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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.09047 |
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| _version_ | 1866918163615055872 |
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| author | Liu, Jiaming Wang, Rui Li, JinJiang Lin, Hong Zhang, Jing Qiu, Kun |
| author_facet | Liu, Jiaming Wang, Rui Li, JinJiang Lin, Hong Zhang, Jing Qiu, Kun |
| contents | We propose a transfer learning-enabled Transformer framework to simultaneously realize accurate modeling and Raman pump design in C+L-band systems. The RMSE for modeling and peak-to-peak GSNR variation/deviation is within 0.22 dB and 0.86/0.1 dB, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09047 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transfer Learning-Enabled Efficient Raman Pump Tuning under Dynamic Launch Power for C+L Band Transmission Liu, Jiaming Wang, Rui Li, JinJiang Lin, Hong Zhang, Jing Qiu, Kun Signal Processing Systems and Control We propose a transfer learning-enabled Transformer framework to simultaneously realize accurate modeling and Raman pump design in C+L-band systems. The RMSE for modeling and peak-to-peak GSNR variation/deviation is within 0.22 dB and 0.86/0.1 dB, respectively. |
| title | Transfer Learning-Enabled Efficient Raman Pump Tuning under Dynamic Launch Power for C+L Band Transmission |
| topic | Signal Processing Systems and Control |
| url | https://arxiv.org/abs/2510.09047 |