Method and system for constructing non-stationary channel of unmanned aerial vehicle to ground based on time-varying randomness
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG UNIV
- Filing Date
- 2026-03-17
- Publication Date
- 2026-06-26
AI Technical Summary
Existing U2G communication channel modeling methods struggle to accurately describe the non-stationary characteristics of UAV communication links in port dry bulk cargo terminal scenarios. In particular, the complex environmental features caused by the high dynamism of mechanical operations and the geometric time-varying nature of stacking distribution lack effective geometric constraints, posing challenges to the computational efficiency and environmental adaptability of existing models.
A time-varying stochastic UAV-to-ground non-stationary channel method is constructed. By performing channel simulations under multiple frequency bands and trajectories, a regularly spaced scatterer distribution and a time-varying elliptical cylindrical geometric model are used to simulate the stacking environment of a dry bulk cargo terminal in a port. The time-varying characteristics of Doppler frequency shift and phase are introduced to reconstruct the U2G channel.
It achieves efficient and accurate characterization of U2G channels in the complex environment of port dry bulk cargo terminals, provides high-precision theoretical model support, provides a theoretical basis for the design and optimization of UAV low-altitude operation systems for smart ports, and ensures communication reliability and coverage performance.
Smart Images
Figure CN122293239A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of wireless communication channel modeling technology, specifically to a method and system for constructing non-stationary UAV-to-ground channels based on time-varying randomness. Background Technology
[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.
[0003] With the booming development of the low-altitude economy and the accelerated construction of "smart ports," unmanned aerial vehicle (UAV) systems deeply integrated with 6th-generation (6G) mobile communication technology have become core equipment driving the intelligent transformation of ports. Especially in the critical logistics node of dry bulk cargo terminals, UAVs demonstrate irreplaceable application value in tasks such as yard mapping, facility inspection, and safety supervision. However, as a typical complex industrial operation scenario, the wireless communication environment of dry bulk cargo terminals is far more complex than that of traditional urban scenarios: on the one hand, the frequent lifting and rotation of large metal machinery such as gantry cranes and ship loaders leads to a highly dynamic propagation environment; on the other hand, the shape and distribution of dry bulk cargo stacks such as coal and ore change in real time during operations, causing severe multipath effects and channel time-varying characteristics in UAV communication links. The high dynamism of mechanical operations and the deep coupling of the geometric time-varying nature of stacking distribution result in significant non-stationary characteristics in the UAV-to-ground (U2G) channel. Therefore, constructing a U2G channel model that can accurately characterize the complex dynamic environment of port dry bulk cargo terminals is the key to ensuring high-reliability communication and efficient operation of U2G at port dry bulk cargo terminals.
[0004] Existing U2G communication channel modeling methods are mainly divided into two categories: deterministic models and statistical models. While deterministic models, such as ray tracing, offer high accuracy, their extremely high computational complexity makes them unsuitable for the real-time requirements of large-scale system simulations at dry bulk cargo terminals in ports. On the other hand, irregular geometric models in statistical models typically utilize birth-death processes to simulate the dynamic changes of scatterers. Although these models can characterize the non-stationarity of the channel to some extent, they still face challenges in terms of computational efficiency and environmental adaptability when dealing with specific application scenarios. (1) Especially for scenarios with significant regular arrangement characteristics, such as dry bulk cargo terminals in ports, the positions of scatterers in irregular geometric models are all randomly generated, resulting in the lack of effective geometric constraints on the topological structure of the physical environment.
[0005] (2) Regular geometric models can approximate complex physical environments using simplified geometric shapes, thereby significantly reducing computational overhead. Among them, the elliptical cylinder model, as a typical regular geometric model, is naturally adapted to the narrow passage characteristics formed by the stacking of cargo at port dry bulk terminals. However, most traditional regular geometric models focus on general scenarios, such as general urban environments or open highways, and lack adaptation mechanisms for the special environmental characteristics of port dry bulk terminals. They are difficult to accurately describe the severe non-stationary characteristics caused by the deep coupling between port stacking distribution and mechanical operations when UAVs pass through high-dynamic operation areas at low altitudes. Summary of the Invention
[0006] To address the aforementioned issues, this disclosure proposes a method and system for constructing a non-stationary UAV-to-ground communication channel based on time-varying stochasticity. It simulates the stacking environment of a port dry bulk cargo terminal by constructing a time-varying elliptical cylindrical geometric space with the UAV and ground base station as the focal points. A regularly spaced scatterer distribution replaces the complex random birth-death process, and the time-varying characteristics of Doppler frequency shift and phase are introduced by updating the elliptical cylindrical geometric parameters in real time. This allows for accurate and efficient reconstruction of the non-stationary U2G communication channel in the port dry bulk cargo terminal scenario, providing precise theoretical model support for the design, network optimization, and performance evaluation of smart port UAV low-altitude operation systems.
