Internet of vehicles communication and sensing integration method based on intelligent surface

Abstract

The invention provides an Internet of Vehicles communication and sensing integration method based on an intelligent surface so as to improve communication quality and environment sensing capability. Firstly, wireless channel models between a base station and an intelligent surface IRS, between the IRS and a vehicle and between the base station and the vehicle are constructed; then, the base station sends a communication signal to the main vehicle and senses a side vehicle by using the transmission signal, and an echo signal generated by the side vehicle is reflected back to the base station through the IRS so as to realize target tracking; under the condition of known imperfect channel state information (CSI), taking the transmission rate and maximization of a main vehicle user as targets, and jointly optimizing a base station beam forming weight vector, an IRS phase shift matrix and a weighting factor; then, aiming at global positioning system errors and CSI uncertainty, an error model is introduced, probability constraints are combined, and system design is optimized to improve robustness; the method is suitable for scenes such as intelligent traffic and automatic driving, communication reliability and environment sensing precision can be improved, and technical support is provided for communication and sensing integration of the 6G Internet of Vehicles.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking communications, and in particular to a vehicle networking tele-sensory integration method based on a smart surface. Background Art

[0002] With the maturity of 5G technology and the rapid development of 6G, the Internet of Vehicles (IoV), a key component of intelligent transportation systems, is placing higher demands on high data rates, ultra-low latency, and high reliability. Traditional IoV systems employ a design architecture that separates communication and perception functions. This design suffers from low spectrum utilization, hardware redundancy, and high system complexity, severely hindering their further development.

[0003] To address this issue, ISAC (Integrated Synaesthesia Calling) technology was proposed and has received widespread attention from academia and industry. ISAC technology aims to integrate wireless communication and environmental perception functions into the same system, giving full play to the complementarity between the two to improve the overall system performance and spectrum utilization. This technology has become one of the key features of 6G networks, with significant advantages in spectrum sharing, hardware resource reuse, and communication perception collaborative optimization. However, in complex urban traffic environments, factors such as channel obstruction, multipath fading, and high-speed mobility still restrict the application of ISAC technology. In particular, in complex scenarios such as high-density urban roads and highways, improving system performance faces huge challenges.

[0004] Intelligent surface (IRS), an emerging reconfigurable metamaterial technology, has attracted considerable attention in recent years. IRS boasts significant advantages such as low power consumption, ease of deployment, and high flexibility. By dynamically adjusting the phase, amplitude, and polarization characteristics of reflected signals, they effectively improve wireless channel conditions, enhance signal quality, and reduce energy consumption. With the assistance of IRS, connected vehicle systems can optimize signal propagation paths without increasing transmit power, improving communication quality and environmental awareness. IRS demonstrates great potential in complex application scenarios such as connected vehicles and autonomous driving.

[0005] Existing IRS-assisted ISAC systems typically use a common single-polarization or dual-polarization channel model for communication and perception fusion, enabling simultaneous communication with multiple users and detection of multiple targets. To maximize the weighted sum of the signal-to-interference-and-noise ratio (SIN) for multi-target detection, these systems optimize the communication and perception beamforming at the base station, as well as the reflection phase shift of the single-polarization IRS, thereby improving the ISAC system's perception performance. This technology is typically designed for traditional communication environments. In the context of the connected vehicle (IoV) environment, it fails to account for issues unique to real-world traffic scenarios, such as on-board GPS errors and channel state information (CSI) uncertainties. Consequently, the model is unsuitable for this real-world IoV environment. Summary of the Invention

[0006] To address the problems of channel obstruction, multipath fading, and high-speed mobility in complex urban traffic environments, this paper proposes a method for integrating telematics and sensing in the Internet of Vehicles (IoV) system based on smart surfaces. Multiple IRSs are used in the IoV system to implement communication and sensing functions. In wireless transmission scenarios where perfect channel state information is known or where channel state information cannot be accurately acquired, a corresponding optimization problem is established. Using mathematical tools such as optimization theory, an optimal design solution for intelligent surface-assisted IoV telematics is obtained to meet the communication and radar sensing needs of future IoV systems. The present invention provides the following technical solutions:

[0007] A method for integrating vehicle-to-vehicle synaesthesia based on a smart surface comprises the following steps:

[0008] Step 1: Build an integrated vehicle-to-vehicle interawareness system assisted by an intelligent surface IRS, including a base station, an IRS, and vehicles. The vehicles include the main vehicle communicating with the base station and the adjacent vehicles sensed by the base station. The channel models between the base station and the IRS, the IRS and the vehicle, and the base station and the vehicle are constructed using the Rice channel model.

