Cooperative environment modeling method and system for 6G low-altitude traffic

Abstract

The invention discloses a 6G low-altitude traffic-oriented collaborative environment modeling method and system, and belongs to the technical field of wireless communication. Comprising the following steps: matching by using scatterer coordinates in an electromagnetic space and a fused laser point cloud in combination with a preset minimum flight height of an unmanned aerial vehicle and a vehicle size, determining statistical distribution of a static scatterer, a ground dynamic scatterer, an air dynamic scatterer and parameters, performing channel modeling, and generating a multi-unmanned aerial vehicle and multi-vehicle communication channel model; and analyzing the multi-unmanned aerial vehicle and multi-vehicle communication channel model in combination with the dynamic change of the time domain and frequency domain characteristics to obtain a time-frequency correlation function, Doppler power spectral density and a time stationary interval so as to dynamically adjust the unmanned aerial vehicle end and the vehicle end. Through deep fusion of sensing information and introduction of a cooperation mechanism, time-frequency non-stability and time consistency of a multi-unmanned-aerial-vehicle communication channel can be accurately described. The problem that the description capability of existing channel modeling on an actual scene is insufficient is solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a collaborative environment modeling method and system for 6G low-altitude traffic. Background Art

[0002] The statements in this section merely mention background art related to the present invention and do not necessarily constitute prior art.

[0003] Low-altitude traffic scenarios are one of the typical application scenarios of the sixth generation of mobile communications (6th-Generation, 6G). The introduction of advanced communication and perception technologies can effectively improve the safety and efficiency of low-altitude aircraft such as drones. In addition, low-altitude traffic has also promoted the development of air-ground integrated networks, enabling low-altitude aircraft to communicate with ground traffic in a timely manner, thereby achieving more accurate flight management and scheduling, and providing strong technical support for application scenarios such as urban logistics, emergency rescue, and environmental monitoring.

[0004] In the 6G low-altitude traffic scenario, drones and ground intelligent vehicles have put forward higher requirements for efficient and accurate information exchange. Especially in the face of complex and dynamic propagation environments, the limitations of traditional single-vehicle and single-drone communication architectures are becoming increasingly apparent. These architectures usually rely on the perception and communication capabilities of a single node, making it difficult to achieve comprehensive environmental perception, resulting in limited coverage, insufficient anti-interference capabilities, and poor dynamic adaptability, which cannot meet the needs of low-altitude traffic for high reliability and low-latency communications. Multi-vehicle and multi-drone collaborative communication technology effectively compensates for the shortcomings of traditional single-vehicle and single-drone communication methods by integrating the perception information and communication resources of multiple nodes, significantly improving communication robustness, coverage, and environmental adaptability, and becoming an important development direction for 6G intelligent transportation systems.

[0005] However, in the collaborative communication of multiple vehicles and multiple drones, the addition of multiple vehicles and multiple drones will introduce more reflection, scattering and diffraction paths, causing the multipath characteristics of the channel to become more complicated; the high-speed movement of multiple vehicles and multiple drones will lead to more complex Doppler frequency shift and delay spread, increasing the difficulty of dynamic changes of the channel; the channel propagation characteristics between multiple drones and vehicles are also different from the communication characteristics of a single drone to the ground.

[0006] Existing channel models usually only rely on RF channel information (such as Doppler frequency shift) to characterize the propagation environment. In complex propagation environments, this method is difficult to distinguish between static scatterers, ground dynamic scatterers, and air dynamic scatterers, resulting in insufficient description of the model for actual scenarios. For example, in single-vehicle and single-drone communications, due to the limited perception field of the single vehicle and single drone, it may not be possible to accurately capture this complex propagation environment, resulting in limited cognition of the surrounding physical environment, and thus unable to accurately characterize and model the dynamic changes of key parameters such as the number, position, angle, and delay of scatterers. Summary of the invention

[0007] In order to address the deficiencies in the prior art, the present invention provides a collaborative environment modeling method, system, electronic device, computer-readable storage medium and computer program product for 6G low-altitude traffic, which can accurately describe the time-frequency non-stationarity and time consistency of multi-UAV and multi-vehicle communication channels.

[0008] In a first aspect, the present invention provides a collaborative environment modeling method for 6G low-altitude traffic;

[0009] A collaborative environment modeling method for 6G low-altitude traffic, including:

[0010] Obtain and fuse the laser point cloud data collected by multiple drones and multiple vehicles, combine the preset minimum flight altitude of the drone and the size of the vehicle, and use the coordinates of the scatterers in the electromagnetic space and the fused laser point cloud to match them to determine the static scatterers, ground dynamic scatterers, and aerial dynamic scatterers;

[0011] Obtain the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers, perform channel modeling, and generate a multi-UAV and multi-vehicle communication channel model;

[0012] The multi-UAV multi-vehicle communication channel model is analyzed in combination with the dynamic changes of time domain characteristics and frequency domain characteristics in multi-vehicle and multi-UAV communications, and the time-frequency correlation function, Doppler power spectrum density and time stationary interval of the multi-UAV multi-vehicle communication channel model are obtained to dynamically adjust the UAV end and the vehicle end.

[0013] In some embodiments, the step of acquiring and fusing laser point cloud data collected by multiple drone terminals and multiple vehicle terminals includes:

[0014] The laser point cloud data collected by the drone and the vehicle are converted into the same coordinate system, and the laser point cloud data after coordinate conversion are spliced ​​to obtain a point cloud set;

[0015] Combined with the distance between the point cloud and the sensor, the overlapping areas in the point cloud set are weighted averaged and the point cloud set is updated to determine the environment perception point cloud.

