Open AccessDe-multiplexing vortex modes in optical communications using transport-based pattern recognition
Author(s) -
Se Rim Park,
Liam Cattell,
Jonathan M. Nichols,
Abbie T. Watnik,
Timothy Doster,
Gustavo K. Rohde
Publication year - 2018
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.26.004004
Subject(s) - multiplexing , computer science , optics , angular momentum , optical communication , physics , free space optical communication , decoding methods , artificial intelligence , algorithm , telecommunications , quantum mechanics
Free space optical communications utilizing orbital angular momentum beams have recently emerged as a new technique for communications with potential for increased channel capacity. Turbulence due to changes in the index of refraction emanating from temperature, humidity, and air flow patterns, however, add nonlinear effects to the received patterns, thus making the demultiplexing task more difficult. Deep learning techniques have been 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.