Abstract
In this work, we experimentally generated Laguerre Gaussian (LG) and its multiplexed form (Mux-LG) in the 1610 nm regime of the optical communication band employing InAs/InP quantum dash laser diode. Later, we investigated the detection of these spatial light modes encoding schemes under smoke channel conditions employing convolutional neural network (CNN) and uNET deep learning algorithms in conjunction with multiple received orbital angular momentum (OAM) modes images as input for the first time. We studied OAM modes classification and visibility estimation and reported identification accuracies of > 92% and > 96%, respectively, with uNET even for a challenging visibility range of 0–50 m. In general, exploiting the similarity of temporally successive images resulted in better performance of deep learning networks than just a single input image. Lastly, we propose a simple yet powerful image processing technique as a pre-processing stage for the received mode patterns for visibility estimation via deep learning regression and showed an improvement of ∼ 4 m in root mean square error (RMSE).
Original language | English |
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Article number | 109933 |
Journal | Optics and Laser Technology |
Volume | 168 |
DOIs | |
State | Published - Jan 2024 |
Bibliographical note
Publisher Copyright:© 2023 Elsevier Ltd
Keywords
- CNN
- Deep learning
- Image processing
- InAs/InP Quantum dash laser
- L-band wavelength
- Orbital angular momentum
- Smoke channel
- uNET
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering