Plateau mountainous environment vehicle-road cooperative anti-interference communication method

Abstract

This invention provides a vehicle-road cooperative anti-interference communication method for high-altitude mountainous environments, relating to the field of vehicle-to-everything (V2X) communication technology. The method includes: acquiring the location coordinates of communication nodes and three-dimensional terrain point cloud data of the communication area; constructing a spatial topology vector for collision detection and generating a link blocking command; extracting surface geometric feature parameters of the local terrain and selecting effective shaped reflective surfaces through reflection gain calculation; fusing node coordinates and spatial pose parameters of the reflective surfaces to construct a terrain-assisted channel model and calculating the dominant reflection path channel state information; calculating the three-dimensional beamforming weights of the transmitter based on the channel state information, guiding the beam to the effective shaped reflective surface, and constructing a redirected non-line-of-sight (NLS) communication link; controlling the receiver to capture the signal, and using the channel state information to perform phase correction and multipath diversity analysis and demodulation. This invention solves the communication interruption problem caused by non-line-of-sight obstruction in high-altitude mountainous areas, significantly improving the robustness of the system.

Description

Technical Field

[0001] This invention relates to the field of vehicle-to-everything (V2X) communication technology, and in particular to a vehicle-road cooperative anti-interference communication method for high-altitude mountainous environments. Background Technology

[0002] Vehicle-to-everything (V2X) communication technologies primarily rely on dedicated short-range communication standards or cellular V2X protocols, with their physical layer designs largely based on orthogonal frequency division multiplexing (OFDM) technology and multiple-input multiple-output (MIMO) antenna architectures. In conventional line-of-sight transmission environments such as structured urban roads or flat highways, existing communication systems can perform channel estimation using pilot sequences and utilize adaptive modulation and coding mechanisms and space-time block coding techniques to ensure reliable data exchange between vehicles and roadside infrastructure.

[0003] As intelligent transportation systems extend to western border regions and high-altitude, complex geographical environments, the deployment scenarios for vehicle-to-everything (V2X) networks are becoming increasingly complex. To address the challenges of non-line-of-sight transmission caused by terrain undulations, academia and industry are dedicated to researching channel modeling techniques based on 3D ray tracing, 3D beamforming techniques for large-scale antenna arrays, and environmental cognitive radio technologies. The aim is to enhance the system's robustness under harsh channel conditions by improving the ability to resolve signals in both the spatial and temporal domains.

[0004] The environment of high-altitude mountainous areas is characterized by significant elevation differences, large road curvature, and dense rock face obstructions. In such scenarios, radio electromagnetic waves inevitably collide with the mountain rock surfaces during propagation, resulting in strong multipath reflection, scattering, and severe shadow fading. Existing anti-interference communication methods typically treat the multipath effects caused by the terrain as destructive interference components in the channel, mainly employing passive suppression methods such as zero-forcing equalization and minimum mean square error filtering to combat them. However, due to the drastic changes in the topology of high-altitude mountainous areas caused by high-speed vehicle movement, the channel exhibits highly dynamic time-varying characteristics and Doppler frequency shift. Traditional filter tap coefficients cannot achieve rapid convergence, leading to a sharp decrease in the signal-to-interference-plus-noise ratio (SNR) of the useful signal. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a vehicle-road cooperative anti-interference communication method in plateau mountainous environments. This invention solves the problem that the prior art has serious multipath interference in high dynamic non-line-of-sight environments in plateau mountainous areas and that the passive suppression mechanism is prone to communication link interruption.

[0006] To achieve the above objectives, the present invention provides the following solution: A vehicle-road cooperative anti-interference communication method for high-altitude mountainous environments includes: Acquire the first position coordinates of the transmitting node, the second position coordinates of the receiving node, and the three-dimensional terrain point cloud data of the current communication area; A spatial topology vector is constructed based on the first and second position coordinates, and collision detection is performed between the spatial topology vector and the three-dimensional terrain point cloud data to determine the link blocking command. Based on the link blocking command, surface geometric feature parameters are extracted from the three-dimensional terrain point cloud data, and reflection gain is calculated for the local terrain corresponding to the surface geometric feature parameters to obtain an effective reflective surface. Extract the spatial pose parameters of the effectively shaped reflective surface, and fuse the first position coordinates and the second position coordinates to construct a terrain-assisted channel model; The dominant reflection path channel state information for the effective shaped reflection surface is calculated based on the terrain-assisted channel model. The three-dimensional beamforming weights of the transmitting node are calculated based on the dominant reflection path channel state information to adjust the beam pointing and guide the communication signal to be transmitted to the effective beamforming reflection surface in order to construct a redirected non-line-of-sight communication link. The receiving node is controlled to capture the arriving signal transmitted via the redirected non-line-of-sight communication link, and the dominant reflection path channel state information is used to perform phase correction and multipath aggregation on the arriving signal to complete the demodulation of the communication signal to be transmitted.

[0007] The present invention discloses the following technical effects: This invention provides a vehicle-road cooperative anti-interference communication method for high-altitude mountainous environments. By introducing three-dimensional terrain point cloud extraction to effectively shape the reflecting surface, and combining terrain-assisted channel modeling with three-dimensional beamforming technology, a redirected non-line-of-sight communication link is accurately constructed. Then, at the receiving end, phase correction and multipath diversity merging are performed using the dominant reflecting path channel state information. This mechanism not only effectively overcomes the communication blind zone caused by physical obstruction of the direct link, but also transforms traditionally destructive multipath interference into substantial spatial diversity gain, fundamentally solving the link interruption problem and significantly improving the robustness of vehicle-road cooperative communication in high-altitude mountainous areas. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart of a vehicle-road cooperative anti-interference communication method for plateau mountainous environments provided in this embodiment of the invention; Figure 2A schematic diagram of the aggregation boundary of three-dimensional terrain point cloud data provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the internal structure of the transmitter node provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the internal structure of the receiving node provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a vehicle-road cooperative anti-interference communication system for a plateau mountainous environment, provided as an embodiment of the present invention.

[0010] Figure label: 1-Data acquisition module, 2-Link blockage detection module, 3-Reflector extraction module, 4-Channel state calculation module, 5-Channel model construction module, 6-Beamforming control module, 7-Signal combining and demodulation module. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] like Figure 1 As shown, this invention provides a vehicle-road cooperative anti-interference communication method for high-altitude mountainous environments, comprising: Step 100: Obtain the first position coordinates of the transmitting node, the second position coordinates of the receiving node, and the three-dimensional terrain point cloud data of the current communication area; Step 200: Construct a spatial topology vector based on the first location coordinates and the second location coordinates, and perform collision detection between the spatial topology vector and the three-dimensional terrain point cloud data to determine the link blocking command; Step 300: Based on the link blocking command, extract the surface geometric feature parameters from the three-dimensional terrain point cloud data, and calculate the reflection gain of the local terrain corresponding to the surface geometric feature parameters to obtain an effective reflective surface; Step 400: Extract the spatial pose parameters of the effectively shaped reflective surface, and fuse the first position coordinates and the second position coordinates to construct a terrain-assisted channel model; Step 500: Calculate the dominant reflection path channel state information for the effective shaped reflection surface based on the terrain-assisted channel model; Step 600: Calculate the three-dimensional beamforming weights of the transmitting node based on the dominant reflection path channel state information, so as to adjust the beam pointing and guide the communication signal to be transmitted to the effective beamforming reflection surface to construct a redirected non-line-of-sight communication link; Step 700: Control the receiving node to capture the arriving signal transmitted via the redirected non-line-of-sight communication link, and use the dominant reflection path channel state information to perform phase correction and multipath aggregation on the arriving signal to complete the demodulation of the communication signal to be transmitted.