[0007] According to some embodiments, the present disclosure adopts the following technical solutions: A method for constructing non-stationary UAV-to-ground channels based on time-varying stochasticity includes: Construct a three-dimensional physical environment model of a port dry bulk cargo terminal scenario; Large-scale channel simulations were performed across multiple frequency bands and trajectories using ray tracing algorithms to extract key channel parameters. Initialize the communication scenario and system parameters, and construct a time-varying elliptic cylindrical regular geometric model based on key channel parameters, focusing on the real-time position of the UAV and the position of the ground base station; A regularly distributed set of scatterers is generated on the elliptical cylindrical surface at uniform angular intervals to simulate the continuous, regularly arranged surface of a dry bulk cargo stack. The line-of-sight component and ground reflection component are calculated based on geometric analytical relationships, and the non-line-of-sight scattering component reflected by the regular elliptical cylindrical scatterer is calculated by introducing Doppler frequency shift and time delay evolution. Taking into account Rice factor, ground reflection loss and phase information of each path, the line-of-sight component, ground reflection component and non-line-of-sight scattering component are superimposed to reconstruct the U2G channel model for the dry bulk cargo terminal of the port.
[0008] According to some embodiments, the present disclosure adopts the following technical solutions: A system for constructing non-stationary UAV-to-ground channels based on time-varying stochasticity includes: The environment model building module is used to build a three-dimensional physical environment model of a port dry bulk cargo terminal scenario; The channel parameter extraction module is used to perform large-scale channel simulations in multiple frequency bands and multiple trajectories using the ray tracing algorithm to extract key channel parameters. The time-varying model construction module is used to initialize communication scenario and system parameters, and to construct a time-varying elliptic cylindrical regular geometric model based on key channel parameters, with the real-time position of the UAV and the position of the ground base station as the focus. The component calculation module is used to generate a set of regularly distributed scatterers on the elliptical cylindrical surface at uniform angular intervals to simulate the continuous and regularly arranged surface of dry bulk cargo stacks; it calculates the line-of-sight component and ground reflection component based on geometric analytical relationships, and introduces Doppler frequency shift and time delay evolution to calculate the non-line-of-sight scattering component reflected by the regular scatterers on the elliptical cylindrical surface. The channel model reconstruction module is used to comprehensively consider Rice factor, ground reflection loss and phase information of each path, and superimpose line-of-sight components, ground reflection components and non-line-of-sight scattering components to reconstruct the U2G channel model for port dry bulk cargo terminals.
[0009] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product includes a computer program that, when executed by a processor, implements the aforementioned method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness.
[0010] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the aforementioned method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness.
[0011] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness.
[0012] Compared with the prior art, the beneficial effects of this disclosure are as follows: This disclosed method for constructing UAV-to-ground non-stationary channels based on time-varying stochastics constructs a dynamic elliptical cylindrical geometric framework that updates in real time with the movement of the UAV. It fully utilizes the low computational complexity advantage of regular geometric models and can accurately characterize the time-frequency non-stationary characteristics of U2G channels in the complex environment of port dry bulk cargo terminals. This provides efficient and accurate theoretical model support for the design, optimization and evaluation of UAV low-altitude operation systems in smart ports.
[0013] This disclosure presents a time-varying stochastic method for constructing U2G non-stationary channels to the ground. It constructs a time-varying elliptic cylindrical geometric space with the UAV and ground base station as the focus, simulating the stacking environment of a port dry bulk cargo terminal. It replaces the complex random birth and death process with a regularly spaced scatterer distribution, which greatly reduces the amount of computation. By updating the elliptic cylindrical geometric parameters in real time, it introduces the time-varying characteristics of Doppler frequency shift and phase, thereby accurately and efficiently reconstructing the U2G communication non-stationary channel in the port dry bulk cargo terminal scenario.
