Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers

Pengfei Fan, Michael Ruddlesden, Yufei Wang, Luming Zhao, Chao Lu, Lei Su

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

We proposed a semi-supervised confidence-based learning approach (SCALA) to overcome the high-temporal-variability of multimode fiber (MMF) information channels, and experimentally demonstrated continuous transmission of high-spatial-density information with accuracy close to 100% over different MMFs.

Original languageEnglish
Title of host publicationOECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Chapter9850043
Pages1-4
ISBN (Electronic)9784885523366
DOIs
Publication statusPublished - Aug 2022
Event27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022 - Toyama, Japan
Duration: 3 Jul 20226 Jul 2022

Publication series

NameOECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022

Conference

Conference27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022
Country/TerritoryJapan
CityToyama
Period3/07/226/07/22

Keywords

  • adaptive semi-supervised learning method
  • deep neural network
  • fiber optics communications
  • space-division multiplexing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

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