[0014] Furthermore, the specific implementation process of step 100 is as follows: In the specific implementation process, for step 100, this embodiment first performs the operation of acquiring the first position coordinates of the transmitting node and the second position coordinates of the receiving node. This embodiment uses carrier phase differential technology combined with an inertial measurement unit to collect the absolute spatial positions of both communicating parties in a global three-dimensional coordinate system in real time, thereby generating the first position coordinates and the second position coordinates. Here, the global three-dimensional coordinate system refers to the geodetic space rectangular coordinate system with the Earth's center of mass as the origin. Its function is to provide a unified and unbiased absolute spatial measurement benchmark for highly dynamic vehicle-road cooperative nodes. In order to ensure the spatial accuracy of subsequent multipath prediction and beamforming, this embodiment strictly controls the positioning error of the coordinates to within 0.1 meters and periodically refreshes the coordinate data at an update frequency of 100 Hz to adapt to the physical characteristics of high-speed vehicle movement in plateau and mountainous areas.

[0015] Subsequently, as Figure 2As shown, this embodiment begins spatial data processing based on the first and second position coordinates to construct the three-dimensional bounding box. This embodiment constructs a centralized three-dimensional bounding box within a complex, multi-layered mountainous terrain background space to precisely define the aggregation range of point cloud data. The three-dimensional bounding box is a hexahedral closed geometry containing dense terrain point cloud data composed of a massive number of discrete point nodes. The point cloud data is aggregated within the gridded transparent boundary of the three-dimensional bounding box. A solidified, layered base structure is provided at the bottom of the three-dimensional bounding box. The three-dimensional bounding box is defined as a virtual spatial geometric enclosure used to define the terrain data extraction range. Its core function is to filter out redundant wide-area terrain data unrelated to the spatial span of the current communication link, thereby significantly reducing the spatiotemporal overhead of subsequent spatial collision calculations. In the specific construction operation, this embodiment first calculates the absolute distance difference between the first and second position coordinates along each spatial axis, using this as a baseline distance scale. Then, based on the baseline distance scale, this embodiment adds a preset horizontal and vertical expansion margin. For example, to ensure that potential terrain reflective surfaces are included, this embodiment sets the lateral extension margin to 500 meters to cover the wide canyon wall area where electromagnetic waves may generate effective lateral reflections; at the same time, the longitudinal extension margin is set to 200 meters to completely cover the top mountain reflection area caused by the elevation difference in the mountain area.

[0016] After obtaining the aforementioned spatial span scale with added expansion margin, this embodiment constructs a three-dimensional bounding box enclosed by six orthogonal boundary planes in the global three-dimensional coordinate system. The specific data processing and mapping process is as follows: This embodiment uses the previously calculated maximum and minimum boundary values ​​in the horizontal direction, the maximum and minimum boundary values ​​in the vertical direction, and the maximum and minimum elevation values ​​in the vertical direction to generate six mutually orthogonal boundary planes: top, bottom, left, right, front, and rear. These six orthogonal boundary planes cut and close each other in three-dimensional space, precisely defining a cuboid space. This embodiment further limits the maximum elevation value of the top boundary plane to an absolute altitude of 3000 meters, while the value of the bottom boundary plane is set at a lower limit of 50 meters below the lowest point of the first and second position coordinates. Through the aforementioned geometric operation loop, this embodiment strictly determines the actual geographical area enclosed and covered by these six orthogonal boundary planes as the current communication area.

[0017] After determining the current communication area, this embodiment performs the operation of retrieving the 3D terrain point cloud data matching the current communication area from a pre-set high-precision 3D map database. The pre-set high-precision 3D map database refers to a collection of spatial geographic information that has been pre-scanned and mapped by airborne lidar and stored in roadside edge computing nodes or cloud storage media. Its function is to provide a static physical environment twin base for the prediction of non-line-of-sight communication channels. To achieve rapid retrieval of massive terrain data, this embodiment adopts a 3D spatial tree-shaped block indexing mechanism. The coordinate extreme values ​​of the six orthogonal boundary planes constituting the 3D bounding box are input into the database as query spatial parameters, and spatial intersection comparison operations are performed. The terrain data stored in the database in this embodiment has extremely high mapping accuracy, with a default basic point cloud density of 25 effective reflection points per square meter, and the minimum grid division scale of the spatial index is limited to 10 meters. This ensures both the retrieval efficiency of the retrieval process and the accuracy of terrain matching.

[0018] Finally, this embodiment completes data indexing and extraction, and formally outputs the 3D terrain point cloud data. The 3D terrain point cloud data refers to a dataset composed of a massive number of discrete spatial point nodes. Each node not only contains precise 3D coordinate parameters but also carries reflection intensity parameters characterizing the properties of rock or vegetation materials. Its function is to provide a high-fidelity data source for subsequent direct link collision detection and refined calculation of the effective reflective surface. Before the data is finally output and passed to the next module, this embodiment also performs consistency threshold filtering on the retrieved preliminary data to remove outbound nodes that are outside the six orthogonal boundary planes. After the above rigorous data processing, this embodiment can extract and cache up to 10,000 high-quality point cloud nodes in a single communication calculation cycle, and strictly suppresses the proportion of outlier noise points that may cause channel prediction bias to below 5%. Thus, this embodiment fully realizes the digital extraction of the physical terrain of the communication area, laying a solid and clean data foundation for subsequent determination of link blocking commands.

[0019] Furthermore, the specific implementation process of step 200 is as follows: After acquiring the location data of the communication node, this embodiment constructs a spatial topology vector with the first location coordinate as the starting point and the second location coordinate as the ending point, based on the first location coordinate and the second location coordinate, to represent the ideal line-of-sight transmission channel. In specific data processing, this embodiment extracts the three-dimensional spatial components of the first location coordinate as the electromagnetic wave emission source point and extracts the three-dimensional spatial components of the second location coordinate as the electromagnetic wave receiving target point. A directed line segment connecting the two points is generated through spatial analytical geometry operations, forming the core skeleton of the spatial topology vector. To ensure that this topology vector truly reflects the spatial propagation physical characteristics of radio waves and achieves physical entity mapping, this embodiment uses the directed line segment as the central axis and, combined with the physical beam divergence angle parameters of the current radio frequency antenna, expands in three-dimensional space to construct a quasi-ellipsoidal spatial energy transmission tube that encloses the central axis. This spatial energy transmission tube serves as the specific boundary representation of the ideal line-of-sight transmission channel, covering the main lobe energy concentration area directly hit by the electromagnetic wave. Its engineering effect lies in accurately converting the invisible abstract radio wave channel into a three-dimensional geometric entity data structure in computer memory that can be quantized, segmented, and intersected.

