Presentation + Paper
26 September 2023 Demultiplexing OAM beams via Fourier optical convolutional neural network
Jiachi Ye, Haoyan Kang, Hao Wang, Chen Shen, Belal Jahannia, Elham Heidari, Navid Asadizanjani, Mohammad-Ali Miri, Volker J. Sorger, Hamed Dalir
Author Affiliations +
Abstract
Here we present an innovative free-space optical (FSO) communication system which is capable of training database in real-time and demultiplex multiplexed spatial structured laser beams such as orbital angular momentum (OAM) beams under varying atmospheric turbulent conditions. The core part of our detection system is heterogeneous convolutional neural network includes an optical 4f system using first Fourier convolution neural network layer driven by kilohertz-fast reprogrammable high-resolution digital micromirror devices (DMDs). This optical-filtering-based convolutional neural network is utilized to realize the training and demultiplexing 4-bit OAM-coded beams under simulated turbulent condition using modified von K´arm´an atmospheric model. The current implementation shows classification accuracy of 89.35% (under weak turbulence) and 38.26% (under strong turbulence).
Conference Presentation
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jiachi Ye, Haoyan Kang, Hao Wang, Chen Shen, Belal Jahannia, Elham Heidari, Navid Asadizanjani, Mohammad-Ali Miri, Volker J. Sorger, and Hamed Dalir "Demultiplexing OAM beams via Fourier optical convolutional neural network", Proc. SPIE 12667, Laser Beam Shaping XXIII, 1266706 (26 September 2023); https://doi.org/10.1117/12.2682108
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KEYWORDS
Machine learning

Free space optics

Angular momentum

Multiplexing

Wireless communications

Nanophotonics

Photonics

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