Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems


We investigate the application of dynamic deep neural networks for nonlinear equalization in long haul transmission systems. Through extensive numerical analysis we identify their optimum dimensions and calculate their computational complexity as a function of system length. Performing comparison with traditional back-propagation based nonlinear compensation of 2 steps-per-span and 2 samples-per-symbol, we demonstrate equivalent mitigation performance at significantly lower computational cost.

Publication DOI: https://doi.org/10.1364/OE.26.032765
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
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Publication ISSN: 1094-4087
Last Modified: 08 Jul 2024 07:45
Date Deposited: 04 Dec 2018 10:28
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Related URLs: https://www.osa ... =oe-26-25-32765 (Publisher URL)
PURE Output Type: Article
Published Date: 2018-12-10
Published Online Date: 2018-11-29
Accepted Date: 2018-10-20
Authors: Sidelnikov, Oleg
Redyuk, Alexey
Sygletos, Stylianos (ORCID Profile 0000-0003-2063-8733)



Version: Published Version

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