Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems

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Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems. / Sidelnikov, Oleg ; Redyuk, Alexey; Sygletos, Stylianos.

In: Optics Express, Vol. 26, No. 25, 10.12.2018, p. 32765-32776.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{31e0bdc2b9734c14a58c65ca004c4497,

title = "Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems",

abstract = "We investigate the application of dynamic deep neural networks for nonlinear equalization in long haul transmission systems. Through extensive numerical analysis we identify their optimum dimensions and calculate their computational complexity as a function of system length. Performing comparison with traditional back-propagation based nonlinear compensation of 2 steps-per-span and 2 samples-per-symbol, we demonstrate equivalent mitigation performance at significantly lower computational cost.",

keywords = "FIBER NONLINEARITY COMPENSATION, DISPERSION, CHANNEL",

author = "Oleg Sidelnikov and Alexey Redyuk and Stylianos Sygletos",

note = "Publisher Copyright: {\textcopyright} 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement",

year = "2018",

month = dec,

day = "10",

doi = "10.1364/OE.26.032765",

language = "English",

volume = "26",

pages = "32765--32776",

journal = "Optics Express",

issn = "1094-4087",

publisher = "The Optical Society",

number = "25",

}

RIS

TY - JOUR

T1 - Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems

AU - Sidelnikov, Oleg

AU - Redyuk, Alexey

AU - Sygletos, Stylianos

PY - 2018/12/10

Y1 - 2018/12/10

N2 - We investigate the application of dynamic deep neural networks for nonlinear equalization in long haul transmission systems. Through extensive numerical analysis we identify their optimum dimensions and calculate their computational complexity as a function of system length. Performing comparison with traditional back-propagation based nonlinear compensation of 2 steps-per-span and 2 samples-per-symbol, we demonstrate equivalent mitigation performance at significantly lower computational cost.

AB - We investigate the application of dynamic deep neural networks for nonlinear equalization in long haul transmission systems. Through extensive numerical analysis we identify their optimum dimensions and calculate their computational complexity as a function of system length. Performing comparison with traditional back-propagation based nonlinear compensation of 2 steps-per-span and 2 samples-per-symbol, we demonstrate equivalent mitigation performance at significantly lower computational cost.

KW - FIBER NONLINEARITY COMPENSATION

KW - DISPERSION

KW - CHANNEL

UR - http://www.scopus.com/inward/record.url?scp=85058151001&partnerID=8YFLogxK

U2 - 10.1364/OE.26.032765

DO - 10.1364/OE.26.032765

M3 - Article

C2 - 30645439

AN - SCOPUS:85058151001

VL - 26

SP - 32765

EP - 32776

JO - Optics Express

JF - Optics Express

SN - 1094-4087

IS - 25

ER -

ID: 17828173