Dynamic complex deep Neural Network Nonlinear Equalizer for 64 QAM Long-haul Transmission Systems

Govind Sharan Yadav, Takehiro Tsuritani, Shohei Beppu, Hidenori Takahashi, Itsuro Morita, Kai Ming Feng, Jhih Heng Yan

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

3 Citations (Scopus)

Abstract

We implemented a two-hidden-layer dynamic complex deep neural network nonlinear equalizer which outperforms the linear equalizer, static and dynamic single hidden layer CDNN-NLE by 1.38-dB, 1.01-dB and 0.62-dB for a 34-GBaud/s, 64-QAM signal over 1200-km.

Original languageEnglish
Title of host publicationOECC/PSC 2019 - 24th OptoElectronics and Communications Conference/International Conference Photonics in Switching and Computing 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9784885523212
DOIs
Publication statusPublished - 2019 Jul
Externally publishedYes
Event24th OptoElectronics and Communications Conference/International Conference Photonics in Switching and Computing, OECC/PSC 2019 - Fukuoka, Japan
Duration: 2019 Jul 72019 Jul 11

Publication series

NameOECC/PSC 2019 - 24th OptoElectronics and Communications Conference/International Conference Photonics in Switching and Computing 2019

Conference

Conference24th OptoElectronics and Communications Conference/International Conference Photonics in Switching and Computing, OECC/PSC 2019
Country/TerritoryJapan
CityFukuoka
Period19/7/719/7/11

Keywords

  • adaptive machine learning method
  • bit error rate
  • complex deep artificial neural network
  • deep learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

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