Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912285821239296 |
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| author | Raj, Agastya Wang, Zehao Slyne, Frank Chen, Tingjun Kilper, Dan Ruffini, Marco |
| author_facet | Raj, Agastya Wang, Zehao Slyne, Frank Chen, Tingjun Kilper, Dan Ruffini, Marco |
| contents | We implement a ML-based attention framework with component-specific decoders, improving optical power spectrum prediction in multi-span networks. By reducing the need for in-depth training on each component, the framework can be scaled to multi-span topologies with minimal data collection, making it suitable for brown-field scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17072 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework Raj, Agastya Wang, Zehao Slyne, Frank Chen, Tingjun Kilper, Dan Ruffini, Marco Machine Learning Networking and Internet Architecture We implement a ML-based attention framework with component-specific decoders, improving optical power spectrum prediction in multi-span networks. By reducing the need for in-depth training on each component, the framework can be scaled to multi-span topologies with minimal data collection, making it suitable for brown-field scenarios. |
| title | Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework |
| topic | Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2503.17072 |