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On the sparse non-parametric model for body-centric ultra-wideband channel

机译:以体为中心的超宽带信道的稀疏非参数模型

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Parametric probability models are usually used to characterize the channels for Wireless Body Area Networks (WBANs). However, one should note that only limited samples can be collected in WBANs; on the other hand, channels for this area can be easily affected by many unexpected factors, such as human body postures, individual difference, etc. In this paper, the authors develop a sparse non-parametric model for UWB WBANs, it is demonstrated that when the coefficients are selected properly, the error of the model is smaller than traditional parametric ones.
机译:参数概率模型通常用于表征无线人体局域网(WBAN)的信道。但是,应该注意的是,在WBAN中只能收集有限的样本;另一方面,该区域的通道很容易受到许多意外因素的影响,例如人体姿势,个体差异等。在本文中,作者针对UWB WBAN开发了一种稀疏的非参数模型,证明了:正确选择系数后,模型的误差小于传统的参数误差。

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