Design method of v2v physical layer security transmission based on joint optimization of active and passive beams

Abstract

The application discloses a kind of V2V physical layer security transmission design methods based on active and passive beam joint optimization, comprising the following steps: establishing intelligent reflecting surface assisted vehicle networking communication system model;According to the power constraint of sending end and the unit phase shift constraint of intelligent reflecting surface, the objective function of the safety rate of vehicle mobile user in vehicle networking communication system model is established;The objective function is solved by using Charnes-Cooper transformation and SDR algorithm, and the optimal sending precoding matrix and the optimal phase shift matrix that meet the objective function are obtained;Vehicle networking communication system model is communicated based on the optimal sending precoding matrix and the optimal phase shift matrix obtained, and the safety rate of vehicle mobile user is maximized.Its remarkable effect is: under the condition of meeting the total power constraint of emission, IRS unit phase shift module one constraint, the safety rate of V2V vehicle mobile user is maximized, compared with traditional technical scheme, the performance of vehicle networking communication system can be greatly improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a V2V physical layer secure transmission design method based on joint optimization of active and passive beams. Background Technology

[0002] With the development of mobile communication and vehicle-to-everything (V2X) communication, vehicle users are increasingly demanding higher speeds, lower latency, higher quality, and wider coverage. This places higher demands on the infrastructure and security of V2X communication networks. Due to limitations in coverage and transmission capacity, traditional V2X communication networks cannot meet the service quality and transmission rate requirements of vehicle users. Furthermore, in complex V2X environments, the high-speed movement of vehicle users, frequent switching of network connection points, and interference and attenuation caused by obstacles and terrain pose significant challenges to V2X communication.

[0003] A smart reflector (IRS) is a planar array composed of numerous passive components. As a key technology in 6G vehicular networks, it can independently control the phase shift and amplitude of the incident signal through an intelligent controller, thereby improving the wireless channel environment. Depending on different needs, the IRS phase can be set to be the same as the direct signal from a legitimate vehicle user to enhance the signal at the receiver; or opposite to the direct signal from an eavesdropping vehicle user to weaken the signal at the receiver. The smart reflector actively modulates the reflected signal, selectively enhancing or suppressing signal propagation, overcoming the uncontrollable nature of traditional wireless channels. This characteristic makes smart reflectors have broader application prospects in the field of wireless communication.

[0004] In recent years, smart reflectors have attracted widespread attention and research in the field of assisted wireless communication, with extensive studies on issues such as channel estimation, channel capacity, spectral efficiency, and secure rates. However, research on applying smart reflectors to vehicle-to-everything (V2V) communication models is relatively limited, and the secure rate issue in existing research remains unresolved for the MISO-based V2V communication model. Therefore, this invention proposes an IRS-assisted transmission design method for secure physical layer communication in the MISO V2V communication model, aiming to achieve more reliable, secure, and higher-speed communication. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a V2V physical layer secure transmission design method based on joint optimization of active and passive beams for communication between V2V vehicle mobile users in the MISO vehicle-to-everything (V2V) communication model. This method enhances the reception of legitimate signals at the receiver and maximizes the secure rate of V2V vehicle mobile users while satisfying the total transmit power constraint and the unit phase shift modulo-one constraint of the IRS. Compared with traditional technical solutions, this method can significantly improve the performance of the vehicle-to-everything (V2X) communication system.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A V2V physical layer secure transmission design method based on joint optimization of active and passive beams, the key of which includes the following steps:

[0008] Step 1: Establish a vehicle-to-everything (V2X) communication system model based on intelligent reflector-assisted communication.

[0009] Step 2: Based on the power constraints of the transmitting end and the unit phase shift constraints of the intelligent reflector, establish the objective function of the safe rate of vehicle mobile users in the vehicle-to-everything (V2X) communication system model;

[0010] Step 3: Solve the objective function using the Charnes-Cooper transform and the SDR algorithm to obtain the optimal transmit precoding matrix and the optimal phase shift matrix that satisfy the objective function;

[0011] Step 4: The vehicle-to-everything (V2X) communication system model communicates based on the obtained optimal transmission precoding matrix and optimal phase shift matrix to maximize the safe speed for vehicle users.

[0012] Furthermore, the expression for the objective function is:

[0013]

[0014] st||w|| 2 ≤P max ,

[0015] |p n |=1,n=1,2,...,N,

[0016] Among them, C sec For the safe rate of the vehicle-moving user, w is the beamforming vector of the transmitter, Φ = diag(p1, p2, ..., p N P is the phase shift matrix of the intelligent reflector. max The maximum power of the transmitting end. These represent the amplitude and phase of the nth element of the intelligent reflector, respectively.