[0016] In some embodiments, the method combines the preset minimum flight altitude of the drone and the vehicle size, matches the scatterer coordinates in the electromagnetic space with the fused laser point cloud, and determines the static scatterers, the ground dynamic scatterers, and the air dynamic scatterers, including:

[0017] The fused laser point cloud is processed by density clustering method to generate multiple point cloud clusters and extract the point cloud cluster size; the point cloud cluster size is compared with the preset minimum flight altitude of the UAV and the vehicle size to screen static objects, ground dynamic objects and aerial dynamic objects;

[0018] The coordinates of the scatterers in the electromagnetic space are matched with the position coordinates of the static objects, the ground dynamic objects and the aerial dynamic objects to determine the static scatterers, the ground dynamic scatterers and the aerial dynamic scatterers.

[0019] In some embodiments, obtaining the statistical distribution of quantity parameters, distance parameters, and angle parameters in static scatterers, ground dynamic scatterers, and air dynamic scatterers and performing channel modeling includes:

[0020] Obtain the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers, and perform clustering and range screening respectively to determine the static scattering clusters, ground dynamic scattering clusters and aerial dynamic scattering clusters in the visible area;

[0021] Using the initial distance between the UAV and the ground vehicle and the moving speed of the UAV and the ground vehicle, the direct path component of the communication link between the UAV and the ground vehicle is obtained;

[0022] The non-direct path component of the communication link between the UAV and the ground vehicle is determined by combining the power parameters, distance parameters and angle parameters of the static scattering cluster, the ground dynamic scattering cluster and the air dynamic scattering cluster;

[0023] The ground reflection component of the communication link between the UAV and the ground vehicle is determined using the distance vector from the transceiver to the ground reflection point;

[0024] The multi-UAV and multi-vehicle communication model is determined by the direct path component, indirect path component and ground reflection component of the communication link between multiple UAVs and multiple ground vehicles.

[0025] In some embodiments, the multi-UAV multi-vehicle communication channel model is analyzed in combination with the dynamic changes of time domain characteristics and frequency domain characteristics in multi-vehicle and multi-UAV communications to obtain the time-frequency correlation function of the multi-UAV multi-vehicle communication channel model. Specifically, the channel impulse response is Fourier transformed and frequency-related factors are introduced to obtain the time-varying transfer function; based on the time-varying transfer function, the time-frequency correlation function is determined.

[0026] In some embodiments, the multi-UAV multi-vehicle communication channel model is analyzed in combination with the dynamic changes of time domain characteristics and frequency domain characteristics in multi-vehicle multi-UAV communication, and the Doppler power spectrum density and time stationary interval of the multi-UAV multi-vehicle communication channel model are obtained, including:

[0027] Based on the time-frequency correlation function, a time autocorrelation function is determined; based on the autocorrelation function, a time interval is Fourier transformed to obtain a Doppler power spectrum density;

[0028] Obtain the path gain of the hybrid twin cluster in the communication link between the UAV and the vehicle, and calculate the time-varying delay spread; Based on the time-varying delay spread, calculate the time stationary interval of the multi-UAV and multi-vehicle communication channel model.

[0029] In a second aspect, the present invention provides a collaborative environment modeling system for 6G low-altitude traffic;

[0030] A collaborative environment modeling system for 6G low-altitude traffic, including:

[0031] The scatterer identification module is configured to: obtain and fuse the laser point cloud data collected by multiple drone terminals and multiple vehicle terminals, combine the preset minimum flight altitude of the drone and the vehicle size, use the scatterer coordinates in the electromagnetic space and the fused laser point cloud to match, and determine the static scatterers, ground dynamic scatterers and air dynamic scatterers;

[0032] The channel model building module is configured to: obtain the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers, perform channel modeling, and generate a multi-UAV multi-vehicle communication channel model;

[0033] The analysis module is configured to analyze the multi-UAV multi-vehicle communication channel model in combination with the dynamic changes of the time domain characteristics and the frequency domain characteristics in the multi-vehicle multi-UAV communication, and obtain the time-frequency correlation function, Doppler power spectrum density and time stationary interval of the multi-UAV multi-vehicle communication channel model to dynamically adjust the UAV side and the vehicle side.

[0034] In a third aspect, the present invention provides an electronic device;

[0035] An electronic device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned collaborative environment modeling method for 6G low-altitude traffic.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium;

[0037] A computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the above-mentioned collaborative environment modeling method for 6G low-altitude traffic.

[0038] In a fifth aspect, the present invention provides a computer program product;

[0039] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned collaborative environment modeling method for 6G low-altitude traffic.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The technical solution provided by the present invention can fully capture the physical environment characteristics by means of multi-view lidar point cloud and RF channel data collected by multiple vehicles and multiple drones in the collaborative communication of multiple vehicles and multiple drones, so as to more finely characterize and model the dynamic changes of key parameters such as the number, position, angle and delay of scatterers, and provide strong support for analyzing the influence of dynamic scatterers in the air and on the ground on drone channel modeling; moreover, it can better characterize the channel evolution process under the collaborative communication of multiple vehicles and multiple drones, so as to complete the non-stationary and consistency modeling of the channel.

[0042] 2. The technical solution provided by the present invention provides a new technical route for 6G intelligent low-altitude transportation through the deep integration of perception information and the introduction of cooperation mechanism in the collaborative communication of multiple vehicles and multiple drones. It can not only effectively improve the communication efficiency, but also bring new research perspectives and solutions to channel modeling in complex dynamic propagation environments. It is of great significance to promote the deep integration of low-altitude transportation and ground transportation, and also lays a solid foundation for the comprehensive upgrade of future intelligent transportation systems.

[0043] 3. The technical solution provided by the present invention constructs a collaborative channel modeling framework that integrates static, ground dynamic and aerial dynamic scattering clusters by statistically analyzing the number, distance, angle and power-delay characteristics of scatterers, overcoming the challenges of modeling the time and space non-stationarity of channels in multi-vehicle and multi-UAV collaborative scenarios. This technology fully considers the phenomenon that multiple vehicles and multiple UAVs share the same scatterers due to different communication links in the same propagation environment, realizes the accurate modeling of multi-vehicle and multi-UAV collaborative communication channels, and further captures the dynamic evolution process of scatterers, thereby characterizing the smooth evolution characteristics of scatterers in a collaborative communication environment.

