Deep-Neural-Network-Based Wavelength Selection and Switching in ROADM Systems
AffiliationUniv Arizona, Coll Opt Sci
MetadataShow full item record
PublisherOPTICAL SOC AMER
CitationWeiyang Mo, Craig L. Gutterman, Yao Li, Shengxiang Zhu, Gil Zussman, and Daniel C. Kilper, "Deep-Neural-Network-Based Wavelength Selection and Switching in ROADM Systems," J. Opt. Commun. Netw. 10, D1-D11 (2018)
Rights© 2018 Optical Society of America
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractRecent 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.
Note12 month embargo; published online: 8 May 2018
VersionFinal accepted manuscript
SponsorsNational Science Foundation (NSF) [CNS-1650669, CNS-1423105, CNS-1650685, PFI-1601784]