A measurements based comparison of new and classical models used to characterize fading in body area networks

Simon Cotton, Seong Ki Yoo, William Scanlon

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

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Abstract

In this paper we compare a number of the classical models used to characterize fading in body area networks (BANs) with the recently proposed shadowed κ-μ fading model. In particular, we focus on BAN channels which are considered to be susceptible to shadowing by the human body. The measurements considered in this study were conducted at 2.45 GHz for hypothetical BAN channels operating in both anechoic and highly reverberant environments while the person was moving. Compared to the Rice, Nakagami and lognormal fading models, it was found that the recently proposed shadowed κ-μ fading model provided an enhanced fit to the measured data.
Original languageEnglish
Title of host publication2014 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-Bio2014)
PublisherIEEE
Pages1-4
Number of pages4
ISBN (Electronic)9781479954476
ISBN (Print)9781479954452
DOIs
Publication statusPublished - 9 Feb 2015
Externally publishedYes
Event2014 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications - London, United Kingdom
Duration: 8 Dec 201410 Dec 2014

Workshop

Workshop2014 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications
Abbreviated titleIMWS-Bio 2014
CountryUnited Kingdom
CityLondon
Period8/12/1410/12/14

Bibliographical note

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Keywords

  • Body centric communications
  • on-body fading channels
  • channel characterization

Cite this

Cotton, S., Yoo, S. K., & Scanlon, W. (2015). A measurements based comparison of new and classical models used to characterize fading in body area networks. In 2014 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applications (IMWS-Bio2014) (pp. 1-4). IEEE. https://doi.org/10.1109/IMWS-BIO.2014.7032411