A Demodulator for UWOC-OAM-SK Systems Based on Convolutional Neural Networks

Jia Ren, Min Chen, Jie Ding, Ziyue Wang, Mengyu Wan Mengyu Wan, Bing Yan, Yan Yan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In order to identify the different modes of vortex beams in the underwater OAM shift keying (OAM-SK) system more conveniently and accurately, a deep learning technique is introduced, and an improved convolutional neural networks (CNN) model is proposed as the demodulator of the communication system. Based on the vortex beam transmission characteristics and the fundamental theory of ocean turbulence, a random phase screen is generated by the power spectrum inversion method with low frequency compensation to simulate the transmission of vortex beam in different ocean turbulence channels. The recognition rate of this CNN model as a system demodulator for the damaged light intensity distribution maps of LG beams, BG beams and superposed state LG beams (S-LG) after passing through ocean turbulence is investigated. The experimental results show that the CNN model has strong generalization ability and can effectively identify different kinds of vortex beams and its modes. The recognition rate can reach more than 97% for superposition state LG beams after 100m transmission in high intensity turbulent environment. The model has some reference value for the research design of OAMSK system.
Original languageEnglish (US)
Title of host publication2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)
ISBN (Print)9781665453110
StatePublished - Dec 9 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2023-07-20
Acknowledgements: This work was supported by the National Program on Key Basic Research Project (2014CB921002), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB07030200), and National Natural Science Foundation of China (51522212, 51421002, and 51102208), and the Fundamental Research Funds for the Central Universities and by King Abdullah University of Science and Technology. Zhenzhong Yang acknowledges support from KAUST during his stay as an exchange student.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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