Presentation Information

[WF1-4]ML-Aided Proactive In-line EDFAs’ Gain Degradation Detection and Localization in Optical Networks

○Hongcheng Wu1, Qi Hu1, Zhuojun Cai1, Gai Zhou2, Kangping Zhong3, Faisal Nadeem Khan1 (1. Tsinghua University (China), 2. Guangdong University of Technology (China), 3. The Hong Kong Polytechnic University (Hong Kong))

Keywords:

Artificial intelligence and machine learning for optical network design,control,and management

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.

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