Efficient identification of orbital angular momentum modes carried by Bessel Gaussian beams in oceanic turbulence channels using convolutional neural network

Abstract

As one of the spatial dimensions, orbital angular momentum (OAM) expands the channel capacity of underwater wireless optical communication (UWOC) system. However, the performance of the OAM-UWOC system is degraded due to the distortion of the OAM by oceanic turbulence. In this paper, we present a 7-layer convolutional neural network (CNN) and validate its ability to identify OAM patterns carried by Bessel Gaussian (BG) beams in oceanic turbulence channels. The trained CNN performs the task of recognizing OAM patterns with an accuracy of over 99% under weak-to-moderate oceanic turbulence at 100 m. Furthermore, in order to demonstrate the performance and robustness of the designed CNN, the effects of training sample number, epoch, turbulence intensity, balance parameter, transmission distance, type of training dataset, and coding method on the recognition ability of CNN are fully investigated. We hope that the method devised in our work will contribute to UWOC and facilitate its further development.