Orbital angular momentum acoustic modes demultiplexing by machine learning methods

Abstract

Orbital angular momentum (OAM) multiplexing is a promising method for MIMO multiplexing strategy. OAM multiplexing has previously been demonstrated for underwater acoustic communication, where data transmission was carried out within a single acoustic beam. Inner-product method is most often used for OAM demultiplexing, but it is sensitive to changes of signal parameters. For example, parameters changes can be associated with wave propagation through heterogeneous medium. I propose and demonstrate an approach using of machine learning methods to increase demultiplexing accuracy to 96% for non-stationary signals. In article presents experimental and numerical investigation results of proposed method.