[0014] This disclosed method for constructing non-stationary UAV-to-ground channels based on time-varying stochastics uses Rizhao Port, a real dry bulk cargo terminal, as a prototype. It utilizes the 3D modeling software Blender to meticulously recreate the physical environment of dry bulk cargo stacking, large machinery, and different yard utilization rates. Through ray tracing technology, it conducts large-scale channel simulations under multiple frequency bands and trajectories, extracts key statistical parameters such as Rice factor and multipath power delay spectrum, and establishes a high-fidelity U2G channel dataset covering multiple frequency bands, multiple altitudes, and different yard utilization rates. This provides scarce data support and verification benchmarks for the study of channel characteristics in complex scenarios at port dry bulk cargo terminals.
[0015] This disclosure presents a time-varying stochastic method for constructing non-stationary UAV-to-ground channels. Based on a dataset, it delves into key statistical characteristics of the channel, such as time autocorrelation, singular value spread, and Doppler power spectral density. It innovatively employs a time-varying elliptic cylindrical regular geometric model to jointly model line-of-sight, ground reflection, and regular scattering non-line-of-sight paths. This significantly reduces computational complexity while accurately characterizing the time-varying and non-stationary characteristics of UAV channels in port dry bulk cargo terminal environments. The model constructed in this disclosure provides high-precision theoretical basis and data support for smart port communication link budgeting, base station deployment optimization, and UAV flight trajectory planning, thereby effectively ensuring the reliability and coverage performance of low-altitude networks at port dry bulk cargo terminals and contributing to the automated transformation of port dry bulk cargo terminals and the high-quality development of the low-altitude economy.
[0016] This disclosed method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness calculates the time-varying lengths of the line-of-sight path, the ground reflection path, and the non-line-of-sight path reflected by a regular elliptical cylindrical scatterer based on geometric analytical relationships. It naturally introduces Doppler frequency shift and time delay evolution by utilizing the continuous change in path length caused by UAV motion. By comprehensively considering Rice factor, ground reflection loss, and phase information of each path, and superimposing the line-of-sight component, the ground reflection component, and the non-line-of-sight scattering component, a full-link channel impulse response containing time-domain non-stationarity and frequency-domain correlation is constructed, achieving low-complexity and high-precision reconstruction of U2G channel characteristics in the complex environment of a port dry bulk cargo terminal. Attached Figure Description
[0017] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.
[0018] Figure 1 This is a flowchart of a method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness according to an embodiment of this disclosure. Detailed Implementation
[0019] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.
[0020] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.
[0021] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0022] Example 1 One embodiment of this disclosure provides a method for constructing a U2G non-stationary channel based on time-varying randomness. It proposes a method for constructing a U2G non-stationary channel based on time-varying elliptic cylindrical regular geometry for dry bulk cargo terminals in ports. The method includes the following steps: Step 1: Construct a 3D physical environment model of the port dry bulk cargo terminal scenario; Step 2: Utilize ray tracing algorithms to perform large-scale channel simulations across multiple frequency bands and trajectories, and extract key channel parameters; Step 3: Initialize the communication scenario and system parameters, and construct a time-varying elliptic cylindrical regular geometric model based on key channel parameters, with the real-time position of the UAV and the position of the ground base station as the focus; Step 4: Generate a regularly distributed set of scatterers on the elliptical cylindrical surface at uniform angular intervals to simulate the continuous and regularly arranged surface of a dry bulk cargo stack. Step 5: Calculate the line-of-sight component and ground reflection component based on geometric analytical relationships, and introduce Doppler frequency shift and time delay evolution to calculate the non-line-of-sight scattering component reflected by the regular elliptical cylindrical scatterer; Step 6: Taking into account Rice factor, ground reflection loss and phase information of each path, superimpose the line-of-sight component, ground reflection component and non-line-of-sight scattering component to reconstruct the U2G channel model for the port dry bulk cargo terminal.
