Sparsity-inspired nonparametric probability characterization for radio propagation in body area networks

Xiaodong Yang, Shuyuan Yang, Qammer Hussain Abbasi, Zhiya Zhang, Aifeng Ren, Wei Zhao, Akram Alomainy

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Parametric probability models are common references for channel characterization. However, the limited number of samples and uncertainty of the propagation scenario affect the characterization accuracy of parametric models for body area networks. In this paper, we propose a sparse nonparametric probability model for body area wireless channel characterization. The path loss and root-mean-square delay, which are significant wireless channel parameters, can be learned from this nonparametric model. A comparison with available parametric models shows that the proposed model is very feasible for the body area propagation environment and can be seen as a significant supplement to parametric approaches.

Original languageEnglish
Article number6847666
Pages (from-to)858-865
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number3
DOIs
Publication statusPublished - 1 May 2015

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Keywords

  • Body area networks
  • nonparametric model
  • radio propagation
  • sparsity
  • support vector

ASJC Scopus subject areas

  • Biotechnology
  • Medicine(all)
  • Computer Science Applications
  • Health Information Management
  • Electrical and Electronic Engineering

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