Rasterization-based 5G tower mast station selection method for unmanned aerial vehicle channel coverage

Abstract

The invention discloses a rasterizing-based 5G tower mast site preferential method for unmanned aerial vehicle channel coverage, and the method comprises the following steps: S1, rasterizing: carrying out the rasterizing of a digital map, an unmanned aerial vehicle channel and a 5G tower mast site, and providing data input for an ITU-R diffraction model; s2, path loss and receiving level calculation: based on an ITU-R diffraction model, calculating the maximum path loss between each 5G tower mast site and an unmanned aerial vehicle channel, and predicting the receiving level of each unmanned aerial vehicle channel in combination with 5G air interface link budget; and S3, site selection and result output: according to the receiving level of each unmanned aerial vehicle channel, generating a 5G tower mast site list for covering the unmanned aerial vehicle channel according to the priority, and calculating the coverage rate. The method has the advantages that the optimal 5G tower mast site is selected efficiently at low cost, and continuous and reliable coverage of the unmanned aerial vehicle channel is achieved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and more specifically, to a method for selecting optimal 5G tower and mast sites based on gridded UAV flight path coverage. Background Technology

[0002] As an emerging comprehensive economic form, the low-altitude economy, driven by various low-altitude flight activities of manned and unmanned aircraft, widely extends to application scenarios such as logistics distribution and aerial monitoring, creating an urgent need for the stability and reliability of communication networks. In the complex urban low-altitude environment, significant differences in building heights and varied terrain structures make wireless signal propagation paths easily obstructed. This is especially true for drone flight paths, where flight altitudes are typically below 100 meters, resulting in signal propagation characteristics fundamentally different from ground-based scenarios. Traditional ground coverage models struggle to accurately predict coverage effectiveness. How to efficiently select optimal 5G tower and mast sites based on the existing complex wireless environment to achieve continuous and reliable coverage of drone flight paths has become a core issue that telecommunications operators urgently need to address.

[0003] In existing technologies, one approach is to model and optimize based on real-time communication status parameters between the UAV and existing 5G base stations, adjusting base station configurations by analyzing indicators such as signal strength and bit error rate. However, this method is only applicable to existing base station networks and cannot be applied to the planning and selection of new site resources, making it difficult to meet the network expansion needs brought about by the rapid expansion of the low-altitude economy. Another approach is to use ray tracing models for coverage prediction. This method relies on high-precision 3D digital maps and dedicated simulation platforms, requiring significant investment in commercial software and supporting data. Furthermore, the algorithm implementation process is opaque and lacks flexibility. Simultaneously, the simulation accuracy of ray tracing models is limited within the low-altitude range, failing to effectively handle the differences in propagation characteristics caused by dynamic changes in UAV flight path altitude, resulting in significant deviations in coverage prediction results.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this invention is to provide a method for selecting optimal 5G tower and mast sites based on gridded UAV flight path coverage. This method has the advantages of selecting the optimal 5G tower and mast sites efficiently and at low cost, thereby achieving continuous and reliable coverage of UAV flight paths.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0007] A method for selecting optimal 5G tower / mast sites based on gridded UAV flight path coverage includes the following steps:

[0008] S1. Rasterization Processing: Rasterize digital maps, UAV flight paths, and 5G tower site locations to provide data input for the ITU-R diffraction model;

[0009] S2. Path loss and received signal level calculation: Based on the ITU-R diffraction model, the maximum path loss between each 5G tower site and the UAV flight path is calculated, and the received signal level of each UAV flight path is predicted by combining the 5G air interface link budget.

[0010] S3. Site Selection and Result Output: Based on the receiving level of each UAV flight path, generate a list of 5G tower mast sites to cover the UAV flight path in priority order, and calculate the coverage rate.

[0011] Furthermore, the specific method for step S1 is as follows:

[0012] S11. Digital map rasterization: The digital map with building heights is sampled using the center latitude and longitude of the map raster, and the building heights are used as the heights of the corresponding map rasters.

[0013] S12. Raster cluster merging process:

[0014] Adjacent map rasters are merged into a raster cluster, and a cluster number is assigned to each raster cluster;

[0015] S13. UAV route gridding: The UAV route is gridded, the number of each route grid is defined, and the route height is used as the height of the route grid.

[0016] S14.5G Tower and Mast Site Grid Processing: The available 5G tower and mast sites around the UAV flight path are gridded. The tower and mast grid number of each 5G tower and mast site is defined. The tower and mast latitude and longitude are used as the center latitude and longitude of the tower and mast grid, and the antenna height is used as the height of the tower and mast grid.

[0017] Furthermore, the specific method for step S2 is as follows:

[0018] S21. Select an uncalculated tower mast grid;

[0019] S22. Select an uncalculated channel grid;

[0020] S23. Select the antenna height of the tower and mast grid and the channel height of the channel grid, and calculate the map grids through which the line connecting the selected tower and mast grid and the channel grid passes.

[0021] The map grates through which the connecting line passes are classified: map grates below the height of the connecting line are marked as unobstructed grates and their number is counted; map grates above the height of the connecting line are marked as obstructed grates, and the cluster number of the grates to which the map grates belong is extracted.

[0022] S24. Calculate the free space loss between the tower / mast grid and the channel grid based on the free propagation space loss calculation method of radio waves;

[0023] S25. Based on the method for calculating diffraction loss of cascaded knife-edge obstacles, calculate the diffraction loss between the tower mast grid and the channel grid;

[0024] S26. The sum of free space loss and diffraction loss is the total propagation loss between the tower mast grid and the channel grid;

[0025] S27. Based on the 5G air interface link budget, calculate the received signal level of the 5G base station at the mast grid of the airway grid receiving tower; and based on the preset service level threshold, determine whether the received signal level meets the standard, and record the determination result;

[0026] S28. Select the next uncalculated channel grid and proceed to steps S23 to S26;

[0027] S29. Select the next uncalculated tower mast grid and proceed to steps S23 through S27.

[0028] Furthermore, the specific method for step S3 is as follows:

[0029] S31. Select a channel grid that is not marked as a grid that cannot be covered;

[0030] S32. Sort the received levels of all tower mast grids corresponding to the channel grid, select the tower mast grid with the highest received level as the optimal tower mast grid of the channel grid and record its number; if the received levels of all tower mast grids do not reach the preset service level threshold, then mark the channel grid as a grid that cannot be covered.

