Sparse Identification for Nonlinear Optical communication systems

Mariia Sorokina*, Stylianos Sygletos, Sergei Turitsyn

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

We have developed a low complexity machine learning based nonlinear impairment equalization scheme and demonstrated its successful performance in SDM transmission links achieving compensation of both inter- and intra- channel Kerr-based nonlinear effects. The method operates in one sample per symbol and in one computational step. It is adaptive, i.e. it does not require a knowledge of system parameters, and it is scalable to different power levels and modulation formats. The method can be straightforwardly expanded to multi-channel systems and to any other type of nonlinear impairment.

Original languageEnglish
Title of host publicationICTON 2017 - 19th International Conference on Transparent Optical Networks
PublisherIEEE
ISBN (Electronic)9781538608586
DOIs
Publication statusPublished - 4 Sep 2017
Event19th International Conference on Transparent Optical Networks, ICTON 2017 - Girona, Catalonia, Spain
Duration: 2 Jul 20176 Jul 2017

Conference

Conference19th International Conference on Transparent Optical Networks, ICTON 2017
CountrySpain
CityGirona, Catalonia
Period2/07/176/07/17

Bibliographical note

© Copyright 2017 IEEE - All rights reserved

Funding: EPSRC project UNLOC EP/J017582/1 and EU-FP7 INSPACE project under grant agreement N.619732

Keywords

  • fiber optic communications
  • machine learning
  • nonlinear analysis
  • spatial division multiplexing

Fingerprint Dive into the research topics of 'Sparse Identification for Nonlinear Optical communication systems'. Together they form a unique fingerprint.

Cite this