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 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 …

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 …

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 investigate the complexity and performance of recurrent neural network (RNN) models
as post-processing units for the compensation of fibre nonlinearities in digital coherent …

Compensating for nonlinear effects using digital signal processing (DSP) is complex and
computationally expensive in long-haul optical communication systems due to intractable …

Optical nonlinearity impairments have been a major obstacle for high-speed, long-haul and
large-capacity optical transmission. In this paper, we propose a novel convolutional neural …

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 …

Kerr nonlinearity in the form of self-and cross-phase modulation imposes a fundamental
limitation to the capacity of wavelength division multiplexed (WDM) optical communication …