Abstract
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 language | English |
|---|---|
| Pages (from-to) | 30433-30443 |
| Number of pages | 11 |
| Journal | Optics Express |
| Volume | 24 |
| Issue number | 26 |
| Early online date | 23 Dec 2016 |
| DOIs | |
| Publication status | Published - 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).