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arXiv · 2509.14559

Radiolunadiff: Estimation of wireless network signal strength in lunar terrain

Paolo Torrado·Anders Pearson·Jason Klein·Alexander Moscibroda·Joshua Smith
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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.

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Paolo Torrado, Anders Pearson, Jason Klein, Alexander Moscibroda, Joshua Smith. 2025-09-18. Radiolunadiff: Estimation of wireless network signal strength in lunar terrain. https://arxiv.org/abs/2509.14559

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