[0044] 4. The technical solution provided by the present invention characterizes the propagation environment under different air and ground traffic densities by flexibly adjusting the model parameters to ensure the accuracy of channel modeling; with the help of perception information, it significantly improves the robustness of multi-vehicle and multi-UAV collaborative communication channel modeling, and provides an effective means for accurately characterizing complex channel characteristics in 6G intelligent transportation scenarios, laying a solid foundation for the deep integration of low-altitude transportation and ground transportation.

[0045] 5. The technical solution provided by the present invention combines the ground reflection path with the direct path in the air to establish a more complete channel model; at the same time, it is necessary to consider the different distributions of various scatterer types and model the distributions of dynamic air, dynamic ground and static scatterers respectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0047] Figure 1 A flowchart of a collaborative environment modeling method for 6G low-altitude traffic provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0049] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0050] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0051] Embodiment 1

[0052] Existing channel modeling methods cannot fully describe the propagation environment, which in turn affects the analysis and understanding of channel characteristics; therefore, the present invention provides a collaborative environment modeling method for 6G low-altitude traffic, which provides more comprehensive propagation environment information through multi-perspective lidar point cloud data, and combines the dynamic changes of time domain characteristics and frequency domain characteristics to perform more accurate multi-UAV and multi-vehicle communication channel analysis.

[0053] Next, combine Figure 1 , a collaborative environment modeling method for 6G low-altitude traffic disclosed in this embodiment is described in detail. The collaborative environment modeling method for 6G low-altitude traffic includes the following steps:

[0054] S1. Obtain laser point cloud data collected by laser radars carried by multiple drones and multiple vehicles and pre-process them, fuse the pre-processed laser point cloud data, and generate an environment perception point cloud.

[0055] In order to fully describe the complex and dynamic propagation environment, a wider range of environmental perception information is obtained by integrating the laser radar point cloud perception information on the vehicle side and the drone side. As an implementation method, S1 includes:

[0056] S101, obtaining laser point cloud data collected by laser radars carried by multiple drones and multiple vehicles and performing preprocessing.

[0057] Specifically, the point cloud data directly collected by the LiDAR contains a large number of redundant ground points, which has no benefit in distinguishing static objects, ground dynamic objects, and aerial dynamic objects. Therefore, the minimum value of all point cloud coordinates plus the defined value is selected as the threshold to identify whether the point is a ground point. If the point coordinates are greater than the threshold, it is retained, otherwise it is deleted.

[0058] S102, according to the position coordinates S(t) of the laser radar carried by the drone and the vehicle during the movement process = [x S (t),y S (t),z S (t)] and the relative coordinates of the LiDAR point cloud L′(t) = [x L (t),y L (t),z L (t)], the absolute coordinates L(t) of the LiDAR point cloud are calculated according to the mapping rules between the coordinate system rules followed by the original collected LiDAR point cloud and the world coordinate system.

[0059] Taking the original lidar point cloud acquisition following the left-handed coordinate system as an example, the x-axis is aligned with the vehicle's forward direction, and the z-axis is vertically upward. When the angle between the front of the drone and the vehicle and the x-axis of the world coordinate system is θ, the absolute coordinates of the lidar point cloud are calculated as follows:

[0060] L(t)=[x L (t)cosθ+y L (t)sinθ+x S (t),x L (t)sinθ+y L (t)cosθ-y S (t),-z L (t)-z S (t)].

[0061] S103: Fusing the pre-processed laser point cloud data of the drone end and the laser point cloud data of the vehicle end to generate an environment perception point cloud.

[0062] Next, the preprocessed laser point cloud data of the drone is represented as P UAV ={(x i ,y i ,z i )}, the pre-processed vehicle-side point cloud data is represented as P vehicle ={(x j ,y j ,z j )}, as an implementation mode, the specific process of S103 is as follows:

[0063] (1) P UAV ={(x i ,y i ,z i )} and P vehicle ={(x j ,y j ,z j )} directly spliced ​​into a point cloud set.

[0064] Furthermore, if the cloud acquisition time of the drone end and the vehicle end is not synchronized, before implementing step (1), it also includes: using time interpolation to synchronize the laser point cloud data and save it, which is expressed as:

[0065] (x i ′,y i ′,z i ′)=(x i ,y i ,z i )+Δt·v UAV ;

[0066] (x j ′,y j ′,z j ′)=(x j ,y j ,z j )+Δt·v vehicle ;

[0067] In the formula, Δt represents the time difference, v UAV With v vehicle Represents the velocity vector.

[0068] (2) For the overlapping areas in the point cloud set, the distance between the drone or vehicle sensor and the point cloud is weighted averaged to update the point cloud set; for the non-overlapping areas in the point cloud set, the point cloud in the corresponding drone or vehicle laser point cloud data is directly retained.

[0069] Exemplarily, for the overlapping area, it is expressed as:

[0070]

[0071]

[0072] In the formula, w UAV (d i ) represents the weight of the point cloud on the drone side, which depends on the distance d between the point and the laser radar on the drone side i , w vehicle (d j ) represents the point cloud weight of the vehicle, which depends on the distance between the point and the laser radar on the vehicle. α and β are distance attenuation factors, which are set according to the characteristics of the laser radar on the unmanned vehicle and the vehicle.

[0073] The point cloud data on the vehicle side and the drone side each have limitations in terms of viewing angle and coverage. The vehicle-side point cloud mainly focuses on the horizontal plane and has difficulty perceiving high-altitude obstacles (such as tree canopies and drones); the drone point cloud has a better bird's-eye view, but the perception of small obstacles on the ground (such as speed bumps and shoulders) is not detailed enough. Therefore, in this embodiment, point cloud fusion is used to solve the limitation of single-viewpoint point clouds. The fused point cloud has a wider field of view. After fusing the vehicle-side and drone-side point clouds, the vehicle side is supplemented with high-altitude information, such as the spatial position of drones and surrounding buildings; drones are supplemented with ground details to make environmental modeling more comprehensive. The generated environmental perception point cloud can provide all-round environmental information from the air to the ground.

