Measurement-Driven Path-Loss Modeling for Low-Altitude UAV Air-to-Ground Links Using Physics-Aware Ensemble Learning and Calibrated Uncertainty

Air-to-ground (A2G) channel modeling supports low-altitude wireless networks for unmanned aerial vehicle (UAV) services in 5G/6G systems. Existing data-driven models are often benchmarked against weak empirical baselines, decoupled from the underlying measurement campaigns, and prone to overfitting on small UAV datasets. This paper presents PACE-A2G, a calibrated deep-tree ensemble on physics-aware features, together with a stricter evaluation methodology. A quadrotor-mounted software-defined-radio measurement system produces five datasets covering 433, 740, 1500, and 3300 MHz in overwater, suburban, and mountainous environments. The deep tabular backbone combines periodic numerical embeddings, feature-group attention, and a parameter-efficient ensembling head; propagation physics enters through the features rather than through the architecture. At inference, the deep estimate is blended with a tree ensemble, and a split-conformal quantile head provides calibrated prediction intervals. Evaluation under random, chronological, and spatial-block protocols shows that random partitions overestimate accuracy: the chronological-split root-mean-square error (RMSE) of the strongest tree-ensemble baseline is 1.9 to 8 times its random-split value. In distribution, the hybrid predictor is statistically on par with the strongest tree ensembles. Across nine cross-scenario and cross-frequency protocols, it transfers more robustly than the tree ensemble on balance, with a mean paired RMSE reduction of 0.34 dB, although the fitted log-distance baseline remains the strongest single predictor under shift. Split-conformal calibration reaches near-nominal 90% coverage in distribution but collapses under domain shift; a feature-space out-of-distribution gate detects the shift on every source–target pair, and ten labelled records of the new domain restore near-nominal coverage.