TY - GEN
T1 - Deep Neural Network based Path Loss Analysis of Magnetic Induction Communication Systems in Underwater Pipeline
AU - Zhou, Wentao
AU - Shin, Yoan
AU - Lee, Inkyu
N1 - 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.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/11
Y1 - 2020/11
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.
KW - deep neural network
KW - Magnetic induction
KW - path loss
KW - software-defined radio
KW - supervised learning
KW - underwater pipeline
UR - http://www.scopus.com/inward/record.url?scp=85101304866&partnerID=8YFLogxK
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U2 - 10.1109/VTC2020-Fall49728.2020.9348496
DO - 10.1109/VTC2020-Fall49728.2020.9348496
M3 - Conference contribution
AN - SCOPUS:85101304866
T3 - IEEE Vehicular Technology Conference
BT - 2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
Y2 - 18 November 2020
ER -