[0074] S2. Combined with the preset minimum flight altitude of the drone and the size of the vehicle, the coordinates of the scatterers in the electromagnetic space and the environmental perception point cloud are matched to determine the static scatterers, ground dynamic scatterers and aerial dynamic scatterers. Specifically, it includes:

[0075] S201. Process the environment perception point cloud by using a density clustering (DBSCAN) method, obtain multiple point cloud clusters and extract the point cloud coordinates within the cluster, and determine the size of the object represented by the point cloud cluster.

[0076] Specifically, first, the density clustering method traverses each point in the environment perception point cloud and assigns the data points to different clusters. After the traversal, a set of point cloud clusters is output. Each point cloud cluster contains points with a sufficiently high density, and the spatial distance between these clusters is relatively far. Then, each point cloud cluster is traversed, and the point cloud coordinates corresponding to the maximum and minimum values ​​are extracted along the x, y, and z axes respectively, and the size of the object represented by the point cloud cluster is calculated.

[0077] This embodiment adopts the existing density clustering method without making any improvements thereto, and its specific processing details are not repeated here.

[0078] S202. Compare the object size with the preset UAV minimum flight height H and the vehicle size threshold. If the range of the current cluster in the three directions of x, y, and z is smaller than the preset vehicle size threshold, and the z value is smaller than H, it is determined to be a dynamic vehicle, that is, a ground dynamic object; if the z value is greater than H, it is determined to be a dynamic UAV, that is, a dynamic object in the air; otherwise, it is determined to be a static object.

[0079] S203. For each pair of communication links in each frame, the coordinates of all scatterers in the electromagnetic space are extracted from the wireless channel data obtained by the ray tracing method, and stored according to different links; data preprocessing is performed on the coordinates of the scatterers between each pair of transceivers that need to be processed (i.e., the UAV end and the vehicle end), and only scatterers at a certain height above the ground are retained. The same threshold as that for removing ground points in the lidar point cloud is used to screen the scatterers, and the coordinates of the scatterers without the influence of ground scattering are obtained.

[0080] Here, wireless channel data is obtained by ray tracing method, and the coordinates of all scatterers in electromagnetic space are extracted by existing ray simulation software. This is a well-known technology in the field, and this embodiment does not make any improvement on this, so it will not be repeated here.

[0081] S204, using the detected UAV position coordinates to match the vehicle position coordinates and the scatterer position coordinates to distinguish static scatterers, ground dynamic scatterers and aerial dynamic scatterers.

[0082] Specifically, the distance between each scatterer and each detected drone and vehicle is calculated. If the distance between a scatterer and a drone or a vehicle is less than a threshold, the scattering point is determined to be an aerial dynamic scatterer or a ground dynamic scatterer. Similarly, static scatterers are identified by determining whether the distance between the scatterer and the static object is less than a preset threshold.

[0083] The remaining scatterers that do not coincide with any object are called unknown scatterers, which are beyond the detection range of the lidar point cloud. Since the position scatterers are usually far away from the transceiver and have very small power, they can be ignored in channel modeling.

[0084] S3. Obtain the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers, and perform channel modeling to generate a multi-UAV and multi-vehicle communication channel model.

[0085] The multi-UAV multi-vehicle communication channel model is represented by a channel impulse response. In this embodiment, in the multi-UAV multi-vehicle communication channel model, the transmitting end includes I UAVs, and the receiving end includes J vehicles. The channel impulse response (Channel Impulse Response, CIR) H(t,τ) of the model is expressed as:

[0086]

[0087] Among them, h j,i (t,τ) represents the CIR of the communication link between the i-th UAV and the j-th vehicle, including the direct path (Line-of-Sight, LoS) component, the non-direct path (NLoS) component through the static scattering cluster, the NLoS component through the hybrid twin cluster, and the ground reflection (GR) component, which can be expressed as:

[0088]

[0089] The hybrid twin cluster is defined as a scattering cluster obtained by randomly matching the sub-cluster around the i-th UAV with the sub-cluster around the j-th vehicle, Ω j,i represents the Rice factor, and η j,i NLoS (t) represent the power ratio of GR component to NLoS component, and satisfy

[0090] S4. Analyze the multi-UAV multi-vehicle communication channel model in combination with the dynamic changes of time domain characteristics and frequency domain characteristics in multi-vehicle multi-UAV communication, and obtain the time-frequency correlation function, Doppler power spectrum density and time stationary interval of the multi-UAV multi-vehicle communication channel model to dynamically adjust the UAV end and the vehicle end. Specifically including:

[0091] S401, perform Fourier transform on the channel impulse response h(t,τ) and introduce frequency-related factors A time-varying transfer function is obtained; and a time-frequency correlation function is determined based on the time-varying transfer function.

[0092] Exemplarily, the time-varying transfer function is expressed as:

[0093]

[0094] The time-frequency correlation function is expressed as:

[0095] R ji,j′i′ (t,f;Δt,Δf)=E[h ji * (t,f)hj′i′ (t+Δt,f+Δf)];

[0096] Among them, E[·] and (·) * denote the expectation operator and the complex conjugate operator respectively.

[0097] Furthermore, the time-frequency correlation function is represented by the LoS component, the ground reflection component and the NLoS component, which is expressed as:

[0098]

[0099] Among them, the LoS component, ground reflection component and NLoS component can be expressed as

[0100]

[0101] S402: Determine a time autocorrelation function based on the time-frequency correlation function; perform Fourier transform on the time interval based on the time autocorrelation function to obtain Doppler power spectrum density.

