L-band InAs/InP quantum dash laser spatial OAM light modes classification under smoke environment: An image processing enhanced deep learning approach

M. Z.M. Khan*, A. M. Ragheb, M. Masood, W. Saif, M. A. Esmail, N. Iqbal, Q. Tareq, A. S. Almaiman, H. Fathallah, S. Alshebeili

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article number109933
JournalOptics and Laser Technology
Volume168
DOIs
StatePublished - 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

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