Machine learning (ML) has disrupted a wide range of science and engineering disciplines in
recent years. ML applications in optical communications and networking are also gaining …

Optical communication systems represent the backbone of modern communication
networks. Since their deployment, different fiber technologies have been used to deal with …

We investigate the application of dynamic deep neural networks for nonlinear equalization
in long haul transmission systems. Through extensive numerical analysis we identify their …

In this work, we proposed two k-means-clustering-based algorithms to mitigate the fiber
nonlinearity for 64-quadrature amplitude modulation (64-QAM) signal, the training-sequence …

In the optical networks, the dynamicity, the complexity and the heterogeneity have
dramatically increased owing to the deployment of advanced coherent techniques, and the …

In this work, a perturbation-based neural network (P-NN) scheme with an embedded
bidirectional long short-term memory (biLSTM) layer is investigated to compensate for the …

We? demonstrated? a support? vector machine (SVM) based machine learning method to
mitigate modulation nonlinearity distortion for PAM-4 and PAM-8 vertical cavity surface …

The computational complexity and system bit-error-rate (BER) performance of four types of
neural-network-based nonlinear equalizers are analyzed for a 50-Gb/s pulse amplitude …

By adopting the cumulative distribution function of the received signal's amplitude as feature,
a support vector machine-based algorithm is proposed to jointly classify the modulation …

A fast Newton-based support vector machine (N-SVM) nonlinear equalizer (NLE) is
experimentally demonstrated, for the first time, in 40 Gb/s 16-quadrature amplitude …