Neural network classification of beams carrying orbital angular momentum after propagating through controlled experimentally generated optical turbulence.

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 n2≅10-11 m -2/3), through a simulated propagation path derived from the Nikishov spectrum (C n2≅10-13 m -2/3), and through optical turbulence from a thermal point source located in a water tank (C n2≅10-10 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.