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-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

Divisions: Engineering & Applied Sciences > Aston Institute of Photonics Technology
Engineering & Applied Sciences
Engineering & Applied Sciences > Electrical, Electronic & Power Engineering
Additional Information: © The Authors
Event Title: 2019 European Conference on Optical Communications
Event Type: Other
Event Location: Royal Dublin Society
Event Dates: 2019-09-22 - 2019-09-26
PURE Output Type: Paper
Published Date: 2019-09-26
Accepted Date: 2019-07-01
Authors: Kotlyar, Oleksandr ( 0000-0002-2744-0132)
Kamalian Kopae, Morteza ( 0000-0002-6278-976X)
Prilepsky, Jaroslaw E. ( 0000-0002-3035-4112)
Pankratova, Maryna ( 0000-0002-5974-6160)
Turitsyn, Sergei K. ( 0000-0003-0101-3834)

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