Convolutional Neural Network to Identify Cylindrical Vector Beam Modes

Cylindrical vector beams (CVBs), which have spatial non-uniform polarization distributions, have gained considerable interest in optical communication owing to their orthogonal vector modes. However, the identification of vector modes is always a challenge owing to the lack of effective mode extraction techniques, which hinders the practical application of CVB communications. Herein we introduce a convolutional neural network (CNN) to identify the vector modes of CVBs and an experiment to demonstrate its application in CVB shift-keying (CVB-SK) communication. The CNN model composed of convolution layers was designed to extract mode features from the petal patterns obtained by CVBs using a Glan prism. The identification accuracy of vector modes ranging from −20 to 20 reached 98.07% with an atmospheric turbulence of
$C_{\textrm {n}}^{2} =1\times 10^{-14}\textrm {m}^{-2/3},\Delta \textrm {z=}2000~\textrm {m}$
. Mapping and encoding a Maxwell grayscale image with 100
$\times100$
pixels to vector modes, we constructed a CVB-SK communication system, and the CVB-SK signals were successfully demodulated by the CNN model with a pixel-error rate of
$5.19\times 10^{-2}$
. Our results indicate that this CNN model can effectively recognize vector modes, which may have application potential in CVB communication, high-dimensional quantum information protocols, etc.