[0009] Step 2: Build wireless transmission and reception signal models between the main vehicle user and the base station, and between the main vehicle user and the IRS, and build a perception model of the base station to the adjacent vehicles.

[0010] Step 3: Construct an optimization problem to maximize the transmission rate of the main vehicle user, while satisfying the constraints of signal-to-interference-noise ratio, beam pattern error, and maximum transmit power.

[0011] In step 4, the optimization problem described in step 3 is split into two sub-problems. The non-convex problem is converted into a convex optimization problem using the Schur complement method. The optimization problem is solved using an alternating iterative method to obtain the base station transmit beamforming matrix, the IRS passive beamforming matrix, and the weighting factors to achieve integrated telematics.

[0012] Preferably, the base station-IRS channel constructed using the Rice channel model in step 1 is expressed as:

[0013]

[0014] Among them, α B2I represents the large-scale fading coefficient, a and b represent the array responses of the base station and IRS, H represents the matrix transpose, and is the effective departure angle in the x-axis and y-axis directions, and is the effective arrival angle in the x-axis and y-axis directions;

[0015] The IRS-vehicle channel is expressed as:

[0016]

[0017] The base station and vehicle are represented as:

[0018]

[0019] Where K is the Rice factor in the Rice channel model.

[0020] Preferably, the wireless transmission and reception signal model between the main vehicle user and the base station and IRS is specifically as follows:

[0021]

[0022] Among them, y U，i represents the signal received by the user of the i-th vehicle, represents the phase shift matrix of IRS and each element thereof should satisfy |φ n |=1,arg(φ)∈[0,2π],the base station transmits a signal w is the active beam, x is the data symbol for the main vehicle user; in the second term of the formula, ii≠i means the sum of all main vehicle users except the i-th main vehicle user. represents additive white Gaussian noise, is the noise variance;

[0023] The signal-to-interference-and-noise ratio expression of the main vehicle user is:

[0024]

[0025] Preferably, the communication signal sent by the base station to the main vehicle user is also used to sense the adjacent vehicle target, and its sensing beam pattern can be expressed as:

[0026]

[0027] in, and represents the sampling angle grid;

[0028] When the accurate angle information is known, the ideal beamforming weight vector w′ i Obtained by Zero-Forcing technology, a sampling angle grid with an angle range of [-π / 2, π / 2] is obtained and The expected sensing beam pattern is:

[0029]

[0030] The square error between the beam pattern and the expected beam pattern is:

[0031]

[0032] β is the weighting factor.

[0033] Preferably, the specific formula of the optimization problem and constraint conditions proposed in step 3 is:

[0034]

[0035] Among them, Λ i is the signal-to-noise ratio threshold of the i-th main vehicle user, ε is the upper limit of the square error allowed between the beam pattern and the expected beam pattern, P max is the maximum transmit power.

[0036] Preferably, a probability constraint is introduced into the optimization problem of step 4, and the specific formula is:

[0037]

[0038] Among them, P i，out is the probability threshold of the i-th main vehicle user.

[0039] Preferably, the specific steps of solving the optimization problem using the alternating iterative method are:

[0040] Step 4.1: Initialize the base station transmit beamforming matrix W i (0) , IRS phase shift matrix U (0) and weighting factor β (0) , and set the calculation accuracy tolerance ε′ and the maximum number of iterations t max ;

[0041] Step 4.2, based on the initial conditions, solve subproblem 1: fix the IRS phase shift matrix U (t-1) , get the optimal beamforming matrix W for the current t-th iteration (t) and weighting factor β (t) ;

[0042] Step 4.3, solve subproblem 2: fix the optimal beamforming matrix W i (t) and weighting factor β (t) , get the optimal IRS phase shift matrix U for the current t-th iteration (t) ;

[0043] Step 4.4, calculate

[0044] Step 4.5, until Δ<ε′ or t=t max End the iteration and get the optimal beamforming matrix W i (t) , IRS phase shift matrix U (t) and weighting factor β (t), use Gaussian randomization or singular value decomposition method to obtain the corresponding base station beamforming weight vector and IRS beamforming vector, otherwise return to step 4.2 to continue iteration.

