| ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS |
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Diffraction deep neural network based orbital angular momentum mode recognition scheme in oceanic turbulence |
| Hai-Chao Zhan(詹海潮)1, Bing Chen(陈兵)1, Yi-Xiang Peng(彭怡翔)1, Le Wang(王乐)1, Wen-Nai Wang(王文鼐)2, and Sheng-Mei Zhao(赵生妹)1,2,† |
1 Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications(NUPT), Nanjing 210003, China;
2 Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education), Nanjing University of Posts and Telecommunications, Nanjing 210003, China |
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Abstract Orbital angular momentum (OAM) has the characteristics of mutual orthogonality between modes, and has been applied to underwater wireless optical communication (UWOC) systems to increase the channel capacity. In this work, we propose a diffractive deep neural network (DDNN) based OAM mode recognition scheme, where the DDNN is trained to capture the features of the intensity distribution of the OAM modes and output the corresponding azimuthal indices and radial indices. The results show that the proposed scheme can recognize the azimuthal indices and radial indices of the OAM modes accurately and quickly. In addition, the proposed scheme can resist weak oceanic turbulence (OT), and exhibit excellent ability to recognize OAM modes in a strong OT environment. The DDNN-based OAM mode recognition scheme has potential applications in UWOC systems.
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Received: 09 July 2022
Revised: 13 September 2022
Accepted manuscript online: 21 September 2022
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PACS:
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42.30.Sy
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(Pattern recognition)
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42.68.-w
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(Atmospheric and ocean optics)
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42.79.Ta
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(Optical computers, logic elements, interconnects, switches; neural networks)
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84.35.+i
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(Neural networks)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61871234 and 62001249), and the Postgraduate Research and Practice Innovation Program of Jiangsu Province, China (Grant No. KYCX200718). |
Corresponding Authors:
Sheng-Mei Zhao
E-mail: zhaosm@njupt.edu.cn
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Cite this article:
Hai-Chao Zhan(詹海潮), Bing Chen(陈兵), Yi-Xiang Peng(彭怡翔), Le Wang(王乐), Wen-Nai Wang(王文鼐), and Sheng-Mei Zhao(赵生妹) Diffraction deep neural network based orbital angular momentum mode recognition scheme in oceanic turbulence 2023 Chin. Phys. B 32 044208
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