Analysis and Prediction of Coverage and Channel Rank for UAV Networks in Rural Scenarios with Foliage

TL;DR

This paper analyzes RF coverage and channel rank for UAV networks in rural foliage environments using ray tracing simulations, proposing a Kriging interpolation scheme and validating results with real-world measurements.

Contribution

It introduces a realistic RT-based analysis of UAV channels in foliage, incorporating custom tree models and a novel Kriging interpolation method for channel rank prediction.

Findings

Tree-induced blockage reduces RF coverage and channel rank at low UAV altitudes

The Kriging interpolation scheme accurately predicts 3D channel rank with low MAE

Simulation results show good agreement with real-world measurements

Abstract

Unmanned aerial vehicles (UAVs) are expected to play a key role in 6G-enabled vehicular-to-everything (V2X) communications requiring high data rates, low latency, and reliable connectivity for mission-critical applications. Multi-input multi-output (MIMO) technology is essential for meeting these demands. However, UAV link performance is significantly affected by environmental factors such as signal attenuation, multipath propagation, and blockage from obstacles, particularly dense foliage in rural areas. In this paper, we investigate RF coverage and channel rank over UAV channels in foliage-dominated rural environments using ray tracing (RT) simulations. We conduct RT-based channel rank and RF coverage analysis over Lake Wheeler Field Labs at NC State University to examine the impact on UAV links. Custom-modeled trees are integrated into the RT simulations using NVIDIA Sionna, Blender, and Open Street Map (OSM) database to capture realistic blockage effects. Results indicate that tree-induced blockage impacts RF coverage and channel rank at lower UAV altitudes. We also propose a Kriging interpolation-based 3D channel rank interpolation scheme, leveraging the observed spatial correlation of channel rank in the given environments. The accuracy of the proposed scheme is evaluated using the mean absolute error (MAE) metric and compared against baseline interpolation methods. Finally, we compare the RT-based received signal strength (RSS) and channel rank results with real-world measurements from the NSF AERPAW testbed demonstrating reasonable consistency between simulation results and the measurements.