[0045] Preferably, the sub-problem 1 is to fix the IRS phase shift matrix and solve the optimal base station beamforming weight vector and weighting factor, which is in the form of:

[0046]

[0047] Among them, {ξ i ,ζ i} is a slack variable used to handle non-convex functions, which is reflected by the constraints C5 and C6. and represents the t-th iteration point after the first-order Taylor expansion;

[0048] Introduce the following variables to simplify the formula: W i =w i w i H , represents H in the Schur complement method i estimated value of;

[0049] is the error matrix, I is the unit matrix; Ω ΔH，i is a real-valued matrix, each element of which [Ω ΔH，i ] j，k =σ i ;

[0050] Preferably, the sub-problem 2 is to fix the base station beamforming weight vector and weighting factor and solve the optimal IRS phase shift matrix, which is in the form of:

[0051]

[0052] The phase shift matrix is expressed as Φ = diag(u), Let U = uu H ,but

[0053] Introduce the following variables to simplify the formula:

[0054] in

[0055]

[0056] in

[0057] Preferably, if the rank of the solved beamforming matrix W and IRS phase shift matrix U is not 1, the Gaussian randomization method is used to obtain the optimal solution; if the rank is 1, the singular value decomposition method is used to obtain the base station beamforming weight vector w and IRS beamforming vector u.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. Based on the characteristics of the Internet of Vehicles environment, channel models are constructed between the base station (BS) and the IRS, the IRS and the vehicle, and the BS and the vehicle. The direct path and the reflected path characteristics are described based on the Rice channel to improve signal transmission efficiency and reduce the impact of channel fading. The modeling is closer to the actual Internet of Vehicles deployment environment and is more suitable for practical applications in the transportation field.

[0060] Second, given perfect channel state information (CSI), an optimization problem is established to maximize the transmission rate of the main vehicle user. The base station beamforming, IRS phase shift matrix, and weighting factors are optimized while satisfying the signal-to-interference-and-noise ratio (SINR), beam pattern error, and transmit power constraints. The constraints are richer, more detailed, and more targeted, improving communication reliability and perception capabilities.

[0061] 3. To address the problem of inaccurate CSI acquisition due to channel estimation error, the present invention introduces an error model, combines probabilistic constraints, and uses methods such as semi-definite relaxation (SDR) and Schur complement to transform the optimization problem into an easy-to-solve convex optimization problem, thereby improving the stability and robustness of the system under channel uncertainty conditions and optimizing performance in uncertain environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0063] Figure 1 It is the overall flow chart of the method of the present invention;

[0064] Figure 2 It is a schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0066] In order to make the above-mentioned objects, features and effects of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] Example 1:

[0068] A vehicle-to-vehicle interawareness integration method based on smart surfaces, such as Figure 1 As shown, the following steps are included:

[0069] Step 1: Build an intelligent surface-assisted vehicle-to-vehicle intersensory integrated system architecture, such as Figure 2 As shown, it includes a base station, an IRS and a vehicle. The vehicle includes a main vehicle user communicating with the base station and a side vehicle sensed by the base station. The base station has radar sensing and communication functions; the main vehicle user obtains the base station transmission signal, and the base station obtains the side vehicle echo signal to detect the side vehicle target. The base station is equipped with a uniform linear array of N antennas and uses unicast communication technology to serve M single-antenna main vehicle users. The base station and the main vehicle user can not only communicate directly but also enhance communication through the assistance of IRS. At the same time, the signal transmitted by the base station is reflected by the IRS to sense L obscured side vehicles. In this scenario, it is assumed that the IRS has N R A reflection unit is placed at a suitable location and ignores the same signal reflected multiple times by the IRS.

[0070] According to the deployment characteristics of IRS in the Internet of Vehicles system, the Rice channel model is used to construct the channel models between the base station and IRS, IRS and vehicle, and base station and vehicle. Specifically, according to the Rice channel model, the channel with LoS component is modeled, and the base station-IRS channel is expressed as:

[0071]

[0072] Among them, α B2I represents the large-scale fading coefficient, a and b represent the array responses of the base station and IRS, H represents the matrix conjugate transpose, and is the effective departure angle in the x-axis and y-axis directions, and is the effective arrival angle in the x-axis and y-axis directions, specifically expressed as:

[0073]

[0074] Among them, θ B2I and φ B2I are the elevation angle and azimuth angle sent by the base station to the IRS, θ B2Ia and φ B2Ia are the elevation and azimuth angles of the base station to the IRS, λ is the carrier wavelength, and d BS and d IRSare the antenna element spacings of the base station and IRS, respectively, which are usually set to λ / 2. The formula can be rewritten as:

[0075]

[0076] The nth element of the array response vector a and the mth element of b are:

[0077]

[0078] in:

[0079]

[0080] Among them, Remainder(·) represents the remainder operation, and Quotient(·) represents the quotient operation.

