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

2025-06-15

AI Summary

Existing UAV channel models neglect the spatial correlation of ground user mobility trajectories, leading to inaccurate characterization of dynamic LOS/NLOS transitions in urban environments and thus distorted modeling of path loss and shadow fading. To address this, we propose a spatially consistent air-to-ground channel model that jointly leverages azimuth and elevation angles to partition LOS/NLOS probabilities—enabling coupled deterministic path loss and stochastic shadow fading modeling. Crucially, it reproduces spatially correlated LOS/NLOS transitions without requiring full 3D environmental reconstruction—a first in the literature. The method integrates geometry-driven simulation, probabilistic modeling, and spatially consistent parameterization. Evaluated across diverse urban scenarios, the model significantly improves fidelity in path loss and shadow fading representation, thereby enabling high-accuracy link outage probability analysis.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Application Domains: Mobility, Driving & Flight

Application Category

• User Modeling, Personalization and Recommendation: User model development and evaluation
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Modeling spatially consistent A2G channels with LOS/NLOS segmentation
• Addressing spatial correlation limitations in UAV channel models
• Capturing environment-specific obstructions without 3D representations

Innovation

• Probabilistic LOS/NLOS segmentation for A2G modeling
• Azimuth/elevation-dependent LOS probabilities without 3D details
• Validated accuracy and efficiency in urban settings