[0020] After completing the physical mapping of the ideal line-of-sight transmission channel, this embodiment performs a spatial comparison between the spatial topology vector and the elevation parameters in the 3D terrain point cloud data to perform collision detection. During the specific collision intersection data processing, this embodiment employs a 3D ray stepping traversal algorithm to project the 3D terrain point cloud data into the local spatial coordinate system where the ideal line-of-sight transmission channel is located. This embodiment calculates the vertical normal projection distance from each discrete terrain spatial node in the point cloud dataset to the central axis of the spatial topology vector, and extracts the absolute altitude value of the corresponding terrain spatial node as the elevation parameter. By dynamically subtracting the elevation parameter from the vector segment height of the spatial topology vector at the same two-dimensional plane projection point, this embodiment accurately quantifies the physical depth at which the geometric surface of the physical mountain peels away each terrain node from the ideal line-of-sight transmission channel. The engineering effect of this functional data processing is that it can faithfully reproduce the real physical cutting state of the complex towering rock walls and protruding mountain surfaces of plateau mountain areas on the direct spatial path of electromagnetic waves, thereby presenting the microscopic spatial topological details of channel physical obstruction in the form of high-precision discrete data.

[0021] Based on the terrain intrusion quantification data obtained from the above spatial comparison, this embodiment further evaluates the spatial link connectivity. When the collision detection result indicates that the depth of terrain intrusion into the ideal line-of-sight transmission channel exceeds the adaptive occlusion threshold calculated based on the current communication wavelength, it is determined that the direct link corresponding to the spatial topology vector is occluded, and a link blocking command is generated. In the data processing for threshold determination, this embodiment abandons the traditional static distance judgment and instead uses the current communication wavelength as a dynamic physical input parameter. Combining the mathematical model for calculating the first Fresnel zone radius in microwave transmission theory, the adaptive occlusion threshold is derived in real time. This embodiment compares the actual calculated maximum intrusion depth with the adaptive occlusion threshold. Once it is confirmed that terrain intrusion causes irreversible severe physical diffraction attenuation and energy blocking in the main lobe signal transmission profile, the underlying control logic of this embodiment immediately determines that the direct link has suffered substantial physical occlusion. Subsequently, this embodiment generates the link blocking instruction containing the link blocking flag and the spatial coordinate parameters of the obstruction point. In engineering applications, this instruction not only serves as a level-triggered switch to block the transmission of data packets from the currently failed line-of-sight communication link, but also as a physical control signaling to forcibly wake up the subsequent terrain reflection gain calculation and spatial beam redirection anti-interference mechanism, ensuring that the high-dynamic vehicle-road cooperative node maintains its communication lifeline in extreme obstruction environments.

[0022] Furthermore, the specific implementation process of step 300 is as follows: Upon receiving the link blocking command, this embodiment immediately locks onto candidate reflection areas around the blocked line-of-sight link and, based on the link blocking command, extracts the surface geometric feature parameters of the local terrain from the 3D terrain point cloud data. In the specific data extraction and materialization process, this embodiment performs Delaunay triangulation on discrete point cloud nodes, transforming disordered spatial points into physically continuous 3D topological surfaces. Subsequently, for each subdivided terrain grid, this embodiment calculates the absolute normal vector, slope value, and surface roughness variance of its geometric center point in 3D space, and uses these three physical quantities together as the surface geometric feature parameters. For example, this embodiment sets the grid division side length benchmark to 2 meters to finely depict the microscopic undulations of the rock walls in plateau mountainous areas; and filters out severely fractured areas with a roughness variance greater than 0.5 meters during the extraction process, retaining relatively smooth rock layers. The engineering effect of this functional processing is that it accurately transforms massive amounts of disordered geographic mapping data into the geometric boundary conditions necessary for electromagnetic wave scattering models, laying a data foundation for finding high-quality natural physical reflectors.

[0023] After obtaining the geometric boundary conditions of the terrain entity, this embodiment performs reflection gain calculation on the local terrain corresponding to the surface geometric feature parameters based on the incident angle and reflection angle, which characterize the electromagnetic wave propagation characteristics. Simultaneously, it dynamically calculates an adaptive reflection gain threshold based on the electromagnetic wave penetration loss of the current communication frequency band and the material statistical characteristics of the local terrain. In the reflection gain data processing link, this embodiment constructs the incident path using rays from the transmitting node to the center of the terrain grid, and the reflection path using rays from the center of the terrain grid to the receiving node. The spatial incident angle and spatial reflection angle are solved by combining the absolute normal vector and spatial geometric laws. Subsequently, this embodiment introduces a microwave rough surface scattering model, using the surface roughness variance to attenuate and correct the ideal specular reflection coefficient, thus obtaining the actual quantized reflection gain value for each terrain grid. Meanwhile, this embodiment extracts the wavelength parameters of the 5900 MHz band specifically used in the current vehicle-to-everything (V2X) network, and combines them with the statistical average dielectric constant of common high-altitude surface materials such as granite and dry soil in this band to deduce the absorption attenuation baseline when radio frequency energy impacts a physical mountain. For example, a baseline electromagnetic wave penetration loss of 15 dB is set, and this baseline is then combined with the current receiver noise floor sensitivity to dynamically generate the adaptive reflection gain threshold that fits the real-time physical environment. The engineering advantage of this computational mechanism is that it eliminates the vulnerability of traditional empirical fixed thresholds in complex time-varying environments, and achieves a high-fidelity energy simulation of the physical scattering and fading process of radio waves in complex mountainous terrain in a specific frequency band.

[0024] After deriving the above-mentioned energy values, this embodiment determines whether the calculated reflection gain is greater than the adaptive reflection gain threshold, and filters out regions where the calculated reflection gain is greater than the adaptive reflection gain threshold to determine the effective shaped reflective surface. In the specific decision and filtering data processing steps, this embodiment inputs the reflection gain value generated by each terrain grid into a numerical comparator and performs a strict differential comparison with the real-time updated adaptive reflection gain threshold. For grid cells with a positive gain difference, this embodiment assigns them an effective reflection label; for grid cells with a negative gain difference, spatial rejection is directly performed. To ensure that the finally selected reflective surface has a physical area margin sufficient to carry high-frequency broadband signals, this embodiment further performs regional connectivity clustering growth processing on adjacent spatial grids with effective reflection labels. For example, this embodiment requires that the physical area of ​​the continuous smooth rock wall after clustering growth must exceed 10 square meters, and the normal deflection angle fluctuation of the overall plane must be less than 5 degrees before it can be independently separated from the local terrain dataset. After this rigorous spatial purification, the effective shaped reflective surface finally output in this embodiment is equivalent to erecting a high-gain passive relay reflector among the mountains at the physical level. Its engineering effect is that it accurately anchors the spatial geometric coordinate reference that can maximize the recovery of multipath energy, providing a direct physical target for the physical closed loop of subsequent active beam redirection and communication anti-interference link.

[0025] Specifically, the surface geometric feature parameters include: the roughness variance, slope value, and three-dimensional spatial normal vector of the local terrain surface corresponding to the three-dimensional terrain point cloud data.

