Long-distance radio frequency antenna communication method for gantry gate

Abstract

The application discloses a long-distance radio frequency antenna communication method for a gantry gate, and belongs to the technical field of wireless communication networks. The method comprises collecting radio frequency environment signals and vehicle dynamic data of an antenna coverage area of the gantry gate and performing analysis, generating channel state characteristics and vehicle dynamic characteristics; using the channel state characteristics and the vehicle dynamic characteristics to perform link quality prediction, generating predicted communication link quality, and constructing a dynamic communication environment model based on the predicted communication link quality; generating an adaptive communication strategy based on the dynamic communication environment model; and controlling the radio frequency antenna system of the gantry gate to perform a communication operation according to the adaptive communication strategy. The radio frequency environment signals and the vehicle dynamic data are jointly analyzed to construct a dynamic communication environment model, and an adaptive communication strategy is generated and executed based on the model, which can predict changes in communication link quality and actively optimize communication parameters, thereby improving the stability and reliability of communication in a high-speed moving and complex multi-vehicle scenario.

Description

Technical Field

[0001] This invention relates to the field of wireless communication network technology, and in particular to a long-distance radio frequency antenna communication method for gantry gates. Background Technology

[0002] Gantry cranes are critical infrastructure in intelligent transportation systems, widely used in scenarios such as highway non-stop toll collection, traffic flow monitoring, and vehicle recognition. The core of these applications is achieving stable and reliable wireless communication between the equipment on the gantry crane and high-speed vehicles. Long-distance radio frequency antenna communication systems for gantry cranes are a key technology for achieving vehicle-to-infrastructure (V2I) communication, and their performance directly determines the efficiency and service quality of the entire intelligent transportation application. To ensure necessary data exchange during high-speed vehicle passage, this type of wireless communication system must be able to maintain a high-quality communication link over long distances, at high speeds, and in complex electromagnetic environments.

[0003] Most existing gantry gate communication systems employ relatively simple communication schemes. These schemes typically deploy radio frequency antennas with fixed coverage areas and use fixed transmit power levels and operating channel frequencies for communication. Upon detecting a vehicle entering its coverage area, the system attempts to establish a communication link and exchange data. While some systems possess a degree of adaptive capability, such as simple power adjustments based on received signal strength or channel switching and retransmission after communication failure, these adjustment strategies are all reactive mechanisms based on the fact that communication quality has already deteriorated.

[0004] However, for high-speed vehicles, their positions and relative angles to the antenna change rapidly, making it difficult for fixed communication parameters to consistently match optimal transmission and reception conditions. This results in short effective communication windows and a high failure rate for link establishment and data exchange. Secondly, the outdoor environment where the gantry is located is complex, with multipath effects caused by reflections from other vehicles and unknown interference from other wireless devices. These dynamically changing channel factors make the link quality of statically configured communication systems extremely unstable. Finally, during peak traffic hours when multiple vehicles pass concurrently, the lack of an effective resource scheduling mechanism easily leads to communication conflicts and resource competition between vehicles, reducing the overall system processing efficiency. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a long-distance radio frequency antenna communication method for gantry gates. This method employs joint analysis of radio frequency environmental signals and vehicle dynamic data to construct a dynamic communication environment model. Based on this model, an adaptive communication strategy is generated and executed. This method can predict changes in communication link quality and proactively optimize communication parameters, thereby improving the stability and reliability of communication in high-speed movement and complex multi-vehicle scenarios.

[0006] The above objectives can be achieved through the following approach:

[0007] A long-distance radio frequency antenna communication method for a gantry gate includes: acquiring radio frequency environmental signals in the antenna coverage area of ​​the gantry gate and analyzing the radio frequency environmental signals to generate channel state characteristics; acquiring vehicle dynamic data in the antenna coverage area and analyzing the vehicle dynamic data to generate vehicle dynamic characteristics; using the channel state characteristics and the vehicle dynamic characteristics to predict link quality, generating predicted communication link quality, and constructing a dynamic communication environment model based on the predicted communication link quality; generating an adaptive communication strategy based on the dynamic communication environment model; and controlling the radio frequency antenna system of the gantry gate to perform communication operations according to the adaptive communication strategy.

[0008] Optionally, the generation of channel state characteristics includes: acquiring radio frequency environmental signals in the coverage area of ​​the gantry gate antenna; measuring the received signal strength indication, signal-to-noise ratio, and phase change of the radio frequency environmental signals to obtain signal quality parameters; identifying and quantifying the interference modes present in the radio frequency environmental signals to obtain interference parameters; and combining the signal quality parameters and the interference parameters to generate channel state characteristics.

[0009] Optionally, acquiring vehicle dynamic data within the antenna coverage area includes: collecting raw sensor data within the antenna coverage area through at least one of video analysis, radar detection, or lidar; processing the raw sensor data to identify the real-time position and speed of the target vehicle; and generating vehicle dynamic data containing vehicle position parameters and vehicle speed parameters based on the real-time position and the speed.

[0010] Optionally, generating vehicle dynamic features includes: extracting vehicle position parameters and vehicle speed parameters from the vehicle dynamic data; performing state estimation on the vehicle position parameters and vehicle speed parameters to calculate vehicle trajectory parameters; and generating vehicle dynamic features based on the vehicle trajectory parameters.

[0011] Optionally, the construction of the dynamic communication environment model includes: calculating the predicted position of the target vehicle at one or more future time points based on the vehicle speed parameters and vehicle position parameters in the vehicle dynamic features; using the channel state features and the predicted position to predict the link quality, generating the predicted communication link quality corresponding to the predicted position; and constructing the dynamic communication environment model based on the predicted position and the predicted communication link quality.

