Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

O S Sidelnikov; A A Redyuk; S Sygletos

doi:10.1070/QEL16535

Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

O S Sidelnikov, A A Redyuk, S Sygletos

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

Abstract

We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

Original language	English
Pages (from-to)	1147-1149
Journal	Quantum Electronics
Volume	47
Issue number	12
DOIs	https://doi.org/10.1070/QEL16535
Publication status	Published - 31 Dec 2017

Bibliographical note

© 2017 Kvantovaya Elektronika, Turpion Ltd and IOP Publishing Ltd.

Funding: Russian Science Foundation (Project No. 17-72-30006).

Keywords

Mathematical modelling. Neural networks Nonlinear effects Optical fibre

Access to Document

10.1070/QEL16535

Cite this

@article{3bf993ca077b4d0c8312f3b169ee23e6,

title = "Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines",

abstract = "We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.",

keywords = "Mathematical modelling. Neural networks Nonlinear effects Optical fibre",

author = "Sidelnikov, {O S} and Redyuk, {A A} and S Sygletos",

note = "{\textcopyright} 2017 Kvantovaya Elektronika, Turpion Ltd and IOP Publishing Ltd. Funding: Russian Science Foundation (Project No. 17-72-30006).",

year = "2017",

month = dec,

day = "31",

doi = "10.1070/QEL16535",

language = "English",

volume = "47",

pages = "1147--1149",

journal = "Quantum Electronics",

issn = "1063-7818",

publisher = "Turpion Ltd.",

number = "12",

}

TY - JOUR

T1 - Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

AU - Sidelnikov, O S

AU - Redyuk, A A

AU - Sygletos, S

PY - 2017/12/31

Y1 - 2017/12/31

N2 - We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

AB - We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

KW - Mathematical modelling. Neural networks Nonlinear effects Optical fibre

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85040975228&doi=10.1070%2fQEL16535&origin=inward&txGid=7d258dc535b8c308eeca4d46782c6f7b

UR - http://iopscience.iop.org/article/10.1070/QEL16535/meta

U2 - 10.1070/QEL16535

DO - 10.1070/QEL16535

M3 - Article

SN - 1063-7818

VL - 47

SP - 1147

EP - 1149

JO - Quantum Electronics

JF - Quantum Electronics

IS - 12

ER -

Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this