[0102] Specifically, first, based on the time-frequency correlation function, let Δf = 0 and Δt = 0, and we can get the time autocorrelation function and frequency cross-correlation function, which respectively characterize the characteristics of the UAV-to-ground channel in the time domain and frequency domain. Then, based on the derived time autocorrelation function, the time interval is Fourier transformed to obtain the Doppler power spectrum density, which is expressed as:

[0103]

[0104] Where fD is the Doppler frequency, ξ(t; Δt) is the time autocorrelation function, and the Doppler power spectral density can characterize the time-varying characteristics of the proposed lidar-assisted multi-UAV and multi-vehicle communication channel model.

[0105] S403, obtaining the path gain of the hybrid twin cluster in the communication link between the UAV and the vehicle, and calculating the time-varying delay spread; based on the time-varying delay spread, calculating the time stationary interval of the multi-UAV multi-vehicle communication channel model.

[0106] If the absolute value of the relative error of the delay spread is not greater than 10%, the channel impulse response can be considered to be stable. In this case, the minimum time interval corresponding to the stable channel impulse response is the time stable interval. The time stable interval of the multi-UAV multi-vehicle communication channel model is expressed as:

[0107]

[0108] Among them, inf{·} represents the infimum of the function, represents the time-varying delay spread, represents the gth hybrid twin cluster in the lth hybrid twin cluster in the communication link between the i-th UAV and the j-th vehicle. l The path gain of a ray.

[0109] Furthermore, the smaller the value of the time-frequency correlation function, the lower the correlation between time and frequency, indicating that the channel fading in some areas is more serious; if the time-frequency correlation function shows that the channel fading in some areas is more serious or the Doppler power spectrum density shows a large frequency expansion, it means that the communication quality in this area is poor; based on this, the flight trajectory of the drone can be optimized, and the drone can change its flight trajectory to avoid these areas; for example, in areas with dense high-rise buildings in the city, the drone can increase its flight altitude to reduce the interference of the multipath effect.

[0110] For example, in the channel analysis, it was found that when the drone was flying at an altitude of 50 meters, the channel Doppler spread was wide (the frequency spread reached 100Hz), causing communication signal distortion. Therefore, when the drone's flight altitude was adjusted to 80 meters, the Doppler spread was reduced to 30Hz, and the channel stability was significantly improved.

[0111] Furthermore, according to the time stability interval, if the channel stability time is short in certain time periods, it indicates that the communication link quality fluctuates greatly and requires higher power compensation; the UAV needs to increase the transmission power or adjust the antenna beam direction in these areas to enhance the link quality.

[0112] For example, if the time stability interval is less than 10ms (high-speed moving scenario), the drone adjusts the antenna beam direction to align with the vehicle end position and increases the transmission power by 3dB to maintain link stability.

[0113] Furthermore, if the Doppler power spectral density shows that the high-frequency channel is greatly affected by the Doppler effect (such as signal power concentrated at 5 GHz and extending to 200 Hz), it is possible to switch to low-frequency communication; the vehicle side switches to a lower frequency channel (such as 2.4 GHz) to reduce the impact of the Doppler effect.

[0114] For example, at a vehicle speed of 120 km / h, the communication quality of the 5 GHz channel decreases; the vehicle end automatically switches to the 2.4 GHz channel, reducing the Doppler spread to 50 Hz, thereby improving communication stability.

[0115] Furthermore, the time-varying characteristics of the channel are determined by the time-frequency correlation function. If the channel frequency selectivity is strong (the channel gain fluctuates significantly with frequency), the modulation order can be reduced or the coding redundancy can be increased; it can be switched to a more robust modulation method (such as switching from 64-QAM to 16-QAM), and low-density parity-check code (LDPC) can be used to improve anti-interference ability.

[0116] For example, channel analysis shows that the bit error rate (BER) of 64-QAM is too high in high-speed scenarios. The vehicle side switches to 16-QAM modulation and enables LDPC coding, which reduces the bit error rate by 30%.

[0117] Furthermore, the time stability interval shows that the channel in a certain area is relatively stable (stable interval>100ms), and resources can be jointly allocated to improve communication efficiency; the UAV and the vehicle side coordinate and adjust to use the channel stable time period for high data rate transmission, or preload the cache to reduce future communication pressure.

[0118] For example, in a low-speed scenario of 30km / h, the time stability interval is 200ms. The drone and the vehicle can collaboratively allocate more resources to transmit high-definition video streams and reduce the risk of cache interruption.

[0119] In some embodiments, obtaining the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers and performing channel modeling to generate a multi-UAV multi-vehicle communication channel model specifically includes:

[0120] Step 1: Obtain the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers.

[0121] Next, taking the i-th UAV as the transmitter and the j-th vehicle as the receiver as an example, the statistical distribution of the parameters is explained in detail.

[0122] (1) For the statistical distribution of quantity parameters, the number of static scatterers, ground dynamic scatterers, and air dynamic scatterers in the communication link between the i-th UAV and the j-th vehicle are expressed as and

[0123] Furthermore, considering the influence of transmission distance, the ratio of the number of static scatterers, ground dynamic scatterers and air dynamic scatterers to the transmission distance is used as the cumulative distribution function of the quantity parameter and Respectively expressed as:

[0124]

[0125] In the formula, and They represent the position of the i-th UAV and the j-th vehicle respectively.

[0126] By extracting the scatterer coordinates between each pair of transceiver communication links in each frame, the number and ratio of all static scatterers, ground dynamic scatterers and air dynamic scatterers under three conditions of high, medium and low ground traffic density and air traffic density are counted, and the cumulative distribution function of the number parameters of static scatterers, ground dynamic scatterers and air dynamic scatterers is calculated for constructing the channel model.