[0023] As one embodiment, this disclosure presents a method for constructing a UAV-to-ground non-stationary channel based on time-varying randomness. Using Rizhao Port, a real dry bulk cargo terminal, as a prototype, it simulates the stacking environment of a dry bulk cargo terminal by constructing a time-varying elliptical cylindrical geometric space with the UAV and ground base station as the focal points. It replaces the complex random birth-death process with a regularly spaced scatterer distribution, significantly reducing the computational load. By updating the elliptical cylindrical geometric parameters in real time, it introduces the time-varying characteristics of Doppler frequency shift and phase, thereby accurately and efficiently reconstructing the U2G communication non-stationary channel in the dry bulk cargo terminal scenario. The specific implementation process is as follows: Step 1: Construct a 3D physical environment model of the port dry bulk cargo terminal scenario; Specifically, taking the dry bulk cargo terminal of Rizhao Port of Shandong Port Group as the physical prototype, a high-precision and high-fidelity three-dimensional simulation digital environment was constructed using the three-dimensional modeling software Blender.
[0024] In the process of high-fidelity 3D digital scene modeling, the scene with a length of 4051 meters and a width of 3407 meters was geometrically restored at a 1:1 scale, strictly based on the publicly available port engineering layout map and high-resolution satellite remote sensing image. The focus was on the detailed modeling of key scattering bodies such as dry bulk cargo stacks, large loading and unloading machinery (gantry cranes, ship loaders), transport vehicles and berthed ships. Two typical yard utilization configurations of 40% (low density) and 70% (high density) were set to simulate the dynamic occlusion environment caused by changes in cargo throughput during the operation of the port's dry bulk cargo terminal.
[0025] Step 2: Use the ray tracing algorithm to perform large-scale channel simulations across multiple frequency bands and trajectories to extract key channel parameters; Based on the high-fidelity 3D digital scene constructed in step 1, channel simulation was performed using the advanced ray tracing simulation software Sionna RT. First, import the 3D scene model built in step 1 into the simulation platform, and configure the corresponding electromagnetic parameters according to the actual materials of different objects.
[0026] Secondly, the simulated carrier frequencies were set to 5.9 GHz and 28 GHz to cover both low-frequency and high-frequency communication bands. Four ground stations (GSs) were deployed as receivers, and six drones equipped with omnidirectional antennas were deployed as transmitters. Based on the inspection needs of the port's dry bulk cargo terminal, six typical flight paths were planned, covering different operating areas from the center to the edge of the yard. The flight altitudes were set at 80 meters and 120 meters, and the flight speed was uniformly set at 20 meters per second.
[0027] Finally, the number of simulation time snapshots was set to 250, with a snapshot interval of 0.02 seconds. The ray tracing algorithm was used to calculate the reflection, diffraction, and scattering propagation paths of the signal in a dense obstacle environment, generating a total of 6000 sets of Channel Impulse Response (CIR) data.
[0028] Based on the Channel Impulse Response (CIR) dataset, key channel parameters, including Rice factor, ground reflection coefficient, and multipath power delay spectrum, are extracted through statistical analysis.
[0029] Step 3: Initialize the communication scenario and system parameters, and construct a time-varying elliptic cylindrical regular geometric model based on key channel parameters, with the real-time position of the UAV and the position of the ground base station as the focus; First, initialize the communication scenario and system parameters, specifically including: Taking the horizontal plane of the dock where GS is located as xy The origin of the coordinate system is the point projected onto the horizontal plane of the dock, with the location of GS as the coordinate axis. z Establish a three-dimensional rectangular coordinate system using axes. The coordinates of GS are denoted as follows: , t The distance vector between the drone and GS at any time z shaft and xy The vectors of the planar projection are denoted as E(t) and A(t), respectively. The absolute position of the UAV can then be expressed as... The initial times are denoted as follows: and The motion of a drone is described by its velocity vector, denoted as . Expanded as:
[0030] in, and They aret The horizontal and pitch angles of the UAV's velocity vector at any given moment. The projection of the UAV's velocity vector onto the horizontal plane. The definition of is:
[0031] Accordingly, distance vector and They are respectively:
[0032]
[0033] Based on this, the line-of-sight distance vector of the UAV is:
[0034] Its horizontal projection distance That is, a vector The modulus length, i.e. .
[0035] Furthermore, a time-varying elliptic cylindrical regular geometric model is constructed, focusing on the real-time location of the UAV and the location of the ground base station, specifically including: Using the calculated horizontal projection distance As the basis for the focal length of an ellipse. At any given moment t Calculate the semi-focal length of the ellipse. Introducing the path redundancy ratio factor Calculate the semi-major axis of the ellipse. and short half shaft Construct the rotation matrix Its rotation angle From vector The direction determines, that is Ensure that the major axis of the ellipse always coincides with the horizontal line connecting the transmitting and receiving ends.
