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Convolutional Neural Network to Identify Cylindrical Vector Beam Modes

Cylindrical vector beams (CVBs), which have spatial non-uniform polarization distributions, have gained considerable interest in optical communication owing to their orthogonal vector modes. However, the identification of vector modes is always a challenge owing to the lack of effective mode extraction techniques, which hinders the practical application of CVB communications. Herein we introduce a convolutional neural network (CNN) to identify the vector modes of CVBs and an experiment to demonstrate its application in CVB shift-keying (CVB-SK) communication. The CNN model composed of convolution layers was designed to extract mode features from the petal patterns obtained by CVBs using a Glan prism. The identification accuracy of vector modes ranging from −20 to 20 reached 98.07% with an atmospheric turbulence of $C_{\textrm {n}}^{2} =1\times 10^{-14}\textrm {m}^{-2/3},\Delta \textrm {z=}2000~\textrm {m}$ . Mapping and encoding a Maxwell grayscale image with 100 $\times100$ pixels to vector modes, we constructed a CVB-SK communication system, and the CVB-SK signals were successfully demodulated by the CNN model with a pixel-error rate of $5.19\times 10^{-2}$ . Our results indicate that this CNN model can effectively recognize vector modes, which may have application potential in CVB communication, high-dimensional quantum information protocols, etc.

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Informasi Detail

Judul Seri

IEEE JOURNAL OF QUANTUM ELECTRONICS; Vol.58 No.1 January 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 6100111

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CONVOLUTIONAL NEURAL NETWORK
CYLINDRICAL VECTOR BEAM
MODE RECOGNITION
SHIFT-KEYING COMMUNICATION

Info Detail Spesifik

DOI: 10.1109/JQE.2021.3130935

Pernyataan Tanggungjawab

Lizhen Chen, Wenjie Xiong, Peipei Wang, Zebin Huang, Yanliang He, Junmin Liu, Huapeng Ye, Ying Li, Dianyuan Fan, Dianyuan Fan

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