Revisiting multi-step nonlinearity compensation with machine learning

Christian Häger, Henry D. Pfister, Rick M. Bütler, Gabriele Liga, Alex Alvarado

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

1 Citation (Scopus)
72 Downloads (Pure)

Abstract

For the efficient compensation of fiber nonlinearity, one of the guiding principles appears to be: fewer steps are better and more efficient. We challenge this assumption and show that carefully designed multi-step approaches can lead to better performance-complexity trade-offs than their few-step counterparts.
Original languageEnglish
Title of host publication45th European Conference on Optical Communication (ECOC 2019)
PublisherInstitution of Engineering and Technology (IET)
Number of pages4
ISBN (Print)978-1-83953-185-9
DOIs
Publication statusPublished - 30 Jun 2020
Event45th European Conference on Optical Communication (ECOC 2019) - Dublin, Ireland
Duration: 22 Sep 201926 Sep 2019
Conference number: 45
https://www.ecoc2019.org/

Publication series

NameIET Conference Publications
NumberCP765

Conference

Conference45th European Conference on Optical Communication (ECOC 2019)
Abbreviated titleECOC 2019
Country/TerritoryIreland
CityDublin
Period22/09/1926/09/19
Internet address

Keywords

  • Deep Learning
  • Low-Complexity Digital Back-Propagation
  • Machine Learning Based Dsp
  • Polarization Mode Dispersion
  • Subband Processing

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