[0026] Specifically, the calculation expression for the adaptive reflection gain threshold is as follows: ; in, Adaptive reflection gain threshold; It is the sum of the two distances; This is a distance-dependent attenuation constant used to control the attenuation rate of the threshold with distance; and This represents the lower / upper limit of the threshold.

[0027] The expression for calculating the reflection gain is: ; in, Indicates reflection gain; To synthesize the Fresnel coefficients, their specific values ​​are determined by polarization; α is the propagation loss coefficient; and D is the sum of the two distances. These are the distances from the transmitter to the reflection point and from the reflection point to the receiver, respectively. The angle of incidence is denoted as .

[0028] Furthermore, the specific implementation process of step 400 is as follows: After successfully locating a natural rock face with high reflectivity potential, this embodiment extracts the three-dimensional center coordinates and plane tilt angle of the effectively shaped reflective surface to determine the spatial pose parameters. In the specific spatial geometric parameter extraction data processing, this embodiment first performs a centroid-weighted average calculation on all discrete spatial grid nodes constituting the effectively shaped reflective surface to derive the three-dimensional center coordinates that represent the overall geometric position of the physical reflective surface. To ensure that the subsequent electromagnetic wave beam can be accurately focused on this physical center, this embodiment strictly controls the spatial quantization calculation error of the center coordinates to within 0.5 meters. Subsequently, this embodiment uses a least-squares-based spatial plane fitting algorithm to construct the optimal approximation plane for the three-dimensional topological nodes within the effectively shaped reflective surface and extracts the normal vector of this optimal approximation plane. By calculating the spatial angle between this normal vector and the horizontal reference plane in the global three-dimensional coordinate system, this embodiment accurately obtains the plane tilt angle representing the spatial tilt state of the physical reflective surface. The engineering effect of this functional data processing is that it performs high-precision mathematical abstraction and physical entity reduction of the irregular natural mountain surface, extracts the core geometric anchor points necessary for beam redirection, and provides a spatial reference for the subsequent precise ejection of electromagnetic rays.

[0029] After obtaining the aforementioned core geometric anchor points, this embodiment performs deep cross-referencing and mapping of spatial topology data, that is, fusing the first position coordinates, the second position coordinates, and the spatial pose parameters to construct the terrain-assisted channel model. In the data fusion process, this embodiment uses the three-dimensional center coordinates as spatial inflection points, the first position coordinates as the electromagnetic wave transmission starting point, and the second position coordinates as the electromagnetic wave reception endpoint, constructing a geometric ray spatial propagation path composed of two directed line segments in a virtual three-dimensional spatial calculation matrix. Along this geometric ray spatial propagation path, this embodiment accurately calculates the absolute physical flight distance of the electromagnetic wave from the transmitting end to the effective shaped reflector, and then from the effective shaped reflector to the receiving end, and derives the spatial propagation delay parameter of the electromagnetic wave accordingly. In this physical deduction calculation, this embodiment sets the effective tracking upper limit of spatial ranging to 2000 meters and refines the calculation resolution of the propagation delay to the order of 10 nanoseconds. The engineering effect of this integration step is that it completely breaks the topological limitation of direct point-to-point transmission between the transmitter and receiver in traditional vehicle-to-everything (V2X) communication. At the physical level, it cleverly weaves the static side mountains into the dynamic vehicle-road cooperative communication link, constructing a complete relay pipeline architecture from the perspective of spatial geometry.

[0030] Based on the geometric ray spatial propagation path and its physical flight parameters generated by the above fusion, this embodiment further integrates the spatial attenuation mechanism of radio frequency energy to improve the terrain-assisted channel model. During the dynamic data mapping process of constructing this model, this embodiment substitutes the previously extracted absolute physical flight distance into the free space loss calculation logic to obtain the basic path attenuation base. Simultaneously, it introduces the atmospheric absorption coefficient specific to the current communication frequency band as an environmental correction parameter. For example, for the 5900 MHz radio frequency band dedicated to vehicle networking, this embodiment sets the atmospheric absorption loss factor to 0.02 dB per kilometer to correct the impact of the unique meteorological environment of plateau and mountainous areas on the dissipation of electromagnetic wave energy. Subsequently, this embodiment performs a multi-dimensional logarithmic superposition operation on the basic path attenuation base, the atmospheric absorption loss factor, and the reflection gain attenuation characteristics of the effectively shaped reflective surface to synthesize the overall system channel fading response characteristic architecture of the redirected reflection path. This channel model is not merely a mathematical simulation framework, but a digital twin of the channel that highly conforms to the real physical environment. Its engineering effect is reflected in the fact that this embodiment successfully transforms the complex and ever-changing physical mountain environment into a radio wave spatial multipath evolution relationship model that can be used by the baseband processor for deterministic calculation, fundamentally eliminating the unpredictability of non-line-of-sight blind zone communication and providing a complete foundation for calculating the channel state information of the dominant reflection path.

[0031] Specifically, the expression for the terrain-assisted channel model is as follows: ; in, The first The transmit / receive direction vector of the path; L is the array response vector; L is the number of effectively formed reflection paths; This represents the path length.

[0032] Furthermore, the specific implementation process of step 500 is as follows: After constructing the terrain-assisted channel model, this embodiment immediately performs the operation of calculating the dominant reflection path channel state information for the effectively shaped reflector based on the terrain-assisted channel model. First, based on the terrain-assisted channel model, this embodiment calculates the electromagnetic wave fading coefficient passing through the effectively shaped reflector. The electromagnetic wave fading coefficient is defined as an approximate large-scale factor characterizing the energy dissipation caused by radio waves traveling long distances in the space medium. Its function is to provide a basic amplitude attenuation weighting benchmark for subsequent signal combining and beam control. At the specific data processing level, this embodiment extracts the total distance of the space ray propagation path and obtains the propagation loss coefficient of the current atmospheric and frequency band environment. This embodiment multiplies the propagation loss coefficient by the total distance to obtain the attenuation exponent, and then performs an exponential function operation with the natural constant as the base and the negative of the attenuation exponent as the exponent, thereby obtaining the electromagnetic wave fading coefficient. For example, when the total single-path distance extracted in this embodiment is 850 meters, and the propagation loss coefficient for the current weather conditions is calibrated to 0.003 per meter, this embodiment can accurately quantify the macroscopic energy attenuation of electromagnetic waves when crossing a canyon through the exponential mapping of the pure real number domain, thus realizing the conversion of physical attenuation to digital weight.

[0033] Next, to compensate for frequency domain distortion caused by the high dynamic environment, this embodiment calculates the Doppler phase deflection matrix through the effectively shaped reflective surface based on the relative motion velocity of the transmitting node and the receiving node. In the pre-processing of solving this matrix, this embodiment first needs to accurately calculate the Doppler frequency shift of a single path. Specifically, this embodiment obtains the relative velocity vectors of the two communicating parties through differential operations, and simultaneously extracts the three-dimensional spatial path unit vector of the current reflection path. This embodiment performs a spatial dot product operation on the relative velocity vector and the path unit vector to extract the projection component of the relative motion velocity in the actual physical propagation direction of the electromagnetic wave. Subsequently, this embodiment divides the projection component by the physical wavelength of the current communication radio frequency signal to obtain the Doppler frequency shift of that specific path. For example, in the radio frequency carrier band dedicated to vehicle networking, the physical wavelength is constant at approximately 0.05 meters. When this embodiment captures in real time that the relative projection velocity of the transmitting and receiving parties along the ray is 33 meters per second, the spatial Doppler frequency shift induced by violent mechanical motion can be accurately obtained through the above division mapping.

