Deep learning assisted OAM modes demultiplexing

Venugopal Raskatla; Vijay Kumar

doi:10.1117/12.2615170

20 December 2021 Deep learning assisted OAM modes demultiplexing

Venugopal Raskatla, Vijay Kumar

Proceedings Volume 12126, Fifteenth International Conference on Correlation Optics; 121260A (2021) https://doi.org/10.1117/12.2615170
Event: Fifteenth International Conference on Correlation Optics, 2021, Chernivtsi, Ukraine

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 (𝐿𝐺_𝑝𝑙) 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.

Citation Download Citation

Venugopal Raskatla and Vijay Kumar "Deep learning assisted OAM modes demultiplexing", Proc. SPIE 12126, Fifteenth International Conference on Correlation Optics, 121260A (20 December 2021); https://doi.org/10.1117/12.2615170

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KEYWORDS

Spiral phase plates

Speckle

Convolution

Optical communications

Spatial light modulators

Free space optical communications

Neural networks

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