Unsupervised and supervised machine learning for performance improvement of NFT optical transmission

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We apply both the unsupervised and supervised
machine learning (ML) methods, in particular, the k-means
clustering and support vector machine (SVM) to improve the
performance of the optical communication system based on the
nonlinear Fourier transform (NFT). The NFT system employs the
continuous NFT spectrum part to carry data up to 1000 km using
the 16-QAM OFDM modulation. We classify the performance of
the system in terms of BER versus signal power dependence.
We show that the NFT system performance can be improved
considerably by means of the ML techniques and that the
more advanced SVM method typically outperforms the k-means
clustering.
Original languageEnglish
Title of host publication2018 British and Irish Conference on Optics and Photonics, BICOP 2018 - Proceedings
PublisherIEEE
ISBN (Electronic)978-153867361-4
ISBN (Print)978-1-5386-7362-1
DOIs
Publication statusPublished - 4 Mar 2019
Event1st IEEE British and Irish Conference on Optics and Photonics (BICOP 2018) - London, United Kingdom
Duration: 12 Dec 201814 Dec 2018

Conference

Conference1st IEEE British and Irish Conference on Optics and Photonics (BICOP 2018)
CountryUnited Kingdom
CityLondon
Period12/12/1814/12/18

Fingerprint

machine learning
Light transmission
Support vector machines
Learning systems
quadrature amplitude modulation
Quadrature amplitude modulation
Optical communication
Orthogonal frequency division multiplexing
learning
optical communication
telecommunication
Fourier transforms
Communication systems
Modulation

Bibliographical note

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


Funding: European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreements No.751561 (MP) and No.713694 (OK), EPSRC project TRANSNET (EP/R035342/1) (OK, MK & SKT) and the Leverhulme Trust project (RPG-2018-063) (JEP & SKT).

Keywords

  • Machine learning
  • k-means clustering
  • nonlinear Fourer transform
  • optical communications
  • support vector machine

Cite this

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title = "Unsupervised and supervised machine learning for performance improvement of NFT optical transmission",
abstract = "We apply both the unsupervised and supervisedmachine learning (ML) methods, in particular, the k-meansclustering and support vector machine (SVM) to improve theperformance of the optical communication system based on thenonlinear Fourier transform (NFT). The NFT system employs thecontinuous NFT spectrum part to carry data up to 1000 km usingthe 16-QAM OFDM modulation. We classify the performance ofthe system in terms of BER versus signal power dependence.We show that the NFT system performance can be improvedconsiderably by means of the ML techniques and that themore advanced SVM method typically outperforms the k-meansclustering.",
keywords = "Machine learning, k-means clustering, nonlinear Fourer transform, optical communications, support vector machine",
author = "Oleksandr Kotlyar and Maryna Pankratova and {Kamalian Kopae}, Morteza and Anastasiia Vasylchenkova and Prilepsky, {Jaroslaw E.} and Turitsyn, {Sergei K.}",
note = "{\circledC} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Funding: European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreements No.751561 (MP) and No.713694 (OK), EPSRC project TRANSNET (EP/R035342/1) (OK, MK & SKT) and the Leverhulme Trust project (RPG-2018-063) (JEP & SKT).",
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Kotlyar, O, Pankratova, M, Kamalian Kopae, M, Vasylchenkova, A, Prilepsky, JE & Turitsyn, SK 2019, Unsupervised and supervised machine learning for performance improvement of NFT optical transmission. in 2018 British and Irish Conference on Optics and Photonics, BICOP 2018 - Proceedings., 8658274, IEEE, 1st IEEE British and Irish Conference on Optics and Photonics (BICOP 2018), London, United Kingdom, 12/12/18. https://doi.org/10.1109/BICOP.2018.8658274

Unsupervised and supervised machine learning for performance improvement of NFT optical transmission. / Kotlyar, Oleksandr; Pankratova, Maryna; Kamalian Kopae, Morteza; Vasylchenkova, Anastasiia; Prilepsky, Jaroslaw E.; Turitsyn, Sergei K.

2018 British and Irish Conference on Optics and Photonics, BICOP 2018 - Proceedings. IEEE, 2019. 8658274.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Prilepsky, Jaroslaw E.

AU - Turitsyn, Sergei K.

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Funding: European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreements No.751561 (MP) and No.713694 (OK), EPSRC project TRANSNET (EP/R035342/1) (OK, MK & SKT) and the Leverhulme Trust project (RPG-2018-063) (JEP & SKT).

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AB - We apply both the unsupervised and supervisedmachine learning (ML) methods, in particular, the k-meansclustering and support vector machine (SVM) to improve theperformance of the optical communication system based on thenonlinear Fourier transform (NFT). The NFT system employs thecontinuous NFT spectrum part to carry data up to 1000 km usingthe 16-QAM OFDM modulation. We classify the performance ofthe system in terms of BER versus signal power dependence.We show that the NFT system performance can be improvedconsiderably by means of the ML techniques and that themore advanced SVM method typically outperforms the k-meansclustering.

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M3 - Conference contribution

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BT - 2018 British and Irish Conference on Optics and Photonics, BICOP 2018 - Proceedings

PB - IEEE

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