[0034] After obtaining the Doppler frequency shifts of each dominant reflection path, this embodiment further constructs the Doppler phase deflection matrix. The Doppler phase deflection matrix is ​​a diagonal data structure that represents the dynamic rotation of phase over time in complex form. Its core function is to provide real-time and high-precision phase anti-deflection compensation for beamforming algorithms. During the data processing of matrix synthesis, this embodiment performs continuous multiplication operations on the Doppler frequency shift, communication observation time variable, and twice the value of pi for each path to calculate the Doppler phase shift of that path at the current time section. Next, this embodiment uses the natural constant as the base and the product of the imaginary unit and the Doppler phase shift as the complex exponent to generate a complex phase rotation factor through complex plane Euler mapping. Finally, this embodiment arranges the complex phase rotation factors of all effective multipaths sequentially on the main diagonal of the matrix, while forcibly filling the remaining off-diagonal elements of the matrix with 0. For example, if this embodiment currently extracts and locks the four strongest effective reflection paths, and the communication observation time update granularity is 0.001 seconds, then this embodiment will construct a four-row, four-column complex diagonal matrix, perfectly mapping the relative motion characteristics in the time domain to the phase deflection tensor in the spatial domain.

[0035] After obtaining the two key matrices and parameters mentioned above, this embodiment performs a data fusion operation, fusing the electromagnetic wave fading coefficient with the Doppler phase deflection matrix to calculate the dominant reflection path channel state information for the effectively shaped reflective surface. To achieve depth alignment of amplitude and phase, this embodiment first performs microscopic correction on the electromagnetic wave fading coefficient. This embodiment extracts the comprehensive Fresnel coefficient obtained in the aforementioned terrain calculation, multiplies it with the electromagnetic wave fading coefficient, and further divides it by the square root of the product of the distance from the transmitter to the reflection point and the distance from the reflection point to the receiver. This complex data operation successfully transforms the large-scale fading factor, which only includes air loss, into a comprehensive amplitude fading weight that includes the specific rock material polarization characteristics and two-segment spatial distance attenuation. For example, this embodiment applies the above correction operation in parallel to the four extracted independent reflection paths, mapping the original pure spatial distance fading to independent amplitude weights that reflect the true physical reflection characteristics of the granite, ensuring that the energy characterization of each ray absolutely matches the actual terrain.

[0036] Finally, this embodiment performs complex-domain multiplication and fusion processing on the combined amplitude fading weights of multiple paths after terrain physical correction and the Doppler phase deflection matrix. The specific data processing procedure is as follows: This embodiment extracts the complex phase rotation factors at each diagonal position in the diagonal matrix and multiplies them by the combined amplitude fading weights of the corresponding paths to generate complex baseband channel coefficients that exhibit both energy attenuation and phase drift characteristics. Subsequently, this embodiment sequentially pushes the complex baseband channel coefficients corresponding to all paths into a one-dimensional array according to the order of path energy magnitude or spatial number, and performs a matrix transpose operation on this one-dimensional array to generate a standardized complex-form column vector dataset. This column vector is the final output of the dominant reflection path channel state information. This channel state information is essentially a high-dimensional complex spatial interface, whose function is to provide the underlying antenna beam controller with complete, digitally twinned spatial channel profile data. Thus, this embodiment has completed the quantitative extraction of channel features in complex multipath environments with a maximum dimension of up to 16 through multi-dimensional data mapping and complex domain transformation, providing a pure mathematical basis without theoretical error for the precise directional guidance of antenna beams in subsequent steps.

[0037] Specifically, the expression for calculating the electromagnetic wave fading coefficient is as follows: ; Where α is the propagation loss coefficient; D is the total distance of the path. It represents the approximate large-scale factor of the fading coefficient of electromagnetic waves.

[0038] The expression for calculating the Doppler phase deflection matrix is ​​as follows: ; in, This is the Doppler phase deflection matrix; For path Doppler phase shift; This is the Doppler frequency shift of the path; The path unit vector; It is a relative velocity vector; λ is the wavelength; t is the time.

[0039] The calculation expression for the dominant reflection path channel state information is as follows: ; in, and As defined above; L represents the number of paths; A vector representing the dominant reflection path channel state information.

[0040] Furthermore, the specific implementation process of step 600 is as follows: After obtaining the dominant reflection path channel state information, this embodiment performs a data processing flow based on the dominant reflection path channel state information, using singular value decomposition (SVD) to calculate the three-dimensional beamforming weights of the transmitting node. This embodiment first integrates the multiple channel state vectors output from the previous steps, which include amplitude attenuation and Doppler shift, to construct a complex-form dominant channel matrix. The dominant channel matrix is ​​a two-dimensional data structure used for holographic mapping of the spatial channel topology response between the transmitting and receiving antenna arrays; its function is to transform multipath physical characteristics into an algebraically solvable characteristic equation matrix. Subsequently, this embodiment performs singular value decomposition (SVD) on the dominant channel matrix. Specifically, this embodiment uses mathematical operations such as conjugate transpose and eigenvalue extraction to precisely decompose the dominant channel matrix into a left singular vector matrix, a diagonal singular value matrix containing the characteristic energy distribution, and a right singular vector matrix. For example, when the transmitting end is equipped with 64 antenna elements and the receiving end is equipped with 16 antenna elements, this embodiment can reduce the complex spatial electromagnetic coupling network into multiple sets of orthogonal independent parallel data stream channels without loss through the matrix decomposition mapping in the pure algebraic domain described above.

[0041] After completing the data processing of the singular value decomposition described above, this embodiment performs a refined extraction operation of beamforming weights. In this embodiment, the first principal eigenvalue with the most concentrated energy is locked in the diagonal singular value matrix according to the descending order of eigenvalue values ​​from largest to smallest. To maximize the use of the spatial channel capacity of this dominant reflection path, this embodiment directly extracts the first column vector in the right singular vector matrix and assigns it as the complete value of the three-dimensional beamforming weight of the transmitting node; simultaneously, this embodiment extracts the first column vector in the left singular vector matrix and defines it as the beam matching weight of the receiving node. The core function of this data extraction mechanism is to physically filter out weak reflection sidelobes and environmental background noise interference with an energy percentage lower than 5% of the total eigenvalues, based on the mathematical energy extreme value distribution characteristics of singular value decomposition, thereby accurately capturing the redirection physical path with the highest spatial gain. Through the above matrix column extraction operation, this embodiment converts the complex channel state characteristics into a set of directly usable complex weight coefficient vectors, providing a theoretically optimal solution for the subsequent physical control of the hardware antenna.

