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 …
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 …
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 …
C Li, Y Wang, J Wang, H Yao, X Liu… - Journal of Lightwave …, 2022 - ieeexplore.ieee.org
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 …
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 investigate the complexity and performance of recurrent neural network (RNN) models as post-processing units for the compensation of fibre nonlinearities in digital coherent …
T Kamiyama, H Kobayashi… - IEEE Photonics …, 2021 - ieeexplore.ieee.org
Recently, neural network (NN) nonlinear equalizers which are expected to reduce computational complexity have attracted attention as fiber nonlinear compensation methods …
X Liu, C Li, Z Jiang, L Han - Electronics, 2023 - mdpi.com
Nonlinear impairments caused by devices and fiber transmission links in a coherent optical communication system can severely limit its transmission distance and achievable capacity …
We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach …