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: Contribution to journal › Conference article › 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
Journal	Proceedings of SPIE - The International Society for Optical Engineering
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

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

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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",

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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

UR - https://www.scopus.com/pages/publications/85117158209

UR - https://www.mendeley.com/catalogue/418d71ec-c8b0-3c5a-8142-0017d0cb4643/

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DO - 10.1117/12.2593811

M3 - Conference article

AN - SCOPUS:85117158209

SN - 0277-786X

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

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

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

Y2 - 1 August 2021 through 5 August 2021

ER -

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

Abstract

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