[0012] Optionally, generating an adaptive communication strategy based on the dynamic communication environment model includes: determining the predicted location where the predicted communication link quality is less than a preset quality of service threshold based on the dynamic communication environment model, so as to generate a predicted communication demand; selecting at least one communication parameter to be adjusted from antenna beam direction, transmit power level, and operating channel frequency according to the predicted communication demand; and generating an adaptive communication strategy based on the selected communication parameter to be adjusted.

[0013] Optionally, controlling the RF antenna system of the gantry gate to perform communication operations according to the adaptive communication strategy includes: when the adaptive communication strategy includes adjusting the antenna beam direction, calculating the phase configuration parameters of the array phase shifter pointing to the predicted position of the target vehicle, and controlling the array phase shifter to form a directional communication beam based on the array phase shifter phase configuration parameters; when the adaptive communication strategy includes adjusting the transmit power level, determining the power amplifier control parameters corresponding to the transmit power level, and adjusting the power amplifier based on the power amplifier control parameters; when the adaptive communication strategy includes adjusting the operating channel frequency, determining the frequency synthesizer configuration parameters corresponding to the operating channel, and adjusting the frequency synthesizer based on the frequency synthesizer configuration parameters; and controlling the RF antenna system of the gantry gate to perform communication operations based on the directional communication beam, the adjusted power amplifier, and the adjusted frequency synthesizer.

[0014] Optionally, the method further includes: obtaining actual communication results including data packet loss rate and communication latency; analyzing the deviation between the actual communication results and the predicted communication link quality to obtain communication difference data; and using the communication difference data to update the dynamic communication environment model.

[0015] Optionally, the method further includes: when there are multiple target vehicles, calculating a priority score for each target vehicle based on the vehicle dynamic characteristics of each target vehicle and the predicted communication link quality; establishing a communication session priority queue for the multiple target vehicles according to the priority score; and allocating an independent communication time slot or communication resource for each target vehicle in the adaptive communication strategy according to the communication session priority queue.

[0016] Based on the same inventive concept, this invention also provides a long-distance radio frequency antenna communication system for gantry gates. The system includes: a channel analysis module for acquiring radio frequency environmental signals within the antenna coverage area of ​​the gantry gate and analyzing the radio frequency environmental signals to generate channel state characteristics; a vehicle analysis module for acquiring vehicle dynamic data within the antenna coverage area and analyzing the vehicle dynamic data to generate vehicle dynamic characteristics; an environment model construction module for using the channel state characteristics and the vehicle dynamic characteristics to predict link quality, generate predicted communication link quality, and construct a dynamic communication environment model based on the predicted communication link quality; an intelligent decision-making module for generating an adaptive communication strategy based on the dynamic communication environment model; and a communication control module for controlling the gantry gate's radio frequency antenna system to perform communication operations according to the adaptive communication strategy.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. This invention achieves forward-looking prediction of communication link quality by constructing a predictive dynamic communication environment model. It can identify potential communication quality degradation areas in advance based on the future movement trajectory of the vehicle and the current channel conditions, thereby getting rid of the limitation of traditional methods that only respond passively after the problem occurs. This makes the adjustment of communication strategy predictable and proactive, and ensures the continuity of communication services in high-speed mobile scenarios.

[0019] 2. This invention improves the accuracy of environmental modeling by deeply integrating radio frequency environmental perception and vehicle dynamic perception. It not only analyzes the channel state characteristics composed of received signal strength, signal-to-noise ratio, phase change and interference mode, but also uses sensor data and state estimation algorithms to obtain vehicle motion trajectory parameters. This fusion of multi-dimensional information provides input for subsequent link quality prediction and is a solid foundation for reliable prediction and decision-making.

[0020] 3. When there are multiple target vehicles, the system establishes a communication service queue by calculating the priority score of each vehicle, and prioritizes the allocation of resources to vehicles with higher communication risks. This ensures that in complex traffic flow, limited communication resources can be allocated to the users who need them most, thereby maximizing the overall communication success rate and throughput of the system.

[0021] 4. By comparing the actual communication results with the predicted link quality, the system can detect deviations in the prediction model and continuously self-correct, enabling the dynamic communication environment model to continuously evolve and automatically adapt to slow changes in the environment. This ensures the accuracy and effectiveness of the communication strategy during long-term operation and improves the robustness of the entire communication system.

[0022] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a long-distance radio frequency antenna communication method for a gantry gate according to an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram illustrating the predicted communication degradation points according to an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of a prior art fixed beam according to an embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram of the adaptive beamforming according to an embodiment of the present invention.

[0028] Figure 5 This is a schematic diagram comparing the communication link quality performance of an embodiment of the present invention.

[0029] Figure 6 This is a schematic diagram of the structure of a long-distance radio frequency antenna communication system for a gantry gate according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0031] Reference Figure 1One embodiment of the present invention proposes a long-distance radio frequency antenna communication method for gantry gates. It adopts joint analysis of radio frequency environmental signals and vehicle dynamic data to construct a dynamic communication environment model, and generates and executes an adaptive communication strategy based on the model. It can predict changes in communication link quality and actively optimize communication parameters, thereby improving the stability and reliability of communication in high-speed movement and complex multi-vehicle scenarios.

[0032] The method described in this embodiment specifically includes:

[0033] The radio frequency environment signal of the coverage area of ​​the gantry gate antenna is collected, and the radio frequency environment signal is analyzed to generate channel state characteristics;

[0034] Acquire vehicle dynamic data within the antenna coverage area, and parse the vehicle dynamic data to generate vehicle dynamic features;

[0035] Link quality is predicted using the channel state characteristics and vehicle dynamic characteristics to generate predicted communication link quality, and a dynamic communication environment model is constructed based on the predicted communication link quality.