[0031] S33. Select the next unsorted channel grid and repeat step S32 until all channel grids that are not marked as not being covered are traversed.

[0032] S34. Count the number of the best tower mast grids corresponding to all the channel grids that are not marked as not being covered, and sort them from high to low according to the number to select the first preferred tower mast grid. Then mark all the channel grids with qualified reception levels that can be covered by the first preferred tower mast grid as the solved coverage grids.

[0033] S35. Perform step S34 to perform statistical processing on all remaining channel grids that are not marked as not being covered and whose coverage has not been resolved;

[0034] S36. Finally, generate a list of 5G tower mast sites that achieve coverage of the waterway grid in priority order, and calculate the coverage rate, which is the number of waterway grids marked as having solved coverage / the total number of waterway grids.

[0035] A controller includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for selecting 5G tower mast sites based on gridded UAV flight path coverage.

[0036] A computer-readable storage medium is characterized in that it stores a computer program thereon, which, when executed by a processor, implements the above-described method for selecting 5G tower mast sites based on gridded UAV flight path coverage.

[0037] In summary, the present invention has the following beneficial effects:

[0038] It uses gridded processing to provide data input, calculates path loss and received signal level based on the ITU-R model, selects the best site and calculates coverage based on the received signal level, thus efficiently solving the coverage problem. It has the advantages of selecting the optimal 5G tower site efficiently and at low cost, and achieving continuous and reliable coverage of UAV flight paths. Attached Figure Description

[0039] Figure 1 This is a flowchart of the 5G tower mast site selection method based on gridded UAV flight path coverage, which is a method of the present invention.

[0040] Figure 2 This is a schematic diagram illustrating the rasterization of an existing digital map according to the present invention.

[0041] Figure 3 This is a schematic diagram of the method for calculating the diffraction loss J at the top of an isolated blocking grid (cluster) according to the present invention.

[0042] Figure 4 This is a schematic diagram of the method for calculating the diffraction loss J at the top of an isolated blocking grid (cluster) according to the present invention.

[0043] Figure 5 This is a schematic diagram of the method for calculating the diffraction loss J on the right side of an isolated blocking grid (cluster) according to the present invention.

[0044] Figure 6 This is a schematic diagram of the method for calculating the diffraction loss J on the left side of an isolated blocking grid (cluster) according to the present invention.

[0045] Figure 7 This is a schematic diagram of the method for calculating the blocking loss L2 of the cascaded grid cluster described in this invention. Detailed Implementation

[0046] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below with reference to the figures and specific embodiments.

[0047] See Figure 1 This invention proposes a method for selecting optimal 5G tower / mast sites for UAV flight path coverage based on gridding. By gridding digital maps, UAV flight paths, and 5G tower / mast sites, data input is provided for the ITU-R diffraction model. Then, based on the ITU-R diffraction model, the maximum path loss between each 5G tower / mast site and the UAV flight path is calculated and the received signal level is predicted. Finally, a list of 5G tower / mast sites that can cover the UAV flight path is generated according to the priority order of the received signal level, and the coverage rate is calculated. This effectively solves the problem of selecting the optimal 5G tower / mast site to achieve effective coverage of UAV flight paths in complex wireless environments.

[0048] For ease of understanding, the following explains some key terms in this embodiment:

[0049] Rasterization is the process of discretizing continuous geospatial information or objects into a series of regular grid cells (i.e., rasters) with the same size and shape. Each raster cell can store specific attribute values, such as height and type, thereby transforming complex geographic environments into computable data structures.

[0050] The ITU-R diffraction model is a model recommended by the International Telecommunication Union Radiocommunication Sector (ITU-R) for predicting propagation loss caused by radio waves diffracting at the edges of obstacles (such as buildings and terrain). This model considers the physical phenomenon of radio waves deviating from a straight propagation path when encountering obstacles, and is of great significance for assessing wireless coverage in complex terrain.

[0051] 5G air interface link budget refers to the calculation process used in a 5G wireless communication system to evaluate the performance of the entire communication link from the transmitter to the receiver. By comprehensively considering various factors such as transmit power, antenna gain, propagation loss, noise figure, and receiver sensitivity, it predicts the signal strength that the receiver can obtain under specific conditions, thereby determining whether the communication link can meet the preset quality of service requirements.

[0052] Path loss refers to the attenuation of radio wave signal energy during its propagation from the transmitting antenna to the receiving antenna due to various effects such as free-space propagation, absorption, reflection, scattering, and diffraction. Path loss is a key parameter in wireless network planning and optimization.

[0053] Received signal strength refers to the intensity of the wireless signal received by the receiving antenna, usually expressed in dBm. The level of received signal strength directly affects the quality and reliability of communication and is an important indicator for evaluating wireless coverage.

[0054] 5G tower / mast sites refer to the physical locations used to deploy 5G base station antennas, typically including towers, masts, or other supporting structures. These sites are the infrastructure that provides 5G network coverage.

[0055] Unmanned aerial vehicle (UAV) flight paths refer to specific spatial routes or areas planned for UAV flight activities. These paths typically have predetermined heights and widths and are essential for ensuring the safe and orderly flight of UAVs.

[0056] Digital maps are geographic information data stored and represented in digital form, which can include various geographic elements such as topography, buildings, roads, and water systems, as well as their attribute information. Digital maps are a fundamental data source for geospatial analysis and wireless network planning.

[0057] In the rasterization process, digital maps, drone flight paths, and 5G tower / mast sites are rasterized to discretize continuous geographic information into unified raster data, providing standardized input for subsequent wireless propagation models. Specifically, the geographic coordinates of digital maps, drone flight paths, and 5G tower / mast sites can be directly mapped to two-dimensional or three-dimensional rasters according to a preset grid size and resolution, either manually or through simple automated scripts. For example, the map can be divided into fixed-size square grids, with each grid assigned an identifier. Drone flight paths can be represented as a series of connected flight path grids, while 5G tower / mast sites are located within their respective tower / mast grids. This approach may require additional processing steps when dealing with complex terrain and building heights.

