Sparsity-Inspired Nonparametric Probability Characterization for Radio Propagation in Body Area Networks

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.