Experimental Demonstration of Linear Inter-Channel Interference Estimation Based on Neural Networks

Abstract

In this paper, an algorithm for the estimation of the linear inter-channel crosstalk in a dense-WDM polarization-multiplexed 16-QAM transmission scenario is proposed and demonstrated. The algorithm is based on the use of a feed-forward neural network (FFNN) inside the coherent digital receiver. Two types of FFNNs were considered, the first based on a regression algorithm and the second based on a classification algorithm. Both FFNN algorithms are applied to features extracted from the histograms of the in-phase and quadrature components of the equalized digital samples. After a simulative investigation, the performance of the channel spacing estimation algorithms was experimentally validated in a 3 × 52 Gbaud 16-QAM WDM system scenario.