Diffraction deep neural network based orbital angular momentum mode recognition scheme in oceanic turbulence

Abstract

Orbital angular momentum (OAM) has the characteristics of mutual orthogonality between modes, and has been applied to underwater wireless optical communication (UWOC) systems to increase the channel capacity. In this work, we propose a diffractive deep neural network (DDNN) based OAM mode recognition scheme, where the DDNN is trained to capture the features of the intensity distribution of the OAM modes and output the corresponding azimuthal indices and radial indices. The results show that the proposed scheme can recognize the azimuthal indices and radial indices of the OAM modes accurately and quickly. In addition, the proposed scheme can resist weak oceanic turbulence (OT), and exhibit excellent ability to recognize OAM modes in a strong OT environment. The DDNN-based OAM mode recognition scheme has potential applications in UWOC systems.