Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Conferences > OFC > 2022 > Th2A > Th2A.35

Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems

Pedro J. Freire, Bernhard Spinnler, Daniel Abode, Jaroslaw E. Prilepsky, Abdallah Ali, Nelson Costa, Wolfgang Schairer, Antonio Napoli, Andrew D. Ellis, and Sergei K. Turitsyn

Not Accessible

Your library or personal account may give you access

Abstract

We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up to 99% training process reduction, which we demonstrate in three experimental setups.

© 2022 The Author(s)

PDF Article
More Like This
Power and Modulation Format Transfer Learning for Neural Network Equalizers in Coherent Optical Transmission Systems

Pedro J. Freire, Daniel Abode, Jaroslaw E. Prilepsky, and Sergei K. Turitsyn
SpM5C.6 Signal Processing in Photonic Communications (SPPCom) 2021

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

Pedro J. Freire, Michael Anderson, Bernhard Spinnler, Thomas Bex, Jaroslaw E. Prilepsky, Tobias A. Eriksson, Nelson Costa, Wolfgang Schairer, Michaela Blott, Antonio Napoli, and Sergei K. Turitsyn
We1C.2 European Conference and Exhibition on Optical Communication (ECOC) 2022

Nonlinear Equalizer by Feature Engineering Based-Deep Neural Network for Coherent Optical Communication System

Xinyu Liu, Yongjun Wang, and Chao Li
M4A.275 Asia Communications and Photonics Conference (ACP) 2020

View More...

Poster Presentation

Media 1: PDF (759 KB)     
Select as filters
Select Topics Cancel
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use