Abstract

Recent advances in software and hardware greatly improve the multi-layer control and management of reconfigurable optical add-drop multiplexer (ROADM) systems facilitating wavelength switching. However, ensuring stable performance and reliable quality of transmission (QoT) remain difficult problems for dynamic operation. Optical power dynamics that arise from a variety of physical effects in the amplifiers and transmission fiber complicate the control and performance predictions in these systems. We present a deep-neural-network-based machine learning method to predict the power dynamics of a 90-channel ROADM system from data collection and training. We further show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions.

© 2018 Optical Society of America

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