[0042] To improve the robustness of the algorithm under extreme terrain obstruction and limited underlying computing power, this embodiment also provides an alternative implementation based on a standardized array response vector to quickly calculate the three-dimensional beamforming weights. In this alternative calculation mapping process, this embodiment pre-extracts the transmit spatial azimuth angle and receive spatial azimuth angle for the effective beamforming reflector. Based on the transmit spatial azimuth angle, this embodiment, combined with the physical geometric spacing parameters of the antenna array, calculates the original transmitter array response vector composed of multiple complex exponential terms. Subsequently, this embodiment calculates the norm scalar of the original transmitter array response vector, where the norm scalar refers to the physical length quantization modulus of the complex vector in Euclidean space. This embodiment divides each element of the original transmitter array response vector by the norm scalar, performing a strict energy normalization division. For example, for an original column vector containing 32 complex dimensions, this embodiment strictly compresses and constrains its overall energy modulus to an absolute value of 1, thereby outputting a standardized transmitter array response vector, which is directly used as the alternative three-dimensional beamforming weights for the transmitter node. This alternative effectively reduces the overhead of high-dimensional matrix operations caused by singular value decomposition, and achieves rapid approximation and output of weight parameters within a microsecond-level operation cycle.

[0043] After successfully calculating the beamforming weights, this embodiment executes a physical control closed-loop process that uses the three-dimensional beamforming weights to control the radiation parameters of the antenna array, thereby adjusting the beam pointing. Figure 3As shown, in this embodiment, the underlying RF architecture of the transmitting node mainly includes a digital baseband processing module, a digital modulator, an up-conversion and RF front-end combination module, a phased array and beamforming processing module, and a high-frequency antenna array, all connected in sequence. During the physical construction of the redirected non-line-of-sight communication link, the communication signal to be transmitted is first input to the digital baseband processing module for source coding and forward error correction, and then flows through the digital modulator to complete the complex plane constellation mapping. The mapped discrete baseband sequence enters the up-conversion and RF front-end combination module, is restored to analog level via a digital-to-analog converter bus, and its carrier frequency is shifted up to the dedicated RF microwave band for vehicle networking by a mixer and power amplifier. Next, the microwave signal carrying high-frequency energy is fed into the phased array and beamforming processing module. In this embodiment, the module's internal analog phase shifter and variable gain amplifier array are activated to calculate the output three-dimensional beamforming weights based on the aforementioned steps, performing stringent amplitude scaling and physical phase shifting on each independent RF branch. Finally, multiple radio frequency microwaves, after precise amplitude and phase modulation, are simultaneously injected into, for example, the high-frequency antenna array containing sixty-four physical elements. Utilizing the coherent superposition enhancement effect of electromagnetic waves, a high-gain directional guiding beam with highly focused energy and precise pointing, effectively shaping the reflector, is excited in physical three-dimensional space. In this embodiment, each complex element in the calculated three-dimensional beamforming weight column vector is decomposed using polar coordinate algebra to precisely separate the target amplitude scaling factor and target phase offset angle corresponding to the antenna element. Subsequently, in this embodiment, these discrete target parameters are sent to the analog phase shifter and variable gain amplifier in the radio frequency front end via a digital-to-analog conversion bus. The function of the phase shifter and amplifier is to perform independent analog domain modulation and voltage-controlled adjustment of the microwave signal in the radio frequency hardware transmission link. In this hardware control stage, this embodiment requires that the phase adjustment control accuracy of the underlying phase shifter must be within 3 degrees, and the dynamic voltage-controlled adjustment range of the amplitude gain must cover an upper and lower 15 dB range. By configuring precise radiation parameters with channel feature compensation for each of the 256 independent radio frequency transmission channels in the antenna array, this embodiment utilizes the coherent superposition enhancement effect generated by multi-source electromagnetic waves in the spatial electromagnetic field, so that the physical beam that was originally axially direct undergoes the expected angle deflection and energy focusing in three-dimensional space, thus accurately reshaping the spatial radiation envelope of the high-frequency signal.

[0044] Finally, in this embodiment, the communication signal to be transmitted is directed to the effective shaped reflective surface according to the adjusted beam direction, and the redirected non-line-of-sight communication link is constructed using the physical reflection characteristics of electromagnetic waves. During the specific link physicalization process, this embodiment controls the transmitter baseband circuit to encode, modulate, and transmit the communication signal into the air medium. The high-frequency electromagnetic main lobe energy, after being precisely deflected by the beam direction, no longer directly impacts the straight mountain wall with severe physical obstruction, but instead is precisely projected with an extremely narrow half-power beamwidth of less than 2 degrees to the pre-calculated and locked geometric center anchor point of the physical rock wall. After impacting the effective shaped reflective surface, the radio frequency microwave energy undergoes a large-angle secondary spatial ejection according to the law of total internal reflection of electromagnetic waves, precisely covering the target receiving node located in the non-line-of-sight blind zone of the deep valley with a zigzag propagation trajectory. Through the above-described redirection and convergence process that converts interference into usable spatial energy, this embodiment effectively compensates for the severe direct path energy loss of over 40 decibels caused by mountain obstruction, completely breaks through the originally physically blocked vehicle network communication physical layer, and successfully constructs the redirected non-line-of-sight communication link with extremely high spatial multiplexing capability and multipath diversity gain.

[0045] Specifically, the calculation expression for the three-dimensional beamforming weights is as follows: ; in, For the dominant channel matrix The singular value decomposition results, and The first columns of the left and right singular vector matrices, respectively, serve as beamforming weights for the transmitter and receiver; if necessary, the normalized array response vector form can also be used, i.e. As an alternative implementation.

[0046] Furthermore, the specific implementation process of step 700 is as follows: After successfully establishing the redirected communication link, this embodiment controls the receiving node to activate the radio frequency receiving channel to capture the arriving signal transmitted via the redirected non-line-of-sight communication link, such as... Figure 4As shown, the underlying RF architecture of the receiving node mainly includes a receiving antenna array, a low-noise amplifier, a mixer / downconverter, an analog-to-digital converter, a phased / beamforming receiving processing module, and a digital baseband processing module, all connected in sequence. During the interaction between the receiving entities in the redirected non-line-of-sight communication link, the non-line-of-sight signal, which is secondarily ejected by the terrain reflector, is first intercepted by the receiving antenna array. Due to long-distance spatial attenuation and physical absorption, the RF microwave signal energy is extremely weak at this point. In this embodiment, the low-noise amplifier is then used to perform initial low-noise energy amplification on this weak high-frequency analog signal. Subsequently, the amplified RF signal flows into the mixer / downconverter, where it is mixed with the local oscillator signal to shift the high-frequency carrier down to the intermediate frequency or baseband band. Then, the analog-to-digital converter converts the downconverted analog level signal into a discrete digital sequence at an extremely high sampling rate. After entering the digital domain, the phased / beamforming receiving and processing module performs precise phase correction and multipath convergence merging on the digital signals of multiple spatial branches based on the previously extracted dominant reflection path channel state information. Finally, the synthesized baseband data stream, after spatial distortion compensation and energy in-phase superposition, is sent to the digital baseband processing module to complete the inverse mapping of quadrature amplitude modulation and forward error correction decoding, thereby completely restoring the original data packet containing high-level semantics.

