Optimized OAM Laguerre-Gauss Alphabets for Demodulation using Machine Learning

Abstract

In orbital angular momentum (OAM) based free-space optical (FSO) communication systems, a CCD camera can be used at the reception side to capture images of the laser beam carrying the transmitted OAM modes. The tasks of extracting features from these images and identifying the transmitted modes are studied in this paper. The ability of machine learning algorithms to perform these tasks is explored. Laguerre-Gauss beams and turbulent channels are considered. Different modulation alphabets formed by using sets of superposed and multiplexed OAM modes are investigated. Appropriate choice of these alphabets can increase data rate transmission.