Machine learning based recognition of Orbital Angular Momentum modes for Satellite Communication

Abstract

Orbital angular momentum (OAM) of light has enabled quantum encoding in higher dimensional Hilbert spaces, opening up new opportunities for high capacity satellite-to-ground quantum communications. In this paper, machine learning based recognition of OAM modes for satellite-to-ground communication is proposed. Most of the machine learning models for OAM mode detection reported in literature directly take intensity and phase images of OAM modes as inputs. A novel attempt is made in the proposed work, wherein Histogram of Gradients (HOG) features are extracted from these images and fed to machine learning models. Two architectures are proposed in this paper for OAM mode detection and their performances are reported. The performances of proposed models are also compared under different atmospheric turbulence conditions, propagation distances and the results are reported. In order to evaluate the efficacy of proposed models, performance results of OAM mode detection using various other methods reported in literature are compared with the results obtained from proposed work. It is observed that the performance of proposed models are almost on par with the models reported in literature. Maximum recognition accuracy of 100% with a prediction time ranging between 83 - 157ms, was achieved using the proposed models. The proposed OAM mode detection models can be used for various other quantum communication applications too, including underwater communication.