Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Improving the Bootstrap of Blind Equalizers with Variational Autoencoders

Not Accessible

Your library or personal account may give you access

Abstract

We evaluate the start-up of blind equalizers at critical working points, analyze the advantages and obstacles of commonly-used algorithms, and demonstrate how the recently-proposed variational autoencoder (VAE) based equalizers can improve bootstrapping.

© 2023 The Author(s)

PDF Article  |   Presentation Video
More Like This
Blind Equalization for Coherent Optical Communications Based on Variational Inference

Vincent Lauinger, Fred Buchali, and Laurent Schmalen
SpTh1D.6 Signal Processing in Photonic Communications (SPPCom) 2021

Deep learning in photoacoustic tomography utilizing variational autoencoders

Teemu Sahlström and Tanja Tarvainen
1263108 European Conference on Biomedical Optics (ECBO) 2023

Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design

Toshiaki Koike-Akino, Minwoo Jung, Ankush Chakrabarty, Ye Wang, Keisuke Kojima, and Matthew Brand
FW4C.7 CLEO: Fundamental Science (CLEO:FS) 2023

Presentation Video

Presentation video access is available to:

  1. Optica Publishing Group subscribers
  2. Technical meeting attendees
  3. Optica members who wish to use one of their free downloads. Please download the article first. After downloading, please refresh this page.

Contact your librarian or system administrator
or
Log in to access Optica Member Subscription or free downloads


More Like This
Blind Equalization for Coherent Optical Communications Based on Variational Inference

Vincent Lauinger, Fred Buchali, and Laurent Schmalen
SpTh1D.6 Signal Processing in Photonic Communications (SPPCom) 2021

Deep learning in photoacoustic tomography utilizing variational autoencoders

Teemu Sahlström and Tanja Tarvainen
1263108 European Conference on Biomedical Optics (ECBO) 2023

Bayesian Optimization for Nested Adversarial Variational Autoencoder in Tunable Nanophotonic Device Design

Toshiaki Koike-Akino, Minwoo Jung, Ankush Chakrabarty, Ye Wang, Keisuke Kojima, and Matthew Brand
FW4C.7 CLEO: Fundamental Science (CLEO:FS) 2023

Blind Adaptive Equalization Algorithm Based on Constellation Transformation for DP 16-QAM Systems

M. A. Rezania, J. H. Ke, A. S. Karar, Y. Gao, and J. C. Cartledge
JTh2A.44 National Fiber Optic Engineers Conference (NFOEC) 2013

Deep variational autoencoders for breast cancer tissue modeling and synthesis in SFDI

Arturo Pardo, José M. López-Higuera, Brian W. Pogue, and Olga M. Conde
11074_50 European Conference on Biomedical Optics (ECBO) 2019

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.