[0058] In the path loss and received signal level calculation steps, based on the ITU-R diffraction model, the maximum path loss between each 5G tower / mast site and the UAV flight path is calculated. Combined with the 5G air interface link budget, the received signal level for each UAV flight path is predicted. Specifically, the center point of the tower / mast grid and the flight path grid can be selected, assuming a straight propagation path exists between them. Along this path, all map grids that could potentially constitute obstructions are identified. Then, based on the ITU-R diffraction model, these obstructions are simplified, for example, only considering the single most significant obstacle for diffraction loss calculation. Free space loss is calculated using the standard free space propagation loss formula. Finally, the free space loss and diffraction loss are simply superimposed to obtain the total propagation loss, and combined with preset 5G air interface link budget parameters, the received signal level is calculated. This method may not accurately reflect the actual propagation loss when dealing with multiple consecutive obstacles or complex diffraction scenarios.

[0059] In the site selection and result output steps, a list of 5G tower mast sites capable of covering the UAV flight paths is generated in priority order based on the received signal levels of each UAV flight path, and the coverage rate is calculated. Specifically, a service level threshold can be set first, and the received signal levels of all flight path grids can be judged. Flight path grids that reach the threshold are marked as coverable. Then, for each coverable flight path grid, the tower mast grid that can provide the highest received signal level is selected as its optimal tower mast grid. Next, the number of flight path grids that each tower mast grid can cover is counted, and they are sorted from high to low based on the number, generating a preliminary list of 5G tower mast sites. The coverage rate is calculated by counting the ratio of all covered flight path grids to the total number of flight path grids. This method may not effectively handle duplicate coverage of the same flight path grid by multiple tower mast sites, or it may not fully consider the overall optimization of the sites.

[0060] This invention effectively solves the problems of traditional methods in planning 5G tower and mast sites in complex low-altitude environments, such as the inability to plan unused sites, high simulation costs, and limited accuracy. It involves rasterizing digital maps, UAV flight paths, and 5G tower and mast sites, and calculating path loss and received signal level based on ITU-R diffraction models and 5G air interface link budgets. Finally, it outputs a list of sites and coverage based on the received signal level. This provides stable and reliable 5G networking technology support for the development of the low-altitude economy.

[0061] In some embodiments of the present invention described above, step S1 (rasterization processing) is proposed to provide data input for the ITU-R diffraction model. However, in its implementation, without a specific rasterization method, inaccurate data sampling and low rasterization processing efficiency may occur, thereby affecting the accuracy of subsequent path loss calculation.

[0062] In this regard, the present invention further proposes a specific method for step S1, including:

[0063] S11. Digital map rasterization: The digital map with building heights is sampled using the center latitude and longitude of the map raster, and the building heights are used as the heights of the corresponding map rasters.

[0064] S12. Raster Cluster Merging Process: Merge adjacent map rasters into a raster cluster and assign a cluster number to each raster cluster;

[0065] S13. UAV route gridding: The UAV route is gridded, the number of each route grid is defined, and the route height is used as the height of the route grid.

[0066] S14.5G Tower and Mast Site Grid Processing: The available 5G tower and mast sites around the UAV flight path are gridded. The tower and mast grid number of each 5G tower and mast site is defined. The tower and mast latitude and longitude are used as the center latitude and longitude of the tower and mast grid, and the antenna height is used as the height of the tower and mast grid.

[0067] Specifically, in the digital map rasterization process S11, the aim is to convert continuous digital map data into a discrete raster format to facilitate subsequent wireless propagation model calculations. This process samples the digital map containing building heights using the center latitude and longitude of the map raster, and uses the sampled building heights as the height of the corresponding map raster. For example, the digital map can be divided into grid cells of a preset size. For each grid cell, the corresponding building height information is extracted from the digital map based on its center latitude and longitude. If multiple buildings exist within a grid cell, the height of the tallest building or the average height can be selected as the height of that map raster. This ensures the accurate acquisition of terrain and building height information, providing accurate basic data for subsequent path loss calculations.

[0068] In raster cluster merging processing S12, the goal is to logically group adjacent map rasters to optimize data processing efficiency and simplify obstacle identification. This process merges adjacent map rasters into a raster cluster and assigns a unique cluster number to each cluster. For example, a connected component labeling algorithm can be used to identify all adjacent map rasters sharing a boundary or vertex as a continuous region and assign it a unique cluster identifier. This method is helpful for identifying large, continuous obstacles, such as dense building clusters. Furthermore, merging can also be based on the height characteristics or land cover type of the map rasters; for example, all rasters representing mountains or densely populated urban areas can be grouped into one category. In this way, redundant processing of individual rasters in subsequent calculations can be reduced, thereby improving overall computational efficiency.

[0069] In the UAV flight path rasterization process S13, the goal is to discretize the continuous flight path of the UAV into a series of flight path grids with defined locations and altitudes, making it compatible with rasterized map data. This process rasterizes the UAV flight path, defines the number of each flight path grid, and uses the flight path altitude as the height of the grid. For example, the continuous flight path of the UAV can be sampled at fixed spatial intervals to generate a series of discrete flight path grid points. The geographic coordinates and preset flight altitude of each sampled point constitute the attributes of a flight path grid. This provides a structured and quantifiable input for the coverage calculation of UAV flight paths, ensuring accurate evaluation of specific points on the flight path.

[0070] In the 5G tower / mast site rasterization process S14, the goal is to standardize all potential 5G base station locations into a raster format for unified processing with map and UAV flight path raster data. This process rasterizes the selectable 5G tower / mast sites around the UAV flight path, defining a raster number for each 5G tower / mast site, using the tower / mast latitude and longitude as the center latitude and longitude of the raster, and the antenna height as the height of the raster. For example, for each candidate 5G tower / mast site, its precise geographic coordinates (latitude and longitude) can be directly used to determine its corresponding map raster, and its antenna height can be used as the height attribute of that raster. This ensures the uniformity and accuracy of all potential base station location data, providing reliable input for subsequent propagation model calculations.

