Deep learning assisted OAM modes demultiplexing

Abstract

Orbital angular momentum (OAM) beams have the potential to increase the information-carrying capacity because of the extra degrees of freedom associated with them. Traditional methods for mode detection and de-multiplexing are complex and require expensive optical hardware. We propose a very simple and cost effective deep learning based model for demultiplexing OAM modes at the receiver. In this method we have used a random phase mask of known inhomogeneity to generate a scattered field of OAM mode and the intensity images of these scattered field are used as an input to the Convolutional Neural Network. The model is trained for various Laguerre-Gaussian (𝐿𝐺𝑝𝑙) modes carrying OAM with 𝑝 = 0 and 𝑙 = 1,2,3,4,5,6,7,8. The model is tested for various set of images and the overall accuracy of each dataset is >99%. To demonstrate the proof of concept we simulated an experiment to generate the speckle field at the receiver of optical communication system for demultiplexing OAM modes and decoding the 3-bit information.