[0036] Based on the dynamic communication environment model, an adaptive communication strategy is generated.

[0037] According to the adaptive communication strategy, the radio frequency antenna system of the gantry gate is controlled to perform communication operations.

[0038] This invention constructs a communication environment model through dual sensing: real-time acquisition and analysis of radio frequency environmental signals to grasp the instantaneous state of the wireless channel; and simultaneous acquisition and analysis of dynamic vehicle driving data to understand the physical trajectory of the communication target. Subsequently, the system fuses these two heterogeneous pieces of information, deducing potential changes in communication link quality that the vehicle might encounter along its future path, and thereby constructing a spatiotemporal communication environment model. Based on this predictive model, the system can anticipate nodes where communication quality is about to deteriorate, thus formulating optimal adaptive communication strategies in advance. Finally, it executes these strategies by controlling various parameters of the radio frequency antenna system, forming a closed loop of sensing, prediction, decision-making, and execution.

[0039] This invention improves the stability and reliability of long-distance radio frequency communication in complex dynamic scenarios for gantry turnstiles. By shifting from passive, reactive adjustments to proactive, predictive optimization, this method avoids sudden communication link interruptions or drastic performance degradation caused by factors such as high-speed vehicle movement, multipath effects, or channel interference. The system can intervene and optimize before communication quality problems occur, ensuring that the antenna beam can accurately track the target, the transmit power can be adjusted as needed, and the working channel can actively avoid interference, thereby providing communication connectivity for moving vehicles and ensuring the continuity and overall performance of communication services.

[0040] Optionally, the generated channel state features include:

[0041] Collect radio frequency environmental signals in the area covered by the gantry gate antenna;

[0042] The received signal strength indication, signal-to-noise ratio, and phase change of the radio frequency environment signal are measured to obtain signal quality parameters;

[0043] The interference modes present in the radio frequency environment signal are identified and quantified to obtain the interference parameters;

[0044] By combining the signal quality parameters and the interference parameters, channel state characteristics are generated.

[0045] Specifically, the RF antenna system of the gantry gate first continuously scans and collects RF environmental signals in the antenna coverage area within a specified operating frequency band. This RF environmental signal is an unprocessed raw electromagnetic wave signal, which is received by the RF front end, down-converted, and digitized by an analog-to-digital converter to form a complex sampling sequence, i.e., an I / Q data stream, which serves as the input for subsequent processing.

[0046] Next, the acquired radio frequency environmental signals are analyzed to obtain signal quality parameters. This process specifically includes measuring the received signal strength indication, signal-to-noise ratio, and phase change. Received signal strength indication... It is an indicator that measures the total power of the received signal. It is calculated by accumulating the energy of digital samples over a period of time. Its calculation method can be expressed as:

[0047] ,

[0048] in and These represent the in-phase and quadrature components at the nth sampling point, respectively. The signal-to-noise ratio (SNR) is the ratio of the target signal power to the background noise power. To calculate this value, the system needs to measure the background noise power during idle time slots when there is no target vehicle communication. Then, when the target vehicle signal is detected, the total received power is measured. Target signal power That is and The difference is used to obtain the signal-to-noise ratio:

[0049] .

[0050] Phase change This reflects the Doppler frequency shift caused by multipath effects and vehicle movement in the channel, and is obtained by calculating the phase difference between consecutive sampling points or consecutive symbols, i.e.:

[0051] ,

[0052] in, This represents the phase change at the nth sampling point. It is the arctangent function in the fourth quadrant. , These represent the in-phase and quadrature components at the (n-1)th sampling point, respectively. These three measurements—received signal strength indication, signal-to-noise ratio, and phase change—together constitute the signal quality parameters.

[0053] Simultaneously, to comprehensively assess the channel, it is necessary to identify and quantify the interference modes present in the radio frequency (RF) environment signals, thereby obtaining interference parameters. This process involves spectral analysis of the RF environment signals, for example, using a Fast Fourier Transform (FFT) to convert the time-domain signal to the frequency domain. By analyzing the spectrum, abnormal energy peaks outside the desired communication frequency band can be identified; these peaks are the interference signals. The system records characteristics such as the center frequency, occupied bandwidth, and signal power of these interference signals. Furthermore, the presence of non-Gaussian noise, such as impulse interference, can be determined by analyzing the statistical characteristics of the signal, such as the kurtosis of the amplitude distribution. The interference type, frequency, bandwidth, and power information obtained through the above analysis and quantification are integrated into interference parameters.

[0054] Finally, the signal quality parameters and interference parameters obtained in the preceding steps are combined to generate the final channel state features. The combination process integrates the signal quality parameters, representing signal integrity, with the interference parameters, representing the degree of channel contamination, into a unified data structure, such as a multi-dimensional feature vector. This vector comprehensively describes the physical layer state of the gantry gate's communication link at the current moment, providing accurate and rich input for subsequent link quality prediction.

[0055] Optionally, acquiring vehicle dynamic data within the antenna coverage area includes:

[0056] Raw sensor data within the antenna coverage area is collected using at least one of video analytics, radar detection, or lidar.

[0057] The raw sensor data is processed to identify the real-time location and speed of the target vehicle;

[0058] Based on the real-time location and the driving speed, vehicle dynamic data including vehicle position parameters and vehicle speed parameters is generated.

[0059] Specifically, at least one environmental sensing sensor is first deployed, such as a high frame rate camera, millimeter-wave radar, or lidar mounted on the gantry. These sensors continuously scan the effective communication area of ​​the gantry gate antenna, collecting raw sensor data. If a video analysis scheme is used, the raw sensor data is continuous video image frames; if a radar detection scheme is used, the raw sensor data is a radar point cloud containing target distance, azimuth, elevation, and Doppler velocity information; if a lidar scheme is used, the raw sensor data is high-density three-dimensional spatial point cloud data.

