Sidelnikov, Oleg, Redyuk, Alexey and Sygletos, Stylianos (2018). Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems. Optics Express, 26 (25), pp. 32765-32776.
Abstract
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) |
| Additional Information: | An OSA-formatted open access journal article PDF may be governed by the OSA Open Access Publishing Agreement signed by the author and any applicable copyright laws. Authors and readers may use, reuse, and build upon the article, or use it for text or data mining without asking prior permission from the publisher or the Author(s), as long as the purpose is non-commercial and appropriate attribution is maintained. |
| Publication ISSN: | 1094-4087 |
| Last Modified: | 05 Feb 2024 08:33 |
| Date Deposited: | 04 Dec 2018 10:28 |
| Full Text Link: | |
| 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 ( 
0000-0003-2063-8733)
|
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)