[0017] Furthermore, the solution process for the objective function is as follows:

[0018] Step 3.1: Divide the objective function into two independent sub-problems: the beamforming vector w at the transmitter and the phase shift matrix Φ of the smart reflector.

[0019] Step 3.2: Fix the phase shift matrix Φ of the smart reflector and optimize the beamforming vector w of the transmitter using the Charnes-Cooper transform method;

[0020] Step 3.3: Fix the beamforming vector w at the transmitting end and optimize the phase shift matrix Φ of the smart reflector;

[0021] Step 3.4: Repeat steps 3.2 and 3.3 to optimize the two subproblems alternately until the convergence condition is met and the optimal solution of the objective function is obtained.

[0022] Furthermore, the specific process of optimizing the beamforming vector w at the transmitting end by fixing the phase shift matrix Φ of the smart reflector in step 3.2 is as follows:

[0023] Step A1: The optimization process of the beamforming vector w at the transmitting end is expressed as problem P2:

[0024] 02:

[0025] st||w|| 2 ≤P max ;

[0026] Step A2, Assume H tr =h TR +f IR ΦG,H te =h TE +f IE ΦG, but Redescribe P2 as P3:

[0027] P3:

[0028] st||w|| 2 ≤P max ;

[0029] Step A3: Using the SDR approach, transform the non-convex problem into a relaxation problem, that is, let W = ww H And apply the Charnes-Cooper transform to P3, that is, assume Then P3 is equivalent to P4:

[0030] P4:

[0031]

[0032] Step A4: Apply rank-one constraints Relaxation is performed, and then P4 is optimized using the CVX solver.

[0033] Furthermore, the rank-one constraint described in step A4 uses Gaussian randomization to separate w from W.

[0034] Furthermore, the specific process of optimizing the phase shift matrix Φ of the smart reflector by fixing the beamforming vector w at the transmitting end in step 3.3 is as follows:

[0035] Step B1: The optimization process of the phase shift matrix Φ of the smart reflector is redescribed as a relaxed form problem P5 by removing the constraint rank(P) = 1.

[0036] P5:

[0037] stP≥0,P n,n =1n=1,2,...,N+1;

[0038] Step B2: Based on the change-of-base formula, rewrite problem P5 as problem P6:

[0039] P6:

[0040] stP≥0,P n,n =1, n=1,2,...,N+1;

[0041] Step B3, let Then problem P6 is transformed into problem P7:

[0042] P7:

[0043] stP≥0,P n,n =1, n=1,2,...,N+1;

[0044] Step B4: For P and t, use an alternating optimization method. When P is fixed, Given t * When the problem of optimizing P is expressed as P8:

[0045] P8:

[0046] stP≥0,P n,n =1, n=1,2,...,N+1;

[0047] Step B5: Solve problem P8 using the CVX convex optimization solver.

[0048] Furthermore, in step B1, the constraint rank(P) = 1 is solved by eigenvalue decomposition and Gaussian randomization to obtain an approximate solution.

[0049] The significant effects of this invention are:

[0050] (1) This invention studies the physical layer security design method in vehicle-to-everything (V2X) network based on intelligent reflector-assisted design. For the MISO system model, in the V2V scenario, considering the constraints of the total power of the mobile user transmitter and the phase shift mode constraint of the intelligent reflector, the active and passive beamforming is jointly designed to maximize the system's safe rate. The physical layer security problem of MISO V2X network communication based on intelligent reflector-assisted design is modeled.

[0051] (2) This invention designs a model for intelligent reflector-assisted vehicle-to-everything (V2V) communication, jointly optimizing the active and passive beamforming vectors to maximize the system's safe rate. Furthermore, by alternately optimizing the transmitter precoding and the phase shift matrix of the intelligent reflector, an optimization algorithm is designed to solve the non-convex problem of maximizing the safe rate. Specifically, the optimal transmitter precoding matrix is ​​obtained by combining the Charnes-Cooper transform with the SDR algorithm; semidefinite relaxation is used to relax the rank-1 constraint in the constraints; mathematical lemma one is used to transform the non-convex objective function into a convex, easily solvable function; finally, the optimized phase shift matrix is ​​obtained. Thus, under the conditions of satisfying the total transmit power constraint and the unit phase shift modulus-1 constraint of the IRS, the antenna beam can be directed, i.e., signal amplification or suppression, maximizing the safe rate of V2V vehicle users and effectively improving the communication quality between vehicle users. Attached Figure Description

[0052] Figure 1 This is a flowchart of the method of the present invention;

[0053] Figure 2 This is a schematic diagram of the vehicle-to-everything (V2X) communication system model of the present invention. Detailed Implementation

[0054] The specific embodiments and working principles of the present invention will be further described in detail below with reference to the accompanying drawings.

