ML-Aided Proactive In-Line EDFAs' Gain Degradation Detection and Localization in Optical Networks

We propose a machine learning-based framework inside standard coherent receivers for proactive detection and localization of in-line EDFAs' gain degradation. 84.2% failures can be detected in advance and 93.1% failures localization accuracy is experimentally demonstrated.

Authors

Kangping Zhong (ORCID: https://orcid.org/0000-0002-9530-1468)
Zhuojun Cai
Hongcheng Wu
Gai Zhou (ORCID: https://orcid.org/0000-0001-6278-5964)
Qi Hu (ORCID: https://orcid.org/0000-0003-3647-8471)
Faisal Khan (ORCID: https://orcid.org/0000-0002-8391-6203)

Institutions

Guangdong University of Technology (CN)
Hong Kong Polytechnic University (HK)
Tsinghua–Berkeley Shenzhen Institute (CN)
Tsinghua University (CN)

Publication Details

Published: 2025-06-29
DOI: https://doi.org/10.23919/oecc/psc62146.2025.11111456
Primary Topic: Photonic and Optical Devices
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Natural Science Foundation of China

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article

ML-Aided Proactive In-Line EDFAs' Gain Degradation Detection and Localization in Optical Networks

Kangping Zhong, Zhuojun Cai, Hongcheng Wu, Gai Zhou et al.

Jun 29, 2025

Photonic and Optical Devices

article

ML-Aided Proactive In-Line EDFAs' Gain Degradation Detection and Localization in Optical Networks

Kangping Zhong, Zhuojun Cai, Hongcheng Wu, Gai Zhou, Qi Hu, Faisal Khan

June 29, 2025article en

Abstract

We propose a machine learning-based framework inside standard coherent receivers for proactive detection and localization of in-line EDFAs' gain degradation. 84.2% failures can be detected in advance and 93.1% failures localization accuracy is experimentally demonstrated.

Guangdong University of Technology (CN), Hong Kong Polytechnic University (HK), Tsinghua–Berkeley Shenzhen Institute (CN), Tsinghua University (CN)

National Natural Science Foundation of China

Openalex Percentile: Top 80%

Photonic and Optical Devices

DOI Source OpenAlex

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