[0071] Through the above technical solution, this invention solves the problems of inaccurate and inefficient data input by specifying the rasterization processing steps, ensuring that subsequent calculations are based on accurate and efficient data. Specifically, sampling the digital map using the center latitude and longitude of the map raster and using building heights as raster heights provides accurate terrain data and avoids sampling bias; merging adjacent map rasters into raster clusters and assigning cluster numbers simplifies data processing and improves computational efficiency; rasterizing UAV flight paths and defining their numbers and heights provides structured input for flight path coverage calculations; and rasterizing 5G tower and mast sites and defining their numbers, locations, and heights ensures the uniformity and operability of site data. These refined rasterization processing steps enable the ITU-R diffraction model to receive high-quality and high-efficiency data input, thereby significantly improving the accuracy of path loss and received signal level calculations, and providing a more reliable basis for the final selection of optimal 5G tower and mast sites.

[0072] In some of the embodiments of the present invention described above, a method for calculating the maximum path loss and predicting the received level between each 5G tower mast site and the UAV flight path is proposed. However, in its implementation, the calculation efficiency is low and the accuracy is insufficient, and it cannot effectively process a large amount of grid data. In particular, when dealing with the influence of obstacles, it lacks a systematic classification and optimization mechanism, resulting in calculation redundancy and inaccurate results.

[0073] In response, this invention further proposes that the steps for calculating the path loss and received level include:

[0074] S21. Select an uncalculated tower / mast grid. This step aims to ensure that all potential 5G tower / mast sites can be traversed, and a comprehensive coverage capability assessment can be performed on each site, avoiding the omission of any candidate sites. In practice, a list of tower / mast grids can be maintained, using a Boolean array or a flag to indicate whether each tower / mast grid has been calculated. Each time a selection is made, the first unmarked tower / mast grid not yet calculated is retrieved from the list. Alternatively, all tower / mast grids can be stored in a queue or stack, with one grid taken from the queue / stack each time for calculation, and removed or marked after calculation.

[0075] S22. Select an uncalculated mast grid. This step ensures that the signal reception capability of each discrete point (i.e., a wayway grid) on the UAV's flight path is evaluated, thus providing a comprehensive understanding of the coverage of the entire wayway. In practice, a list of wayway grids can be maintained, using flags to track which wayway grids have been calculated by the current mast grid. Alternatively, a temporary list containing all wayway grids can be initialized each time a mast grid is processed, and each grid can be selected for calculation one by one, ensuring that each wayway grid is evaluated.

[0076] S23. Select the antenna height of the tower / mast grid and the channel height of the channel grid, and calculate the map grids traversed by the line connecting the selected tower / mast grid and channel grid. Classify the map grids traversed by the line: map grids below the height of the line are marked as unobstructed grids and their numbers are counted; map grids above the height of the line are marked as obstructed grids, and the cluster number of the grid cluster to which the map grid belongs is extracted. This step aims to accurately identify obstacles in the signal propagation path, providing key input for subsequent path loss calculation. Simultaneously, through classification and cluster number extraction, it provides structured data for diffraction loss calculation, improving computational efficiency and accuracy. For each traversed map grid, compare its height with the height of the corresponding point on the line, and classify and mark it.

[0077] S24. Calculate the free-space loss between the tower / mast grid and the channel grid based on the free-propagation space loss calculation method for radio waves. This step aims to assess the basic energy attenuation of radio waves from the transmitter to the receiver in an ideal, unobstructed environment, providing a benchmark for calculating the total propagation loss. Specifically, the Friis transmission equation can be used to calculate the free-space loss, which considers factors such as frequency, distance, and antenna gain. Alternatively, a simplified free-space path loss model can be used; for example, at a specific frequency, the loss is proportional to the logarithm of the distance, allowing for rapid estimation via table lookup or empirical formulas.

[0078] S25. Calculate the diffraction loss between the tower / mast grid and the channel grid based on the cascaded knife-edge obstacle diffraction loss calculation method. This step aims to accurately quantify the additional loss caused by obstacles such as buildings and terrain on radio wave propagation, especially in non-line-of-sight propagation scenarios, which is crucial for predicting the actual received signal level. Specifically, a multi-knife-edge diffraction model from ITU-R Recommendation P.526-15, such as the Deygout model or the Epstein-Peterson model, can be used. Based on the obstructing grids and their cluster numbers identified in the previous steps, consecutive obstacles are treated as a series of knife edges, and the accumulated diffraction loss is calculated. Alternatively, a simpler single-knife-edge diffraction model (such as the Fresnel-Kirchhoff diffraction theory) can be used, and multiple obstacles are iteratively calculated or superimposed to approximate the diffraction effect of cascaded knife-edge obstacles.

[0079] S26. The sum of free-space loss and diffraction loss is the total propagation loss between the tower / mast grid and the channel grid. This step aims to comprehensively consider the basic attenuation under ideal propagation conditions and the additional attenuation caused by obstacles in the real environment, thereby obtaining a signal propagation loss value that is closer to reality. In practice, the calculated free-space loss value and the calculated diffraction loss value can be directly linearly superimposed. Alternatively, the two losses can be weighted before superposition, for example, by assigning different weight coefficients according to the complexity of the propagation environment or the type of obstacle.

[0080] S27. Based on the 5G air interface link budget, calculate the received signal level of the 5G base station at the mast grid of the airway grid receiving tower; and based on the preset service level threshold, determine whether the received signal level meets the standard, and record the judgment result. This step aims to convert the calculated total propagation loss into an actual measurable received signal strength, compare it with the service quality requirements, directly evaluate the coverage effect, and provide a decision-making basis for subsequent site selection. Specifically, the received signal level can be calculated by subtracting the total propagation loss from the transmit power, and then considering factors such as transmit antenna gain, receive antenna gain, and feeder loss. By comparing the received signal level with the threshold value, it is determined whether it meets the standard. The judgment result can be recorded as a Boolean value (meets the standard / does not meet the standard) or a specific received signal level value.

[0081] S28. Select the next uncalculated channel grid and perform the steps of calculating the map grids, classification, free space loss, and diffraction loss traversed by the above calculation connection. This step aims to ensure that, under the currently selected tower / mast grid, path loss and received signal level are calculated for all unassessed channel grids, achieving a comprehensive evaluation of the coverage capability of a single tower / mast site. In practice, all channel grids can be iterated over, and the corresponding calculation logic can be called for each channel grid. Alternatively, an iterator or pointer can be used to point to the currently processed channel grid and move it to the next uncalculated channel grid after the calculation is completed.