[0055] This embodiment addresses the MISO vehicle-to-everything (V2V) communication model. In the context of communication between V2V vehicle mobile users, it proposes an IRS-assisted transmission design method for secure physical layer communication in the V2V network, enhancing the reception of legitimate signals at the receiver. This method maximizes the secure rate for V2V vehicle mobile users while satisfying the total transmit power constraint and the IRS's unit phase shift modulo-one constraint. Compared to traditional technical solutions, this significantly improves the performance of the V2V communication system. Specifically, it addresses three technical problems: 1) designing the optimal transmit precoding for V2V communication; 2) designing the optimal intelligent reflector phase shift matrix for V2V communication; and 3) jointly designing the optimal transmit precoding and IRS phase shift matrix for V2V communication.

[0056] like Figure 1 As shown in the figure, this embodiment proposes a V2V physical layer secure transmission design method based on joint optimization of active and passive beams. The specific steps are as follows:

[0057] Step 1: Establish a vehicle-to-everything (V2X) communication system model based on intelligent reflector-assisted communication. Specifically:

[0058] Considering the downlink V2V scenario of an IRS-assisted MISO system, the communication model is as follows: V2V transmission exists within the C-V2X architecture, and its frequency, power, and other resource allocation are controlled by the base station. An IRS is deployed on the roadside to assist vehicle communication. The IRS contains N reflecting units, and their phases are adjusted by the IRS controller based on base station control information. Vehicle mobile user T sends information to vehicle mobile user R, while mobile user E is eavesdropping on the signal.

[0059] Considering the downlink V2V scenario of an IRS-assisted MISO system, the communication model is as follows: V2V transmission exists within the C-V2X architecture, and its frequency, power, and other resource allocation are controlled by the base station. An IRS is deployed on the roadside to assist vehicle communication. The IRS contains N reflecting units, and their phases are adjusted by the IRS controller based on base station control information. Vehicle mobile user T sends information to vehicle mobile user R, while mobile user E is eavesdropping on the signal.

[0060] The vehicle-to-everything (V2X) communication system model is as follows: Figure 2 As shown, the channels between user T and user R, user T and user E, and user T and IRS are respectively denoted as... and The channels between the IRS and vehicle user R and vehicle user E are denoted as f, respectively. IR ∈ 1×N and The phase shift diagonal matrix of the IRS is Φ = diag(p1, p2, ..., p N ), Where βn ∈[0,1], θ n ∈[0,2π] represent the amplitude and phase of the nth element of the IRS, respectively. For simplicity, assume that each element maximizes signal reflection, i.e., β n =1, IRS maximizes the incident signal by adjusting the phase of the reflecting element.

[0061] Assuming the CSI of all channels is known, the results presented in this paper can serve as an upper bound on system performance when the CSI is not ideal in practice. All channels are assumed to be Ricean channels. A Ricean channel can be represented as:

[0062]

[0063] Where, k h This represents the Ricean fading factor. h LoS A line-of-sight link (direct-fire link) can be represented by antennas or phase-shift arrays at the transmitting and receiving ends. The placement angle of the antennas determines the arrival and departure angles of the signal. NLoS Multipath effects are caused by reflection, diffraction, and scattering. The equivalent multipath link resulting from the superposition of multiple reflection paths is a non-line-of-sight random scattering path, which can be represented by a standard complex Gaussian distribution. Assuming the transceiver antennas and IRS phase-shifting array are arranged in a uniform linear array (ULA), then:

[0064]

[0065] Where d is the spacing between antenna elements, λ is the carrier wavelength, and m represents the dimension of the vector. This represents the beam angle of arrival (AoA) or departure angle (AoD) for a single array element or antenna. Assuming d / λ = 1 / 2, a line-of-sight link can be represented as follows:

[0066]

[0067]

[0068]

[0069] in, These are the outbound and return angles from mobile users T and IRS to users R and E, respectively. These are the angle of arrival and the angle of departure for user T from IRS, respectively.