[0081] Similarly, the IRS-vehicle channel is expressed as:

[0082]

[0083] The base station and vehicle are represented as:

[0084]

[0085] Where K is the Rice factor in the Rice channel model.

[0086] Step 2: Build wireless transmission and reception signal models between the main vehicle user and the base station, and between the main vehicle user and the IRS, and build a perception model of the base station to the adjacent vehicles.

[0087] Specifically, the base station transmits a signal w is the active beam, and x is the data symbol for the main vehicle user. The signal received by the main vehicle user can be expressed as:

[0088]

[0089] Among them, y U，i represents the signal received by the user of the i-th vehicle, represents the phase shift matrix of IRS and each element thereof should satisfy |φ n |=1,arg(φ)∈[0,2π],ii≠i in the second term of the formula means summing up the terms whose subscript is not equal to i, that is, summing up the main car users other than the main car user of the i-th car, represents additive white Gaussian noise, is the noise variance.

[0090] Based on the main vehicle user receiving signal model, the main vehicle user signal-to-interference-and-noise ratio is given by the following formula:

[0091]

[0092] The communication signal sent by the base station to the main vehicle user is also used to sense L adjacent vehicles to meet the service quality requirements in sensing. Its sensing beam pattern can be expressed as:

[0093]

[0094] in, and Represents the sampling angle grid.

[0095] When the accurate angle information is known, the ideal beamforming weight vector w i ′ can be obtained by Zero-Forcing (ZF) technology, so w i ′ is a priori known quantity, so we can get the angle grid in the angle range of [-π / 2, π / 2] and The desired sensing beam pattern is:

[0096]

[0097] In the sensing process, the base station first obtains the rough angle information of the target through a wide beam in the detection phase, and then sends a sensing signal to track the target. This application focuses on the tracking phase, aiming to ensure the beam gain at the angle of interest. The square error between the beam pattern and the expected beam pattern is:

[0098]

[0099] β is the weighting factor.

[0100] Step 3: Under the condition of perfect channel state information, construct an optimization problem to maximize the transmission rate of the main vehicle user, and meet the constraints of signal-to-interference-noise ratio, beam pattern error, and maximum transmit power. Solve the optimization problem to obtain the base station transmit beamforming weight vector, IRS phase shift matrix, and weighting factor, as follows:

[0101] In a wireless transmission scenario with known perfect channel state information, the CSI-based intelligent surface-assisted vehicle-to-vehicle interawareness integrated design optimization problem is constructed and expressed as:

[0102]

[0103] Among them, Λ i is the signal-to-noise ratio threshold of the i-th main vehicle user, ε is the upper limit of the square error allowed between the beam pattern and the expected beam pattern, P max is the maximum transmit power.

[0104] In step 4, considering the estimation errors in the use of on-board GPS technology in actual IoV, an error model is introduced into the optimization problem in step 3, and the optimization problem is split into two sub-problems. The optimization problem is solved using an alternating iterative method to obtain the base station transmit beamforming matrix, the IRS passive beamforming matrix, and the weighting factors, thereby realizing the integration of IoV telemetry.

[0105] Specifically, the process of introducing the error model is as follows: an angle detection method based on the vehicle's GPS and the facility's geographic location information is used to estimate the effective angle, and further derive the effective angle from the IRS to the vehicle. During the angle estimation period, the IRS is turned off and the vehicle sends a signal with a power of P to the base station. q The unmodulated carrier, then the baseband signal received by the base station is expressed as:

[0106]

[0107] in is the baseband equivalent representation of the unmodulated carrier, n BS It is subject to mean zero and variance Gaussian noise, * represents the complex conjugate form of the matrix.

[0108] The signal received by the nth antenna is:

[0109]

[0110] The phase of the signal received by the nth antenna can be decomposed into:

[0111]

[0112] Among them, e n represents the phase uncertainty caused by noise and NLoS paths.

