Optical Signal Spectrum Prediction Using Machine Learning and In-line Channel Monitors in a Multi-span ROADM System

Zehao Wang; Emmanuel Akinrintoyo; Dan Kilper; Tingjun Chen

European Conference on Optical Communication (ECOC) 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper We3B.5

Optical Signal Spectrum Prediction Using Machine Learning and In-line Channel Monitors in a Multi-span ROADM System

Zehao Wang, Emmanuel Akinrintoyo, Dan Kilper, and Tingjun Chen

European Conference and Exhibition on Optical Communication 2022

Basel Switzerland
18–22 September 2022
ISBN: 978-1-957171-15-9

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Z. Wang, E. Akinrintoyo, D. Kilper, and T. Chen, "Optical Signal Spectrum Prediction Using Machine Learning and In-line Channel Monitors in a Multi-span ROADM System," in European Conference on Optical Communication (ECOC) 2022, J. Leuthold, C. Harder, B. Offrein, and H. Limberger, eds., Technical Digest Series (Optica Publishing Group, 2022), paper We3B.5.

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Abstract

We measure the performance of separately characterized machine learning-based EDFA models for predicting the optical power spectrum evolution in a 5-span system with six ROADM nodes deployed in the COSMOS testbed, which achieve a mean absolute error of 0.6–0.7 dB after 10 EDFAs under varying channel loading configurations.

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