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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
Titel21st International Conference on Transparent Optical Networks, ICTON 2019
Plaats van productiePiscataway
UitgeverijIEEE Computer Society
Aantal pagina's4
ISBN van elektronische versie978-1-7281-2779-8
DOI's
StatusGepubliceerd - 1 jul 2019
Evenement21st International Conference on Transparent Optical Networks, ICTON 2019 - Angers, Frankrijk
Duur: 9 jul 201913 jul 2019
http://www.icton2019.com/index.php

Congres

Congres21st International Conference on Transparent Optical Networks, ICTON 2019
Verkorte titelICTON2019
LandFrankrijk
StadAngers
Periode9/07/1913/07/19
Internet adres

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Citeer dit

Amari, A., Lin, X., Dobre, O. A., Venkatesan, R., & Alvarado, A. (2019). Fiber nonlinearity mitigation via the parzen window classifier for dispersion managed and unmanaged links. In 21st International Conference on Transparent Optical Networks, ICTON 2019 [Fr.C3.1] Piscataway: IEEE Computer Society. https://doi.org/10.1109/ICTON.2019.8840250