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 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 …

The deployment of artificial neural networks-based optical channel equalizers on edge-
computing devices is critically important for the next generation of optical communication …

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 …

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 …

We propose a convolutional-recurrent channel equalizer and experimentally demonstrate
1dB Q-factor improvement both in single-channel and 96× WDM, DP-16QAM transmission …

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 …

The neural network (NN) has been widely used as a promising technique in fiber optical
communication owing to its powerful learning capabilities. The NN-based equalizer is …

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 …