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

Dynamic mitigation of EDFA power excursions with machine learning

Yishen Huang, Craig L. Gutterman, Payman Samadi, Patricia B. Cho, Wiem Samoud, Cédric Ware, Mounia Lourdiane, Gil Zussman, and Keren Bergman
Opt. Express 25(3) 2245-2258 (2017)

Deep-Neural-Network-Based Wavelength Selection and Switching in ROADM Systems

Weiyang Mo, Craig L. Gutterman, Yao Li, Shengxiang Zhu, Gil Zussman, and Daniel C. Kilper
J. Opt. Commun. Netw. 10(10) D1-D11 (2018)

Optical Physical Layer SDN [Invited]

Yao Li and Daniel C. Kilper
J. Opt. Commun. Netw. 10(1) A110-A121 (2018)

Using Machine Learning to Balance Energy Cost and Emissions in Optical Networks

Arash Deylamsalehi, Dylan A. P. Davis, Pegah Afsharlar, Mehdi Bahrami, Wei-Peng Chen, and Vinod M. Vokkarane
J. Opt. Commun. Netw. 10(10) D72-D83 (2018)

Experimental Demonstration of Machine-Learning-Aided QoT Estimation in Multi-Domain Elastic Optical Networks with Alien Wavelengths

Roberto Proietti, Xiaoliang Chen, Kaiqi Zhang, Gengchen Liu, M. Shamsabardeh, Alberto Castro, Luis Velasco, Zuqing Zhu, and S. J. Ben Yoo
J. Opt. Commun. Netw. 11(1) A1-A10 (2019)