Abstract

We generate an alphabet of spatially multiplexed Laguerre–Gaussian beams carrying orbital angular momentum, which are demultiplexed at reception by a convolutional neural network (CNN). In this investigation, a methodology for optimizing alphabet design for best classification rates is proposed, and three 256-symbol alphabets are designed for performance evaluation in optical turbulence. The beams were propagated in three environments: through underwater optical turbulence generated by Rayleigh–Bénard (RB) convection (${C}_{n}^{2}\cong 1{0}^{-11}\;{\rm m}^{-2/3}$), through a simulated propagation path derived from the Nikishov spectrum (${C}_{n}^{2}\cong 1{0}^{-13}\; {\rm m}^{-2/3}$), and through optical turbulence from a thermal point source located in a water tank (${C}_{n}^{2}\cong 1{0}^{-10}\;{\rm m}^{-2/3}$). We report a classification accuracy of 93.1% for the RB environment, 99.99% in simulation, and 48.5% in the point source environment. The project demonstrates that the CNN can classify the complex alphabet symbols in a practical turbulent flow that exhibits strong optical turbulence, provided sufficient training data is available and testing data is representative of the specific environment. We find the most important factor in a high classification accuracy is a diversification in the intensity profiles of the alphabet symbols.

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