Machine learning for performance improvement of periodic NFT-based communication system

Research output: Contribution to conferencePaper

Abstract

We compare performance of several machine learning methods, including support vector machine, k-nearest neighbours, k-means
clustering, and Gaussian mixture model, used for increasing transmission reach in the optical communication system based on
the periodic nonlinear Fourier transform signal processing
Original languageEnglish
Number of pages4
Publication statusPublished - 26 Sep 2019
Event2019 European Conference on Optical Communications - Royal Dublin Society, Dublin, Ireland
Duration: 22 Sep 201926 Sep 2019
Conference number: 45
https://www.ecoc2019.org/

Conference

Conference2019 European Conference on Optical Communications
Abbreviated titleECOC 2019
CountryIreland
CityDublin
Period22/09/1926/09/19
Internet address

Fingerprint

Optical communication
Support vector machines
Learning systems
Fourier transforms
Communication systems
Signal processing

Bibliographical note

© The Authors

Cite this

@conference{34a70072d59744fab01bcef4e3b781d3,
title = "Machine learning for performance improvement of periodic NFT-based communication system",
abstract = "We compare performance of several machine learning methods, including support vector machine, k-nearest neighbours, k-meansclustering, and Gaussian mixture model, used for increasing transmission reach in the optical communication system based onthe periodic nonlinear Fourier transform signal processing",
author = "Oleksandr Kotlyar and {Kamalian Kopae}, Morteza and Prilepsky, {Jaroslaw E.} and Maryna Pankratova and Turitsyn, {Sergei K.}",
note = "{\circledC} The Authors; 2019 European Conference on Optical Communications, ECOC 2019 ; Conference date: 22-09-2019 Through 26-09-2019",
year = "2019",
month = "9",
day = "26",
language = "English",
url = "https://www.ecoc2019.org/",

}

Kotlyar, O, Kamalian Kopae, M, Prilepsky, JE, Pankratova, M & Turitsyn, SK 2019, 'Machine learning for performance improvement of periodic NFT-based communication system' Paper presented at 2019 European Conference on Optical Communications, Dublin, Ireland, 22/09/19 - 26/09/19, .

Machine learning for performance improvement of periodic NFT-based communication system. / Kotlyar, Oleksandr; Kamalian Kopae, Morteza; Prilepsky, Jaroslaw E.; Pankratova, Maryna; Turitsyn, Sergei K.

2019. Paper presented at 2019 European Conference on Optical Communications, Dublin, Ireland.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Machine learning for performance improvement of periodic NFT-based communication system

AU - Kotlyar, Oleksandr

AU - Kamalian Kopae, Morteza

AU - Prilepsky, Jaroslaw E.

AU - Pankratova, Maryna

AU - Turitsyn, Sergei K.

N1 - © The Authors

PY - 2019/9/26

Y1 - 2019/9/26

N2 - We compare performance of several machine learning methods, including support vector machine, k-nearest neighbours, k-meansclustering, and Gaussian mixture model, used for increasing transmission reach in the optical communication system based onthe periodic nonlinear Fourier transform signal processing

AB - We compare performance of several machine learning methods, including support vector machine, k-nearest neighbours, k-meansclustering, and Gaussian mixture model, used for increasing transmission reach in the optical communication system based onthe periodic nonlinear Fourier transform signal processing

M3 - Paper

ER -