Machine learning-based classification of structured light modes under turbulence and eavesdropping effects

Abstract

This paper considers the classification of multiplexed structured light modes, aiming to bolster communication reliability and data transfer rates, particularly in challenging scenarios marked by turbulence and potential eavesdropping. An experimental free-space optic (FSO) system is established to transmit 16 modes [8-ary Laguerre Gaussian (LG) and 8-ary superposition LG (Mux-LG) mode patterns] over a 3-m FSO channel, accounting for interception threats and turbulence effects. To the best of authors’ knowledge, this paper is the first to consider both factors concurrently. We propose four machine/deep learning algorithms—artificial neural network, support vector machine, 1D convolutional neural network, and 2D convolutional neural network—for classification purposes. By fusing the outputs of these methods, we achieve promising classification results exceeding 92%, 81%, and 69% in cases of weak, moderate, and strong turbulence, respectively. Structured light modes exhibit significant potential for a variety of real-world applications where reliable and high-capacity data transmission is crucial.