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

Paper Details

Date Published: 6 September 2019
PDF: 7 pages
Proc. SPIE 11133, Laser Communication and Propagation through the Atmosphere and Oceans VIII, 1113305 (6 September 2019); doi: 10.1117/12.2529303
Show Author Affiliations
Jose Delpiano, Univ. de los Andes (Chile)
Advanced Ctr. for Electrical and Electronic Engineering (Chile)
Gustavo L. Funes, Univ. de los Andes (Chile)
Jaime E. Cisternas, Univ. de los Andes (Chile)
Sebastian Galaz, Univ. de los Andes (Chile)
Jaime A. Anguita, Univ. de los Andes (Chile)
Millennium Institute for Research in Optics (Chile)


Published in SPIE Proceedings Vol. 11133:
Laser Communication and Propagation through the Atmosphere and Oceans VIII
Jeremy P. Bos; Alexander M. J. van Eijk; Stephen Hammel, Editor(s)

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