An Empirical Study on Radio Propagation Estimation for UAVs Flying at Low-Altitude with Machine Learning

This paper presents a hybrid approach with machine learning and light-weight radio propagation model to estimate wireless communication range between Unmanned Aerial Vehicles (UAVs) flying at low altitude and their operators for crop-spraying in orchards. Additionally, it clarifies key factors to realize the approach by comparing measured data in a real field and simulation results. Simulation and measurement results shown in this paper reveals significant discrepancies between Irregular Terrain Model (ITM) and raytracing simulation results and some different patterns of signal attenuation due to vegetation and artificial obstacles such as trees and buildings between UAVs and their operators. The authors show that 3D special information, which is voxelized point cloud data measured in a real field, can improve the accuracy of results of the light-weight radio propagation model and discuss on detailed design of our approach with consideration of radio propagation characteristics.