Diffraction deep neural network-based classification for vector vortex beams

• Published in: Chinese physics B Vol. 33; no. 3; pp. 34205 - 436
• Main Authors: Peng, Yixiang, Chen, Bing, Wang, Le, Zhao, Shengmei
• Format: Journal Article
• Language: English
• Published: Chinese Physical Society and IOP Publishing Ltd 01.03.2024
• Subjects: atmospheric turbulence; classification; diffractive deep neural network; vector vortex beam; vector vortex beam; classification; atmospheric turbulence; diffractive deep neural network
• ISSN: 1674-1056; 2058-3834
• DOI: 10.1088/1674-1056/ad0142

The 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, a 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 classify the intensity distribution of the input distorted VVBs, and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the DDNN. The numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification tasks. The energy distribution percentage remains above 95% from weak to medium AT, and the classification accuracy can remain above 95% for various strengths of turbulence. It has a faster convergence and better accuracy than that based on a convolutional neural network.