[0060] Subsequently, the system performs deep processing on the collected raw sensor data to accurately identify and track each target vehicle within the area and extract its motion state. Taking video analysis as an example, the processing flow includes using deep learning target detection algorithms, such as YOLO or SSD, to identify vehicles in each frame and output their bounding boxes. Next, using multi-target tracking algorithms, such as DeepSORT, the same vehicle in different frames is associated, thereby assigning a unique identifier to each vehicle and establishing its motion trajectory. Based on camera calibration parameters, the vehicle position in the image coordinate system, such as the center point of the bounding box, is transformed to a preset world coordinate system to obtain the vehicle's real-time position. The driving speed is obtained by calculating the difference between the real-time positions at consecutive moments. If radar or lidar is used, the processing flow includes background filtering of the point cloud data, clustering (e.g., using the DBSCAN algorithm) to segment individual vehicle targets, and then calculating the centroid of each vehicle's clustered point cloud as its real-time position. Radar can directly use the Doppler effect to measure the radial velocity of the target; combined with position changes, the vehicle's driving speed can be accurately calculated. The driving speed calculation can be expressed as:

[0061] ,

[0062] in It is the current speed. It is the real-time location at the current moment. It is the real-time position at the previous sampling time. It is the time interval between two samples.

[0063] Finally, the system encapsulates the processed real-time location and speed into structured data to generate vehicle dynamics data. This data is typically a timestamp-containing data structure that explicitly includes vehicle position parameters, such as the vehicle's coordinates in a three-dimensional world coordinate system. And vehicle speed parameters, namely the vehicle's velocity components on each coordinate axis. This vehicle dynamics data, containing precise spatiotemporal information, provides crucial input for subsequent vehicle trajectory prediction and dynamic communication environment modeling.

[0064] Optionally, the generated vehicle dynamic features include:

[0065] Extract the vehicle position parameters and vehicle speed parameters from the vehicle dynamic data;

[0066] State estimation is performed on the vehicle position parameters and the vehicle speed parameters to calculate the vehicle motion trajectory parameters;

[0067] Based on the vehicle motion trajectory parameters, vehicle dynamic features are generated.

[0068] Specifically, the method first extracts vehicle position and velocity parameters at discrete time points from the vehicle dynamic data obtained in the previous step. While these parameters reflect the instantaneous state of the vehicle, they typically contain sensor measurement noise and lack a description of the continuity of vehicle motion. To overcome this limitation, this method then performs state estimation on the extracted vehicle position and velocity parameters. State estimation is a technique that uses mathematical models to filter out noise and model and predict the dynamic behavior of a system. Here, we typically use a Kalman filter or its variants, such as the extended Kalman filter. The system defines the vehicle's motion state as a state vector, which at least contains the vehicle's position and velocity. The core of state estimation lies in predicting the vehicle's state at the next moment using a pre-defined motion model and correcting the prediction using new measurements. This process is repeated cyclically, outputting a smoothed and filtered vehicle trajectory parameter that better conforms to the laws of physical motion. The calculation and prediction steps of the vehicle trajectory parameter can be described by the following state transition equation:

[0069] ,

[0070] in, It is the vehicle state vector predicted at time k, which typically includes the predicted position and velocity; It is the vehicle state vector at time k-1 after filtering and optimization; This is the state transition matrix, constructed based on Newton's laws of motion, describing the state evolution of the vehicle from time k-1 to time k. For example, in a uniform motion model, The velocity from the previous moment can be superimposed onto the position to predict the current position. Through this process, the vehicle trajectory parameters obtained by the system are not only the best estimate of the current state, but also contain a prediction of the movement trend in the short term.

[0071] Finally, based on the calculated vehicle trajectory parameters, the final vehicle dynamic features are generated. These vehicle dynamic features are the optimized state vector output from the state estimation process. They characterize the vehicle's dynamic behavior in a structured data form, such as a feature vector containing smoothed position, velocity, and even acceleration components. Compared to the original vehicle dynamic data, this feature vector has higher accuracy and stronger predictive power.

[0072] Optionally, the construction of the dynamic communication environment model includes:

[0073] Based on the vehicle speed and vehicle position parameters in the vehicle dynamic characteristics, calculate the predicted position of the target vehicle at one or more future time points;

[0074] The link quality is predicted by using the channel state features and the predicted location, and the predicted communication link quality corresponding to the predicted location is generated.

[0075] A dynamic communication environment model is constructed based on the predicted location and the predicted communication link quality.

[0076] Specifically, to construct a dynamic communication environment model capable of predicting future communication states, this method first calculates the predicted position of the target vehicle at one or more future time points based on previously generated vehicle dynamic features, particularly the optimized vehicle speed and position parameters. This calculation process utilizes fundamental kinematic principles and is implemented through a state prediction model. A simplified linear prediction model can be expressed as:

[0077] ,

[0078] in, In the future Predicted location after time; It is the precise position parameters of the vehicle at the current moment, obtained from the vehicle's dynamic characteristics; It is the precise speed parameter of the vehicle at the current moment, obtained from the vehicle's dynamic characteristics; It refers to the prediction time step; the system can set one or more different steps. The value is used to obtain a sequence of predicted locations at a series of future time points.

[0079] After obtaining the predicted vehicle location, the system uses these predicted locations and the currently acquired channel state features to predict link quality. This step is accomplished through a pre-established link quality prediction model. This model is typically a machine learning model trained on historical data, such as a gradient boosting tree or a neural network. The model's input is multi-dimensional, including channel state features describing the current wireless environment and the predicted location describing the vehicle's future spatial position. By learning the complex nonlinear relationship between various channel states and the actual communication link quality under different combinations of vehicle locations in historical data, the model can generate a quantified predicted communication link quality for new input combinations, such as predicted signal-to-noise ratio, throughput, or packet loss rate.

