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 performs a detailed, multi-faceted analysis of key challenges and common
design caveats related to the development of efficient neural networks (NN) based nonlinear …

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

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

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 …

Nonlinear impairment in a high-speed orbital angular momentum (OAM) mode-division
multiplexing (MDM) optical fiber communication system presents high complexity and strong …

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 …

For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity
compensation are implemented in an FPGA, with a level of complexity comparable to that of …

We introduce the domain adaptation and randomization approach for calibrating neural
network-based equalizers for real transmissions, using synthetic data. The approach …