Reconstructing wireless signals for low altitude networks using small language models

Wireless signal reconstruction is essential for RF-based positioning in GPS-denied environments. However, multipath propagation, shadowing, and non-Gaussian noise complicate this, and traditional methods require extensive site-specific calibration that precludes rapid deployment. We present In-Context Signal Completion (ICSC), demonstrating that small language models fine-tuned with Group Relative Policy Optimization and physics-informed rewards can reconstruct RSSI across sequential extrapolation and spatial interpolation tasks. Our 0.5B-parameter model attains 55% recall within 2 dB and a 2.85 dB mean absolute error on sequential prediction. This achieves a 49% error reduction over the untrained baseline, performing on par with GPT-4o (51%) with fewer parameters. Successful zero-shot transfer to spatial interpolation indicates the model acquires transferable physical reasoning rather than task-specific memorization. Operating at 3 ms latency for real-time edge inference, ICSC reduces deployment from weeks of per-site data collection to immediate inference using sequential context.