[0082] S29. Select the next uncalculated tower / mast grid and perform the steps described above for calculating the map grids traversed by the connection, classification, free space loss calculation, diffraction loss calculation, and received signal level calculation and judgment. This step aims to ensure that all candidate 5G tower / mast sites are systematically evaluated, and to complete the path loss and received signal level calculations between all tower / mast grids and all channel grids, providing a complete dataset for the final site selection. In specific implementation, all tower / mast grids can be iterated over, and for each tower / mast grid, the corresponding calculation logic can be called, where the logic itself includes the traversal of the channel grids. Alternatively, a nested loop structure can be used, with the outer loop traversing the tower / mast grids and the inner loop traversing the channel grids, ensuring that all combinations are calculated.

[0083] Through the above technical solution, this invention provides a systematic and efficient calculation method that effectively solves the problems of low efficiency and insufficient accuracy in path loss and received level calculation. Specifically, by using an iterative processing mechanism of selecting uncalculated tower and mast grids and selecting the next uncalculated tower and mast grid, all candidate tower and mast grids are fully evaluated, avoiding calculation omissions. Simultaneously, the nested iteration of selecting uncalculated channel grids and selecting the next uncalculated channel grid ensures that the coverage capability of each tower and mast grid for all channel grids is accurately calculated, thereby improving the integrity of data processing. In the critical obstacle handling stage, by selecting the antenna height of the tower and mast grid and the channel height of the channel grid, the map grids traversed by the connecting line are calculated, and the map grids are classified based on height comparison, accurately identifying unobstructed and obstructed grids, and extracting the cluster number of the grid cluster to which the obstructed grid belongs. This significantly reduces redundant calculations and optimizes obstacle identification and processing efficiency. Building upon this foundation, a method for calculating spatial loss in free-propagation radio waves is used to assess the basic loss. A method for calculating diffraction loss due to cascaded knife-edge obstacles is used to precisely quantify the additional loss caused by obstacles. Combining these two methods yields the total propagation loss, ensuring the comprehensiveness and high accuracy of the loss assessment. Finally, the received signal level is calculated based on the 5G air interface link budget and its compliance is determined, directly supporting subsequent coverage decisions. This enables the entire calculation process to efficiently and accurately predict the signal reception status of UAV flight paths when processing large amounts of grid data, providing reliable data support for the optimal selection of 5G tower / mast sites.

[0084] In some of the above-mentioned solutions of the present invention, site selection and result output are proposed to generate a list of 5G tower mast sites and calculate coverage. However, in this process, there are shortcomings in how to efficiently process uncovered waterway grids, optimize the site selection order, and ensure full coverage, resulting in low coverage optimization efficiency and omission of some waterways.

[0085] In this regard, the present invention further proposes a specific method for step S3 above, including:

[0086] S31. Select a channel grid that is not marked as a grid that cannot be covered;

[0087] S32. Sort the received levels of all tower mast grids corresponding to the channel grid, select the tower mast grid with the highest received level as the optimal tower mast grid of the channel grid and record its number; if the received levels of all tower mast grids do not reach the preset service level threshold, then mark the channel grid as a grid that cannot be covered.

[0088] S33. Select the next unsorted channel grid and repeat step S32 until all channel grids that are not marked as not being covered are traversed.

[0089] S34. Count the number of the best tower mast grids corresponding to all the channel grids that are not marked as not being covered, and sort them from high to low according to the number to select the first preferred tower mast grid. Then mark all the channel grids with qualified reception levels that can be covered by the first preferred tower mast grid as the solved coverage grids.

[0090] S35. Perform step S34 to perform statistical processing on all remaining channel grids that are not marked as not being covered and whose coverage has not been resolved;

[0091] S36. Finally, generate a list of 5G tower mast sites that achieve coverage of the waterway grid in priority order, and calculate the coverage rate, which is the number of waterway grids marked as having solved coverage / the total number of waterway grids.

[0092] Specifically, in step S31, a channel grid that is not marked as uncoverable is selected to initiate the evaluation of potentially coverable areas. This step ensures that subsequent computational resources are focused on channel grids that are likely to be covered, avoiding ineffective processing of known uncoverable areas. One implementation is that the system maintains a list of channel grids and associates a status flag, such as "unprocessed," "covered," or "cannot be covered," with each grid associating it with a status flag. At the start of each iteration, the system iterates through the list and selects the first channel grid with a status of "unprocessed" for further processing. Another implementation is that the system can filter channel grids that are not yet marked as "cannot be covered" based on geographic location information, such as from the coordinate set of channel grids, and process them sequentially in a preset order.

[0093] In step S32, the received signal levels of all tower mast grids corresponding to the channel grid are sorted, and the tower mast grid with the highest received signal level is selected as the optimal tower mast grid for that channel grid and its number is recorded. If the received signal levels of all tower mast grids do not reach the preset service level threshold, the channel grid is marked as an uncoverable grid. The core of this step is to find the potential service tower mast grid with the best signal quality for each channel grid and to promptly identify channel grids that cannot be effectively covered by any tower mast grid. By sorting, the optimal signal source can be selected, and the service level threshold serves as a key criterion for judging the effectiveness of coverage. One implementation is that, for the selected channel grid, the system extracts relevant values ​​from the pre-calculated received signal level data between all tower mast grids and the channel grid, and sorts them using a descending order sorting algorithm (such as quicksort or mergesort). After sorting, the tower mast grid at the top of the list is selected as the optimal tower mast grid, and its unique identifier (number) is stored. Simultaneously, it checks whether all received signal levels are below the service level threshold. If this condition is met, the status marker of the channel grid is updated to "cannot be covered grid". Alternatively, the system can traverse all mast grids associated with the channel grid, dynamically comparing and updating the current maximum received signal level and its corresponding mast grid number during the traversal. After the traversal, the optimal mast grid can be determined. If, during the traversal, the received signal levels of all mast grids fail to reach the service level threshold, the channel grid is marked as "cannot be covered grid".

