Today's telecommunication networks have become sources of enormous amounts of widely
heterogeneous data. This information can be retrieved from network traffic traces, network …

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 …

In long-haul optical communication systems, compensating nonlinear effects through digital
signal processing (DSP) is difficult due to intractable interactions between Kerr nonlinearity …

We propose a bi-directional gated recurrent unit neural network based nonlinear equalizer
(bi-GRU NLE) for coherent optical communication systems. The performance of bi-GRU NLE …

Optical networks generate a vast amount of diagnostic, control, and performance monitoring
data. When information is extracted from these data, reconfigurable network elements and …

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 …

Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the
main workhorse within data centers, supercomputers, and even vehicles, providing low-cost …

Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-
solving in many applications. Such approaches found fertile ground in optical …

Fiber-induced intra-and interchannel nonlinearities are experimentally tackled using blind
nonlinear equalization (NLE) by unsupervised machine-learning-based clustering (MLC) …

With the rapid development of machine learning technologies in recent years, different types
of neural network (NN)-based equalizers have been proposed and proved to be efficient …