Toward Intelligent and Efficient Optical Networks: Performance Modeling, Co-Existence, and Field Trials
Optical transmission networks require intelligent traffic adaptation and efficient spectrum usage. We present scalable machine learning (ML) methods for network performance modeling, and field trials of distributed fiber sensing and classic optical network traffic coexistence.
Authors
- Giacomo Borraccini (ORCID: https://orcid.org/0000-0002-6920-7873)
- Yoshiaki Aono
- Andrea D’Amico (ORCID: https://orcid.org/0000-0003-0828-6157)
- Daniel C. Kilper (ORCID: https://orcid.org/0000-0003-3542-5335)
- Gil Zussman (ORCID: https://orcid.org/0000-0002-1845-4460)
- Zhenzhou Qi (ORCID: https://orcid.org/0000-0001-6330-5184)
- Agastya Raj (ORCID: https://orcid.org/0000-0003-3651-6837)
- Marco Ruffini (ORCID: https://orcid.org/0000-0001-6220-0065)
- Koji Asahi (ORCID: https://orcid.org/0000-0002-3067-9750)
- Zehao Wang
- Hideo Kageshima
- Ting Wang (ORCID: https://orcid.org/0000-0002-4658-7789)
- Shaobo Han (ORCID: https://orcid.org/0000-0003-2545-7114)
- Yue-Kai Huang (ORCID: https://orcid.org/0000-0003-2762-9223)
- Ezra Ip (ORCID: https://orcid.org/0000-0001-7108-8888)
- Tingjun Chen
Institutions
- NEC (Japan) (JP)
- Duke University (US)
- Princeton University (US)
- Trinity College Dublin (IE)
- Columbia University (US)
Publication Details
- Published
- 2025-06-29
- DOI
- https://doi.org/10.23919/oecc/psc62146.2025.11110008
- Citations
- 2
- Primary Topic
- Optical Network Technologies
- Type
- article
- Field-Weighted Citation Impact
- 1.46
Funders
- National Science Foundation