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Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM. / Giacoumidis, Elias ; Yi-lin; Wei, Jinlong ; Aldaya, Ivan ; Tsokanos, Athanasios; Barry, Liam.

In: Future internet, Vol. 11, No. 1, 20.12.2018, p. 1-20.

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Giacoumidis, Elias ; Yi-lin ; Wei, Jinlong ; Aldaya, Ivan ; Tsokanos, Athanasios ; Barry, Liam. / Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM. In: Future internet. 2018 ; Vol. 11, No. 1. pp. 1-20.

Bibtex

@article{21d8a9fe0eea4087b2f4f857bbc0f508,
title = "Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM",
abstract = "Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM{\textquoteright}s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions. ",
author = "Elias Giacoumidis and Yi-lin and Jinlong Wei and Ivan Aldaya and Athanasios Tsokanos and Liam Barry",
note = "{\textcopyright} 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).",
year = "2018",
month = dec,
day = "20",
doi = "10.3390/fi11010002",
language = "English",
volume = "11",
pages = "1--20",
journal = "Future internet",
issn = "1999-5903",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "1",

}

RIS

TY - JOUR

T1 - Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

AU - Giacoumidis, Elias

AU - Yi-lin, null

AU - Wei, Jinlong

AU - Aldaya, Ivan

AU - Tsokanos, Athanasios

AU - Barry, Liam

N1 - © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

PY - 2018/12/20

Y1 - 2018/12/20

N2 - Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.

AB - Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.

U2 - 10.3390/fi11010002

DO - 10.3390/fi11010002

M3 - Article

VL - 11

SP - 1

EP - 20

JO - Future internet

JF - Future internet

SN - 1999-5903

IS - 1

ER -