# Sparse Identification for Nonlinear Optical Communication Systems: SINO Method

## TL;DR

The paper presents SINO, a low-complexity machine learning approach that identifies minimal degrees of freedom needed to mitigate nonlinear impairments in optical fiber communication systems, improving data recovery.

## Contribution

Introduction of SINO, a sparse identification method that simplifies nonlinear impairment mitigation in optical systems by determining optimal degrees of freedom.

## Key findings

- Effective in standard fiber communication links
- Successful application to spatial-division-multiplexing systems
- Reduces complexity of nonlinear impairment mitigation

## Abstract

We introduce low complexity machine learning based approach for mitigating nonlinear impairments in optical fiber communications systems. 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 and for few-mode spatial-division-multiplexing systems.