Radiolunadiff: Estimation of wireless network signal strength in lunar terrain
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911161108135936 |
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| author | Torrado, Paolo Pearson, Anders Klein, Jason Moscibroda, Alexander Smith, Joshua |
| author_facet | Torrado, Paolo Pearson, Anders Klein, Jason Moscibroda, Alexander Smith, Joshua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14559 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Radiolunadiff: Estimation of wireless network signal strength in lunar terrain Torrado, Paolo Pearson, Anders Klein, Jason Moscibroda, Alexander Smith, Joshua Signal Processing Machine Learning 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. |
| title | Radiolunadiff: Estimation of wireless network signal strength in lunar terrain |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2509.14559 |