Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications

09/17/2021
by   Pedro J. Freire, et al.
0

Addressing the neural network-based optical channel equalizers, we quantify the trade-off between their performance and complexity by carrying out the comparative analysis of several neural network architectures, presenting the results for TWC and SSMF set-ups.

READ FULL TEXT

page 1

page 2

page 3

research
06/24/2022

Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems

For the first time, recurrent and feedforward neural network-based equal...
research
08/27/2023

A Comparison of Neural Networks for Wireless Channel Prediction

The performance of modern wireless communications systems depends critic...
research
04/13/2023

Neural Network Architectures for Optical Channel Nonlinear Compensation in Digital Subcarrier Multiplexing Systems

In this work, we propose to use various artificial neural network (ANN) ...
research
02/10/2021

Resilient Architectures for Free Space Optical Wireless Interconnection Systems

In this paper, we propose the use of two Passive Optical Network (PON)-b...
research
12/05/2022

Geometric Constellation Shaping with Low-complexity Demappers for Wiener Phase-noise Channels

We show that separating the in-phase and quadrature component in optimiz...
research
05/06/2021

Pathloss modeling for in-body optical wireless communications

Optical wireless communications (OWCs) have been recognized as a candida...
research
09/09/2020

Revealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Data

The paper presents and comparatively analyses several deep learning appr...

Please sign up or login with your details

Forgot password? Click here to reset