Nonlinearity Mitigation Using a Machine Learning Detector Based on <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math> </inline-formula>-Nearest Neighbors

Abstract

A powerful machine learning detector based on the k-nearest neighbors (KNN) algorithm is proposed to overcome system impairments. The zero-dispersion link (ZDL), dispersion managed link (DML), and dispersion unmanaged link (DUL) are considered. Meanwhile, an improved algorithm, the distance-weight KNN, is introduced, which outperforms the conventional maximum likelihood-post compensation approach. The numerical results show that KNN is feasible for overcoming various impairments, especially for non-Gaussian symmetric noise, such as laser phase noise and nonlinear phase noise in the ZDL or DML.