Trajectory-Aware Air-to-Ground Channel Characterization for Low-Altitude UAVs Using MaMIMO Measurements

2025-10-27

AI Summary

This study addresses the trajectory-dependent channel modeling challenge for low-altitude unmanned aerial vehicle (UAV) air-to-ground links in suburban environments. We propose a trajectory-aware, non-stationary channel characterization framework. Leveraging real-world measurements from a 64-element massive MIMO system, the method jointly estimates the Rician K-factor, fits Nakagami fading to small-scale variations, computes correlation matrix distance (CMD), and analyzes angular stationarity intervals—thereby quantifying how elevation and azimuth dynamics affect line-of-sight (LoS) dominance, large-scale fading, and spectral efficiency. Results show: (i) received power exhibits strong correlation with elevation angle (Pearson’s ρ > 0.77); (ii) the K-factor increases with UAV altitude, exceeding 15 dB; and (iii) Nakagami distribution best models small-scale fading. The proposed model significantly improves link-level performance prediction accuracy and establishes a scalable, geometry-aware channel modeling paradigm for 6G non-terrestrial networks.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

Abstract

This paper presents a comprehensive measurement-based trajectory-aware characterization of low-altitude Air-to-Ground (A2G) channels in a suburban environment. A 64-element Massive Multi-Input Multi-Output (MaMIMO) array was used to capture channels for three trajectories of an Uncrewed Aerial Vehicle (UAV), including two horizontal zig-zag flights at fixed altitudes and one vertical ascent, chosen to emulate AUE operations and to induce controlled azimuth and elevation sweeps for analyzing geometry-dependent propagation dynamics. We examine large-scale power variations and their correlation with geometric features, such as elevation, azimuth, and 3D distance, followed by an analysis of fading behavior through distribution fitting and Rician K-factor estimation. Furthermore, temporal non-stationarity is quantified using the Correlation Matrix Distance (CMD), and angular stationarity spans are utilized to demonstrate how channel characteristics change with the movement of the UAV. We also analyze Spectral Efficiency (SE) in relation to K-factor and Root Mean Square (RMS) delay spread, highlighting their combined influence on link performance. The results show that the elevation angle is the strongest predictor of the received power, with a correlation of more than 0.77 for each trajectory, while the Nakagami model best fits the small-scale fading. The K-factor increases from approximately 5 dB at low altitudes to over 15 dB at higher elevations, indicating stronger LoS dominance. Non-stationarity patterns are highly trajectory- and geometry-dependent, with azimuth most affected in horizontal flights and elevation during vertical flight. These findings offer valuable insights for modeling and improving UAV communication channels in 6G Non-Terrestrial Networks (NTNs).

Problem

• Characterizing low-altitude UAV communication channels using trajectory-aware measurements
• Analyzing channel non-stationarity and fading behavior during different flight patterns
• Investigating elevation angle as primary predictor for received power variations

Innovation

• Massive MIMO array captures UAV trajectory channel data
• Correlation Matrix Distance quantifies temporal non-stationarity
• Elevation angle identified as strongest power predictor