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

doi:10.1109/ACAIT56212.2022.10137921

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 proceeding › Conference contribution

Abstract

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 language	English (US)
Title of host publication	2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)
Publisher	IEEE
ISBN (Print)	9781665453110
DOIs	https://doi.org/10.1109/ACAIT56212.2022.10137921
State	Published - Dec 9 2022
Externally published	Yes

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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10.1109/ACAIT56212.2022.10137921

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@inproceedings{c209f2d32370430585e20184720903a6,

title = "A Demodulator for UWOC-OAM-SK Systems Based on Convolutional Neural Networks",

abstract = "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.",

author = "Jia Ren and Min Chen and Jie Ding and Ziyue Wang and Wan, {Mengyu Wan Mengyu} and Bing Yan and Yan Yan",

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.",

year = "2022",

month = dec,

day = "9",

doi = "10.1109/ACAIT56212.2022.10137921",

language = "English (US)",

isbn = "9781665453110",

booktitle = "2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)",

publisher = "IEEE",

}

TY - GEN

T1 - A Demodulator for UWOC-OAM-SK Systems Based on Convolutional Neural Networks

AU - Ren, Jia

AU - Chen, Min

AU - Ding, Jie

AU - Wang, Ziyue

AU - Wan, Mengyu Wan Mengyu

AU - Yan, Bing

AU - Yan, Yan

N1 - 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.

PY - 2022/12/9

Y1 - 2022/12/9

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/693117

UR - https://ieeexplore.ieee.org/document/10137921/

UR - http://www.scopus.com/inward/record.url?scp=85162177924&partnerID=8YFLogxK

U2 - 10.1109/ACAIT56212.2022.10137921

DO - 10.1109/ACAIT56212.2022.10137921

M3 - Conference contribution

SN - 9781665453110

BT - 2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)

PB - IEEE

ER -

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

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