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

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

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

Bibliographical note

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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

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