[0113] When the Rice factor and the received signal-to-noise ratio are large, the phase uncertainty approximately obeys the mean of zero and the variance of Gaussian distribution. Any two antennas receive signals r n and r m The phase difference between them is:

[0114]

[0115] in, Next, we will use the phase difference To estimate and

[0116] and The ML estimate of is:

[0117]

[0118] and can be broken down into:

[0119]

[0120] Among them, ∈ x-B2U and ∈ y-B2U Represents the estimation error, and has a mean of zero and a variance of Gaussian distribution of , the estimation error between vehicles is the same as above.

[0121] After obtaining the valid angle and distance, the vehicle's position can be estimated

[0122]

[0123] Assume that the base station is at the coordinate origin (0, 0, 0) and the estimated vehicle position is known And the perfect IRS position (x I ,y I , z I ), d I2U represents the distance between the vehicle and the IRS, then the effective angle from the IRS to the vehicle is:

[0124]

[0125] in Then, the effective angle from IRS to the host vehicle can be decomposed into:

[0126]

[0127] Among them, They can be expressed as:

[0128] From the above description, we can see that the error model can be established in the following form:

[0129] h′ B2U,i =h B2U,i ⊙Δh1

[0130] h′ I2U,i =h I2U,i ⊙Δh2

[0131] in and The error model provides a theoretical method for calculating the estimation error and proves the existence of the estimation error. Based on the existence of the estimation error, probabilistic constraints are introduced in the subsequent model.

[0132] Estimation error is introduced based on the known perfect channel state information. The system's synaesthesia integration mode and other system parameters are assumed to remain unchanged. Due to the estimation error of the interference channel, a probabilistic constraint is introduced to reflect the robustness of the system. The optimization problem can be expressed as:

[0133]

[0134] Among them, P i，out is the probability threshold of the i-th main vehicle user.

[0135] The non-convexity of the objective function and constraint function makes this problem difficult to solve. Using the idea of alternating iteration, we can gradually split the original problem into smaller parts, find the optimal solution to each sub-problem, and then try to find the optimal solution to the original problem. The specific steps are as follows:

[0136] The following variables are introduced to simplify the formula:

[0137]

[0138] Signal-to-Interference-Noise Ratio Expression

[0139]

[0140] Constraint C4 is a non-convex constraint. To convert it into a convex constraint, the complex phase is expressed in complex exponential form:

[0141]

[0142] Define the phase shift matrix Φ = diag(u), Φ = diag(u); let U = uu H , then constraint C4 can be transformed into:

[0143]

[0144] rank(U)=1,U>0

[0145] Subproblem 1: Fix the IRS phase shift matrix and find the optimal base station beamforming weight vector and weighting factor:

[0146] Since the IRS phase shift is fixed, there are only two variables to be optimized. First, we introduce the following variables and matrix equivalent transformations to simplify the formula to facilitate the subsequent solution:

[0147] make W i =w i w i H , then the original problem can be expressed as:

[0148]

[0149] Constraint C1 is a probabilistic constraint and has no closed-form formula. It can be transformed using the Schur complement method to make it easier to solve. The specific transformation process is as follows:

[0150] Convert the constraints to semidefinite programming form:

[0151]

[0152] in represents H in the Schur complement method i The estimated value of is caused by estimation error;

[0153] Further converted into a definite form, that is

[0154]

[0155] Using the Schur complement method, it is transformed into a linear matrix inequality form:

[0156]

[0157] in is the error matrix, I is the unit matrix; Ω ΔH,i is a real-valued matrix, each element of which [Ω ΔH,i ] j,k =σ i ;

[0158] Since the optimization objective is a complex non-convex function, the slack variable {ξ i ,ξ i} to deal with non-convex functions, the original problem becomes:

[0159]

[0160] At this time, the objective function and C1-C4 constraints are already convex functions, but due to the introduction of slack variables {ξ i ,ξ i}, resulting in C5 and C6 becoming non-convex constraints. For these two non-convex constraints, the first-order Taylor expansion method is used to obtain the following expressions:

[0161]

[0162] in, Represents the t-th iteration point after the first-order Taylor expansion.

