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


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.

Publication DOI: https://doi.org/10.1109/BICOP.2018.8658274
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 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).
Event Title: 1st IEEE British and Irish Conference on Optics and Photonics (BICOP 2018)
Event Type: Other
Event Dates: 2018-12-12 - 2018-12-14
Uncontrolled Keywords: Machine learning,k-means clustering,nonlinear Fourer transform,optical communications,support vector machine,Electronic, Optical and Magnetic Materials,Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics
ISBN: 978-1-5386-7362-1, 978-153867361-4
Last Modified: 01 Apr 2024 07:53
Date Deposited: 05 Dec 2018 10:14
Full Text Link:
Related URLs: https://ieeexpl ... ocument/8658274 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2019-03-04
Published Online Date: 2018-12-14
Accepted Date: 2018-11-26
Authors: Kotlyar, Oleksandr (ORCID Profile 0000-0002-2744-0132)
Pankratova, Maryna (ORCID Profile 0000-0002-5974-6160)
Kamalian Kopae, Morteza (ORCID Profile 0000-0002-6278-976X)
Vasylchenkova, Anastasiia (ORCID Profile 0000-0002-6997-9427)
Prilepsky, Jaroslaw E. (ORCID Profile 0000-0002-3035-4112)
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)



Version: Accepted Version

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