Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission

Mohannad Abu-romoh; Nelson Costa; Antonio Napoli; João Pedro; Yves Jaouën; Mansoor Yousefi

doi:10.1364/SPPCOM.2021.SpM5C.5

OSA Advanced Photonics Congress 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper SpM5C.5
•https://doi.org/10.1364/SPPCOM.2021.SpM5C.5

Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission

Mohannad Abu-romoh, Nelson Costa, Antonio Napoli, João Pedro, Yves Jaouën, and Mansoor Yousefi

Signal Processing in Photonic Communications 2021

Washington, DC United States
26–29 July 2021
ISBN: 978-1-943580-94-1

From the session
Neural Networks Applications for Photonic Systems (SpM5C)

Not Accessible

Your library or personal account may give you access

Get PDF
Email
Share
Get Citation
Copy Citation Text
M. Abu-romoh, N. Costa, A. Napoli, J. Pedro, Y. Jaouën, and M. Yousefi, "Low Complexity Convolutional Neural Networks for Equalization in Optical Fiber Transmission," in OSA Advanced Photonics Congress 2021, B. Yang, F. Leo, A. Tauke-Pedretti, S. Arafin, M. Clerici, L. Caspani, L. Busse, M. Kats, J. Fan, W. Ming (Steve) Lee, D. Caplan, N. Parsons, W. Freude, C. Tremblay, D. Hillerkuss, K. Igarashi, H. Elgala, N. Guerrero Gonzalez, R. "Patty" Stabile, K. Jaeger, R. Saive, H. Aziz, and H. Tan, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper SpM5C.5.

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Get Video
Citation alert
Save article

Abstract

A convolutional neural network is proposed to mitigate fiber transmission ef- fects, achieving a five-fold reduction in trainable parameters compared to alternative equal- izers, and 3.5 dB improvement in MSE compared to DBP with comparable complexity.

PDF Article | Presentation Video

More Like This

Attention-Based Neural Network Equalization in Fiber-Optic Communications

Abtin Shahkarami, Mansoor I. Yousefi, and Yves Jaouen
M5H.3 Asia Communications and Photonics Conference (ACP) 2021

Experimental Study of Deep Neural Network Equalizers Performance in Optical Links

Pedro J. Freire, Yevhenii Osadchuk, Bernhard Spinnler, Wolfgang Schairer, Antonio Napoli, Nelson Costa, Jaroslaw E. Prilepsky, and Sergei K. Turitsyn
M3H.2 Optical Fiber Communication Conference (OFC) 2021

Deep Convolutional Recurrent Neural Network For Fiber Nonlinearity Compensation

Prasham Jain, Lutz Lampe, and Jeebak Mitra
We1C.5 European Conference and Exhibition on Optical Communication (ECOC) 2022

Previous Article Next Article

Presentation Video

Presentation video access is available to:

Optica Publishing Group subscribers
Technical meeting attendees
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