Multiparameter Monitoring of PAM4 Signals Using Deep Learning

  • Si Ao Li
  • , Yuanpeng Liu
  • , Yiwen Zhang
  • , Wenqian Zhao
  • , Tongying Shi
  • , Xiao Han
  • , Ivan B. Djordjevic
  • , Yongxiong Ren
  • , Changjing Bao
  • , Zhongqi Pan
  • , Yang Yue

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

PAM4 signal performance monitoring is demonstrated using CNN-based deep learning. A 98.51% prediction accuracy is achieved for jointly monitoring multiple parameters including baud rate, probabilistic shaping, roll-off factor, optical signal-to-noise ratio, and chromatic dispersion.

Original languageEnglish (US)
Title of host publication2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781957171050
StatePublished - 2022
Event2022 Conference on Lasers and Electro-Optics, CLEO 2022 - San Jose, United States
Duration: May 15 2022May 20 2022

Publication series

Name2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings

Conference

Conference2022 Conference on Lasers and Electro-Optics, CLEO 2022
Country/TerritoryUnited States
CitySan Jose
Period5/15/225/20/22

ASJC Scopus subject areas

  • Instrumentation
  • Spectroscopy
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Management, Monitoring, Policy and Law
  • Materials Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Atomic and Molecular Physics, and Optics

Fingerprint

Dive into the research topics of 'Multiparameter Monitoring of PAM4 Signals Using Deep Learning'. Together they form a unique fingerprint.

Cite this