Fiber nonlinearity mitigation via the parzen window classifier for dispersion managed and unmanaged links

Abdelkerim Amari, Xiang Lin, Octavia A. Dobre, Ramachandran Venkatesan, Alex Alvarado

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects. In this work, a machine learning-based classification technique, known as the Parzen window (PW) classifier, is applied to mitigate the nonlinear effects in the optical channel. The PW classifier is used as a detector with improved nonlinear decision boundaries more adapted to the nonlinear fiber channel. Performance improvement is observed when applying the PW in the context of dispersion managed and dispersion unmanaged systems.

Original languageEnglish
Title of host publication21st International Conference on Transparent Optical Networks, ICTON 2019
Place of PublicationPiscataway
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)978-1-7281-2779-8
DOIs
Publication statusPublished - 1 Jul 2019
Event21st International Conference on Transparent Optical Networks, ICTON 2019 - Angers, France
Duration: 9 Jul 201913 Jul 2019
http://www.icton2019.com/index.php

Conference

Conference21st International Conference on Transparent Optical Networks, ICTON 2019
Abbreviated titleICTON2019
CountryFrance
CityAngers
Period9/07/1913/07/19
Internet address

Keywords

  • Fiber nonlinearity
  • Machine learning
  • Optical communications
  • Parzen window

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