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

TL;DR

This work addresses accurate path loss modelling for UAV-based A2G links in urban environments with random obstacles. It introduces the Manhattan Random Simulator (MRS) to extend ITU-style Manhattan layouts by incorporating irregular building shapes, nonuniform spacing, and randomly placed obstacles such as trees and streetlights, enabling robust $P_{LoS}$ estimation across elevation angles. The authors derive an empirical path loss model at 28 GHz that accounts for LoS and multiple NLoS components (buildings, trees, with leaves emphasized), showing that trees can induce median extra loss of about $2.74$ dB and up to $2.99$ dB at the 95th percentile, while streetlights have negligible impact. The proposed approach yields practical, environment-specific coefficients for UAV mmWave deployments and highlights the importance of realistic urban geometry in designing robust aerial wireless networks.

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.

Figures (6)

• Figure 1: Proposed random Manhattan layout in MRS with varying building areas, shapes, and random obstacles.
• Figure 2: $P_{LoS}$ in standard urban environments with multiple obstacles, where $n_{trees} = 200$, and $n_{lights} = 500$.
• Figure 3: Additional $P_{NLoS, T}$ for 200 trees for three urban environments.
• Figure 4: Impact of tree density on $P_{LoS}$ in dense urban environment.
• Figure 5: $PL$ in standard urban environments with 200 trees.