Deep learning for image-based classification of OAM modes in laser beams propagating through convective turbulence

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Free-space optical communications are highly sensitive to distortions induced by atmospheric turbulence. This is particularly relevant when using orbital angular momentum (OAM) to send information. As current machine learning techniques for computer vision allow for accurate classification of general images, we have studied the use of a convolutional neural network for recognition of intensity patterns of OAM states after propagation experiments in a laboratory. The effect of changes in magnification and level of turbulence were explored. An error as low as 2.39% was obtained for a low level of turbulence when the training and testing data came from the same optical setup. Finally, in this article we suggest data augmentation procedures to face the problem of training before the final calibration of a communication system, with no access to data for the actual magnification and level of turbulence of real application conditions.

Original languageEnglish
Title of host publicationLaser Communication and Propagation through the Atmosphere and Oceans VIII
EditorsJeremy P. Bos, Alexander M. J. van Eijk, Stephen Hammel
PublisherSPIE
ISBN (Electronic)9781510629592
DOIs
StatePublished - 2019
EventLaser Communication and Propagation through the Atmosphere and Oceans VIII 2019 - San Diego, United States
Duration: 12 Aug 201914 Aug 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11133
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceLaser Communication and Propagation through the Atmosphere and Oceans VIII 2019
CountryUnited States
CitySan Diego
Period12/08/1914/08/19

Bibliographical note

Funding Information:
The authors thankfully acknowledge conversations with F. Bernuy on data augmentation techniques for deep learning. This work was partially funded by the Millennium Institute for Research in Optics (MIRO) and the Advanced Center of Electrical and Electronic Engineering (AC3E) (CONICYT/FB0008).

Publisher Copyright:
© 2019 SPIE.

Keywords

  • Laser beam transmission
  • digital image processing
  • neural networks
  • optical vortices
  • pattern recognition

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