[0036] Step 4: Generate a regularly distributed set of scatterers on the elliptical cylindrical surface at uniform angular intervals to simulate the continuous and regularly arranged surface of a dry bulk cargo stack. Specifically, the number of [something] generated on the elliptical cylindrical surface is fixed. A set of regular scattering bodies to simulate the surface of a continuous, regularly arranged dry bulk cargo stack: First, in the interval The azimuth angle of the inner elliptical cylinder is uniformly divided to generate a regular angle sequence:
[0037] in, .
[0038] Secondly, based on the height distribution characteristics of the dry bulk cargo terminal stacks at the port, the height of each scattering body is generated. ,include: Considering that in real-world scenarios, stacking height is limited by the operational limits of large loading and unloading machinery, resulting in a clear physical boundary, while its surface exhibits random fluctuations due to varying yard utilization rates, a truncated normal distribution is used to model the randomness of the height to accurately characterize this geometric distribution feature. The probability density function is expressed as:
[0039] In the formula, The average stack height corresponding to the current yard utilization rate; The standard deviation of height characterizes the degree of surface undulation of the stack; and These represent the maximum and minimum physical height of the stack, determined by the limits of mechanical operation; and Let represent the probability density function and cumulative distribution function of the standard normal distribution, respectively. By randomly sampling from this distribution, the height values of each scatterer can be obtained.
[0040] Finally, calculate the first... Time-varying position coordinates of the scatterers in the global coordinate system :
[0041] In the formula, These are the coordinates of the center of the ellipse.
[0042] Step 5: Calculate the line-of-sight component and ground reflection component based on geometric analytical relationships, and introduce Doppler frequency shift and time delay evolution to calculate the non-line-of-sight scattering component reflected by the regular elliptical cylindrical scatterer; First, (1) calculate the channel parameters of the line-of-sight (LoS) component, specifically: 1) The Doppler frequency shift of the LoS component is:
[0043] in, λ Represents wavelength, Indicates the inner product.
[0044] 2) The time-varying phase of the LoS component is calculated as follows:
[0045] 3) The time delay for calculating the Loss component is:
[0046] in, This represents the phase shift at the initial moment. It represents the speed of light.
[0047] 4) The channel complex gain of the LoS component can be expressed as:
[0048] (2) Calculate the channel parameters of the ground reflection (GR) component, specifically including: set up t The drone's current altitude is The horizontal distance is The distance between the UAV and the reflection point on the ground, and the distance between the center of the GS end and the reflection point on the ground. yz Projection of a plane and They are respectively:
[0049]
[0050] Corresponding propagation distance and They are respectively:
[0051]
[0052] Furthermore, the corresponding distance vector can be obtained. Expanded to:
[0053] In the formula, , These are the horizontal and vertical angles of the distance vector.
[0054] Furthermore, the Doppler frequency shift, time delay, and phase of the ground reflection (GR) component are as follows:
[0055]
[0056]
[0057] The channel complex gain of the GR component can then be expressed as:
[0058] (3) Calculate the channel parameters of the regular geometric non-line-of-sight (NLoS) component, specifically: Iterate through the generated set of regular elliptical cylindrical scatterers, calculating the geometric and physical parameters of the non-line-of-sight path reflected by each scatterer. For the ... Regular scattering bodies Calculate the distance vector from the UAV to the scatterer. and the distance vector from the scatterer to the receiver. Represented as:
[0059]
[0060] The instantaneous total length of this NLoS path The sum of the incident path length and the exit path length:
[0061] Using the velocity vector of the drone Doppler frequency shift is calculated by projection along the transmission direction. Since the scatterers are distributed on a regular elliptical cylindrical surface and the base station is fixed, the Doppler effect is mainly caused by the high-speed motion of the UAV. The Doppler frequency shift, time delay, and phase of the NLoS component are as follows:
[0062]
[0063]
[0064] The complex channel gain of the NLoS component is:
[0065] Step 6: Taking into account Rice factor, ground reflection loss, and phase information of each path, superimpose the line-of-sight component, ground reflection component, and non-line-of-sight scattering component to reconstruct the U2G channel model for the dry bulk cargo terminal of the port. Specifically, this includes: Inspection drones are always t CIR matrix for:
[0066] Complex channel gain It consists of the LoS component, the ground reflection component, and the NLoS component:
[0067] In the formula, The Rice factor is the ratio of the power of the LoS component to that of other components between the UAV and GS. and The power ratios of the GR and NLoS components were defined, and ; , and These represent the complex channel gain of the GR component, LoS component, and NLoS component between the UAV and GS terminals, respectively.
