首页|Diffraction deep neural network-based classification for vector vortex beams
Diffraction deep neural network-based classification for vector vortex beams
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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.
vector vortex beamdiffractive deep neural networkclassificationatmospheric turbulence
彭怡翔、陈兵、王乐、赵生妹
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Institute of Signal Processing and Transmission,Nanjing University of Posts and Telecommunications(NJUPT),Nanjing 210003,China
Key Laboratory of Broadband Wireless Communication and Sensor Network Technology,Ministry of Education,Nanjing 210003,China
National Laboratory of Solid State Microstructures,Nanjing University,Nanjing 210093,China
国家自然科学基金国家自然科学基金Open Research Fund of National Laboratory of Solid State Microstructures
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