Orbital angular momentum optical communications enhanced by artificial intelligence

Abstract

Angular momentum of light can be divided into spin angular momentum and orbital angular momentum (OAM). Due to the theoretically unlimited orthogonal states, the physical dimension of OAM provides a potential solution to boost the information capacity. The OAM multiplexing and modulation techniques have been implemented to meet the continuous growth of bandwidth requirements, resulting in the concept of OAM optical communication. However, the performances of the traditional optical OAM detection techniques degrade seriously in the practical application of OAM optical communications. Thanks to the powerful data analysis advantages, the cutting-edge machine learning (ML) algorithms have been widely used in the field of image processing, laying the technical foundation for OAM recognition. This paper reviews the recent advances on OAM optical communications that are enhanced by ML methods. More than the traditional OAM detection methods, the OAM demodulation methods based on multiple network architectures, including the support vector machine, self-organizing map, feed-forward neural network, convolutional neural network, and diffractive deep optical neural network (D2NN), have been summarized. We also discuss the development of the spiking neural network and on-chip D2NN, opening a possible way to facilitate the future ultra-low power and ultra-fast OAM demodulation technology.