2026
RADIOLUNADIFF: ESTIMATION OF WIRELESS NETWORK SIGNAL STRENGTH IN LUNAR TERRAIN
ICASSP 2026poster
In this paper, we propose a novel physics-informed deep learning architecture for predicting radio maps over lunar terrain. Our approach integrates a physics-based lunar terrain generator, which produces realistic topography informed by publicly available NASA data, with a ray-tracing engine to crea…