by projecting the legitimate user's channel onto the active eavesdropping user's null space:

[0110]

[0111] Where the matrix The M-1 columns constitute the basis vectors of the active eavesdropping users.

[0112] Step 4: Establish a joint optimization algorithm for transmit beamforming and reflective beamforming vectors. The maximization problem proposed in Step 3 is divided into two sub-problems for solution: optimizing the reflective beamforming vector at RIS and optimizing the transmit beamforming vector at the base station.

[0113] In step four, the parameters of the RIS-assisted downlink MISO secure transmission system, the range of values ​​for the optimization variables, and the constraints are set; the optimization algorithm includes the following steps:

[0114] (a) Optimize the reflected beamforming vector at RIS;

[0115] In this embodiment, in step (a):

[0116] With the transmit beamforming vector ω fixed, the optimization problem model for maximizing the signal-to-noise ratio (SNR) received by legitimate users with RIS assistance can be transformed into:

[0117]

[0118]

[0119]

[0120] Introducing an auxiliary variable t can transform the problem into:

[0121]

[0122]

[0123]

[0124]

[0125] The optimal reflected beamforming vector is obtained by solving a convex optimization problem using the semi-definite relaxation (SDR) method, and a rank-one υ is generated through a Gaussian randomization process. * Specifically, it includes:

[0126] We further employ the semi-definite relaxation (SDR) method to solve the convex optimization problem:

[0127]

[0128]

[0129]

[0130]

[0131] in

[0132] Depend on remember The optimization problem can then be further transformed into the following problem:

[0133]

[0134]

[0135]

[0136] |V n,n |≤1 (11d)

[0137] V N+1,N+1 =1 (11e)

[0138]

[0139] rank(V)≤1 (11g)

[0140] Since formula (11g) is non-convex, by relaxing this formula, the above problem is transformed into a feasibility problem for solution. A binary search is performed on the auxiliary variable t, ultimately yielding the optimal solution set {V} of the above optimization problem. * ,t * For the optimal solution set {V} * ,t *}, using a Gaussian random process, generate a high-quality rank-one V * And obtain the optimal reflected beamforming vector υ from it.* .

[0141] (b) Optimize the transmit beamforming vector at the base station.

[0142] In step (b):

[0143] Based on the optimal reflected beamforming vector υ obtained in step (a) * This is achieved by optimizing the transmit beamforming vector ω to maximize the received signal-to-noise ratio at legitimate users. Furthermore, the transmit beamforming weighting factor is defined. Relationship with the transmitted beamforming vector ω:

[0144]

[0145] in MN A The columns form the basis vectors for actively eavesdropping on users, leading to the following optimization problem:

[0146]

[0147]

[0148]

[0149] Due to the complexity of nonconvex problems, the optimal transmit beamforming weight vector 'a' is obtained by using the SDP method based on the CCCP architecture. * And according to formula (12), the optimal transmit beamforming vector ω is obtained. * .

[0150] To further illustrate the effect of this embodiment, a simulation analysis was performed, with the scene set at 100×100m. 2 Within the area, there is a base station equipped with M=10 antennas, a RIS with N=20 reflector elements, and a single-antenna legal user, N A = 2 active eavesdropping users, and an unknown number of passive eavesdropping users with unknown location information. System channel model parameters: α bk =3.5, α br =2.0 and α rk =2.5. The mathematical model for the optimization problem involves the following parameters: ξ = 15 dB; P B =25W.

[0151] like Figure 3 As shown, the variation of the received signal-to-noise ratio (SNR) for legitimate users under different base station transmit power budgets was investigated. The maximum SNR at legitimate users steadily increases with the continuous increase of the transmit power upper limit. This is because when P... BWhen the power is sufficiently large, the base station cannot fully utilize the transmission power budget; otherwise, the proposed algorithm cannot meet the received signal-to-noise ratio constraints of passively eavesdropping users. Overall, the proposed RIS-assisted zero-forcing beamforming method outperforms the baseline algorithm. Since the RIS-assisted zero-forcing beamforming method can eliminate co-channel interference by optimizing the reflected beamforming vector, the zero-forcing beamforming method without RIS assistance (W / O RIS) is not an asymptotically optimal solution.

[0152] This invention applies the RIS-assisted zero-forcing beamforming method to physical layer security technology, establishes a RIS-assisted downlink MISO secure transmission system model, and constructs a mathematical optimization problem to maximize the signal-to-noise ratio (SNR) received by legitimate users. An efficient iterative optimization algorithm is used to jointly optimize the transmit beamforming vector and the reflective beamforming vector. Under the conditions of limited SNR received by passively eavesdropping users, phase amplitude constraints of RIS reflective units, and total transmit power limitations, the optimization objective of maximizing the SNR received by legitimate users is achieved.