[0080] Finally, the system correlates and integrates the predicted locations and their corresponding predicted communication link qualities obtained in the preceding steps to construct the final dynamic communication environment model. This model is essentially a spatiotemporal mapping, stored in the form of a data structure, describing the expected communication service quality at different locations along the target vehicle's future travel path. This model is dynamic; it updates as the vehicle moves and the radio frequency environment changes, consistently providing a dynamic view of future communication environment changes.

[0081] Optionally, generating an adaptive communication strategy based on the dynamic communication environment model includes:

[0082] Based on the dynamic communication environment model, the predicted location where the predicted communication link quality is less than a preset service quality threshold is determined, so as to generate predicted communication demand.

[0083] Based on the predicted communication requirements, at least one communication parameter to be adjusted is selected from antenna beam direction, transmit power level, and operating channel frequency;

[0084] An adaptive communication strategy is generated based on the selected communication parameters to be adjusted.

[0085] Specifically, the analysis begins based on a pre-constructed dynamic communication environment model. This model includes a series of predicted future locations for the target vehicle and the corresponding predicted communication link quality at these locations. The system examines each set of data in the model, comparing each predicted communication link quality with a preset quality of service (QoS) threshold. This QoS threshold is the minimum performance standard set by the system to ensure stable communication, such as a minimum acceptable signal-to-noise ratio or a maximum tolerable packet loss rate. When the system finds that the predicted communication link quality for a certain predicted location is lower than this threshold, it marks that predicted location as a potential communication degradation point, such as... Figure 2As shown. By aggregating all these potential degradation points and their occurrence times, the system generates predictive communication requirements that clearly indicate the time and space nodes where future interventions are needed.

[0086] Next, based on this predicted communication demand, the system intelligently selects the communication parameters that need adjustment. The available library of adjustable communication parameters includes at least antenna beam direction, transmit power level, and operating channel frequency. The selection is based on the root cause of the predicted communication link quality degradation. For example, if the model shows a strong correlation between link quality degradation and the vehicle moving out of the coverage area of ​​the current antenna beam's main lobe, the system will prioritize adjusting the antenna beam direction. If the vehicle's location is ideal but the predicted signal strength is generally weak, for example due to long-distance attenuation or weather conditions, the system will choose to increase the transmit power level. If channel state characteristics indicate that strong interference exists or is about to occur on the current operating channel, the system will choose to switch the operating channel frequency to avoid interference. The system can select one or more parameters for combined adjustment based on preset rules or a more complex decision model.

[0087] Finally, based on the selected communication parameters to be adjusted, the system generates a specific adaptive communication strategy. This strategy is a precise, timestamped set of instructions. For example, if adjusting the antenna beam direction is selected, the strategy will include instructions to point the antenna beam to a specific new azimuth angle at a specific future time. If adjusting the transmit power level is selected, the strategy will include instructions to increase the transmit power to a specific level at a specific time. These instructions constitute the final adaptive communication strategy, ready to be issued to the communication control module for execution.

[0088] Optionally, controlling the RF antenna system of the gantry gate to perform communication operations according to the adaptive communication strategy includes:

[0089] When the adaptive communication strategy includes adjusting the direction of the antenna beam, the phase configuration parameters of the array phase shifter pointing to the predicted position of the target vehicle are calculated, and the array phase shifter is controlled based on the phase configuration parameters of the array phase shifter to form a directional communication beam.

[0090] When the adaptive communication strategy includes adjusting the transmit power level, the power amplifier control parameters corresponding to the transmit power level are determined, and the power amplifier is adjusted based on the power amplifier control parameters;

[0091] When the adaptive communication strategy includes adjusting the working channel frequency, determine the frequency synthesizer configuration parameters corresponding to the working channel, and adjust the frequency synthesizer based on the frequency synthesizer configuration parameters;

[0092] Based on the directional communication beam, the adjusted power amplifier, and the adjusted frequency synthesizer, the radio frequency antenna system of the gantry gate is controlled to perform communication operations.

[0093] Specifically, the first step is to analyze the specific instructions contained in the strategy. This instruction set specifies the need to adjust one or more communication parameters, indicating the target values ​​and execution times. When the adaptive communication strategy involves adjusting the antenna beam direction, based on the predicted target vehicle position given in the strategy and combined with the geometric coordinates of the gantry gate antenna, the required beam azimuth and elevation angles pointing to that predicted position are calculated. Based on these two angles, the system uses phased array antenna theory to calculate the required phase delay for each radiating element in the antenna array. This set of phase delays constitutes the phase configuration parameters of the array phase shifter. The calculation formula can be:

[0094] ,

[0095] in It is the phase shift that needs to be applied to the nth antenna element. It is the wave number, which is equal to 2π divided by the signal wavelength. It is the position of the nth unit relative to the reference point. This is the angle between the target beam pointing direction and the antenna normal direction. After calculation, these parameters are converted into digital control signals and sent to each independent array phase shifter in the antenna array to precisely adjust the phase of the radio frequency signal in each channel. This ensures that the signals of all elements achieve constructive interference in the direction of the predicted position of the target vehicle, forming a high-gain directional communication beam. A schematic diagram of the beam in the prior art is shown below. Figure 3 As shown, the beam diagram of the present invention is as follows: Figure 4 As shown.

[0096] When the adaptive communication strategy includes adjusting the transmit power level, the communication control module reads the target transmit power value from the strategy. Internally, the system maintains a calibration lookup table or function that establishes a mapping between the desired transmit power level and key control parameters of the power amplifier. These control parameters are typically the gate bias voltage or a specific value of the digital gain control word of the power amplifier. The module queries or calculates the corresponding power amplifier control parameters based on the target power value and loads them into the digital-to-analog converter or digital control interface, thereby adjusting the power amplifier's operating point to precisely match the strategy requirements with its output power.

