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 proceedingConference contribution

2 Citations (Scopus)


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 languageEnglish
Title of host publication2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194844
Publication statusPublished - 2020 Nov
Event92nd IEEE Vehicular Technology Conference, VTC 2020-Fall - Virtual, Victoria, Canada
Duration: 2020 Nov 18 → …

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference92nd IEEE Vehicular Technology Conference, VTC 2020-Fall
CityVirtual, Victoria
Period20/11/18 → …

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.


  • 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


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