Learning Enabled Continuous Transmission of Spatially Distributed Information through Multimode Fibers

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Multimode fibers (MMF) are high-capacity channels and are promising to transmit spatially distributed information, such as an image. However, continuous transmission of randomly distributed information at a high-spatial density is still a challenge. Here, a high-spatial-density information transmission framework employing deep learning for MMFs is proposed. A proof-of-concept experimental system is presented to demonstrate up to 400-channel simultaneous data transmission with accuracy close to 100% over MMFs of different types, diameters, and lengths. A scalable semi-supervised learning model is proposed to adapt the convolutional neural network to the time-varying MMF information channels in real-time to overcome the instabilities in the lab environment. The preliminary results suggest that deep learning has the potential to maximize the use of the spatial dimension of MMFs for data transmission.

Original languageEnglish
Article number2000348
JournalLaser and Photonics Reviews
Volume15
Issue number4
DOIs
Publication statusPublished - Apr 2021

Keywords

  • deep learning
  • information transmission
  • multimode optical fibers
  • neural networks
  • single multimode fiber imaging

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Condensed Matter Physics

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