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

Jaime E. Cisternas; Javier I. Espinoza; Jaime A. Anguita

doi:10.1117/12.2593811

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

Jaime E. Cisternas, Javier I. Espinoza, Jaime A. Anguita

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

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 original	Inglés
Título de la publicación alojada	Laser Communication and Propagation through the Atmosphere and Oceans X
Editores	Jaime A. Anguita, Jeremy P. Bos, David T. Wayne
Editorial	SPIE
ISBN (versión digital)	9781510645066
DOI	https://doi.org/10.1117/12.2593811
Estado	Publicada - 2021
Evento	Laser Communication and Propagation through the Atmosphere and Oceans X 2021 - San Diego, Estados Unidos Duración: 1 ago. 2021 → 5 ago. 2021

Serie de la publicación

Nombre	Proceedings of SPIE - The International Society for Optical Engineering
Volumen	11834
ISSN (versión impresa)	0277-786X
ISSN (versión digital)	1996-756X

Conferencia

Conferencia	Laser Communication and Propagation through the Atmosphere and Oceans X 2021
País/Territorio	Estados Unidos
Ciudad	San Diego
Período	1/08/21 → 5/08/21

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Publisher Copyright:
© 2021 SPIE.

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10.1117/12.2593811

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Cisternas, J. E., Espinoza, J. I., & Anguita, J. A. (2021). Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors. En J. A. Anguita, J. P. Bos, & D. T. Wayne (Eds.), Laser Communication and Propagation through the Atmosphere and Oceans X Artículo 118340I (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11834). SPIE. https://doi.org/10.1117/12.2593811

Cisternas, Jaime E. ; Espinoza, Javier I. ; Anguita, Jaime A. / Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors. Laser Communication and Propagation through the Atmosphere and Oceans X. editor / Jaime A. Anguita ; Jeremy P. Bos ; David T. Wayne. SPIE, 2021. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{3917e4ed0bcc447a903e378b3bd37d92,

title = "Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors",

abstract = "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.",

keywords = "FSO communications, Orbital angular momentum, Turbulence-induced distortions",

author = "Cisternas, {Jaime E.} and Espinoza, {Javier I.} and Anguita, {Jaime A.}",

note = "Funding Information: This work was supported by CONICYT-Chile (FR-1210297) and by ANID gram ICN17-012. Publisher Copyright: {\textcopyright} 2021 SPIE.; Laser Communication and Propagation through the Atmosphere and Oceans X 2021 ; Conference date: 01-08-2021 Through 05-08-2021",

year = "2021",

doi = "10.1117/12.2593811",

language = "English",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "SPIE",

editor = "Anguita, {Jaime A.} and Bos, {Jeremy P.} and Wayne, {David T.}",

booktitle = "Laser Communication and Propagation through the Atmosphere and Oceans X",

address = "United States",

}

Cisternas, JE, Espinoza, JI & Anguita, JA 2021, Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors. En JA Anguita, JP Bos & DT Wayne (eds.), Laser Communication and Propagation through the Atmosphere and Oceans X., 118340I, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11834, SPIE, Laser Communication and Propagation through the Atmosphere and Oceans X 2021, San Diego, Estados Unidos, 1/08/21. https://doi.org/10.1117/12.2593811

Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors. / Cisternas, Jaime E.; Espinoza, Javier I.; Anguita, Jaime A.
Laser Communication and Propagation through the Atmosphere and Oceans X. ed. / Jaime A. Anguita; Jeremy P. Bos; David T. Wayne. SPIE, 2021. 118340I (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11834).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors

AU - Cisternas, Jaime E.

AU - Espinoza, Javier I.

AU - Anguita, Jaime A.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - FSO communications

KW - Orbital angular momentum

KW - Turbulence-induced distortions

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DO - 10.1117/12.2593811

M3 - Conference contribution

AN - SCOPUS:85117158209

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Laser Communication and Propagation through the Atmosphere and Oceans X

A2 - Anguita, Jaime A.

A2 - Bos, Jeremy P.

A2 - Wayne, David T.

PB - SPIE

T2 - Laser Communication and Propagation through the Atmosphere and Oceans X 2021

Y2 - 1 August 2021 through 5 August 2021

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Cisternas JE, Espinoza JI, Anguita JA. Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors. En Anguita JA, Bos JP, Wayne DT, editores, Laser Communication and Propagation through the Atmosphere and Oceans X. SPIE. 2021. 118340I. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2593811

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

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