We review application of machine learning methods to tackle fiber linear/nonlinear impairments as well as to estimate crucial signal parameters in optical networks. Recent works involving hierarchical learning approaches are also discussed.

© 2017 Optical Society of America

PDF Article
More Like This
Machine-learning-based Coherent Optical Communication System

Wei Chen, Junfeng Zhang, Mingyi Gao, Yang Ye, Xiaoyi Chen, and Bowen Chen
M3G.6 Asia Communications and Photonics Conference (ACPC) 2017

Application of Machine Learning Techniques in Fiber-Optic Communication Systems

Alan Pak Tao Lau, Faisal Nadeem Khan, Qirui Fan, and Chao Lu
SpW4G.1 Signal Processing in Photonic Communications (SPPCom) 2018

Machine learning concepts in coherent optical communication systems

Darko Zibar and Christian Schäffer
ST2D.1 Signal Processing in Photonic Communications (SPPCom) 2014


You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access Optica Member Subscription