Nonlinearity Compensation Techniques Using Machine Learning

Stylianos Sygletos; Alexey A. Redyuk; Oleg Sidelnikov

Nonlinearity Compensation Techniques Using Machine Learning

Stylianos Sygletos, Alexey A. Redyuk, Oleg Sidelnikov

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

We discuss our recent work on machine learning based nonlinear equalization in long haul transmission sytems. We show that dynamic multi-perceptron networks can deal with the memory properties of the fibre channel and provide efficient mitigation of nonlinear impairments at lower computational cost when compared to conventional digital back propagation methods.

Original language	English
Title of host publication	Signal Processing in Photonic Communications 2019
Publication status	Published - 29 Jul 2019

Cite this

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title = "Nonlinearity Compensation Techniques Using Machine Learning",

abstract = "We discuss our recent work on machine learning based nonlinear equalization in long haul transmission sytems. We show that dynamic multi-perceptron networks can deal with the memory properties of the fibre channel and provide efficient mitigation of nonlinear impairments at lower computational cost when compared to conventional digital back propagation methods.",

author = "Stylianos Sygletos and Redyuk, {Alexey A.} and Oleg Sidelnikov",

year = "2019",

month = jul,

day = "29",

language = "English",

isbn = "978-1-943580-64-4",

booktitle = "Signal Processing in Photonic Communications 2019",

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T1 - Nonlinearity Compensation Techniques Using Machine Learning

AU - Sygletos, Stylianos

AU - Redyuk, Alexey A.

AU - Sidelnikov, Oleg

PY - 2019/7/29

Y1 - 2019/7/29

N2 - We discuss our recent work on machine learning based nonlinear equalization in long haul transmission sytems. We show that dynamic multi-perceptron networks can deal with the memory properties of the fibre channel and provide efficient mitigation of nonlinear impairments at lower computational cost when compared to conventional digital back propagation methods.

AB - We discuss our recent work on machine learning based nonlinear equalization in long haul transmission sytems. We show that dynamic multi-perceptron networks can deal with the memory properties of the fibre channel and provide efficient mitigation of nonlinear impairments at lower computational cost when compared to conventional digital back propagation methods.

UR - https://www.osapublishing.org/abstract.cfm?uri=SPPCom-2019-SpT2E.2

M3 - Conference publication

SN - 978-1-943580-64-4

BT - Signal Processing in Photonic Communications 2019

ER -

Nonlinearity Compensation Techniques Using Machine Learning

Abstract

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