Sparse identification for nonlinear optical communication systems:SINO method


We introduce a low complexity machine learning method method (based on lasso regression, which promotes sparsity, to identify the interaction between symbols in different time slots and to select the minimum number relevant perturbation terms that are employed) for nonlinearity mitigation. The immense intricacy of the problem calls for the development of "smart"methodology, simplifying the analysis without losing the key features that are important for recovery of transmitted data. The proposed sparse identification method for optical systems (SINO) allows to determine the minimal (optimal) number of degrees of freedom required for adaptive mitigation of detrimental nonlinear effects. We demonstrate successful application of the SINO method both for standard fiber communication links (over 3 dB gain) and for fewmode spatial-division-multiplexing systems.

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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: © 2016 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited. Funding: EPSRC (UNLOC EP/J017582/1); and EU-FP7 INSPACE (N.619732).
Uncontrolled Keywords: Atomic and Molecular Physics, and Optics
Publication ISSN: 1094-4087
Last Modified: 22 Apr 2024 07:15
Date Deposited: 30 Jan 2017 11:50
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2016-12-26
Published Online Date: 2016-12-23
Accepted Date: 2016-12-23
Submitted Date: 2016-11-14
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

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