Fiber Nonlinearity Mitigation via the Parzen Window Classifier for Dispersion Managed and Unmanaged Links
This work addresses optical communication impairments for improved data transmission, but it is incremental as it applies an existing machine learning method to a known problem.
The authors tackled fiber nonlinearity in optical channels by applying the Parzen window classifier as a detector with improved nonlinear decision boundaries, resulting in performance improvements for both dispersion managed and unmanaged systems.
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.