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

TL;DR

The paper tackles trajectory-aware characterization of low-altitude A2G channels in a suburban setting using a 64-element MaMIMO system. It introduces a geometry- and non-stationarity-driven analysis framework that jointly examines large-scale power, small-scale fading (best modeled by Nakagami), K-factor evolution with height, CMD-based stationarity spans, and spectral efficiency. Key findings show elevation is the strongest predictor of received power, K-factor grows with altitude, and non-stationarity is highly trajectory-dependent, with azimuth driving changes in horizontal flights and elevation in vertical ascent. The work provides empirical foundations for geometry-aware UAV link design and beam management in 6G NTN ecosystems, offering practical guidance for NTN-based UAV communications and signaling strategies. Future work will extend to urban scenarios and explore UAV orientation and polarization effects.

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).

Figures (11)

• Figure 1: Overview of the measurement equipment and its deployment in the field. The GBS antenna array with upward bore perpendicular to the ground plane is located in a parking lot. The elevation and azimuth angles, $\theta$ and $\varphi$.
• Figure 2: Illustration of UAV trajectories during the measurement campaign. Aerial view showing H49, H59, and V59 trajectories. (b) 3D MATLAB rendering of the same trajectories relative to the ground GBS (red star).
• Figure 3: Correlation of received power with elevation angle, azimuth angle, and 3D distance for different UAV trajectories (H49, H59, V59).
• Figure 4: Correlation of received power with elevation, azimuth, and 3D distance across all trajectories (H59, H49, V59).
• Figure 5: Measured received power as a function of azimuth and elevation angles for three UAV trajectories. These heatmaps highlight angular power distributions with variations introduced by the mobility and geometry of UAVs.