A Statistical Impulse Response Model Based on Empirical Characterization of Wireless Underground Channels

Abdul Salam, Mehmet C. Vuran, Suat Irmak

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Wireless underground sensor networks (WUSNs) are becoming ubiquitous in many areas. The design of robust systems requires an extensive understanding of the underground (UG) channel characteristics. In this article, the UG channel impulse response is modeled and validated via extensive experiments in indoor and field testbed settings. Three distinct types of soils are selected with sand contents ranging from 13% to 86%, and clay contents ranging from 3% to 32%. The impacts of changes in soil texture and soil moisture are investigated with more than 1, 200 measurements in a novel UG testbed at the University of Nebraska-Lincoln that allows flexibility in soil moisture control. Moreover, the time-domain characteristics of the channel, such as the RMS delay spread, coherence bandwidth, and multipath power gain, are analyzed. The power delay profile analysis validates the three main components of the UG channel: direct, reflected, and lateral waves. Furthermore, it is shown that the RMS delay spread follows a log-normal distribution. The coherence bandwidth ranges between 650 kHz and 1.15 MHz for soil paths of up to 1 m and decreases to 418 kHz for distances above 10 m. Soil moisture is shown to affect the RMS delay spread non-linearly, which provides opportunities for soil moisture-based dynamic adaptation techniques. A statistical channel model for the wireless underground channel has been developed based on the measurements and analysis. The statistical model shows good agreement with the measurement data. The model and analysis pave the way for tailored solutions for data harvesting, UG sub-carrier communication, and UG beamforming.

Original languageEnglish (US)
Article number9109746
Pages (from-to)5966-5981
Number of pages16
JournalIEEE Transactions on Wireless Communications
Volume19
Issue number9
DOIs
StatePublished - Sep 2020

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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