Abstract

Flexgrid optical networking relies on spectrum defragmentation to groom channel wavelengths and improve spectral efficiency. Various defragmentation methods—hop, make-before-break, and sweep—interact with the power dynamics of erbium-doped fiber amplifiers (EDFA) differently and result in undesired power excursions that exacerbate the post-EDFA power variance. We present a machine learning engine that characterizes the channel dependence of power excursions from historical data. We further demonstrate that post-EDFA power variance during hop, make-before-break, and sweep defragmentation methods can be greatly mitigated by the trained machine learning engine with automated and expedited power adjustment predictions.

© 2017 Optical Society of America

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