Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network

In underwater wireless optical communication (UWOC), a vortex beam carrying orbital angular momentum has a spatial spiral phase distribution, which provides spatial freedom for UWOC and, as a new information modulation dimension resource, it can greatly improve channel capacity and spectral efficiency. In a case of the disturbance of a vortex beam by ocean turbulence, where a Laguerre–Gaussian (LG) beam carrying orbital angular momentum (OAM) is damaged by turbulence and distortion, which affects OAM pattern recognition, and the phase feature of the phase map not only has spiral wavefront but also phase singularity feature, the convolutional neural network (CNN) model can effectively extract the information of the distorted OAM phase map to realize the recognition of dual-mode OAM and single-mode OAM. The phase map of the Laguerre–Gaussian beam passing through ocean turbulence was used as a dataset to simulate and analyze the OAM recognition effect during turbulence caused by different temperature ratios and salinity. The results showed that, during strong turbulence [Formula: see text] , when different [Formula: see text] = −1.75, the recognition rate of dual-mode OAM ([Formula: see text] = ±1~±5, ±1~±6, ±1~±7, ±1~±8, ±1~±9, ±1~±10) had higher recognition rates of 100%, 100%, 100%, 100%, 98.89%, and 98.67% and single-mode OAM ([Formula: see text] = 1~5, 1~6, 1~7, 1~8, 1~9, 1~10) had higher recognition rates of 93.33%, 92.77%, 92.33%, 90%, 87.78%, and 84%, respectively. With the increase in [Formula: see text] , the recognition accuracy of the CNN model will gradually decrease, and in a fixed case, the dual-mode OAM has stronger anti-interference ability than single-mode OAM. These results may provide a reference for optical communication technologies that implement high-capacity OAM.