Spatially Consistent Air-to-Ground Channel Modeling with Probabilistic LOS/NLOS Segmentation

TL;DR

This paper introduces a probabilistic LOS/NLOS segmentation model for UAV air-to-ground channels that captures spatial correlation and environment-specific obstructions without detailed 3D data, validated through simulations.

Contribution

The paper proposes a novel spatially consistent A2G channel model that accounts for LOS/NLOS transitions using probabilistic segmentation based on azimuth and elevation, improving realism over existing models.

Findings

Accurately reproduces LOS/NLOS transitions in urban environments.

Demonstrates high accuracy and computational efficiency in validation.

Enables realistic path loss and shadow fading analysis.

Abstract

In this paper, we present a spatially consistent A2G channel model based on probabilistic LOS/NLOS segmentation to parameterize the deterministic path loss and stochastic shadow fading model. Motivated by the limitations of existing Unmanned Aerial Vehicle (UAV) channel models that overlook spatial correlation, our approach reproduces LOS/NLOS transitions along ground user trajectories in urban environments. This model captures environment-specific obstructions by means of azimuth and elevation-dependent LOS probabilities without requiring a full detailed 3D representation of the surroundings. We validate our framework against a geometry-based simulator by evaluating it across various urban settings. The results demonstrate its accuracy and computational efficiency, enabling further realistic derivations of path loss and shadow fading models and thorough outage analysis.