Sparse Identification for Nonlinear Optical communication systems


We have developed a low complexity machine learning based nonlinear impairment equalization scheme and demonstrated its successful performance in SDM transmission links achieving compensation of both inter- and intra- channel Kerr-based nonlinear effects. The method operates in one sample per symbol and in one computational step. It is adaptive, i.e. it does not require a knowledge of system parameters, and it is scalable to different power levels and modulation formats. The method can be straightforwardly expanded to multi-channel systems and to any other type of nonlinear impairment.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © Copyright 2017 IEEE - All rights reserved Funding: EPSRC project UNLOC EP/J017582/1 and EU-FP7 INSPACE project under grant agreement N.619732
Event Title: 19th International Conference on Transparent Optical Networks, ICTON 2017
Event Type: Other
Event Dates: 2017-07-02 - 2017-07-06
Uncontrolled Keywords: fiber optic communications,machine learning,nonlinear analysis,spatial division multiplexing,Computer Networks and Communications,Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials
ISBN: 9781538608586
Last Modified: 11 Mar 2024 08:08
Date Deposited: 30 Oct 2017 09:35
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2017-09-04
Accepted Date: 2017-09-04
Authors: Sorokina, Mariia (ORCID Profile 0000-0001-6082-0316)
Sygletos, Stylianos (ORCID Profile 0000-0003-2063-8733)
Turitsyn, Sergei (ORCID Profile 0000-0003-0101-3834)



Version: Accepted Version

| Preview

Export / Share Citation


Additional statistics for this record