33
| dc.contributor.advisor | 33 | |
| dc.contributor.author | Cisternas, Jaime E. | |
| dc.contributor.author | Espinoza, Javier I. | |
| dc.contributor.author | Anguita, Jaime A. | |
| dc.coverage | DOI: 10.1117/12.2593811 | |
| dc.date | 2021 | |
| dc.date.accessioned | 05-01-2026 18:18 | |
| dc.date.available | 05-01-2026 18:18 | |
| dc.date.issued | 33 | |
| dc.description.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. | |
| dc.description.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. | |
| dc.identifier | https://investigadores.uandes.cl/en/publications/3917e4ed-0bcc-447a-903e-378b3bd37d92 | |
| dc.identifier.citation | 33 | |
| dc.identifier.uri | 33 | |
| dc.language | eng | |
| dc.language.iso | 33 | |
| dc.publisher | 33 | |
| dc.relation | 33 | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | |
| dc.source | -2021 | |
| dc.subject | FSO communications | |
| dc.subject | Orbital angular momentum | |
| dc.subject | Turbulence-induced distortions | |
| dc.title | 33 | |
| dc.title | Machine learning identification of multiple-state OAM superpositions detected with spatial mode sensors | eng |
| dc.title | 33 | spa |
| dc.title | 33 | und |
| dc.type | 33 | |
| dc.type | Conference article | eng |
| dc.type | Artículo de la conferencia | spa |