[0127] (2) For the statistical distribution of the distance parameter, the transmission distance parameter from the i-th UAV and the j-th vehicle to the l-th static scatterer, the m-th ground dynamic scatterer and the n-th aerial dynamic scatterer is defined as and Respectively expressed as:

[0128]

[0129] in, and represents the position of the lth static scatterer, the mth ground dynamic scatterer and the nth aerial dynamic scatterer in the transmission link between the i-th UAV and the j-th vehicle, and ||·|| represents the Frobenius norm. Similarly, the cumulative distribution function of the distance parameters between the static scatterer and the aerial dynamic scatterer and the ground dynamic scatterer is calculated.

[0130] The cumulative distribution functions of the distance parameters between static scatterers and dynamic scatterers in the air and on the ground conform to the Gamma distribution, Rayleigh distribution, and Rayleigh distribution, respectively, and are expressed as:

[0131]

[0132] in, and They respectively represent the shape parameter and scale parameter of the Gamma distribution, Γ(·) and γ(·) represent the gamma function and the lower incomplete gamma function, Represents the scale parameter of the Rayleigh distribution.

[0133] (3) The angular characteristics between static scatterers and ground dynamic scatterers and aerial dynamic scatterers include analysis of the horizontal angle of arrival (Azimuth Angle of Arrival, AAoA), the horizontal angle of departure (Azimuth Angle of Departure, AAoD), the elevation angle of arrival (Elevation Angle of Arrival, EAoA) and the elevation angle of departure (Elevation Angle of Departure, EAoD).

[0134] Taking AAoD as an example, for the statistical distribution of angle parameters, the ratios of the AAoD from the i-th UAV and the j-th vehicle to the l-th static scatterer, the m-th ground dynamic scatterer and the n-th aerial dynamic scatterer to the distance between the transmitter and the receiver are defined as angle parameters and Respectively expressed as:

[0135]

[0136]

[0137] in, and is the AAoD from the i-th UAV and the j-th vehicle to the l-th static scatterer, the m-th ground dynamic scatterer and the n-th aerial dynamic scatterer. Similarly, the cumulative distribution function of the angle parameter between each communication link in each frame is calculated.

[0138] In the above formula and Replace with and or and or and The cumulative distribution function corresponding to the angle parameter AaoA, EaoA or EaoD can be obtained.

[0139] Furthermore, through the above steps, the statistical distribution of relevant parameters of static scatterers, ground dynamic scatterers and aerial dynamic scatterers can be obtained. Based on this, the number, distance, angle and power of static scatterers, ground dynamic scatterers and aerial dynamic scatterers under three types of air traffic density, high, medium and low and ground traffic density are generated respectively.

[0140] Specifically, first, the number of static scatterers, ground dynamic scatterers and air dynamic scatterers at the initial time t0 is generated according to the distribution of the number of scatterers, that is, and According to the distance parameter, the distances of the lth static scatterer, the mth ground dynamic scatterer and the nth aerial dynamic scatterer at the initial time t0 are generated respectively, that is, and Generate angle parameter AAoDs according to angle parameter distribution AAoAs EAoAs and EAoDs

[0141] Then, the static scatterers, ground dynamic scatterers and air dynamic scatterers are clustered to obtain static scattering clusters, ground dynamic scattering clusters and air dynamic scattering clusters respectively; at the same time, the ratio of the number of scattering clusters to the transmission distance of the static scattering clusters, ground dynamic scattering clusters and air dynamic scattering clusters is used as the number parameter of the scattering clusters and

[0142] Furthermore, the power-delay characteristics of static scatterers, ground dynamic scatterers and air dynamic scatterers are obtained.

[0143] Specifically, the delay and power information of each path in the propagation environment is extracted from the wireless channel data, and the path power is an exponential function of the path delay.

[0144] For example, the path power in the communication link passing through the lth static scatterer, the mth ground dynamic scatterer and the nth air dynamic scatterer is: and It is expressed as:

[0145]

[0146] Among them, ξ s / td / ad With η s / td / ad are the delay-related parameters of static, ground dynamic and aerial dynamic scatterers, The time delay of the path through the lth static path, the mth ground dynamic path and the nth air dynamic path, parameter Z s / td / ad Obey Gaussian distribution

[0147] To apply the linear regression algorithm based on the least squares method, the formula is transformed into:

[0148]

[0149]

[0150] Step 2: The static scattering clusters, ground dynamic scattering clusters and air dynamic scattering clusters are screened by the visibility region (VR) method to obtain the scattering clusters within the visibility region at the current moment.

[0151] In the VR algorithm, only when the scattering cluster is located inside the visible area will it affect the channel as a visible cluster. With the movement of the visible area and the scattering cluster, the set of visible clusters changes smoothly and continuously, thereby modeling the temporal non-stationarity and consistency of the channel.

[0152] The UAV and vehicle are used as the transmitter and receiver, and their visible areas are modeled as a hemisphere, with the center being the transmitting and receiving UAV and vehicle. Specifically, the visible area radius of the i-th UAV and the j-th vehicle is defined as and They represent the maximum distances between the static, ground dynamic, and air dynamic scattering clusters and the transceiver at the initial moment, respectively, and are expressed as:

[0153]

[0154] in, and They represent the initial positions of the o / p / qth static scattering cluster, the ground dynamic scattering cluster, and the air dynamic scattering cluster at the initial moment. or , it is in the visible cluster set.

[0155] Due to the high-speed movement of drones and vehicles, the visible area is constantly changing. During the time t0+Δt, if the distance between the scattering cluster and the transmitter and receiver is still less than the radius of the visible area, the scattering cluster is still a visible cluster. The surviving static scattering clusters, ground dynamic scattering clusters, and aerial dynamic scattering clusters, that is, the scattering clusters that are in the visible area at t0 and t0+Δt, are defined as and At the same time, the static scattering cluster, ground dynamic scattering cluster and air dynamic scattering cluster generated according to the statistical distribution in S4 at time t0+Δt are defined as and If the number of generated scattering clusters is greater than the number of surviving scattering clusters, the static, ground dynamic, and air dynamic scattering clusters at time t are calculated as:

[0156]

[0157] In this case, there are a total of and Static scattering clusters, ground dynamic scattering clusters and air dynamic scattering clusters have an impact on the channel.

