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

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.

Classifying beams carrying orbital angular momentum with machine learning: tutorial.

This tutorial discusses optical communication systems that propagate light carrying orbital angular momentum through random media and use machine learning (aka artificial intelligence) to classify the distorted images of the received alphabet symbols. We assume the reader is familiar with either optics or machine learning but is likely not an expert in both. We review select works on machine learning applications in various optics areas with a focus on beams that carry orbital angular momentum. We then discuss optical experimental design, including generating Laguerre-Gaussian beams, creating and characterizing optical turbulence, and engineering considerations when capturing the images at the receiver. We then provide an accessible primer on convolutional neural networks, a machine learning technique that has proved effective at image classification. We conclude with a set of best practices for the field and provide an example code and a benchmark dataset for researchers looking to try out these techniques.

Machine learning-aided classification of beams carrying orbital angular momentum propagated in highly turbid water: publisher's note.

This publisher's note corrects the name of an author of J. Opt. Soc. Am. A37, 1662 (2020)JOAOD60740-323210.1364/JOSAA.401153.