Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems

Evgeny Averyanov, Alexey Redyuk, Oleg Sidelnikov, Mariia Soroklna, Mikhail Fedoruk, Sergei Turitsyn

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

4 Citations (Scopus)

Abstract

We propose a perturbation-based receiver-side machine-learning equalizer for inter- and intra-channel nonlinearity compensation in WDM systems. We show 1.6 dB and 0.6 dB Q2-factor improvement compared with linear equalization and DBP respectively for 1000km transmission of 3× 128Gbit/s DP-16QAM signal.

Original languageEnglish
Title of host publication2018 European Conference on Optical Communication, ECOC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2018-September
ISBN (Electronic)9781538648629
DOIs
Publication statusPublished - 14 Nov 2018
Event2018 European Conference on Optical Communication, ECOC 2018 - Rome, Italy
Duration: 23 Sep 201827 Sep 2018

Conference

Conference2018 European Conference on Optical Communication, ECOC 2018
CountryItaly
CityRome
Period23.09.201827.09.2018

Fingerprint

Dive into the research topics of 'Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems'. Together they form a unique fingerprint.

Cite this