Deep learning-based approach for detection of turbulence-induced distortions in free-space optical communication links

Abstract

Abstract This study aims to develop a deep-learning-based approach that is able to detect turbulence-induced mode distortion in orbital angular momentum-based free-space optical communication links. The proposed and tuned deep learning-based models have been trained with a dataset that is created based on the intensity beam profiles that propagate 5 Km in four different levels of turbulent atmosphere. The random search algorithm has been adopted for conducting a hyperparameter tuning process to select the best structures for two groups of deep learning models each of which contains three different deep learning models. The proposed approach is able to not only distinguish the distorted beams but also to recognize the level of distortion. The obtained results indicate that the utilized tuned models gave 100% classification accuracy in terms of detecting distorted beams. Besides that, the proposed and tuned models obtained a very high classification accuracy reaching 97, 94.99, and 97.78% in terms of assessing the amount of distortion exposed by the transmitted beams. We believe that the obtained results will be a milestone in free-space optical communication systems that utilize orbital angular momentum.