[0068] Example 2 One embodiment of this disclosure provides a UAV-to-ground non-stationary channel construction system based on time-varying randomness, including: The environment model building module is used to build a three-dimensional physical environment model of a port dry bulk cargo terminal scenario; The channel parameter extraction module is used to perform large-scale channel simulations in multiple frequency bands and multiple trajectories using the ray tracing algorithm to extract key channel parameters. The time-varying model construction module is used to initialize communication scenario and system parameters, and to construct a time-varying elliptic cylindrical regular geometric model based on key channel parameters, with the real-time position of the UAV and the position of the ground base station as the focus. The component calculation module is used to generate a set of regularly distributed scatterers on the elliptical cylindrical surface at uniform angular intervals to simulate the continuous and regularly arranged surface of dry bulk cargo stacks; it calculates the line-of-sight component and ground reflection component based on geometric analytical relationships, and introduces Doppler frequency shift and time delay evolution to calculate the non-line-of-sight scattering component reflected by the regular scatterers on the elliptical cylindrical surface. The channel model reconstruction module is used to comprehensively consider Rice factor, ground reflection loss and phase information of each path, and superimpose line-of-sight components, ground reflection components and non-line-of-sight scattering components to reconstruct the U2G channel model for port dry bulk cargo terminals.
[0069] Example 3 One embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness.
[0070] Example 4 One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions. When these computer instructions are executed by a processor, they implement the aforementioned method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness.
[0071] Example 5 One embodiment of this disclosure provides an electronic device, including a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness.
[0072] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0074] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.
Claims
1. A method for constructing a non-stationary UAV-to-ground channel based on time-varying stochasticity, characterized in that, include: Construct a three-dimensional physical environment model of a port dry bulk cargo terminal scenario; Large-scale channel simulations were performed across multiple frequency bands and trajectories using ray tracing algorithms to extract key channel parameters. Initialize the communication scenario and system parameters, and construct a time-varying elliptic cylindrical regular geometric model based on key channel parameters, focusing on the real-time position of the UAV and the position of the ground base station; A regularly distributed set of scatterers is generated on an elliptical cylindrical surface at uniform angular intervals to simulate the surface of a continuous, regularly arranged dry bulk cargo stack. The line-of-sight component and ground reflection component are calculated based on geometric analytical relationships, and the non-line-of-sight scattering component reflected by the regular elliptical cylindrical scatterer is calculated by introducing Doppler frequency shift and time delay evolution. Taking into account Rice factor, ground reflection loss and phase information of each path, the line-of-sight component, ground reflection component and non-line-of-sight scattering component are superimposed to reconstruct the U2G channel model for the dry bulk cargo terminal of the port.
2. The method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness as described in claim 1, characterized in that, The construction of a three-dimensional physical environment model for a port dry bulk cargo terminal scenario includes: acquiring the target port, using the three-dimensional modeling software Blender to construct a high-precision three-dimensional simulation environment, and, based on publicly available port engineering layout maps and high-resolution satellite remote sensing images, performing a 1:1 geometric reconstruction of the scene with a set length and width, focusing on detailed modeling of key scattering bodies such as dry bulk cargo stacks, large loading and unloading machinery, transport vehicles, and berthed ships, and setting two typical yard utilization configurations of low density and high density to simulate the dynamic occlusion environment caused by changes in cargo throughput during the operation of the port dry bulk cargo terminal.
