Fiber Nonlinearity Mitigation via the Parzen Window Classifier for Dispersion Managed and Unmanaged Links

TL;DR

This paper applies a machine learning-based Parzen window classifier to mitigate fiber nonlinearities in optical communication links, demonstrating improved performance in both dispersion managed and unmanaged systems.

Contribution

It introduces the use of the Parzen window classifier for nonlinear mitigation in optical channels, offering a novel approach compared to traditional methods.

Findings

Performance improvement in nonlinear fiber channels

Effective in both dispersion managed and unmanaged links

Enhanced nonlinear decision boundaries

Abstract

Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects. In this work, a machine learning-based classification technique, known as the Parzen window (PW) classifier, is applied to mitigate the nonlinear effects in the optical channel. The PW classifier is used as a detector with improved nonlinear decision boundaries more adapted to the nonlinear fiber channel. Performance improvement is observed when applying the PW in the context of dispersion managed and dispersion unmanaged systems.