[0094] In step S33, the next unsorted channel grid is selected, and step S32 is repeated until all channel grids not marked as undisturbed are traversed. This step ensures a systematic evaluation of all potentially undisturbed channel grids, thus laying a comprehensive data foundation for subsequent site selection. One implementation is that the system maintains a queue or list of channel grids to be processed. After processing the current channel grid, the next unsorted channel grid not marked as undisturbed is removed from the queue, and step S32 continues. This process is repeated until the queue is empty. Another implementation is that the system can use a boolean array or hash table to record whether each channel grid has been processed or marked. In each iteration, the system finds the index of the next unprocessed channel grid not marked as undisturbed and uses it as the current processing object until all eligible channel grids have been processed.

[0095] In step S34, the number of optimal mast grids corresponding to all channel grids not marked as uncoverable is counted, and the grids are sorted from highest to lowest quantity to select the first preferred mast grid. Then, all channel grids with acceptable reception levels covered by the first preferred mast grid are marked as resolved coverage grids. This step aims to identify the mast site that provides optimal coverage for the most channel grids, thereby prioritizing site deployment. By prioritizing the mast grid with the widest coverage area, initial coverage benefits can be maximized. One implementation is that the system first creates a mapping table that associates each mast grid number with the number of channel grids it corresponds to as the optimal mast grid. Then, the mast grids in this mapping table are sorted in descending order of quantity. After sorting, the mast grid at the top of the list is selected as the first preferred mast grid. Next, the system iterates through all channel grids, identifies those channel grids that have the first preferred mast grid as their optimal mast grid and whose received signal level meets the requirements, and updates their status flag to "resolved coverage grid". Alternatively, the system can maintain an array of counters, with each counter corresponding to a mast grid. After step S32, for the optimal mast grid of each channel grid, its corresponding counter is incremented. After traversing all channel grids, the mast grid with the largest counter value is identified as the first preferred mast grid. Subsequently, the system can query pre-stored received signal level data between mast grids and channel grids, filter out channel grids covered by the first preferred mast grid and whose received signal level meets the requirements, and update the status flags of these channel grids.

[0096] In step S35, all remaining channel grids that are neither marked as uncoverable nor have their coverage resolved are processed using step S34. This step introduces an iterative optimization mechanism to ensure that after selecting an optimal mast site and resolving its coverage area, the remaining uncovered but coverable channel grids can be re-evaluated, thereby gradually expanding the coverage area. One implementation is that after completing step S34, the system re-selects all channel grids whose status is neither "uncoverable grid" nor "resolved coverage grid". Then, these remaining channel grids are used as a new processing set, and step S34 is executed again to select the next optimal mast site and resolve its coverage. This process continues until no remaining channel grids need to be processed. Another implementation is that the system can maintain a dynamically updated set of channel grids. Whenever a channel grid is marked as "resolved coverage grid" or "uncoverable grid", it is removed from this set. Step S35 involves executing the logic of step S34 again on the channel grids in the set if the set is not empty, thereby achieving iterative processing.

[0097] In step S36, a final list of 5G tower and mast sites that cover the waterway grids is generated in priority order, and the coverage rate is calculated as: Coverage rate = Number of waterway grids marked as "solved coverage" / Total number of waterway grids. This final step integrates the results of the entire optimization process, providing a clear and actionable list of tower and mast site deployment recommendations, and evaluating the effectiveness of the solution through a quantified coverage metric. One implementation is that after all iterations, the system arranges all tower and mast grids selected as "preferred tower and mast grids" according to their selection order (i.e., priority), forming a 5G tower and mast site list. Simultaneously, the number of all waterway grids marked as "solved coverage" is counted and divided by the total number of waterway grids to calculate the coverage rate. Another implementation is that the system can maintain a list, adding each "preferred tower and mast grid" selected to the list. Ultimately, this list constitutes the 5G tower and mast site list arranged in priority order. Coverage can be calculated by querying the status flags of all channel grids, counting the number of "resolved coverage grids", and then performing a division operation.

[0098] Through the above technical solution, this invention can efficiently process uncovered airway grids, optimize site selection order, and ensure comprehensive coverage. Specifically, by first selecting airway grids that are not marked as uncoverable, calculations for invalid areas are avoided, improving processing efficiency. Next, by selecting the optimal tower / mast grid with the highest received signal level for each airway grid and promptly marking uncoverable airway grids, signal quality is ensured and subsequent redundant calculations are reduced. Iterative execution of this process ensures a comprehensive evaluation of all potentially coverable airway grids. Furthermore, by counting the number of optimal tower / mast grids and prioritizing tower / mast sites covering the most airway grids, this invention can achieve maximum coverage benefits with the fewest number of sites, thereby optimizing the site selection order. Continuous iterative processing of remaining uncovered areas ensures coverage integrity and avoids omissions. Finally, a priority-ranked list of 5G tower / mast sites and quantified coverage rates are generated, providing clear and efficient guidance for actual deployment and significantly improving the efficiency and effectiveness of site selection for UAV airway coverage.

[0099] The following example will provide a more detailed explanation of the above technical solution:

[0100] Suppose a telecommunications operator needs to provide stable and reliable 5G communication coverage for a pre-designed drone inspection flight path within an industrial park at location A. The flight path is located 100 meters above the ground, and the industrial park contains several high-rise buildings. The operator has multiple alternative tower sites available for deploying 5G base stations.

[0101] The system performs rasterization processing.

[0102] The first step is digital map rasterization. See also... Figure 2 The system rasterizes the digital map of location A, for example, dividing it into a 5m x 5m grid. For each map grid, the system extracts its center latitude and longitude, and uses the height of the tallest building within that grid as the height of that map grid.

[0103] The second step is raster cluster merging. The system identifies and merges adjacent map rasters representing continuous obstacles to form raster clusters, and assigns a unique cluster number to each raster cluster. For example, a long, narrow building might be merged into a single raster cluster.

[0104] The third step is the UAV route gridding process. The system discretizes the UAV inspection route into a series of route grids. Each route grid is assigned a unique number, and its preset flight altitude (e.g., 100 meters) is used as the height of that route grid.

[0105] The fourth step is the 5G tower site rasterization process. The system also rasterizes all available candidate 5G tower sites around the industrial park (e.g., Tower Site 1, Tower Site 2, Tower Site 3). Each candidate site is defined as a tower grid, assigned a unique number, and its latitude and longitude are used as the center latitude and longitude of the tower grid. The planned antenna height is used as the height of the tower grid. Through this rasterization process, all geographic and spatial information is converted into a unified, discrete data format, providing standardized input for subsequent wireless propagation model calculations and avoiding the reliance on expensive 3D digital maps and opaque algorithms inherent in traditional ray tracing models.

