Neural network classification of beams carrying orbital angular momentum after propagating through controlled experimentally generated optical turbulence
William A. Jarrett, Svetlana Avramov-Zamurovic, Joel M. Esposito, K. Peter Judd, Charles Nelson
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 ≅10 −11 m −2/3 ), through a simulated propagation path derived from the Nikishov spectrum ( C n 2 ≅10 −13 m −2/3 ), and through optical turbulence from a thermal point source located in a water tank ( C n 2 ≅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.