Diffraction deep neural network based classification for vector vortex beams

Abstract

Abstract Vector vortex beam (VVB) has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum communications. However, VVB is unavoidably affected by atmospheric turbulence (AT) when it propagates through the free-space optical communication environment, which results in detection errors at the receiver. In this paper, we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT, where a diffractive deep neural network (DDNN) is designed and trained to achieve the classification information of the input distorted VVBs’ intensity distribution, and the horizontal polarization direction of the input distorted beam is adopted as the feature for the classification through DDNN. The numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification tasks. The energy distribution percentage(EDP) remains above 95% from the weak to medium, and the classification accuracy can keep above 95% under various levels of turbulence strengths. It has a faster convergence and better accuracy than that based on a convolutional neural network.