[0097] When the adaptive communication strategy includes adjusting the operating channel frequency, the communication control module resolves the new operating channel frequency specified in the strategy. The frequency in the RF system is generated by a frequency synthesizer, typically a phase-locked loop (PLL) circuit. Based on the target frequency, the module calculates a set of parameters required to configure the frequency synthesizer, known as the frequency synthesizer configuration parameters. These parameters mainly include the count values ​​of the PLL's internal reference divider and feedback divider. These count values ​​are written into the frequency synthesizer's control register, causing the PLL to lock onto the new target frequency, thus completing the switching of the operating channel.

[0098] Finally, the system integrates one or more of the above adjustments. Based on the newly formed directional communication beam, the adjusted power amplifier, and the adjusted frequency synthesizer, the RF antenna system executes subsequent data transmission and reception tasks according to the new configuration. This series of precise hardware-level controls constitutes the final closed-loop execution of the adaptive communication strategy, ensuring that the communication link is optimized in real time according to predictions. The performance comparison of this invention with existing methods in terms of communication link quality is as follows: Figure 5 As shown.

[0099] Optionally, the method further includes:

[0100] Obtain the actual communication results, including data packet loss rate and communication latency;

[0101] Analyze the deviation between the actual communication results and the predicted communication link quality to obtain communication difference data;

[0102] The dynamic communication environment model is updated using the communication difference data.

[0103] Specifically, firstly, after the RF antenna system executes communication operations according to the adaptive communication strategy, the system continuously monitors and acquires the actual communication results. These actual communication results are key indicators for measuring the true performance of the communication link, mainly including the data packet loss rate and communication latency obtained through statistics from the MAC layer or higher protocol layers. The data packet loss rate refers to the proportion of data packets sent that fail to be successfully acknowledged by the receiver within a certain period of time; the communication latency refers to the time elapsed from when a data packet is sent from the sender to when it is successfully received by the receiver. This data is collected in real time and associated with the corresponding timestamps and vehicle locations.

[0104] Next, the system compares the actual communication results with the predicted communication link quality generated during the construction of the dynamic communication environment model. For example, when a vehicle reaches a specific location, the system compares the actual packet loss rate at that location with the model's predicted packet loss rate. By calculating the difference between the two, quantified communication difference data is obtained. This difference may be positive, indicating that the actual performance is better than the prediction, or negative, indicating that the actual performance is worse than the prediction. The calculation of communication difference data can be expressed as:

[0105] ,

[0106] in It is communication difference data. It is a quantified value of the actual communication result. It is a quantified value of the predicted communication link quality at the corresponding time and location.

[0107] Finally, the system uses the analyzed communication discrepancy data to update and adjust the dynamic communication environment model. This update process can be viewed as retraining or fine-tuning the link quality prediction model. The communication discrepancy data is used as an error signal, fed back to the machine learning model used to generate predictions of communication link quality. The model adjusts its internal parameters, such as the weights of the neural network or the splitting criteria of the decision tree, based on these error signals to reduce the gap between future predictions and actual results. For example, if the system finds that predictions in a specific region are always too optimistic—that is, the predicted link quality is always higher than the actual value—the model will learn to make a more conservative estimate of the link quality prediction for that region. By continuously and iteratively using actual communication results to correct the prediction model, the dynamic communication environment model can gradually approximate the laws of the real physical environment.

[0108] Optionally, the method further includes:

[0109] When there are multiple target vehicles, a priority score for each target vehicle is calculated based on the vehicle dynamic characteristics and the predicted communication link quality of each target vehicle.

[0110] Based on the priority scores, a communication session priority queue is established for multiple target vehicles;

[0111] Based on the communication session priority queue, an independent communication time slot or communication resource is allocated to each target vehicle in the adaptive communication strategy.

[0112] Specifically, the system first calculates a comprehensive priority score for each target vehicle within its field of view, based on its individual vehicle dynamic characteristics and predicted communication link quality. This score quantifies the urgency and importance of each vehicle's communication needs and can be represented by a weighting function:

[0113] ,

[0114] in, It is a priority score calculated for a specific vehicle; and These are preset weighting coefficients used to adjust the proportion of dynamic factors and link factors in the final score; It is a dynamic risk function based on vehicle dynamic features, whose input is the vehicle speed extracted from the vehicle dynamic features. and location The value of this function increases as the vehicle speed increases or the vehicle approaches the edge of the communication coverage area, reflecting the risk that it is about to leave the service area; It is a link risk function based on the predicted communication link quality, and its input is the predicted communication link quality of the vehicle. The function is designed to be inversely proportional, meaning that the worse the predicted communication link quality, the higher the function value, indicating a greater risk of communication interruption. Using this formula, the system can generate a quantitative score for each vehicle that comprehensively reflects its motion state and the expected communication environment.

[0115] After calculating priority scores for all target vehicles, the system sorts them in descending order based on these scores, thus establishing a dynamic communication session priority queue. The vehicle with the highest priority score is placed at the head of the queue, indicating that it has the highest communication priority and needs to be served first. This queue is not static but is periodically recalculated and reordered as vehicle dynamic characteristics and channel state characteristics are updated in real time.

[0116] Finally, the system allocates resources when generating adaptive communication strategies based on this real-time updated communication session priority queue. Specifically, the system allocates independent communication resources to each target vehicle sequentially, starting from the head of the queue. For example, in a system using time-division multiple access, the system allocates the next available communication time slot to the highest-priority vehicle and generates corresponding adaptive communication strategy instructions for it. Then, it allocates the next communication time slot to the next vehicle in the queue, and so on. In this way, the final generated adaptive communication strategy not only includes beam, power, or frequency adjustment instructions for each vehicle, but also includes explicit, priority-based timing scheduling arrangements.