[0163] At this point, only C4 is non-convex. We can relax this constraint first to find the optimal solution to the corresponding problem. At this point, all functions in the original optimization problem are convex. We can now use a convex optimization toolbox (such as CVX) to find the corresponding optimal solution. The original problem can be restated at this point:

[0164]

[0165] Using a relaxed approach to solve the problem may result in the final solution W not satisfying the rank constraint of 1. If the rank is not 1, Gaussian randomization can be used to obtain the optimal solution. If the rank is 1, the beamforming weight vector w can be obtained using singular value decomposition.

[0166] Subproblem 2: Fix the base station beamforming weight vector and weighting factor and solve the optimal IRS phase shift matrix:

[0167] To simplify the formula, make the following transformation:

[0168] in

[0169] so

[0170] in So we can get:

[0171]

[0172] Further introduce U=uu H , and introduce the constraint rank(U)=1,U>0

[0173]

[0174] The above problem can be transformed into:

[0175]

[0176] Constraint C1 is a probabilistic constraint and has no closed-form formula. It can be converted into a linear matrix inequality form using the Schur complement method:

[0177]

[0178] in

[0179] The final form of the optimization problem is:

[0180]

[0181] Therefore, removing constraint C4 from the above problem is a standard convex semidefinite programming problem. Similarly, a convex optimization toolbox (e.g., CVX) can be used to solve this problem. For the rank-one constraint violation caused by relaxation, Gaussian randomization can be used to find the optimal solution that satisfies the rank-one constraint.

[0182] After transforming the original non-convex problem into a convex optimization problem using methods such as the Schur complement method and first-order Taylor expansion, the onboard user transmission rate and maximization problem can be solved through an alternating iterative method when channel state information cannot be accurately obtained, completing the key technology research on the integrated design of vehicle-to-vehicle interawareness assisted by smart surfaces. The process includes:

[0183] Step 4.1, initialize the beamforming matrix W i (0) , IRS passive beamforming matrix U (0) and weighting factor β (0) , and set the calculation accuracy tolerance ε′ and the maximum number of iterations t max .

[0184] Step 4.2, based on the initial conditions, solve the (t-1) Ignoring the rank-one constraint subproblem 1, we obtain the optimal beamforming matrix W for the current t-th iteration: (t) and weighting factor β (t) .

[0185] Step 4.3, solve the optimal beamforming matrix W with a fixed i (t) and weighting factor β (t) Sub-problem 2: Get the optimal IRS passive beamforming matrix U for the current t-th iteration (t) .

[0186] Step 4.4, calculate

[0187] Step 4.5, until Δ<ε′ or t=t max End the iteration and get the optimal beamforming matrix W i (t) , IRS passive beamforming matrix U (t) and weighting factor β (t) , use Gaussian randomization and other methods to obtain the corresponding base station beamforming weight vector and IRS beamforming vector respectively, otherwise return to step 4.2 to continue iteration.

[0188] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A vehicle network synaesthesia integration method based on smart surfaces, characterized by: The following steps are involved: Step 1: Build an integrated vehicle-to-vehicle interawareness system assisted by an intelligent surface IRS, including a base station, an IRS, and vehicles. The vehicles include the main vehicle communicating with the base station and the adjacent vehicles sensed by the base station. The channel models between the base station and the IRS, the IRS and the vehicle, and the base station and the vehicle are constructed using the Rice channel model. Step 2: Build wireless transmission and reception signal models between the main vehicle user and the base station, and between the main vehicle user and the IRS, and build a perception model of the base station to the adjacent vehicles. Step 3: Construct an optimization problem to maximize the transmission rate of the main vehicle user, while satisfying the constraints of signal-to-interference-noise ratio, beam pattern error, and maximum transmit power. In step 4, the optimization problem described in step 3 is split into two sub-problems. The non-convex problem is converted into a convex optimization problem using the Schur complement method. The optimization problem is solved using an alternating iterative method to obtain the base station transmit beamforming matrix, the IRS passive beamforming matrix, and the weighting factors to achieve integrated telematics.

2. The method for integrating vehicle network and synaesthesia based on smart surface according to claim 1, characterized in that: The base station-IRS channel constructed using the Rice channel model in step 1 is expressed as: Among them, α B2I represents the large-scale fading coefficient, a and b represent the array responses of the base station and IRS, H represents the matrix transpose, and is the effective departure angle in the x-axis and y-axis directions, and is the effective arrival angle in the x-axis and y-axis directions; The IRS-vehicle channel is expressed as: The base station and vehicle are represented as: Where K is the Rice factor in the Rice channel model.