3. The method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness as described in claim 1, characterized in that, The method utilizes ray tracing algorithms to perform large-scale channel simulations across multiple frequency bands and trajectories, extracting key channel parameters. This includes: based on a constructed high-fidelity 3D physical environment model, channel simulation is conducted using ray tracing simulation software. The 3D physical environment model is imported into the simulation platform, and corresponding electromagnetic parameters are configured according to the actual materials of different objects. Multiple ground base stations are deployed in the scenario as receivers, and multiple UAVs equipped with omnidirectional antennas are deployed as transmitters. Multiple typical flight trajectories are planned, covering different work areas from the center to the edge of the yard. Ray tracing algorithms are used to calculate the reflection, diffraction, and scattering propagation paths of signals in a dense obstacle environment, generating multiple sets of channel impulse response data. Based on the channel impulse response data, key channel parameters, including Rice factor, ground reflection coefficient, and multipath power delay spectrum, are extracted through statistical analysis.
4. The method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness as described in claim 1, characterized in that, The initialization of communication scenarios and system parameters involves constructing a time-varying elliptic-cylindrical regular geometric model based on key channel parameters, focusing on the real-time location of the UAV and the location of the ground base station. This includes: Taking the horizontal plane of the dock where the ground base station is located as the reference point xy The origin of the coordinate system is the projection point on the horizontal plane of the dock. A three-dimensional rectangular coordinate system is established with the location of the ground base station as the z-axis. The horizontal projection distance is calculated and used as the focal length basis of the ellipse. At any time, the half focal length of the ellipse is calculated. The path redundancy ratio factor is introduced to calculate the major and minor axes of the ellipse. A rotation matrix is constructed, and its rotation angle is determined by the direction of the vector to ensure that the major axis of the ellipse always coincides with the horizontal line connecting the transmitting and receiving ends.
5. The method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness as described in claim 1, characterized in that, The method of generating a regularly distributed set of scatterers at uniform angular intervals on the elliptical cylindrical surface to simulate a continuous, regularly arranged surface of a dry bulk cargo stack includes: First, the azimuth angle of the elliptical cylinder is uniformly divided within the interval to generate a regular angle sequence; Secondly, the height of each scatterer is generated based on the height distribution characteristics of the dry bulk cargo terminal stacks at the port. Finally calculate the... m The time-varying position coordinates of a scatterer in the global coordinate system.
6. The method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness as described in claim 1, characterized in that, The line-of-sight component and ground reflection component are calculated based on geometric analytical relationships. Doppler frequency shift and time delay evolution are introduced to calculate the non-line-of-sight scattering component reflected from a regular elliptical cylindrical scatterer, including: The time-varying lengths of the line-of-sight path, the ground reflection path, and the non-line-of-sight path reflected by the regular scatterer of the elliptical cylindrical surface are calculated based on geometric analytical relationships. By leveraging the continuous change in path length caused by the movement of the UAV to naturally introduce Doppler frequency shift and time delay evolution, the geometric and physical parameters of the non-line-of-sight path reflected by each scatterer are calculated by traversing the generated set of regular elliptical cylindrical scatterers.
7. A system for constructing a non-stationary UAV-to-ground channel based on time-varying stochasticity, characterized in that, include: The environment model building module is used to build a three-dimensional physical environment model of a port dry bulk cargo terminal scenario; The channel parameter extraction module is used to perform large-scale channel simulations in multiple frequency bands and multiple trajectories using the ray tracing algorithm to extract key channel parameters. The time-varying model construction module is used to initialize communication scenario and system parameters, and to construct a time-varying elliptic cylindrical regular geometric model based on key channel parameters, with the real-time position of the UAV and the position of the ground base station as the focus. The component calculation module is used to generate a set of regularly distributed scatterers on the elliptical cylindrical surface at uniform angular intervals to simulate the continuous and regularly arranged surface of dry bulk cargo stacks; it calculates the line-of-sight component and ground reflection component based on geometric analytical relationships, and introduces Doppler frequency shift and time delay evolution to calculate the non-line-of-sight scattering component reflected by the regular scatterers on the elliptical cylindrical surface. The channel model reconstruction module is used to comprehensively consider Rice factor, ground reflection loss and phase information of each path, and superimpose line-of-sight components, ground reflection components and non-line-of-sight scattering components to reconstruct the U2G channel model for port dry bulk cargo terminals.
8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness as described in any one of claims 1-6.
9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the UAV-to-ground non-stationary channel construction method based on time-varying randomness as described in any one of claims 1-6.
10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the method for constructing a non-stationary UAV-to-ground channel based on time-varying randomness as described in any one of claims 1-6.