Identifying structured light modes in a desert environment using machine learning algorithms
Esmail, Maged Abdullah
Ooi, Boon S.
KAUST DepartmentCommunication Theory Lab
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Online Publication Date2020-03-20
Print Publication Date2020-03-30
Permanent link to this recordhttp://hdl.handle.net/10754/662291
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AbstractThe 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.
CitationRagheb, 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
SponsorsDeanship of Scientific Research, King Saud University (grant no. RG-1440-112); King Abdullah University of Science and Technology (KKI2 special initiative)
PublisherThe Optical Society