[0070] The transmitted signal of the vehicle mobile user T is: x = ws;

[0071] The signals received by vehicle mobile user R and vehicle mobile user E are represented as follows:

[0072] y R =(hTR +f IR ΦG)x+n R (6)

[0073] y E =(h TE +f IE ΦG)x+n E (7)

[0074] in, Let T represent the beamforming vector at vehicle user T, and satisfy the power constraint. P max Let represent the maximum power transmitted by vehicle user T. Let s represent the confidential information transmitted by vehicle user T, satisfying... n R n E Let represent the additive complex white Gaussian noise at the receiver, with a mean of zero and a variance of σ. 2 .

[0075] The information rate between the legitimate user R and the eavesdropping user E is expressed as follows:

[0076]

[0077]

[0078] Therefore, the safe rate of the vehicle-to-everything (V2X) communication model is represented as follows:

[0079]

[0080] in,{.} + =max{.,0};

[0081] Step 2: Based on the power constraints of the transmitting end and the unit phase shift constraints of the intelligent reflector, establish the objective function of the safe rate of vehicle mobile users in the vehicle-to-everything (V2X) communication system model;

[0082] Based on the above scenario, under the constraints of power and unity modulus of the phase shift matrix, the system's safe rate is maximized by optimizing the beamforming vector of the transmitter T and the phase shift matrix of the reflector. Therefore, the corresponding optimization problem can be expressed as problem P1, which is the objective function:

[0083] P1:

[0084] st||w|| 2 ≤P max (11b)

[0085] |p n |=1,n=1,2,...,N (11c)

[0086] Step 3: Solve the objective function using the Charnes-Cooper transform and the SDR algorithm to obtain the optimal transmit precoding matrix and the optimal phase shift matrix that satisfy the objective function;

[0087] Observing problem P1, we can see that the two constraints are independent, namely the power constraint and the phase shift constraint, but the objective function is coupled with these two constraints, making direct solution to the optimization problem very difficult. Therefore, an alternating optimization method can be used to solve the overall problem. That is, the overall objective function is divided into two independent subproblems, and the two subproblems are continuously optimized alternately until the convergence condition is met, obtaining the optimal solution to the overall objective function.

[0088] The basic idea is to first fix the phase shift matrix Φ of the smart reflector and optimize the beamforming vector of the transmitter using the Charnes-Cooper transform method; then fix the beamforming vector w of the transmitter to optimize the IRS reflection beamforming vector.

[0089] Therefore, the process of solving the objective function is as follows:

[0090] Step 3.1: Divide the objective function into two independent sub-problems: the beamforming vector w at the transmitter and the phase shift matrix Φ of the smart reflector.

[0091] Step 3.2: Fix the phase shift matrix Φ of the smart reflector, and optimize the beamforming vector w of the transmitter using the Charnes-Cooper transform method. The specific process is as follows:

[0092] Step A1: The optimization process of the beamforming vector w at the transmitting end is expressed as problem P2:

[0093] P2:

[0094] st||w|| 2 ≤P max (12b)

[0095] Step A2: Obviously, the objective function (12a) is non-convex. For ease of subsequent solution, assume H tr =h TR +f IR ΦG,H te =h TE +f IE ΦG, but Because of the monotonically increasing nature of the logarithmic function, omitting the logarithm will not change the performance. Therefore, problem P2 can be rewritten as P3:

[0096] P3:

[0097] st||w|| 2 ≤P max (13b)

[0098] Step A3: To solve the non-convex optimization problem, the SDR approach is used to transform the non-convex problem into a relaxation problem, that is, let W = ww H And apply the Charnes-Cooper transform to the objective function (13a), that is, assume Then P3 is equivalent to P4:

[0099] P4:

[0100]

[0101]

[0102] Step A4: Apply rank-one constraints Relaxation is performed, and then P4 is optimized using the CVX solver.

[0103] Will With this constraint relaxed, problem P4 can be directly optimized using the CVX solver. The omitted rank-one constraint can be separated from W using Gaussian randomization.

