中国物理B ›› 2024, Vol. 33 ›› Issue (3): 34205-034205.doi: 10.1088/1674-1056/ad0142

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Diffraction deep neural network-based classification for vector vortex beams

Yixiang Peng(彭怡翔)1, Bing Chen(陈兵)1, Le Wang(王乐)1, and Shengmei Zhao(赵生妹)1,2,3,†   

  1. 1 Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210003, China;
    2 Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing 210003, China;
    3 National Laboratory of Solid State Microstructures, Nanjing University, Nanjing 210093, China
  • 收稿日期:2023-06-25 修回日期:2023-09-26 接受日期:2023-10-09 出版日期:2024-02-22 发布日期:2024-02-22
  • 通讯作者: Shengmei Zhao E-mail:zhaosm@njupt.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 62375140 and 62001249) and the Open Research Fund of National Laboratory of Solid State Microstructures (Grant No. M36055).

Diffraction deep neural network-based classification for vector vortex beams

Yixiang Peng(彭怡翔)1, Bing Chen(陈兵)1, Le Wang(王乐)1, and Shengmei Zhao(赵生妹)1,2,3,†   

  1. 1 Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications (NJUPT), Nanjing 210003, China;
    2 Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing 210003, China;
    3 National Laboratory of Solid State Microstructures, Nanjing University, Nanjing 210093, China
  • Received:2023-06-25 Revised:2023-09-26 Accepted:2023-10-09 Online:2024-02-22 Published:2024-02-22
  • Contact: Shengmei Zhao E-mail:zhaosm@njupt.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 62375140 and 62001249) and the Open Research Fund of National Laboratory of Solid State Microstructures (Grant No. M36055).

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摘要： 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.

关键词: vector vortex beam, diffractive deep neural network, classification, atmospheric turbulence

Abstract: 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.

Key words: vector vortex beam, diffractive deep neural network, classification, atmospheric turbulence

中图分类号:  (Pattern recognition)

  • 42.30.Sy
42.79.Ta (Optical computers, logic elements, interconnects, switches; neural networks) 42.68.-w (Atmospheric and ocean optics) 84.35.+i (Neural networks)