An OAM Classification Technique using CNN Approach

Abstract

Orbital angular momentum (OAM) of light has drawn increasing attention due to its intriguingly rich potential for a variety of communication applications. In this paper, we propose a state-of-the-art OAM classification technique using a convolution neural network (CNN) approach for decoding OAM carrying Laguerre-Gaussian beams. We evaluate how well the transmitted alphabet encoded on LG beams is decoded on a noisy channel. From the simulation results, we demonstrate that the OAM beams with different values of OAM mode indexes can readily be classified (or decoded) using the proposed CNN-based approach with average classification accuracy greater than 95%.