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

8 Citations (Scopus)

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

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10.1070/QEL16535

Cite this

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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",

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

Abstract

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