3. The method for integrating vehicle network and synaesthesia based on smart surface according to claim 2, characterized in that: The wireless transmission and reception signal model between the main vehicle user and the base station and IRS is as follows: Among them, y U,i represents the signal received by the user of the i-th vehicle, represents the phase shift matrix of IRS and each element thereof should satisfy |φ n |=1,arg(φ)∈[0,2π],the base station transmits a signal w is the active beam, x is the data symbol for the main vehicle user; in the second term of the formula, ii≠i means the sum of all main vehicle users except the i-th main vehicle user. represents additive white Gaussian noise, is the noise variance; The signal-to-interference-and-noise ratio expression of the main vehicle user is:

4. The method for integrating vehicle network and synaesthesia based on smart surface according to claim 3, characterized in that: The communication signal sent by the base station to the main vehicle user is also used to perceive the adjacent vehicle target. Its perception beam pattern can be expressed as: in, and represents the sampling angle grid; When the accurate angle information is known, the ideal beamforming weight vector w i ′ is obtained by the Zero-Forcing technique, which results in a sampling angle grid in the angle range [-π / 2,π / 2] and The expected sensing beam pattern is: The square error between the beam pattern and the expected beam pattern is: β is the weighting factor.

5. The method for integrating vehicle network and synaesthesia based on smart surface according to claim 4, characterized in that: The specific formula of the optimization problem and constraints proposed in step 3 is: Among them, Λ i is the signal-to-noise ratio threshold of the i-th main vehicle user, ε is the upper limit of the square error allowed between the beam pattern and the expected beam pattern, P max is the maximum transmit power.

6. The method for integrating vehicle network and synaesthesia based on smart surface according to claim 5, characterized in that: Probabilistic constraints are introduced into the optimization problem of step 4. The specific formula is: Among them, P i，out is the probability threshold of the i-th main vehicle user.

7. The method for integrating vehicle network and synaesthesia based on smart surface according to claim 6, characterized in that: The specific steps of solving the optimization problem using the alternating iterative method are as follows: Step 4.1: Initialize the base station transmit beamforming matrix W i (0) , IRS phase shift matrix U (0) and weighting factor β (0) , and set the calculation accuracy tolerance ε′ and the maximum number of iterations t max ; Step 4.2, based on the initial conditions, solve subproblem 1: fix the IRS phase shift matrix U (t-1) , get the optimal beamforming matrix W for the current t-th iteration (t) and weighting factor β (t) ; Step 4.3, solve subproblem 2: fix the optimal beamforming matrix W i (t) and weighting factor β (t) , get the optimal IRS phase shift matrix U for the current t-th iteration (t) ; Step 4.4, calculate Step 4.5, until Δ<ε′ or t=t max End the iteration and get the optimal beamforming matrix W i (t) , IRS phase shift matrix U (t) and weighting factor β (t) , use Gaussian randomization or singular value decomposition method to obtain the corresponding base station beamforming weight vector and IRS beamforming vector, otherwise return to step 4.2 to continue iteration.

8. The method for integrating vehicle network and synaesthesia based on smart surface according to claim 7, characterized in that: The sub-problem 1 is to solve the optimal base station beamforming weight vector and weighting factor with a fixed IRS phase shift matrix, which is in the form of: Among them, {ξ i ,ξ i } is a slack variable used to handle non-convex functions, which is reflected by the constraints C5 and C6. and represents the t-th iteration point after the first-order Taylor expansion; Introduce the following variables to simplify the formula: represents H in the Schur complement method i estimated value of; is the error matrix, I is the unit matrix; Ω ΔH，i is a real-valued matrix, each element of which 9. The method for integrating vehicle network and synaesthesia based on smart surface according to claim 7, characterized in that: The sub-problem 2 is to solve the optimal IRS phase shift matrix by fixing the base station beamforming weight vector and weighting factor, which is in the form of: The phase shift matrix is expressed as Φ = diag(u), Let U = uu H ,but Introduce the following variables to simplify the formula: in in 10. A vehicle network synaesthesia integration method based on a smart surface according to any one of claims 7 to 9, characterized in that: If the rank of the solved beamforming matrix W and IRS phase shift matrix U is not 1, the Gaussian randomization method is used to obtain the optimal solution; if the rank is 1, the singular value decomposition method is used to obtain the base station beamforming weight vector w and IRS beamforming vector u.

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