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Area of Science:

Optical communication
Machine learning applications
Atmospheric optics

Background:

Structured light beams offer unique information-carrying capabilities.
Free-space optical communication faces challenges from atmospheric conditions like dust, hindering mode demultiplexing.
Conventional methods struggle with signal degradation in adverse weather.

Purpose of the Study:

To investigate the detection of Laguerre Gaussian and Hermite Gaussian beams under dust storm conditions.
To evaluate the effectiveness of machine learning algorithms for structured light detection in degraded visibility.
To assess the potential for machine learning to improve free-space optical communication reliability.

Main Methods:

Experimental investigation of structured light beam detection.
Application of machine learning algorithms: convolutional neural network (CNN), support vector machine, and k-nearest neighbor.
Testing under simulated dust storm conditions with varying visibility levels.

Main Results:

Achieved 99% identification accuracy for structured light beams at a visibility of 9 meters.
Demonstrated the capability of CNNs to effectively distinguish between different beam types.
Successfully employed CNNs for estimating the visibility range in dusty communication channels.

Conclusions:

Machine learning algorithms, particularly CNNs, show high efficacy in detecting structured light beams under dust storm conditions.
This approach significantly enhances the robustness of free-space optical communication systems.
The developed methods can improve communication reliability and enable visibility range estimation in challenging environments.