Low Complexity Convolutional neural networks for equalization in optical fiber transmission

Mohannad Abu-Romoh; Nelson Costa; Antonio Napoli; Jõao Pedro; Yves Jaoüen; Mansoor Yousefi

Low Complexity Convolutional neural networks for equalization in optical fiber transmission

Mohannad Abu-Romoh
, Nelson Costa
, Antonio Napoli
, Jõao Pedro
, Yves Jaoüen
, Mansoor Yousefi

Telecom Paris
INFINERA UNIPESSOAL LDA
Infinera

Résultats de recherche: Contribution à un journal › Article de conférence › Revue par des pairs

Résumé

A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3.5 dB improvement in MSE compared to DBP with comparable complexity. 1.

langue originale	Anglais
Numéro d'article	SpM5C.5
journal	Optics InfoBase Conference Papers
état	Publié - 1 janv. 2021
Evénement	Signal Processing in Photonic Communications, SPPCom 2021 - Part of OSA Advanced Photonics Congress 2021 - Virtual, Online, États-Unis Durée: 26 juil. 2021 → 29 juil. 2021

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@article{e701b3533b7841938f74a3ce1ea22015,

title = "Low Complexity Convolutional neural networks for equalization in optical fiber transmission",

abstract = "A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3.5 dB improvement in MSE compared to DBP with comparable complexity. 1.",

author = "Mohannad Abu-Romoh and Nelson Costa and Antonio Napoli and J{\~o}ao Pedro and Yves Jao{\"u}en and Mansoor Yousefi",

note = "Publisher Copyright: {\textcopyright} 2021 The Author(s).; Signal Processing in Photonic Communications, SPPCom 2021 - Part of OSA Advanced Photonics Congress 2021 ; Conference date: 26-07-2021 Through 29-07-2021",

year = "2021",

month = jan,

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language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

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TY - JOUR

T1 - Low Complexity Convolutional neural networks for equalization in optical fiber transmission

AU - Abu-Romoh, Mohannad

AU - Costa, Nelson

AU - Napoli, Antonio

AU - Pedro, Jõao

AU - Jaoüen, Yves

AU - Yousefi, Mansoor

PY - 2021/1/1

Y1 - 2021/1/1

N2 - A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3.5 dB improvement in MSE compared to DBP with comparable complexity. 1.

AB - A convolutional neural network is proposed to mitigate fiber transmission effects, achieving a five-fold reduction in trainable parameters compared to alternative equalizers, and 3.5 dB improvement in MSE compared to DBP with comparable complexity. 1.

UR - https://www.scopus.com/pages/publications/85120562447

M3 - Conference article

AN - SCOPUS:85120562447

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - SpM5C.5

T2 - Signal Processing in Photonic Communications, SPPCom 2021 - Part of OSA Advanced Photonics Congress 2021

Y2 - 26 July 2021 through 29 July 2021

ER -

Low Complexity Convolutional neural networks for equalization in optical fiber transmission

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