Optical Nonlinearity Compensation Using Nonlinear Equalizers Based on Machine Learning Technology

Abstract

We introduce our studies on optical nonlinearity compensation schemes that are based on machine learning technology for optical communication systems. We examine the performance of nonlinear equalization using an artificial neural network (ANN) by comparing our scheme with others, including a support vector machine (SVM) and the Volterra series transfer function (VSTF). We describe an SVM-based nonlinear equalizer and our improved version. We also discuss the problem of the computational complexity of a conventional VSTF-based nonlinear equalizer and introduce our investigation for improving VSTF’s computational complexity