[0158] Otherwise, if the number of generated scattering clusters is less than the number of surviving scattering clusters, the static scattering clusters, ground dynamic scattering clusters, and air dynamic scattering clusters at time t are calculated as:

[0159]

[0160] In this case there is Static scattering clusters, ground dynamic scattering clusters and air dynamic scattering clusters have an impact on the channel.

[0161] Step 3: Based on the visible clusters at the current moment obtained by the VR algorithm, random matching is performed to obtain hybrid twin clusters.

[0162] Specifically, at time t0+Δt, in the communication link between the i-th UAV as the transmitter and the j-th vehicle as the receiver, the static scattering cluster in the visible area and the dynamic scattering cluster in the air are subclusters around the transmitter, while in the visible area, the static scattering cluster and the ground dynamic scattering cluster are subclusters around the receiver; the subclusters around the i-th UAV and the subclusters around the j-th vehicle are randomly matched as visible hybrid twin clusters, defined as

[0163] Step 4: Construct a multi-UAV and multi-vehicle communication channel model assisted by LiDAR point cloud. Specifically include:

[0164] Step 401: Model the LoS component in the laser radar point cloud-assisted multi-UAV multi-vehicle communication channel model.

[0165] For example, the distance between the UAV and the ground vehicle at the initial moment is defined as Considering the high dynamics of the transmitting and receiving ends, the function of the distance from the drone to the ground base station over time can be expressed as

[0166]

[0167] In the formula, and are the speeds of the UAV and ground vehicle, respectively.

[0168] The channel complex gain of the LoS component is expressed as:

[0169]

[0170] Where Q(t) is the rectangular window function, expressed as:

[0171]

[0172] Where T0 represents the observation time interval.

[0173] Finally, the LoS component is expressed as:

[0174]

[0175] Step 402: Modeling the NLoS component in the multi-UAV multi-vehicle communication channel model assisted by the LiDAR point cloud.

[0176] For example, first, for the communication link between the i-th UAV and the j-th vehicle, calculate the g-th scattering cluster in the l-th scattering cluster. l The complex channel gain of a scatterer If the lth scattering cluster belongs to but It is expressed as:

[0177]

[0178] otherwise,

[0179] In the formula, represents the set of visible twin clusters in the communication link between the i-th UAV and the j-th vehicle at time t; represents the Doppler frequency at the transmitting end, represents the Doppler frequency at the receiving end, Indicates that the gth scattering cluster in the lth scattering cluster l The phase shift of each scatterer is Indicates that the gth scattering cluster in the lth scattering cluster l The time delay of a scatterer.

[0180] Among them, the Doppler frequency of the transmitter and the receiver and Respectively expressed as:

[0181]

[0182] Through the gth in the lth scattering cluster l The phase shift of the scatterer and delay Respectively expressed as:

[0183]

[0184] In the formula, Indicates the gth scattering cluster in the lth scattering cluster from the transceiver l The distance between scatterers is expressed as:

[0185]

[0186] in, represents the delay of the virtual link through the oth static scattering cluster, Indicates the power parameter, represents the distance parameter, and Represents the angle parameter, generated by the statistical distribution in step 1.

[0187] Then the NLoS component is expressed as:

[0188]

[0189] Step 403: Modeling the GR component in the multi-UAV and multi-vehicle communication channel model assisted by the LiDAR point cloud.

[0190] Exemplarily, the complex channel gain of the ground reflection component can be expressed as

[0191]

[0192] in, They represent the power of the ground reflection component, the Doppler frequency shift, phase offset and time delay between the transmitter and the receiver, respectively, and are expressed as:

[0193]

[0194] Represents the distance vector from the transceiver to the ground reflection point, calculated as:

[0195]

[0196] Then the GR component is expressed as:

[0197]

[0198] Embodiment 2

[0199] This embodiment discloses a collaborative environment modeling system for 6G low-altitude traffic, including:

[0200] The scatterer identification module is configured to: obtain and fuse the laser point cloud data collected by multiple drone terminals and multiple vehicle terminals, combine the preset minimum flight altitude of the drone and the vehicle size, use the scatterer coordinates in the electromagnetic space and the fused laser point cloud to match, and determine the static scatterers, ground dynamic scatterers and air dynamic scatterers;

[0201] The channel model building module is configured to: obtain the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers, perform channel modeling, and generate a multi-UAV multi-vehicle communication channel model;

[0202] The analysis module is configured to analyze the multi-UAV multi-vehicle communication channel model in combination with the dynamic changes of the time domain characteristics and the frequency domain characteristics in the multi-vehicle and multi-UAV communication, and obtain the time-frequency correlation function, Doppler power spectrum density and time stationary interval of the multi-UAV multi-vehicle communication channel model to dynamically adjust the UAV side and the vehicle side. It should be noted here that the above-mentioned scatterer identification module, scattering cluster matching module and channel model construction module correspond to the steps in Example 1, and the examples and application scenarios implemented by the above-mentioned modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0203] Embodiment 3

[0204] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and running on the processor. When the computer instructions are executed by the processor, the steps of the above-mentioned collaborative environment modeling method for 6G low-altitude traffic are completed.

[0205] Embodiment 4

[0206] Embodiment 4 of the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the above-mentioned collaborative environment modeling method for 6G low-altitude traffic are completed.

[0207] Embodiment 5

[0208] Embodiment 5 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned collaborative environment modeling method for 6G low-altitude traffic.

