De-Multiplexing Vortex Modes in Optical Communications Using Transport-Based Pattern Recognition
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 07, 2018
- Accession Number
- AD1102571
Entities
People
- Abbie Watnik
- Gustavo K. Rohde
- Jonathan M. Nichols
- Liam Cattell
- Se R. Park
- Timothy Doster
Organizations
- United States Naval Research Laboratory