This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …

We present the results of the comparative performance-versus-complexity analysis for the
several types of artificial neural networks (NNs) used for nonlinear channel equalization in …

We propose a new machine-learning approach for fiber-optic communication systems
whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our …

Optical transmission links are generally composed of optical fibers, optical amplifiers, and
optical filters. In this paper, we present a channel reconstruction method (CRM) that extracts …

In this work, we address the question of the adaptability of artificial neural networks (NNs)
used for impairments mitigation in optical transmission systems. We demonstrate that by …

We examine here what type of predictive modelling, classification, or regression, using
neural networks (NN), fits better the task of soft-demap** based post-processing in …

Current management of optical communication systems is conservative, manual-based, and
time-consuming. To improve this situation, building an intelligent closed-loop control system …

Machine Learning (ML) algorithms have shown to complement standard digital signal
processing (DSP) tools in mitigating fiber nonlinearity and improving long-haul transmission …

In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward
neural network (NN)-based equalizers for nonlinearity compensation in coherent optical …

Derived from the regular perturbation treatment of the nonlinear Schrödinger equation, a
machine learning-based scheme to mitigate the intra-channel optical fiber nonlinearity is …