[0117] Based on the same inventive concept, such as Figure 6 As shown, the present invention also provides a long-distance radio frequency antenna communication system for gantry gates, the system comprising:

[0118] The channel analysis module is used to collect radio frequency environmental signals in the coverage area of ​​the gantry gate antenna, and analyze the radio frequency environmental signals to generate channel state characteristics;

[0119] The vehicle analysis module is used to acquire vehicle dynamic data within the antenna coverage area, and to analyze the vehicle dynamic data to generate vehicle dynamic features.

[0120] An environment model building module is used to predict link quality using the channel state characteristics and vehicle dynamic characteristics, generate predicted communication link quality, and build a dynamic communication environment model based on the predicted communication link quality.

[0121] The intelligent decision-making module is used to generate an adaptive communication strategy based on the dynamic communication environment model.

[0122] The communication control module is used to control the radio frequency antenna system of the gantry gate to perform communication operations according to the adaptive communication strategy.

[0123] To verify the feasibility of this invention in practice, it was applied to a gantry toll collection system. This gantry experiences high traffic volume and speed, and is surrounded by a complex electromagnetic environment. This often leads to problems such as weak signal, susceptibility to interference, and insufficient communication distance when traditional fixed-beam antennas communicate with the onboard units in vehicles, resulting in a high transaction failure rate.

[0124] To verify the effectiveness of the invention, continuous communication tests and data acquisition were conducted on vehicles passing through the gantry during a certain period. During the test, the system recorded data under various vehicle types, speeds, and complex traffic flow scenarios.

[0125] In this embodiment, the system first acquires the radio frequency environmental signals within the coverage area of ​​the gantry antenna through the channel analysis module. For example, at 10:05 AM on a certain day, the system detects a persistent interference signal with a center frequency of 5.805 GHz and a bandwidth of 10 MHz within the current 5.8 GHz operating frequency band. Simultaneously, the system analyzes its own received signal, measuring the received signal strength indication, signal-to-noise ratio, and phase change. These parameters, along with the characteristics of the interference signal, constitute the channel state characteristics. Meanwhile, the vehicle analysis module acquires vehicle dynamic data within the antenna coverage area using a millimeter-wave radar and a high-definition camera mounted on the gantry. At 10:05:12 AM, the system identifies a heavy truck approaching from a distance at a speed of 100 km / h. Using the YOLOv5 target detection algorithm and a Kalman filter, the system accurately estimates and smooths the truck's trajectory parameters, generating vehicle dynamic features including real-time position and speed.

[0126] Subsequently, the environment model building module uses the aforementioned channel state characteristics and vehicle dynamic characteristics to predict link quality. Based on the truck's vehicle dynamic characteristics, the system calculates the predicted positions of the truck in 0.5 seconds, 1.0 seconds, and 1.5 seconds. Next, the system inputs these predicted positions and the current channel state characteristics into a pre-trained neural network model to predict the communication link quality at the corresponding future positions. The prediction results show that in 1.5 seconds, because the vehicle will travel to the edge region of the current antenna beam and be affected by detected interference signals, the signal-to-noise ratio (SNR) in the predicted communication link quality will decrease from the current 25 dB to 14 dB, below the preset 15 dB service quality threshold. Based on this, the system constructs a dynamic communication environment model incorporating this prediction information.

[0127] Based on this model, the intelligent decision-making module generates an adaptive communication strategy. Since the main causes of the predicted link quality degradation are beam mismatch and channel interference, the module decides to generate a combined strategy: after 1.4 seconds, adjust the antenna beam direction from the current 0-degree main direction to point at the -15-degree direction of the truck's predicted position 1.5 seconds later, and at the same time switch the working channel frequency from 5.80GHz to a cleaner 5.82GHz backup channel.

[0128] Finally, the communication control module executes specific operations according to the adaptive communication strategy. At a predetermined time point, the module calculates the phase configuration parameters required to drive the antenna array phase shifter to form a -15 degree beam, and simultaneously calculates the configuration parameters required to adjust the frequency synthesizer to 5.82 GHz. These parameters are then sent to the hardware of the RF antenna system. The system communicates with the truck according to the new configuration.

[0129] This invention also verified its priority scheduling capability in a multi-vehicle scenario. At 3:30 PM one afternoon, two vehicles, vehicle A and vehicle B, entered the coverage area almost simultaneously. Vehicle A had a speed of 120 km / h and a good predicted link quality; vehicle B had a speed of 90 km / h, but its predicted link quality was poor. The system calculated the priority scores for both vehicles. Due to vehicle B's poor predicted link quality, its link risk function... The higher value resulted in vehicle B having a higher final priority score than vehicle A. Therefore, the system placed vehicle B ahead of vehicle A in the communication session priority queue and allocated communication time slots and resources to it first, ensuring the communication success rate of vehicles with poorer links.

[0130] The closed-loop feedback mechanism also significantly improves the system's long-term accuracy. After completing a communication, the system obtained an actual data packet loss rate of 0.1%, while the predicted value was 0.05%. The system calculates this communication discrepancy and feeds it back as an error signal to the link quality prediction model for parameter fine-tuning. Through thousands of such iterative learning iterations, the model's prediction accuracy continues to improve, and its environmental adaptability is enhanced.

