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

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 languageEnglish
Title of host publication2020 IEEE 92nd Vehicular Technology Conference, VTC 2020-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194844
DOIs
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
Volume2020-November
ISSN (Print)1550-2252

Conference

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

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

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