[0106] Perform path loss and received signal level calculations.

[0107] The system first selects an uncalculated tower / mast grid, such as tower / mast site 1. Then, it selects an uncalculated channel grid. The system obtains the antenna height of tower / mast site 1 and the channel height of the current channel grid, and calculates all map grids traversed by the virtual line connecting these two points. These map grids are classified: map grids below the height of the connecting line are marked as unobstructed grids and their numbers are counted; map grids above the height of the connecting line are marked as obstructed grids, and their cluster numbers are extracted.

[0108] Based on the method for calculating free-propagation space loss of radio waves, the free-space loss between tower / mast site 1 and the current channel grid is calculated. Then, using the method for calculating diffraction loss from cascaded knife-edge obstacles, the diffraction loss between tower / mast site 1 and the current channel grid is calculated according to the obstructing grid and its cluster number. The sum of the free-space loss and the diffraction loss is the total propagation loss between the two.

[0109] The diffraction loss calculation method involved in this invention is as follows:

[0110] 1) Method for calculating free space propagation loss L1.

[0111] Calculate the free-space radio wave loss between the selected tower / mast grid and the channel grid using the following formula:

[0112] L1 = 32.4 + 20 * log 10 (f)+20*log 10 (D)

[0113] Where f is the 5G base station frequency in MHz, and D is the straight-line distance from the tower mast grid to the channel grid in km.

[0114] 2) Calculation method for diffraction loss caused by isolated blocking grid (cluster) blocking

[0115] The method for calculating diffraction loss caused by isolated blocking grids (clusters) refers to the method for calculating diffraction loss of a single knife-edge obstacle in ITU-R Recommendation P.526-11.

[0116] (1) Calculation method for diffraction loss J_top at the top of isolated blocking grid (cluster)

[0117] like Figure 3 and Figure 4 As shown, let the center vertex of the tower mast station grid be A, the center vertex of the channel grid be C, and connect A and C. The center vertex of the blocking grid that the line passes through is B, and connect AB and AC to obtain the length parameters d1 and d2; the angle parameters θ, α1, α2 and the radio wave wavelength λ. According to formula (1), the intermediate value v is obtained, and according to formula (2), the diffraction loss value J_top at the top of the independent blocking grid (cluster) is obtained.

[0118] v= (1)

[0119] J = 6.9 + 20 * log (2)

[0120] (2) Calculation method for diffraction loss J on the right side of isolated blocking grid (cluster)

[0121] like Figure 5 As shown, let the center vertex of the tower mast station grid be A, and the center vertex of the channel grid be C. Connect A and C, and let the center of the first blocking grid on the right boundary of the blocking grid that the line passes through be B. Connect AB and AC to obtain the length parameters d1 and d2; the angle parameters θ, α1, α2 and the radio wave wavelength λ. According to formula (1), the intermediate value v is obtained, and according to formula (2), the diffraction loss value J_right at the right end of the independent blocking grid (cluster) is obtained.

[0122] (3) Calculation method for diffraction loss J on the left side of isolated blocking grid (cluster)

[0123] like Figure 6 As shown, let the center vertex of the tower mast station grid be A, and the center vertex of the channel grid be C. Connect A and C, and let the center of the first blocking grid on the left boundary of the blocking grid that the line passes through be B. Connect AB and AC to obtain the length parameters d1 and d2; the angle parameters θ, α1, α2 and the radio wave wavelength λ. According to formula (1), the intermediate value v is obtained, and according to formula (2), the diffraction loss value J_left at the left end of the independent blocking grid (cluster) is obtained.

[0124] 4) Calculation method for blocking loss L2 of cascaded grid clusters

[0125] like Figure 7 As shown, when there are multiple obstructing grids (clusters) between the tower mast site grid and the channel grid, it is necessary to refer to the ITU-R Recommendation P.526-11 regarding the calculation method for diffraction loss of cascaded knife-edge obstacles.

[0126] The following method is repeated 1 to 3 times based on the path profile of the tower mast site grid and the waterway grid.

[0127] The method for calculating diffraction loss caused by isolated blocking grids (clusters) is to calculate v of all blocking grid clusters in the path profile from the channel grid to the base station, and select the blocking grid with the largest v, which is defined as vp.

[0128] Using the blocking grid where vp is located as the channel grid, calculate the v of all blocking grids between the base station and the blocking grid of vp according to the diffraction loss calculation method caused by isolated blocking grids (clusters), and select the largest one as vt;

[0129] Using the blocking grid where vp is located as the base station, the v of all blocking grid clusters between the vp blocking grid and the original channel grid is calculated according to the single-edge blocking method, and the largest value is selected as vr.

[0130] Give the diffraction loss value J of the cascaded lattice cluster.

[0131] When calculating the top resistance, we get:

[0132] J(top) = Jtop(vp) + T(Jtop(vt) + Jtop(vr) + C;

[0133] Where C = 10.0 + 0.04D; D is the total path length;

[0134] T = 1.0 - exp[-J(vp) / 6];

[0135] When calculating the right-side obstruction, we get:

[0136] J(right) = Jright(vp) + Tright(J(vt) + Jright(vr) + C;

[0137] When calculating the left-side obstruction, we get:

[0138] J(left) = Jleft(vp) + Tleft(J(vt) + Jleft(vr) + C;

[0139] J(end) = MIN[J(top), J(right), J(left)].

[0140] Based on this total propagation loss and the 5G air interface link budget, the system predicts the received signal level of the 5G base station at tower site 1 at this airway grid. This received signal level is compared with a preset service level threshold (e.g., -95dBm) to determine whether it meets the standard, and the result is recorded.

[0141] This process is iterative: First, the next uncalculated channel grid is selected, and the above calculation steps are repeated until the links between mast site 1 and all channel grids are traversed. Then, the next uncalculated mast grid (e.g., mast site 2) is selected, and the above calculation steps are repeated until the links between all candidate mast sites and all channel grids have been calculated. This calculation method based on the ITU-R diffraction model can more accurately predict the signal propagation characteristics in complex low-altitude environments, overcoming the limitations of existing ray tracing models in terms of simulation altitude.