[0104] Step 3.3: Fix the beamforming vector w at the transmitting end and optimize the phase shift matrix Φ of the smart reflector. The specific process is as follows:

[0105] From reflection link f IR As can be seen from ΦGw, the active and passive beamforming vectors w and Φ are coupled together; therefore, Φ needs to be separated from the reflection link. Define p. H =[p1,p2,...,p N ], Then we have:

[0106]

[0107] The information rate at mobile user R can be expressed as:

[0108]

[0109] Among them, G R =diag(f IR )G,

[0110] Similarly, The information rate at mobile user E can be expressed as:

[0111]

[0112] Among them, G E =diag(f IE )G,

[0113] Step B1: The optimization process of the phase shift matrix Φ of the smart reflector is redescribed as a relaxed form problem P5 by removing the constraint rank(P) = 1.

[0114] P5:

[0115] stP≥0,P n,n =1n=1,2,...,N+1 (18b)

[0116] Step B2, according to mathematical lemma one: for the function If and only if t = x -1 ,have Therefore, by applying the change-of-base formula to the objective function (18a), it can be rewritten as:

[0117]

[0118] Omitting ln2 does not affect the overall optimality, and the method with a maximum value using Lemma 1 is C. sec The function has an optimal solution. Optimization problem P5 is reformulated as P6:

[0119] P6:

[0120] stP≥0,P n,n =1,n=1,2,...,N+1 (20b)

[0121] Step B3, let Referring to the mathematical lemma one above, the objective function (20a) is rewritten as:

[0122]

[0123] The optimization problem is transformed into a P7 problem:

[0124] P7:

[0125] stP≥0,P n,n =1,n=1,2,...,N+1 (22b)

[0126] Step B4: For P and t, use an alternating optimization method. When P is fixed, Given t * When the problem of optimizing P is expressed as P8:

[0127] P8:

[0128] stP≥0,P n,n =1,n=1,2,...,N+1 (23b)

[0129] Step B5: At this point, the objective function (23a) is a convex semidefinite programming problem, so it can be solved using the CVX convex optimization solver. Since the semidefinite relaxation technique was used to relax the rank(P) = 1 constraint during the optimization process, it cannot be guaranteed that P is a matrix that satisfies the constraint. Therefore, eigenvalue decomposition and Gaussian randomization can be used to obtain an approximate solution to the original problem.

[0130] Step 3.4: Repeat steps 3.2 and 3.3 to optimize the two subproblems alternately until the convergence condition is met and the optimal solution of the objective function is obtained.

[0131] Step 4: The vehicle-to-everything (V2X) communication system model communicates based on the obtained optimal transmission precoding matrix and optimal phase shift matrix to maximize the safe speed for vehicle users.

[0132] The experimental conditions corresponding to the embodiments of the present invention are shown in Table 1:

[0133] Table 1: Experimental Parameter Table

[0134] Parameter name Value Parameter name Value Number of antennas at the mobile user transmitter T 3 average speed 70 km / h Number of receiving antennas for mobile user R and user E 1 Number of lanes 2 Noise power spectral density -174dBm / Hz Coordinates of mobile user T (0,6,1.5) Channel bandwidth Hz Coordinates of mobile user R (50,6,1.5) noise power -80dBm Coordinates of mobile user E (30,3,1.5) Number of IRS reflective units 30 IRS coordinates (30,0,20) Channel fading model Rice's decline Road length 200m Path loss index of direct links 3.75 Road width 8m Path loss index of the reflection link 2.2 Number of vehicle terminals 3 Rice's decline factor 10 Error accuracy 0.001 Vehicle antenna height 1.5m Monte Carlo times 1000

[0135] In summary, this invention, based on intelligent reflector-assisted vehicle-to-everything (V2V) communication, establishes a multi-input single-output (MIB) communication system model to maximize the safe speed for vehicle users. The proposed optimization problem is solved through joint active and passive beamforming. Due to the non-convexity of the objective function, an alternating active and passive beamforming optimization method is used to address the difficulties arising from beam coupling. The active beamforming subproblem is optimized using an algorithm combining Charnes-Cooper transform and SDR; the reflected beamforming subproblem is optimized using mathematical lemmas and Gaussian randomization. Thus, while satisfying the total transmit power constraint and the unit phase shift modulo-one constraint of the IRS, the safe speed for V2V vehicle users is maximized. Compared to traditional technical solutions, this significantly improves the performance of the vehicle-to-everything (V2X) communication system, achieving more reliable, safer, and higher-speed communication.

[0136] The technical solution provided by this invention has been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make several improvements and modifications to this invention without departing from the principles of this invention, and these improvements and modifications also fall within the protection scope of the claims of this invention.

