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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Laser Communication and Propagation through the Atmosphere and Oceans X
Editors	Jaime A. Anguita, Jeremy P. Bos, David T. Wayne
Publisher	SPIE
ISBN (Electronic)	9781510645066
DOIs	https://doi.org/10.1117/12.2593811
State	Published - 2021
Event	Laser Communication and Propagation through the Atmosphere and Oceans X 2021 - San Diego, United States Duration: 1 Aug 2021 → 5 Aug 2021

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	11834
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Laser Communication and Propagation through the Atmosphere and Oceans X 2021
Country/Territory	United States
City	San Diego
Period	1/08/21 → 5/08/21

Bibliographical note

Funding Information:
This work was supported by CONICYT-Chile (FR-1210297) and by ANID gram ICN17-012.

Publisher Copyright:
© 2021 SPIE.

Keywords

FSO communications
Orbital angular momentum
Turbulence-induced distortions

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10.1117/12.2593811

Cite this

Cisternas, J. E., Espinoza, J. I., & Anguita, J. A. (2021). Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors. In J. A. Anguita, J. P. Bos, & D. T. Wayne (Eds.), Laser Communication and Propagation through the Atmosphere and Oceans X Article 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).

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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",

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}

Cisternas, JE, Espinoza, JI & Anguita, JA 2021, Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors. in 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, United States, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Cisternas, Jaime E.

AU - Espinoza, Javier I.

AU - Anguita, Jaime A.

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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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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.

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Cisternas JE, Espinoza JI, Anguita JA. Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors. In Anguita JA, Bos JP, Wayne DT, editors, 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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