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Sparsity-Inspired Nonparametric Probability Characterization for Radio Propagation in Body Area Networks | IEEE Journals & Magazine | IEEE Xplore
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Sparsity-Inspired Nonparametric Probability Characterization for Radio Propagation in Body Area Networks


Abstract:

Parametric probability models are common references for channel characterization. However, the limited number of samples and uncertainty of the propagation scenario affec...Show More

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.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 19, Issue: 3, May 2015)
Page(s): 858 - 865
Date of Publication: 02 July 2014

ISSN Information:

PubMed ID: 25014979

Funding Agency:


References

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