Publication
Measurement-Calibrated Synthetic Pretraining for Rapid Adaptation of UAV Channel Predictors
Qiuheng Zhou; Wei Jiang; Donglin Wang; Hans Dieter Schotten
In: The 2026 IEEE 104th Vehicular Technology Conference. IEEE Vehicular Technology Conference (VTC-2026), IEEE, 2026.
Abstract
Unmanned aerial vehicle (UAV) links can change faster than route-specific channel data can be collected, so a channel predictor must often be adapted from only a short targetflight calibration segment. This paper studies measurementcalibrated synthetic pretraining as an offline source prior for rapid adaptation of channel state information (CSI) predictors. The synthetic source is calibrated to flight-only power evolution, temporal shape variation, frequency selectivity, and phase disturbance, and is then combined with measured source-flight fine-tuning before target-flight adaptation. Under chronological held-out flight splits, synthetic-to-real (S2R) fine-tuning reduces the measured-data budget needed to reach useful prediction in the low-data regime. For 50 ms-ahead prediction, it crosses a latestchannel-estimate persistence baseline with 50 measured training windows, roughly 0.5 s of 10 ms-spaced supervised windows, whereas training from scratch requires 600 windows. A targetprefix adaptation study further shows that a lightweight gated recurrent unit predictor can reuse the pretrained source prior with one prediction-layer update epoch, positioning measurementcalibrated synthetic pretraining as a reusable prior for scarce target-flight measurements.