Recognition of OAM state using CNN based deep learning for OAM shift keying FSO system with pointing error and limited receiving aperture

Biao Gong; Biao Gong; Shanyong Cai; Shanyong Cai; Ziyang Xiao; Xin Wang; Xin Wang; Luming Li; Zhiguo Zhang; Zhiguo Zhang

doi:10.1364/CLEO_AT.2021.JTu3A.169

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper JTu3A.169
•https://doi.org/10.1364/CLEO_AT.2021.JTu3A.169

Recognition of OAM state using CNN based deep learning for OAM shift keying FSO system with pointing error and limited receiving aperture

Biao Gong, Shanyong Cai, Ziyang Xiao, Xin Wang, Luming Li, and Zhiguo Zhang

CLEO: Applications and Technology 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Joint Poster Session I (JTu3A)

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B. Gong, S. Cai, Z. Xiao, X. Wang, L. Li, and Z. Zhang, "Recognition of OAM state using CNN based deep learning for OAM shift keying FSO system with pointing error and limited receiving aperture," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JTu3A.169.

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Abstract

In this paper, we study the performance of OAM shift keying FSO system with pointing error and limited receiving aperture using CNN based demodulator. The results show that the recognition accuracy can reach 98% with pointing error and weak turbulence.

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