[0153] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A RIS-assisted zero-forcing beamforming method based on physical layer security technology, characterized in that, Includes the following steps: A RIS-assisted downlink MISO secure transmission system model is constructed. The downlink MISO secure transmission system model is equipped with a base station with multiple antennas and a RIS with multiple reflection units. The base station transmits downlink data with a legitimate user with a single antenna. There are multiple active eavesdropping users and passive eavesdropping users around the legitimate user. A mathematical model of passive eavesdropping users based on stochastic geometry is constructed, and channels from the base station to the user, from the base station to the RIS, and from the RIS to the user are established. The signal received at the user's location is represented as: ； ； in, Indicates user Gaussian white noise at that location Indicates noise power. Represents a set of users. Indicates legitimate users, Indicates known CSI eavesdropping users, This indicates that the user being monitored by the unknown CSI is unknown. Represents the reflected beamforming vector. and This represents the amplitude and phase of the Nth reflecting unit. This represents the base station transmit beamforming vector. This indicates the expected signal of legitimate users. G represents the channel from RIS to the user, and G represents the channel from the base station to the user. This indicates the channel from the base station to the RIS; Construct the signal-to-noise ratio expressions for legitimate users and eavesdropping users; user The general formula for the received signal-to-noise ratio at a given location is: ； For passive eavesdropping users with unknown CSI, a homogeneous Poisson process is used for modeling. The average signal-to-noise ratio is introduced to examine the eavesdropping behavior of the passive users. The user is recorded... Combined channel vector at the location The average signal-to-noise ratio at the passive eavesdropping user is: ； in, Indicates the average effective channel from the base station to the passively eavesdropping user, subscript This indicates that the CSI is eavesdropping on an unknown user. Construct an optimization problem model for maximizing the signal-to-noise ratio received by legitimate users, including determining the optimization variables, objective function, and constraints, and pre-designing the transmit beamforming vector based on the zero-forcing algorithm; A joint optimization algorithm for transmit beamforming and reflective beamforming vectors is constructed. The optimization problem of maximizing the signal-to-noise ratio received by legitimate users is divided into two sub-problems for solution: optimizing the reflective beamforming vector at the RIS and optimizing the transmit beamforming vector at the base station.

2. The RIS-assisted zero-forcing beamforming method based on physical layer security technology according to claim 1, characterized in that, The effective channel from the base station to the user via RIS is represented as follows: ； ； The channel model between the base station and the user is represented as follows: ； ； in, G represents the channel from RIS to the user, and G represents the channel from the base station to the user. This indicates the channel from the base station to the RIS. Represents the reflected beamforming vector. and This represents the amplitude and phase of the Nth reflecting unit. This represents the path loss value at a reference distance of 1m. The distance between the base station and the user. The path loss factor between the base station and the user. Rice factor, For the direct path component, This represents the Rayleigh fading component.

3. The RIS-assisted zero-forcing beamforming method based on physical layer security technology according to claim 1, characterized in that, Construct an optimization problem model to maximize the signal-to-noise ratio received by legitimate users, specifically including: ； ； ； ； ； in, Indicates the signal-to-noise ratio received by legitimate users. This indicates the received signal-to-noise ratio of the passively eavesdropping user. This represents the set threshold for the received signal-to-noise ratio of a passively eavesdropping user. This indicates the signal-to-noise ratio received by the user being actively eavesdropped on. Indicates the reflection phase of RIS. P represents the base station transmit beamforming vector. B This indicates the set threshold for the transmit power at the base station.

4. The RIS-assisted zero-forcing beamforming method based on physical layer security technology according to claim 1, characterized in that, The transmit beamforming vector is pre-designed based on the zero-forcing algorithm, specifically including: Assuming a RIS-assisted downlink MISO secure transmission system model has an active eavesdropping user, the designed zero-forcing beamforming vector is represented by projecting the legitimate user's channel onto the active eavesdropping user's null space: ； Among them, matrix of The columns form the basis vectors for actively eavesdropping on users. This indicates the channel from the base station to the legitimate user.

5. The RIS-assisted zero-forcing beamforming method based on physical layer security technology according to claim 1, characterized in that, Optimizing the reflected beamforming vector at RIS specifically includes: Fixed transmit beamforming vector The optimization problem model for maximizing the signal-to-noise ratio received by legitimate users with RIS assistance is transformed into: ； ； ； Introducing auxiliary variables The optimization problem model for maximizing the signal-to-noise ratio received by legitimate users with RIS assistance is transformed into: ； ； ； ； in, Represents the reflected beamforming vector. and This represents the amplitude and phase of the Nth reflecting unit. , , Indicates the channel from the base station to the user. This represents the channel from RIS to the legitimate user. This indicates that RIS is eavesdropping on users' channels through an unknown CSI. This represents the base station transmit beamforming vector. Indicates noise power. This represents the set threshold for the received signal-to-noise ratio of a passively eavesdropping user. Indicates the reflection phase of RIS; The optimal reflected beamforming vector is obtained by solving a convex optimization problem using a semidefinite relaxation method, and a rank-one vector is generated through a Gaussian randomization process. ; For auxiliary variables By performing a binary search, the optimal solution set for the above optimization problem can be obtained. For the optimal solution set A rank-one random number is generated through a Gaussian random process. And obtain the optimal reflected beamforming vector from it. .

6. The RIS-assisted zero-forcing beamforming method based on physical layer security technology according to claim 5, characterized in that, The optimal reflected beamforming vector is obtained by solving a convex optimization problem using a semi-definite relaxation method, specifically including: The semidefinite relaxation method is used to solve the convex optimization problem, which is expressed as: ； ； ； ； Among them, P B This indicates the set threshold for the transmit power at the base station. , , ; Depend on , ,remember The optimization problem then transforms into the following problem: ； ； ； ； ； ； ； By relaxing the formula, the optimization problem is transformed into a feasibility problem for solution.

7. The RIS-assisted zero-forcing beamforming method based on physical layer security technology according to claim 1, characterized in that, Optimizing the transmit beamforming vector at the base station specifically includes: Based on the obtained optimal reflection beamforming vector, the transmission beamforming vector is optimized. To maximize the received signal-to-noise ratio at legitimate users, construct the transmit beamforming weighting factor. With transmitted beamforming vector The relationship is represented as: ； in, of The columns form the basis vectors for actively eavesdropping on users, leading to the following optimization problem: ； ； ； in, This represents the equivalent channel between the base station and the legitimate user. P represents the equivalent channel between the base station and the unknown CSI eavesdropping user. B This indicates the set threshold for the transmit power at the base station. Based on the CCCP architecture, the optimal transmit beamforming weight vector is obtained by using the SDP method. And obtain the optimal transmit beamforming vector. .

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