[0047] Specifically, this embodiment utilizes the analog-to-digital converter array of the underlying baseband processor to convert the analog high-frequency electromagnetic waves intercepted by the antenna physical element into discrete complex digital baseband sequences within a 20 MHz channel bandwidth. These digital sequences represent the arriving signal. Since electromagnetic waves reflected from rock walls in highly dynamic mountainous environments can cause severe transmission delays and spatial phase distortions, this embodiment performs phase correction on the arriving signal based on the channel phase response parameters in the dominant reflection path channel state information to compensate for the spatial distortions. In the specific data mapping and processing, this embodiment extracts the channel phase response parameters from the preceding calculations. These parameters are a set of complex angle values ​​reflecting specific multipath physical time-of-flight differences and Doppler shifts. This embodiment performs a conjugate inversion operation on these complex angle values ​​to generate a phase compensation factor, and then uses a digital multiplier to perform element-wise complex plane multiplication of the phase compensation factor with the arriving signal of the corresponding spatial branch. By forcibly rotating the originally scattered microwave phase to the absolute reference phase point, this embodiment perfectly smooths out the electromagnetic wave distortion caused by physical space refraction in the digital domain with a phase adjustment accuracy of 0.1 degrees, clearing the interference obstacle for subsequent multipath energy in-phase superposition.

[0048] After achieving absolute spatial phase alignment, this embodiment performs multipath aggregation merging on the phase-corrected signal according to the maximum ratio merging criterion. The maximum ratio merging criterion is a physical layer resource scheduling and data fusion logic aimed at maximizing the output signal-to-interference-plus-noise ratio (SINR). Its core function is to adaptively weight the received energy based on the signal quality of different spatial transmission physical branches. In the specific data processing link, this embodiment extracts the channel state information of the dominant reflection path again, and parses the amplitude fading weight parameter corresponding to each physical reflection branch. This embodiment sets the square of this amplitude fading weight parameter as the branch energy confidence level, and linearly multiplies this confidence level with the baseband data stream that has already undergone phase correction in the corresponding branch, thereby giving a greater decision weight to high-intensity physical reflection paths and suppressing weak sidelobe environmental noise in deep fading. Subsequently, this embodiment uses a digital accumulator to perform scalar summation on the weighted multipath complex baseband sequences in the time dimension and outputs the result. For example, when this embodiment captures four effective three-dimensional terrain reflection multipaths in a harsh canyon environment, through the above-mentioned maximum ratio merging data processing mechanism, this embodiment can fuse four weak physical echoes that are originally very easy to cause destructive interference into a strong synthetic baseband data stream, so that the overall signal-to-noise ratio of the receiving node can be substantially improved by up to 12 dB. This physical materialization operation completely transforms the multipath interference in the traditional sense into spatial diversity gain that ensures the reliability of the communication link.

[0049] After acquiring a high-energy-density synthesized baseband data stream, this embodiment demodulates the communication signal to be transmitted based on the merged baseband signal. In the specific demodulation digital processing flow, this embodiment first performs a constellation diagram inverse mapping operation of quadrature amplitude modulation on the merged baseband signal. This embodiment extracts the amplitude values ​​of the in-phase and quadrature components in the baseband signal and physically projects them onto a preset 64-point complex plane coordinate system. By calculating the minimum Euclidean geometric distance between the actual projection point and the physical anchor point of the standard constellation, the continuous physical level signal is accurately cut and quantized into a discrete hard-decision bit stream. In order to further eliminate the residual physical errors caused by electromagnetic background noise in complex high-altitude spaces, this embodiment then inputs the hard-decision bit stream into a forward error correction decoder for high-density iterative verification processing. Based on the additional redundant check bits, it automatically reverse-corrects the physical bit flips that occurred during air transmission. After the above rigorous baseband data processing, this embodiment successfully and losslessly restores radio waves that are completely physically blocked by towering mountains and only reflected by complex terrain into high-level semantic data packets containing the vehicle's absolute position, driving speed, and cooperative driving intentions. The engineering effect of this step is that it ensures an ultra-reliable, low-latency physical closed loop for the underlying control commands of high-level autonomous driving in non-line-of-sight environments, even in extreme terrain where line-of-sight is completely blocked.

[0050] like Figure 5 As shown, this embodiment also provides a vehicle-road cooperative anti-interference communication system for plateau and mountainous environments, including: Data acquisition module 1 is used to acquire the first position coordinates of the transmitting node, the second position coordinates of the receiving node, and the three-dimensional terrain point cloud data of the current communication area; Link blocking detection module 2 is used to construct a spatial topology vector based on the first position coordinates and the second position coordinates, and to perform collision detection between the spatial topology vector and the three-dimensional terrain point cloud data to determine the link blocking command. The reflective surface extraction module 3 is used to extract surface geometric feature parameters from the three-dimensional terrain point cloud data based on the link blocking command, and to calculate the reflection gain of the local terrain corresponding to the surface geometric feature parameters to obtain an effective reflective surface. Channel model construction module 4 is used to extract the spatial pose parameters of the effectively shaped reflective surface and fuse the first position coordinates and the second position coordinates to construct a terrain-assisted channel model; Channel state calculation module 5 is used to calculate the dominant reflection path channel state information for the effective shaped reflection surface based on the terrain-assisted channel model. The beamforming control module 6 is used to calculate the three-dimensional beamforming weights of the transmitting node based on the dominant reflection path channel state information, so as to adjust the beam pointing and guide the communication signal to be transmitted to the effective beamforming reflection surface to construct a redirected non-line-of-sight communication link. The signal combining and demodulation module 7 is used to control the receiving node to capture the arriving signal transmitted via the redirected non-line-of-sight communication link, and to perform phase correction and multipath combination on the arriving signal using the dominant reflection path channel state information to complete the demodulation of the communication signal to be transmitted.

[0051] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0052] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A vehicle-road cooperative anti-interference communication method for high-altitude mountainous environments, characterized in that, include: Acquire the first position coordinates of the transmitting node, the second position coordinates of the receiving node, and the three-dimensional terrain point cloud data of the current communication area; A spatial topology vector is constructed based on the first and second position coordinates, and collision detection is performed between the spatial topology vector and the three-dimensional terrain point cloud data to determine the link blocking command. Based on the link blocking command, surface geometric feature parameters are extracted from the three-dimensional terrain point cloud data, and reflection gain is calculated for the local terrain corresponding to the surface geometric feature parameters to obtain an effective reflective surface. Extract the spatial pose parameters of the effectively shaped reflective surface, and fuse the first position coordinates and the second position coordinates to construct a terrain-assisted channel model; The dominant reflection path channel state information for the effective shaped reflection surface is calculated based on the terrain-assisted channel model. The three-dimensional beamforming weights of the transmitting node are calculated based on the dominant reflection path channel state information to adjust the beam pointing and guide the communication signal to be transmitted to the effective beamforming reflection surface in order to construct a redirected non-line-of-sight communication link. The receiving node is controlled to capture the arriving signal transmitted via the redirected non-line-of-sight communication link, and the dominant reflection path channel state information is used to perform phase correction and multipath aggregation on the arriving signal to complete the demodulation of the communication signal to be transmitted.

