Physics-Constrained Inc-GAN for Tunnel Propagation Modeling from Sparse Line Measurements
Fuente:
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_ | 1866915531900059648 |
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| author | Zhou, Yang Wu, Haochang Mu, Yunxi Qin, Hao Zhang, Xinyue Zhang, Xingqi |
| author_facet | Zhou, Yang Wu, Haochang Mu, Yunxi Qin, Hao Zhang, Xinyue Zhang, Xingqi |
| contents | High-speed railway tunnel communication systems require reliable radio wave propagation prediction to ensure operational safety. However, conventional simulation methods face challenges of high computational complexity and inability to effectively process sparse measurement data collected during actual railway operations. This letter proposes an inception-enhanced generative adversarial network (Inc-GAN) that can reconstruct complete electric field distributions across tunnel cross-sections using sparse value lines measured during actual train operations as input. This directly addresses practical railway measurement constraints. Through an inception-based generator architecture and progressive training strategy, the method achieves robust reconstruction from single measurement signal lines to complete field distributions. Numerical simulation validation demonstrates that Inc-GAN can accurately predict electric fields based on measured data collected during actual train operations, with significantly improved computational efficiency compared to traditional methods, providing a novel solution for railway communication system optimization based on real operational data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03019 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Constrained Inc-GAN for Tunnel Propagation Modeling from Sparse Line Measurements Zhou, Yang Wu, Haochang Mu, Yunxi Qin, Hao Zhang, Xinyue Zhang, Xingqi Signal Processing High-speed railway tunnel communication systems require reliable radio wave propagation prediction to ensure operational safety. However, conventional simulation methods face challenges of high computational complexity and inability to effectively process sparse measurement data collected during actual railway operations. This letter proposes an inception-enhanced generative adversarial network (Inc-GAN) that can reconstruct complete electric field distributions across tunnel cross-sections using sparse value lines measured during actual train operations as input. This directly addresses practical railway measurement constraints. Through an inception-based generator architecture and progressive training strategy, the method achieves robust reconstruction from single measurement signal lines to complete field distributions. Numerical simulation validation demonstrates that Inc-GAN can accurately predict electric fields based on measured data collected during actual train operations, with significantly improved computational efficiency compared to traditional methods, providing a novel solution for railway communication system optimization based on real operational data. |
| title | Physics-Constrained Inc-GAN for Tunnel Propagation Modeling from Sparse Line Measurements |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.03019 |