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

Amr Ragheb, Waddah Saif, Abderrahmen Trichili, Islam Ashry, Maged Abdullah Esmail, Majid Altamimi, Ahmed Almaiman, Essam Altubaishi, Boon S. Ooi, Mohamed-Slim Alouini, Saleh Alshebeili

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)9753
JournalOptics Express
Issue number7
StatePublished - Mar 12 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Deanship of Scientific Research, King Saud University (grant no. RG-1440-112); King Abdullah University of Science and Technology (KKI2 special initiative)


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