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    Identifying structured light modes in a desert environment using machine learning algorithms

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    oe-28-7-9753.pdf
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    Description:
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    Type
    Article
    Authors
    Ragheb, Amr cc
    Saif, Waddah cc
    Trichili, Abderrahmen cc
    Ashry, Islam cc
    Esmail, Maged Abdullah cc
    Altamimi, Majid
    Almaiman, Ahmed cc
    Altubaishi, Essam cc
    Ooi, Boon S. cc
    Alouini, Mohamed-Slim cc
    Alshebeili, Saleh
    KAUST Department
    Communication Theory Lab
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Photonics Laboratory
    Date
    2020-03-20
    Online Publication Date
    2020-03-20
    Print Publication Date
    2020-03-30
    Submitted Date
    2020-01-28
    Permanent link to this record
    http://hdl.handle.net/10754/662291
    
    Metadata
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    Abstract
    The unique orthogonal shapes of structured light beams have attracted researchers to use as information carriers. Structured light-based free space optical communication is subject to atmospheric propagation effects such as rain, fog, and rain, which complicate the mode demultiplexing process using conventional technology. In this context, we experimentally investigate the detection of Laguerre Gaussian and Hermite Gaussian beams under dust storm conditions using machine learning algorithms. Different algorithms are employed to detect various structured light encoding schemes including the use of a convolutional neural network (CNN), support vector machine, and k-nearest neighbor. We report an identification accuracy of 99% under a visibility level of 9 m. The CNN approach is further used to estimate the visibility range of a dusty communication channel.
    Citation
    Ragheb, A., Saif, W., Trichili, A., Ashry, I., Esmail, M. A., Altamimi, M., … Alshebeili, S. (2020). Identifying structured light modes in a desert environment using machine learning algorithms. Optics Express, 28(7), 9753. doi:10.1364/oe.389210
    Sponsors
    Deanship of Scientific Research, King Saud University (grant no. RG-1440-112); King Abdullah University of Science and Technology (KKI2 special initiative)
    Publisher
    The Optical Society
    Journal
    Optics Express
    DOI
    10.1364/oe.389210
    Additional Links
    https://www.osapublishing.org/abstract.cfm?URI=oe-28-7-9753
    ae974a485f413a2113503eed53cd6c53
    10.1364/oe.389210
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Communication Theory Lab; Photonics Laboratory; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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