{"ID":2869110,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.14559","arxiv_id":"2509.14559","title":"Radiolunadiff: Estimation of wireless network signal strength in lunar terrain","abstract":"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.","short_abstract":"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 d...","url_abs":"https://arxiv.org/abs/2509.14559","url_pdf":"https://arxiv.org/pdf/2509.14559v1","authors":"[\"Paolo Torrado\",\"Anders Pearson\",\"Jason Klein\",\"Alexander Moscibroda\",\"Joshua Smith\"]","published":"2025-09-18T02:44:05Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false}