title: 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 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.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Yang
authors.forename: Shuyuan
authors.initials: S
authors.lastname: Yang
authors.forename: Qammer Hussain
authors.initials: QH
authors.lastname: Abbasi
authors.forename: Zhiya
authors.initials: Z
authors.lastname: Zhang
authors.forename: Aifeng
authors.initials: A
authors.lastname: Ren
authors.forename: Wei
authors.initials: W
authors.lastname: Zhao
authors.forename: Akram
authors.initials: A
authors.lastname: Alomainy
mesh_terms: D003195:Computer Communication Networks; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D011846:Radio Waves; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D060388:Support Vector Machine