De-multiplexing vortex modes in optical communications using transport-based pattern recognition
| Autor: | Se Rim Park, Gustavo K. Rohde, Timothy Doster, Jonathan M. Nichols, Liam Cattell, Abbie T. Watnik |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Electromagnetic field
Photon Artificial neural network business.industry Computer science Optical communication Image processing 02 engineering and technology 01 natural sciences Convolutional neural network Multiplexing Atomic and Molecular Physics and Optics 010309 optics Channel capacity 020210 optoelectronics & photonics Optics 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Bit error rate Light beam business Refractive index Algorithm Decoding methods Free-space optical communication |
| Zdroj: | Optics express. 26(4) |
| ISSN: | 1094-4087 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|