[0131] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0132] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A long-range radio antenna communication method for a portal gate, characterized by, The method comprises: Collecting radio frequency environment signals of the antenna coverage area of the gantry barrier gate, and analyzing the radio frequency environment signals to generate channel state features; Obtaining vehicle dynamic data in the antenna coverage area, and analyzing the vehicle dynamic data to generate vehicle dynamic features; Using the channel state features and the vehicle dynamic features to predict link quality, generating predicted communication link quality, and constructing a dynamic communication environment model based on the predicted communication link quality; including calculating the predicted position of the target vehicle at one or more future time points based on the vehicle speed parameter and the vehicle position parameter in the vehicle dynamic features; using the channel state features and the predicted position to predict link quality, generating the predicted communication link quality corresponding to the predicted position; and constructing a dynamic communication environment model based on the predicted position and the predicted communication link quality; Based on the dynamic communication environment model, an adaptive communication strategy is generated; which includes determining the predicted position where the predicted communication link quality is less than the preset service quality threshold based on the dynamic communication environment model to generate a predicted communication demand; selecting at least one communication parameter to be adjusted from the antenna beam direction, the transmission power level and the working channel frequency according to the predicted communication demand; and generating an adaptive communication strategy based on the selected communication parameter to be adjusted; According to the adaptive communication strategy, the radio frequency antenna system of the gantry barrier gate is controlled to perform communication operation.

2. The long-range radio antenna communication method for a portal gate according to claim 1, characterized by, The generation of channel state features comprises: Collecting radio frequency environment signals of the antenna coverage area of the gantry barrier gate; Measuring the received signal strength indication, signal-to-noise ratio and phase change of the radio frequency environment signals to obtain signal quality parameters; Identifying and quantifying the interference patterns existing in the radio frequency environment signals to obtain interference parameters; Jointly generating channel state features based on the signal quality parameters and the interference parameters.

3. The long-range radio antenna communication method for a portal gate according to claim 1, characterized by, The acquisition of vehicle dynamic data in the antenna coverage area comprises: Collecting raw sensor data in the antenna coverage area by at least one of video analysis, radar detection or laser radar; Processing the raw sensor data to identify the real-time position and driving speed of the target vehicle; Based on the real-time position and the driving speed, vehicle dynamic data containing vehicle position parameters and vehicle speed parameters are generated.

4. The long-range radio antenna communication method for a portal gate according to claim 3, wherein The generation of vehicle dynamic features comprises: Extracting the vehicle position parameters and vehicle speed parameters from the vehicle dynamic data; Performing state estimation on the vehicle position parameters and the vehicle speed parameters to calculate vehicle motion trajectory parameters; Based on the vehicle motion trajectory parameters, vehicle dynamic features are generated.

5. The long-range radio antenna communication method for a portal gate according to claim 4, wherein According to the adaptive communication strategy, the radio frequency antenna system of the gantry barrier gate is controlled to perform communication operation, which comprises: When the adaptive communication strategy contains adjusting the antenna beam direction, the array phase shifter phase configuration parameters pointing to the predicted position of the target vehicle are calculated, and the array phase shifter is controlled based on the array phase shifter phase configuration parameters to form a directional communication beam; determining a power amplifier control parameter corresponding to the transmit power level when the adaptive communication strategy comprises adjusting the transmit power level, and adjusting a power amplifier based on the power amplifier control parameter; determining a frequency synthesizer configuration parameter corresponding to the operating channel when the adaptive communication strategy comprises adjusting the operating channel frequency, and adjusting a frequency synthesizer based on the frequency synthesizer configuration parameter; controlling the radio frequency antenna system of the barrier gate to perform a communication operation based on the directional communication beam, the adjusted power amplifier, and the adjusted frequency synthesizer.

6. The long-range radio antenna communication method for a portal gate according to claim 1, characterized by, The method further comprises: obtaining actual communication results including data packet loss rate and communication delay; analyzing the deviation between the actual communication results and the predicted communication link quality to obtain communication difference data; updating the dynamic communication environment model using the communication difference data.

7. The long-range radio antenna communication method for a portal gate according to claim 1, wherein The method further comprises: when there are multiple target vehicles, calculating a priority score for each target vehicle based on the vehicle dynamic characteristics and the predicted communication link quality of each target vehicle; establishing a communication session priority queue for multiple target vehicles according to the priority score; allocating an independent communication time slot or communication resource for each target vehicle in the adaptive communication strategy according to the communication session priority queue.

8. A long-range radio antenna communication system for a portal gate, characterized in that The system comprises: a channel analysis module for collecting radio frequency environment signals in the antenna coverage area of the barrier gate and analyzing the radio frequency environment signals to generate channel state characteristics; a vehicle analysis module for obtaining vehicle dynamic data in the antenna coverage area and analyzing the vehicle dynamic data to generate vehicle dynamic characteristics; an environment model construction module for predicting link quality using the channel state characteristics and the vehicle dynamic characteristics to generate predicted communication link quality, and constructing a dynamic communication environment model based on the predicted communication link quality; wherein the vehicle speed parameter and the vehicle position parameter in the vehicle dynamic characteristics are used to calculate the predicted position of a target vehicle at one or more future time points, the channel state characteristics and the predicted position are used to predict link quality to generate predicted communication link quality corresponding to the predicted position, and the predicted position and the predicted communication link quality are used to construct a dynamic communication environment model; an intelligent decision-making module for generating an adaptive communication strategy based on the dynamic communication environment model; wherein the predicted positions where the predicted communication link quality is less than a preset service quality threshold are determined based on the dynamic communication environment model to generate predicted communication demand, at least one communication parameter to be adjusted is selected from the antenna beam direction, the transmit power level, and the operating channel frequency according to the predicted communication demand, and an adaptive communication strategy is generated based on the selected communication parameter to be adjusted; a communication control module for controlling the radio frequency antenna system of the barrier gate to perform a communication operation according to the adaptive communication strategy.

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