De-multiplexing vortex modes in optical communications using transport-based pattern recognition

Free space optical communications utilizing orbital angular momentum beams have recently emerged as a new technique for communications with the potential for increased channel capacity. Turbulence due to changes in the index of refraction emanating from temperature, humidity, and air flow patterns, however, adds nonlinear effects to the received patterns, thus making the demultiplexing task more difficult. Deep learning techniques have previously been applied to solve the demultiplexing problem as an image classification task. Here we make use of a newly developed theory suggesting a link between image turbulence and photon transport through the continuity equation to describe a method that utilizes a shallow learning method instead. The decoding technique is tested and compared against previous approaches using deep convolutional neural networks. Results show that the new method can obtain similar classification accuracies (bit error ratio) at a small fraction (1/90) of the computational cost, thus enabling higher bit rates. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement