Sparse identification for nonlinear optical communication systems: SINO method

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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.

Original languageEnglish
Pages (from-to)30433-30443
Number of pages11
JournalOptics Express
Issue number26
Early online date23 Dec 2016
Publication statusPublished - 26 Dec 2016

Bibliographical note

© 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).


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