Mitigation of Nonlinear Distortion in Unrepeatered Interconnects Employing Clustering Algorithms

Abstract

This work compares clustering techniques for selfphase modulation (SPM) mitigation, evaluating k-means, Gaussian mixture models (GMM), density-based spatial clustering for applications with noise (DBSCAN), and ordering points to identify the clustering structure (OPTICS) on experimental 32 GBd dual polarization 16-ary quadrature amplitude modulation (DP-16QAM) signals. Results show all clustering methods surpass maximum likelihood detection, with GMM and DBSCAN achieving the best performance. GMM outperforms at higher powers, while DBSCAN excels at lower powers but demands careful tuning.