Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

When propagated through atmospheric turbulence, Orbital Angular Momentum (OAM) modes suffer a loss of orthogonality that can compromise their detection and classification. The problem is more challenging when user information encoded on multi-state OAM superpositions needs to be detected with high probability. Optical sensors like the Shack-Hartmann detector or the Mode Sorter are candidates for such task. We describe how OAM histograms derived from such detectors can be used for decoding the original data symbols. We propose Machine Learning strategies for a reliable classification of the histogram patterns obtained with 4-mode superpositions propagated over a 1 km range in weak to intermediate turbulence. © 2021 SPIE.
Idioma originalInglés
Título de la publicación alojadaLaser Communication and Propagation through the Atmosphere and Oceans X
EditoresJaime A. Anguita, Jeremy P. Bos, David T. Wayne
EditorialSPIE
ISBN (versión digital)9781510645066
DOI
EstadoPublicada - 2021
EventoLaser Communication and Propagation through the Atmosphere and Oceans X 2021 - San Diego, Estados Unidos
Duración: 1 ago 20215 ago 2021

Serie de la publicación

NombreProceedings of SPIE - The International Society for Optical Engineering
Volumen11834
ISSN (versión impresa)0277-786X
ISSN (versión digital)1996-756X

Conferencia

ConferenciaLaser Communication and Propagation through the Atmosphere and Oceans X 2021
País/TerritorioEstados Unidos
CiudadSan Diego
Período1/08/215/08/21

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Publisher Copyright:
© 2021 SPIE.

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