2. The vehicle-road cooperative anti-interference communication method for plateau mountainous environments according to claim 1, characterized in that, The acquisition of the first position coordinates of the transmitting node, the second position coordinates of the receiving node, and the three-dimensional terrain point cloud data of the current communication area includes: Obtain the first position coordinates of the transmitting node and the second position coordinates of the receiving node; Based on the first and second position coordinates, a three-dimensional bounding box consisting of six orthogonal boundary planes is constructed in the global three-dimensional coordinate system, and the geographical area covered by the three-dimensional bounding box is determined as the current communication area. Retrieve the three-dimensional terrain point cloud data that matches the current communication area from a pre-set high-precision three-dimensional map database.

3. The vehicle-road cooperative anti-interference communication method for plateau mountainous environments according to claim 1, characterized in that, The step of constructing a spatial topology vector based on the first and second position coordinates, and performing collision detection between the spatial topology vector and the three-dimensional terrain point cloud data to determine a link blocking command includes: Based on the first position coordinates and the second position coordinates, a spatial topology vector is constructed with the first position coordinates as the starting point and the second position coordinates as the ending point to represent the ideal line-of-sight transmission channel; The spatial topology vector is spatially compared with the elevation parameters in the three-dimensional terrain point cloud data to perform the collision detection; When the collision detection result indicates that the depth of terrain intrusion into the ideal line-of-sight transmission channel exceeds the adaptive occlusion threshold calculated based on the current communication wavelength, it is determined that the direct link corresponding to the spatial topology vector is occluded, so as to generate the link blocking command.

4. The vehicle-road cooperative anti-interference communication method for plateau mountainous environments according to claim 1, characterized in that, The step of extracting surface geometric feature parameters from the 3D terrain point cloud data based on the link blocking command, and calculating the reflection gain of the local terrain corresponding to the surface geometric feature parameters to obtain an effectively shaped reflecting surface includes: Based on the link blocking command, the surface geometric feature parameters of the local terrain in the three-dimensional terrain point cloud data are extracted; Based on the incident angle and reflection angle, which characterize the propagation properties of electromagnetic waves, the reflection gain is calculated for the local terrain corresponding to the surface geometric feature parameters. Based on the electromagnetic wave penetration loss of the current communication frequency band and the material statistical characteristics of the local terrain, the adaptive reflection gain threshold is dynamically calculated. Determine whether the result of the calculated reflection gain is greater than the adaptive reflection gain threshold, and filter out the regions where the result of the calculated reflection gain is greater than the adaptive reflection gain threshold, so as to determine the effective reflective surface.

5. The vehicle-road cooperative anti-interference communication method for plateau mountainous environments according to claim 1, characterized in that, The step of extracting the spatial pose parameters of the effectively shaped reflective surface and fusing the first position coordinates and the second position coordinates to construct a terrain-assisted channel model includes: Extract the three-dimensional center coordinates and plane tilt angle of the effectively shaped reflective surface to determine the spatial pose parameters; The terrain-assisted channel model is constructed by fusing the first location coordinates, the second location coordinates, and the spatial pose parameters.

6. The anti-interference communication method for vehicle-road cooperative communication in plateau mountainous environments according to claim 1, characterized in that, The calculation of the dominant reflection path channel state information for the effective shaped reflector based on the terrain-assisted channel model includes: Based on the terrain-assisted channel model, the electromagnetic wave fading coefficient after passing through the effective shaped reflector is calculated; Based on the relative motion velocity between the transmitting node and the receiving node, the Doppler phase deflection matrix after passing through the effective shaped reflective surface is calculated; By fusing the electromagnetic wave fading coefficient with the Doppler phase deflection matrix, the dominant reflection path channel state information for the effectively shaped reflective surface is calculated.

7. The vehicle-road cooperative anti-interference communication method for plateau mountainous environments according to claim 1, characterized in that, The step of calculating the three-dimensional beamforming weights of the transmitting node based on the dominant reflection path channel state information to adjust the beam pointing and guide the communication signal to be transmitted to the effectively beamforming reflection surface to construct a redirected non-line-of-sight communication link includes: Based on the dominant reflection path channel state information, the singular value decomposition method is used to calculate the three-dimensional beamforming weights of the transmitting node. The radiation parameters of the antenna array are controlled by the three-dimensional beamforming weights to adjust the beam pointing. According to the adjusted beam direction, the communication signal to be transmitted is directed to the effective reflective surface, and the redirected non-line-of-sight communication link is constructed by utilizing the physical reflection characteristics of electromagnetic waves.

8. The vehicle-road cooperative anti-interference communication method for plateau mountainous environments according to claim 1, characterized in that, The control of the receiving node to capture the arriving signal transmitted via the redirected non-line-of-sight communication link, and to perform phase correction and multipath diversity combining on the arriving signal using the dominant reflection path channel state information, in order to demodulate the communication signal to be transmitted, includes: The receiving node is controlled to activate the radio frequency receiving channel to capture the arriving signal transmitted via the redirected non-line-of-sight communication link; Based on the channel phase response parameters in the dominant reflection path channel state information, the phase correction is performed on the arriving signal to compensate for spatial distortion. The multipath combinatorial process is performed on the phase-corrected signal according to the maximum ratio combining criterion. The demodulation of the communication signal to be transmitted is completed based on the merged baseband signal.

9. A vehicle-road cooperative anti-interference communication method for plateau mountainous environments according to claim 1, characterized in that, The surface geometric feature parameters include: The three-dimensional terrain point cloud data corresponds to the roughness variance, slope value, and three-dimensional spatial normal vector of the local terrain surface.

10. A vehicle-road cooperative anti-interference communication system for high-altitude mountainous environments, characterized in that, include: The data acquisition module is used to acquire the first position coordinates of the transmitting node, the second position coordinates of the receiving node, and the three-dimensional terrain point cloud data of the current communication area; The link blocking detection module is used to construct a spatial topology vector based on the first position coordinates and the second position coordinates, and to perform collision detection between the spatial topology vector and the three-dimensional terrain point cloud data to determine the link blocking command. The reflective surface extraction module is used to extract surface geometric feature parameters from the three-dimensional terrain point cloud data based on the link blocking command, and to calculate the reflection gain of the local terrain corresponding to the surface geometric feature parameters to obtain an effectively shaped reflective surface. The channel model construction module is used to extract the spatial pose parameters of the effectively shaped reflective surface and fuse the first position coordinates and the second position coordinates to construct a terrain-assisted channel model. The channel state calculation module is used to calculate the dominant reflection path channel state information for the effective shaped reflection surface based on the terrain-assisted channel model. The beamforming control module is used to calculate the three-dimensional beamforming weights of the transmitting node based on the dominant reflection path channel state information, so as to adjust the beam pointing and guide the communication signal to be transmitted to the effective beamforming reflection surface to construct a redirected non-line-of-sight communication link. The signal combining and demodulation module is used to control the receiving node to capture the arriving signal transmitted via the redirected non-line-of-sight communication link, and to perform phase correction and multipath combination on the arriving signal using the dominant reflection path channel state information to complete the demodulation of the communication signal to be transmitted.

Images