Path Loss Modelling for UAV Communications in Urban Scenarios with Random Obstacles

TL;DR

This paper develops a realistic path loss model for UAV communications in urban areas by incorporating irregular obstacles and non-uniform building distributions, improving accuracy over simplified models.

Contribution

It introduces a Manhattan Random Simulator (MRS) that accounts for irregular building shapes, spacing, and additional obstacles, enhancing urban UAV path loss modeling.

Findings

The MRS provides more accurate PLoS estimates in complex urban environments.

Empirical path loss models are derived for environments with and without obstacles.

The approach improves UAV communication performance predictions in realistic urban scenarios.

Abstract

Path Loss (PL) is vital to evaluate the performance of Unmanned Aerial Vehicles (UAVs) as Aerial Base Stations (ABSs), particularly in urban environments with complex propagation due to various obstacles. Accurately modeling PL requires a generalized Probability of Line-of-Sight (PLoS) that can consider multiple obstructions. While the existing PLoS models mostly assume a simplified Manhattan grid with uniform building sizes and spacing, they overlook the real-world variability in building dimensions. Furthermore, such models do not consider other obstacles, such as trees and streetlights, which may also impact the performance, especially in millimeter-wave (mmWave) bands. This paper introduces a Manhattan Random Simulator (MRS) to estimate PLoS for UAV-based communications in urban areas by incorporating irregular building shapes, non-uniform spacing, and additional random obstacles to create a more realistic environment. Lastly, we present the PL differences with and without obstacles for standard urban environments and derive the empirical PL for these environments.