# A Machine Learning-Based Detection Technique for Optical Fiber Nonlinearity Mitigation

## TL;DR

This paper presents a machine learning-based detection method using Parzen window to mitigate fiber nonlinearity in optical communication systems, improving signal quality in both dispersion managed and unmanaged scenarios.

## Contribution

It introduces a novel application of Parzen window classification for fiber nonlinearity mitigation and combines it with digital back propagation for enhanced performance.

## Key findings

- Up to 0.4 dB Q factor improvement achieved.
- Effective mitigation of stochastic nonlinear effects.
- Enhanced detection accuracy in nonlinear fiber channels.

## Abstract

We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection at the receiver side, and deals with the non-Gaussian nonlinear effects by designing improved decision boundaries. We also propose a two-stage mitigation technique using digital back propagation and Parzen window for dispersion unmanaged systems. In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal-noise interactions, which are not taken into account by digital back propagation. A performance improvement up to 0:4 dB in terms of Q factor is observed.