Radiolunadiff: Estimation of wireless network signal strength in lunar terrain
This addresses the challenge of wireless network planning for lunar missions, though it appears incremental as it builds on existing deep learning and physics-based techniques.
The paper tackles the problem of predicting radio signal strength over lunar terrain by developing a physics-informed deep learning architecture that integrates a terrain generator and ray-tracing engine to create a dataset, and shows it outperforms existing deep learning methods on this dataset.
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 create a high-fidelity dataset of radio propagation scenarios. Building on this dataset, we introduce a triplet-UNet architecture, consisting of two standard UNets and a diffusion network, to model complex propagation effects. Experimental results demonstrate that our method outperforms existing deep learning approaches on our terrain dataset across various metrics.