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 …

Experimental results based on offline processing reported at optical conferences
increasingly rely on neural network-based equalizers for accurate data recovery. However …

This paper introduces a novel methodology for developing low-complexity neural network
(NN) based equalizers to address impairments in high-speed coherent optical transmission …

Despite the increased exploration of machine learning (ML) techniques for the realization of
autonomous optical networks, less attention has been paid to data quality, which is critical …

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

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 propose a deep learning-based phase retrieval method to accurately reconstruct the
optical field of a single-sideband minimum-phase signal from the directly detected intensity …

We implement a new variant of the end-to-end learning approach for the performance
improvement of an optical coherent-detection communication system. The proposed solution …

Digital twin (DT) has revolutionized optical communication networks by enabling their full life-
cycle management, including planning, prediction, optimization, upgrade, and …

The transition to the edge-cloud era makes ultra-high data rate signals indispensable for
covering the immense and increasing traffic demands created. This ecosystem also seeks …