Identifying structured light modes in a desert environment using machine learning algorithms
Type
ArticleAuthors
Ragheb, Amr
Saif, Waddah

Trichili, Abderrahmen

Ashry, Islam

Esmail, Maged Abdullah

Altamimi, Majid
Almaiman, Ahmed

Altubaishi, Essam

Ooi, Boon S.

Alouini, Mohamed-Slim

Alshebeili, Saleh
KAUST Department
Communication Theory LabComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Photonics Laboratory
Date
2020-03-20Online Publication Date
2020-03-20Print Publication Date
2020-03-30Submitted Date
2020-01-28Permanent link to this record
http://hdl.handle.net/10754/662291
Metadata
Show full item recordAbstract
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.389210Sponsors
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 SocietyJournal
Optics ExpressAdditional Links
https://www.osapublishing.org/abstract.cfm?URI=oe-28-7-9753ae974a485f413a2113503eed53cd6c53
10.1364/oe.389210