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

2025-01-24

AI Summary

Existing millimeter-wave (mmWave) propagation models for unmanned aerial vehicles (UAVs) serving as aerial base stations in complex urban environments suffer from inaccurate predictions due to oversimplified assumptions—particularly the uniform grid model of building layouts. Method: This work departs from conventional assumptions by introducing, for the first time, the Manhattan Random Simulator (MRS) to explicitly capture stochastic building geometries and the spatial distribution of unstructured obstacles (e.g., trees, streetlights). Leveraging stochastic geometry and Monte Carlo simulation, we derive a generalized line-of-sight (LoS) probability model and calibrate path loss using empirical measurements. Contribution/Results: The resulting empirically grounded urban channel model is deployable in practice. Experimental validation shows a 38% average reduction in prediction error compared to classical approaches, significantly improving path loss accuracy in multi-obstacle scenarios. This provides both theoretical foundations and a practical tool for designing and evaluating low-altitude urban communication systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Signal Propagation
• Path Loss Model
• Urban Drone Communications

Innovation

• MRS Simulator
• Urban Environment Modeling
• Path Loss Optimization