Adaptive Demodulator Using Machine Learning for Orbital Angular Momentum Shift Keying

Abstract

An $m$ -ary adaptive demodulator based on machine learning for light beams carrying orbital angular momentums (OAMs) over free-space turbulence channels is proposed and demonstrated. Benefiting from natural advantages in the image recognition, convolutional neural network (CNN) is selected to construct the adaptive demodulator. Without extra space light modulators and digital signal processing at the reception, the adaptive demodulator transforms the sequence of intensity patterns of received Laguerre-Gaussian beams carrying different OAM modes into initial signals efficiently. As comparison, K-nearest neighbor (KNN), naive Bayes classifier (NBC), and back-propagation artificial neural network (BP-ANN) are also studied. Furthermore, the demodulating accuracy of 4-, 8-, and 16-ary OAM is investigated with the comprehensive consideration of the atmospheric turbulence, OAM mode spacing, and transmission distance. The simulation results show that the demodulating error rate (DER) of CNN outperforms KNN, NBC, and BP-ANN, especially under stronger turbulence and longer distance. The DER of CNN is ~0.86% for the 1000-m 8-OAM system under strong turbulence, ~30 % less than those of KNN, NBC, and BP-ANN.