The Return on Remembering: Reusing UAV Channel Models
TL;DR for operators A UAV moving repeatedly between suburban and densely built areas encounters communication conditions it has experienced before. Yet a conventional online predictor may have forgotten those conditions while adapting to more recent measurements. LiLib retains small, environment-specific regression models and reactivates them when familiar conditions return. In simulations, this reduces prediction RMSE during the first 50 observations after a revisit from 12.29 to 5.70 dB relative to a sliding-window baseline. The improvement also carries into simulated throughput and outage, while four learned models can be transferred between UAVs using 480 bytes. The operational opportunity is selective model reuse rather than repeated retraining, provided real-world channels exhibit sufficiently recognizable recurring regimes. ...