[0209] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0210] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0211] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0212] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0213] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A collaborative environment modeling method for 6G low-altitude traffic, characterized in that: include: Obtain and fuse the laser point cloud data collected by multiple drones and multiple vehicles, combine the preset minimum flight altitude of the drone and the size of the vehicle, and use the coordinates of the scatterers in the electromagnetic space and the fused laser point cloud to match them to determine the static scatterers, ground dynamic scatterers, and aerial dynamic scatterers; Obtain the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers, perform channel modeling, and generate a multi-UAV and multi-vehicle communication channel model; The multi-UAV multi-vehicle communication channel model is analyzed in combination with the dynamic changes of time domain characteristics and frequency domain characteristics in multi-vehicle and multi-UAV communications, and the time-frequency correlation function, Doppler power spectrum density and time stationary interval of the multi-UAV multi-vehicle communication channel model are obtained to dynamically adjust the UAV end and the vehicle end.

2. The collaborative environment modeling method for 6G low-altitude traffic according to claim 1, characterized in that: The step of obtaining and fusing laser point cloud data collected by multiple drone terminals and multiple vehicle terminals includes: The laser point cloud data collected by the drone and the vehicle are converted into the same coordinate system, and the laser point cloud data after coordinate conversion are spliced ​​to obtain a point cloud set; Combined with the distance between the point cloud and the sensor, the overlapping areas in the point cloud set are weighted averaged and the point cloud set is updated to determine the environment perception point cloud.

3. The collaborative environment modeling method for 6G low-altitude traffic according to claim 1, characterized in that: The method combines the preset minimum flight altitude of the UAV and the vehicle size, matches the scatterer coordinates in the electromagnetic space with the fused laser point cloud, and determines the static scatterers, the ground dynamic scatterers, and the aerial dynamic scatterers, including: The fused laser point cloud is processed by density clustering method to generate multiple point cloud clusters and extract the point cloud cluster size; the point cloud cluster size is compared with the preset minimum flight altitude of the UAV and the vehicle size to screen static objects, ground dynamic objects and aerial dynamic objects; The static scatterers, the ground dynamic scatterers and the air dynamic scatterers are determined by matching the scatterer coordinates in the electromagnetic space with the position coordinates of the static objects, the ground dynamic objects and the air dynamic objects.

4. The collaborative environment modeling method for 6G low-altitude traffic according to claim 1, characterized in that: The obtaining of the statistical distribution of quantity parameters, distance parameters and angle parameters in the static scatterers, the ground dynamic scatterers and the air dynamic scatterers and performing channel modeling comprises: Obtain the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers, and perform clustering and range screening respectively to determine the static scattering clusters, ground dynamic scattering clusters and aerial dynamic scattering clusters in the visible area; Using the initial distance between the UAV and the ground vehicle and the moving speed of the UAV and the ground vehicle, the direct path component of the communication link between the UAV and the ground vehicle is obtained; The non-direct path component of the communication link between the UAV and the ground vehicle is determined by combining the power parameters, distance parameters and angle parameters of the static scattering cluster, the ground dynamic scattering cluster and the air dynamic scattering cluster; The ground reflection component of the communication link between the UAV and the ground vehicle is determined using the distance vector from the transceiver to the ground reflection point; The multi-UAV and multi-vehicle communication model is determined by the direct path component, indirect path component and ground reflection component of the communication link between multiple UAVs and multiple ground vehicles.

5. The collaborative environment modeling method for 6G low-altitude traffic according to claim 1, characterized in that: The multi-UAV multi-vehicle communication channel model is analyzed by combining the dynamic changes of time domain characteristics and frequency domain characteristics in multi-vehicle and multi-UAV communication to obtain the time-frequency correlation function of the multi-UAV multi-vehicle communication channel model. Specifically, the channel impulse response is Fourier transformed and frequency-related factors are introduced to obtain the time-varying transfer function; based on the time-varying transfer function, the time-frequency correlation function is determined.

6. The collaborative environment modeling method for 6G low-altitude traffic according to claim 1, characterized in that: Combined with the dynamic changes of time domain characteristics and frequency domain characteristics in multi-vehicle and multi-UAV communication, the multi-UAV and multi-vehicle communication channel model is analyzed to obtain the Doppler power spectrum density and time stationary interval of the multi-UAV and multi-vehicle communication channel model, including: Based on the time-frequency correlation function, a time autocorrelation function is determined; based on the autocorrelation function, a time interval is Fourier transformed to obtain a Doppler power spectrum density; The path gain of the hybrid twin cluster in the communication link between the UAV and the vehicle is obtained, and the time-varying delay spread is calculated; based on the time-varying delay spread, the time stationary interval of the multi-UAV and multi-vehicle communication channel model is calculated.

7. A collaborative environment modeling system for 6G low-altitude traffic, characterized in that: include: The scatterer identification module is configured to: obtain and fuse the laser point cloud data collected by multiple drone terminals and multiple vehicle terminals, combine the preset minimum flight altitude of the drone and the vehicle size, use the scatterer coordinates in the electromagnetic space and the fused laser point cloud to match, and determine the static scatterers, ground dynamic scatterers and air dynamic scatterers; The channel model building module is configured to: obtain the statistical distribution of quantity parameters, distance parameters and angle parameters in static scatterers, ground dynamic scatterers and aerial dynamic scatterers, perform channel modeling, and generate a multi-UAV multi-vehicle communication channel model; The analysis module is configured to analyze the multi-UAV multi-vehicle communication channel model in combination with the dynamic changes of time domain characteristics and frequency domain characteristics in multi-vehicle and multi-UAV communication, obtain the time-frequency correlation function, Doppler power spectrum density and time stationary interval of the multi-UAV multi-vehicle communication channel model, so as to dynamically adjust the UAV end and the vehicle end.

8. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the collaborative environment modeling method for 6G low-altitude traffic as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the steps of the collaborative environment modeling method for 6G low-altitude traffic as described in any one of claims 1-6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the collaborative environment modeling method for 6G low-altitude traffic as described in any one of claims 1-6 are implemented.

Images