[0142] Perform site selection and result output.

[0143] The system selects a channel grid that is not marked as undisturbed. For this channel grid, the system sorts the received signal levels of all candidate mast sites, selects the mast site with the highest received signal level as the optimal mast site for this channel grid, and records its number. If the received signal levels of all candidate mast sites do not reach a preset service level threshold, the channel grid is marked as an undisturbed grid. The system repeats this process until all channel grids not marked as undisturbed have been traversed.

[0144] The system counts the number of optimal mast sites corresponding to all channel grids that are not marked as uncoverable. For example, it counts the number of times each mast site is considered an optimal site. The system sorts these optimal mast sites from highest to lowest based on the count, selecting the mast site with the most covered channel grids as the first preferred mast site. Subsequently, all channel grids that can be covered by this first preferred mast site and whose received signal level meets the requirements are marked as resolved coverage grids.

[0145] The system repeats the above statistical processing procedure for all remaining channel grids that are not marked as not being covered and whose coverage has not been resolved, and selects the second and third preferred tower mast sites in sequence.

[0146] Finally, a list of 5G tower sites for achieving drone flight path coverage is generated according to priority, and the coverage rate is calculated. The coverage rate is calculated as the number of flight path grids with resolved coverage divided by the total number of flight path grids. This list and coverage prediction results provide telecommunications operators with clear site selection recommendations, enabling efficient solutions to drone flight path coverage issues.

[0147] In this document, the terms "upper," "lower," "front," "back," "left," "right," "top," "bottom," "inner," "outer," "vertical," and "horizontal," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only used for the clarity of expressing the technical solution and for the convenience of description, and therefore should not be construed as limiting the present invention.

[0148] In this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, which includes not only the elements listed but also other elements not expressly listed.

[0149] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimal selection of 5G tower / mast sites based on gridded UAV flight path coverage, characterized in that, Includes the following steps: S1. Rasterization Processing: Rasterize digital maps, UAV flight paths, and 5G tower site locations to provide data input for the ITU-R diffraction model; S2. Path loss and received signal level calculation: Based on the ITU-R diffraction model, the maximum path loss between each 5G tower site and the UAV flight path is calculated, and the received signal level of each UAV flight path is predicted by combining the 5G air interface link budget. S3. Site Selection and Result Output: Based on the receiving level of each UAV flight path, generate a list of 5G tower mast sites to cover the UAV flight path in priority order, and calculate the coverage rate.

2. The method for selecting optimal 5G tower / mast sites based on gridded UAV flight path coverage according to claim 1, characterized in that, The specific method for step S1 is as follows: S11. Digital map rasterization: The digital map with building heights is sampled using the center latitude and longitude of the map raster, and the building heights are used as the heights of the corresponding map rasters. S12. Raster cluster merging process: Adjacent map rasters are merged into a raster cluster, and a cluster number is assigned to each raster cluster; S13. UAV route gridding: The UAV route is gridded, the number of each route grid is defined, and the route height is used as the height of the route grid. S14.5G Tower and Mast Site Grid Processing: The available 5G tower and mast sites around the UAV flight path are gridded. The tower and mast grid number of each 5G tower and mast site is defined. The tower and mast latitude and longitude are used as the center latitude and longitude of the tower and mast grid, and the antenna height is used as the height of the tower and mast grid.

3. The method for selecting optimal 5G tower / mast sites based on gridded UAV flight path coverage according to claim 2, characterized in that, The specific method for step S2 is as follows: S21. Select an uncalculated tower mast grid; S22. Select an uncalculated channel grid; S23. Select the antenna height of the tower and mast grid and the channel height of the channel grid, and calculate the map grids through which the line connecting the selected tower and mast grid and the channel grid passes. The map grates through which the connecting line passes are classified: map grates below the height of the connecting line are marked as unobstructed grates and their number is counted; map grates above the height of the connecting line are marked as obstructed grates, and the cluster number of the grates to which the map grates belong is extracted. S24. Calculate the free space loss between the tower / mast grid and the channel grid based on the free propagation space loss calculation method of radio waves; S25. Based on the method for calculating diffraction loss of cascaded knife-edge obstacles, calculate the diffraction loss between the tower mast grid and the channel grid; S26. The sum of free space loss and diffraction loss is the total propagation loss between the tower mast grid and the channel grid; S27. Based on the 5G air interface link budget, calculate the received signal level of the 5G base station at the mast grid of the airway grid receiving tower; Based on a preset service level threshold, it determines whether the received level meets the standard and records the result. S28. Select the next uncalculated channel grid and proceed to steps S23 to S26; S29. Select the next uncalculated tower mast grid and proceed to steps S23 through S27.

4. The method for selecting optimal 5G tower / mast sites based on gridded UAV flight path coverage according to claim 1, characterized in that, The specific method for step S3 is as follows: S31. Select a channel grid that is not marked as a grid that cannot be covered; S32. Sort the receiving levels of all tower mast grids corresponding to the channel grid, select the tower mast grid with the highest receiving level as the optimal tower mast grid for the channel grid and record its number. If the average received power of all tower and mast grids does not reach the preset service level threshold, the channel grid will be marked as an uncoverable grid. S33. Select the next unsorted channel grid and repeat step S32 until all channel grids that are not marked as not being covered are traversed. S34. Count the number of the best tower mast grids corresponding to all the channel grids that are not marked as not being covered, and sort them from high to low according to the number to select the first preferred tower mast grid. Then mark all the channel grids with qualified reception levels that can be covered by the first preferred tower mast grid as the solved coverage grids. S35. Perform step S34 to perform statistical processing on all remaining channel grids that are not marked as not being covered and whose coverage has not been resolved; S36. Finally, generate a list of 5G tower mast sites that achieve coverage of the waterway grid in priority order, and calculate the coverage rate, which is the number of waterway grids marked as having solved coverage / the total number of waterway grids.

5. A controller, characterized in that, The controller includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the 5G tower mast site selection method based on gridded UAV flight path coverage as described in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the 5G tower mast site selection method based on gridded UAV flight path coverage as described in any one of claims 1-4.

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