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.
|Title of host publication||Laser Communication and Propagation through the Atmosphere and Oceans X|
|Editors||Jaime A. Anguita, Jeremy P. Bos, David T. Wayne|
|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
|Name||Proceedings of SPIE - The International Society for Optical Engineering|
|Conference||Laser Communication and Propagation through the Atmosphere and Oceans X 2021|
|Period||1/08/21 → 5/08/21|
Bibliographical noteFunding Information:
This work was supported by CONICYT-Chile (FR-1210297) and by ANID gram ICN17-012.
© 2021 SPIE.
- FSO communications
- Orbital angular momentum
- Turbulence-induced distortions