Deep Neural Network based Path Loss Analysis of Magnetic Induction Communication Systems in Underwater Pipeline

Wentao Zhou; Yoan Shin; Inkyu Lee

doi:10.1109/VTC2020-Fall49728.2020.9348496

Deep Neural Network based Path Loss Analysis of Magnetic Induction Communication Systems in Underwater Pipeline

Wentao Zhou, Yoan Shin, Inkyu Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Magnetic induction (MI) communication uses the mutual inductance between coil antennas to achieve the communication process. As MI communication is not affected by most factors of the propagation environment, we can achieve the detection and monitoring tasks through a stealth operation. In the MI system, the path loss is the most important parameter when estimating the channel and the communication range. The pipeline is used to transport the liquid, and thus it is a special scenario of the underwater communication. Since the index of refraction of the boundaries is different, there are three possible scenarios at the boundaries, i.e. semi-reflection, total reflection, and no reflection. Hence, the distribution of the magnetic field is changed and the path loss is difficult to be estimated. In this paper, we build an MI-based software-defined radio (SDR) system testbed in a water tank to simulate the underwater pipeline. Then, we adopt a deep neural network (DNN) with supervised learning to estimate the path loss of the MI communication. Also we discuss the communication range in the theoretical path loss model and our proposed model.

Original language	English
Title of host publication	2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728194844
DOIs	https://doi.org/10.1109/VTC2020-Fall49728.2020.9348496
Publication status	Published - 2020 Nov
Event	92nd IEEE Vehicular Technology Conference, VTC 2020-Fall - Virtual, Victoria, Canada Duration: 2020 Nov 18 → …

Publication series

Name	IEEE Vehicular Technology Conference
Volume	2020-November
ISSN (Print)	1550-2252

Conference

Conference	92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
Country/Territory	Canada
City	Virtual, Victoria
Period	20/11/18 → …

Bibliographical note

Funding Information:
The work of W. Zhou and I. Lee was supported by the National Research Foundation through the Ministry of Science, ICT, and Future Planning (MSIP), Korean Government under Grant 2017R1A2B3012316. The work of Y. Shin was supported by the NRF grant funded by the Korea government (MSIT) (2020R1A2C2010006).

Publisher Copyright:
© 2020 IEEE.

Keywords

Magnetic induction
deep neural network
path loss
software-defined radio
supervised learning
underwater pipeline

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1109/VTC2020-Fall49728.2020.9348496

Cite this

Zhou, W., Shin, Y., & Lee, I. (2020). Deep Neural Network based Path Loss Analysis of Magnetic Induction Communication Systems in Underwater Pipeline. In 2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings Article 9348496 (IEEE Vehicular Technology Conference; Vol. 2020-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTC2020-Fall49728.2020.9348496

Deep Neural Network based Path Loss Analysis of Magnetic Induction Communication Systems in Underwater Pipeline. / Zhou, Wentao; Shin, Yoan; Lee, Inkyu.
2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9348496 (IEEE Vehicular Technology Conference; Vol. 2020-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhou, W, Shin, Y & Lee, I 2020, Deep Neural Network based Path Loss Analysis of Magnetic Induction Communication Systems in Underwater Pipeline. in 2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings., 9348496, IEEE Vehicular Technology Conference, vol. 2020-November, Institute of Electrical and Electronics Engineers Inc., 92nd IEEE Vehicular Technology Conference, VTC 2020-Fall, Virtual, Victoria, Canada, 20/11/18. https://doi.org/10.1109/VTC2020-Fall49728.2020.9348496

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title = "Deep Neural Network based Path Loss Analysis of Magnetic Induction Communication Systems in Underwater Pipeline",

abstract = "Magnetic induction (MI) communication uses the mutual inductance between coil antennas to achieve the communication process. As MI communication is not affected by most factors of the propagation environment, we can achieve the detection and monitoring tasks through a stealth operation. In the MI system, the path loss is the most important parameter when estimating the channel and the communication range. The pipeline is used to transport the liquid, and thus it is a special scenario of the underwater communication. Since the index of refraction of the boundaries is different, there are three possible scenarios at the boundaries, i.e. semi-reflection, total reflection, and no reflection. Hence, the distribution of the magnetic field is changed and the path loss is difficult to be estimated. In this paper, we build an MI-based software-defined radio (SDR) system testbed in a water tank to simulate the underwater pipeline. Then, we adopt a deep neural network (DNN) with supervised learning to estimate the path loss of the MI communication. Also we discuss the communication range in the theoretical path loss model and our proposed model.",

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note = "Funding Information: The work of W. Zhou and I. Lee was supported by the National Research Foundation through the Ministry of Science, ICT, and Future Planning (MSIP), Korean Government under Grant 2017R1A2B3012316. The work of Y. Shin was supported by the NRF grant funded by the Korea government (MSIT) (2020R1A2C2010006). Publisher Copyright: {\textcopyright} 2020 IEEE.; 92nd IEEE Vehicular Technology Conference, VTC 2020-Fall ; Conference date: 18-11-2020",

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N2 - Magnetic induction (MI) communication uses the mutual inductance between coil antennas to achieve the communication process. As MI communication is not affected by most factors of the propagation environment, we can achieve the detection and monitoring tasks through a stealth operation. In the MI system, the path loss is the most important parameter when estimating the channel and the communication range. The pipeline is used to transport the liquid, and thus it is a special scenario of the underwater communication. Since the index of refraction of the boundaries is different, there are three possible scenarios at the boundaries, i.e. semi-reflection, total reflection, and no reflection. Hence, the distribution of the magnetic field is changed and the path loss is difficult to be estimated. In this paper, we build an MI-based software-defined radio (SDR) system testbed in a water tank to simulate the underwater pipeline. Then, we adopt a deep neural network (DNN) with supervised learning to estimate the path loss of the MI communication. Also we discuss the communication range in the theoretical path loss model and our proposed model.

AB - Magnetic induction (MI) communication uses the mutual inductance between coil antennas to achieve the communication process. As MI communication is not affected by most factors of the propagation environment, we can achieve the detection and monitoring tasks through a stealth operation. In the MI system, the path loss is the most important parameter when estimating the channel and the communication range. The pipeline is used to transport the liquid, and thus it is a special scenario of the underwater communication. Since the index of refraction of the boundaries is different, there are three possible scenarios at the boundaries, i.e. semi-reflection, total reflection, and no reflection. Hence, the distribution of the magnetic field is changed and the path loss is difficult to be estimated. In this paper, we build an MI-based software-defined radio (SDR) system testbed in a water tank to simulate the underwater pipeline. Then, we adopt a deep neural network (DNN) with supervised learning to estimate the path loss of the MI communication. Also we discuss the communication range in the theoretical path loss model and our proposed model.

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M3 - Conference contribution

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BT - 2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings

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Deep Neural Network based Path Loss Analysis of Magnetic Induction Communication Systems in Underwater Pipeline

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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