Claims

1. A V2V physical layer secure transmission design method based on joint optimization of active and passive beamforming, characterized in that, Includes the following steps: Step 1: Establish a vehicle-to-everything (V2X) communication system model based on intelligent reflector-assisted communication. Step 2: Based on the power constraints of the transmitting end and the unit phase shift constraints of the intelligent reflector, establish the objective function of the safe rate of vehicle mobile users in the vehicle-to-everything (V2X) communication system model; Step 3: Solve the objective function using the Charnes-Cooper transform and the SDR algorithm to obtain the optimal transmit precoding matrix and the optimal phase shift matrix that satisfy the objective function; Step 4: The vehicle-to-everything (V2X) communication system model communicates based on the obtained optimal transmission precoding matrix and optimal phase shift matrix to maximize the safe speed for vehicle users.

2. The V2V physical layer secure transmission design method based on joint optimization of active and passive beams as described in claim 1, characterized in that, The expression for the objective function is: s.t.||w|| 2 ≤P max , |p n |=1,n=1,2,...,N, Among them, C sec For the safe rate of the vehicle-moving user, w is the beamforming vector of the transmitter, Φ = diag(p1, p2, ..., p N P is the phase shift matrix of the intelligent reflector. max The maximum power of the transmitting end. n = 1, 2, ..., N, β n ∈[0,1]、θ n ∈[0,2π] represents the amplitude and phase of the nth element of the intelligent reflector, respectively.

3. The V2V physical layer secure transmission design method based on joint optimization of active and passive beams as described in claim 1 or 2, characterized in that, The process of solving the objective function is as follows: Step 3.1: Divide the objective function into two independent sub-problems: the beamforming vector w at the transmitter and the phase shift matrix Φ of the smart reflector. Step 3.2: Fix the phase shift matrix Φ of the smart reflector and optimize the beamforming vector w of the transmitter using the Charnes-Cooper transform method; Step 3.3: Fix the beamforming vector w at the transmitting end and optimize the phase shift matrix Φ of the smart reflector; Step 3.4: Repeat steps 3.2 and 3.3 to optimize the two subproblems alternately until the convergence condition is met and the optimal solution of the objective function is obtained.

4. The V2V physical layer secure transmission design method based on joint optimization of active and passive beams according to claim 3, characterized in that, The specific process of optimizing the beamforming vector w at the transmitting end by fixing the phase shift matrix Φ of the smart reflector in step 3.2 is as follows: Step A1: The optimization process of the beamforming vector w at the transmitting end is expressed as problem P2: P2： s.t.||w|| 2 ≤P max ; Step A2, Assume H tr =h TR +f IR ΦG,H te =h TE +f IE ΦG, but Redescribe P2 as P3: P3： s.t.||w|| 2 ≤P max ; Step A3: Using the SDR approach, transform the non-convex problem into a relaxation problem, that is, let W = ww H And apply the Charnes-Cooper transform to P3, that is, assume Then P3 is equivalent to P4: P4： Step A4: Apply rank-one constraints Relaxation is performed, and then P4 is optimized using the CVX solver.

5. The V2V physical layer secure transmission design method based on joint optimization of active and passive beams according to claim 4, characterized in that, The rank-one constraint mentioned in step A4 uses Gaussian randomization to separate w from W.

6. The V2V physical layer secure transmission design method based on joint optimization of active and passive beams according to claim 3, characterized in that, The specific process of optimizing the phase shift matrix Φ of the smart reflector by fixing the beamforming vector w at the transmitting end in step 3.3 is as follows: Step B1: The optimization process of the phase shift matrix Φ of the smart reflector is redescribed as a relaxed form problem P5 by removing the constraint rank(P) = 1. P5： s.t.P≥0,P n,n =1n=1,2,...,N+1; Step B2: Based on the change-of-base formula, rewrite problem P5 as problem P6: P6： s.t.P≥0,P n,n =1,n=1,2,...,N+1; Step B3, let Then problem P6 is transformed into problem P7: P7： s.t.P≥0,P n,n =1,n=1,2,...,N+1; Step B4: For P and t, use an alternating optimization method. When P is fixed, Given t * When the problem of optimizing P is expressed as P8: P8： s.t.P≥0,P n,n =1,n=1,2,...,N+1; Step B5: Solve problem P8 using the CVX convex optimization solver.

7. The V2V physical layer secure transmission design method based on joint optimization of active and passive beams according to claim 6, characterized in that, In step B1, the constraint rank(P) = 1 is solved by eigenvalue decomposition and Gaussian randomization to obtain an approximate solution.

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