Simplifying the Supervised Learning of Kerr Nonlinearity Compensation Algorithms by Data Augmentation


We propose a data augmentation technique to improve performance and decrease complexity of the supervised learning of nonlinearity compensation algorithms. We demonstrate both numerically and experimentally that the augmentation allows reducing the training dataset size up to 6 times while keeping the same post-compensation bit-error rate.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
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Event Title: 2020 European Conference on Optical Communications
Event Type: Other
Event Dates: 2020-12-06 - 2020-12-10
Uncontrolled Keywords: Computer Networks and Communications,Electronic, Optical and Magnetic Materials,Instrumentation,Atomic and Molecular Physics, and Optics
ISBN: 978-1-7281-7362-7, 978-1-7281-7361-0
Last Modified: 16 Apr 2024 07:42
Date Deposited: 21 Jul 2021 13:34
Full Text Link:
Related URLs: https://ieeexpl ... ocument/9333417 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2021-02-04
Accepted Date: 2020-12-01
Authors: Neskorniuk, Vladislav (ORCID Profile 0000-0002-3358-153X)
Freire de Carvalho Souza, Pedro Jorge (ORCID Profile 0000-0003-3145-1018)
Napoli, Antonio
Spinnler, Bernhard
Schairer, Wolfgang
Prilepsky, Jaroslaw E. (ORCID Profile 0000-0002-3035-4112)
Costa, Nelson
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)



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