arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1902.00149eess.SP
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智能无线身体中心网络

Towards Smart Wireless Body-Centric Networks

Samiya M. Shimly, David B. Smith

摘要:

我们研究了无线以身体为中心的信道的“长记忆”或长距离依赖性(LRD)的存在,例如,身体上,身体对身体(B2B),从10个共同定位的无线体域网或BAN(配备可穿戴传感器的人)收集的真实实验数据集。为此,我们研究了两个不同的因素,如:衰减自相关函数(ACF)和赫斯特指数的模式。从实验结果中,我们发现,ACF衰减的身体为中心的通道遵循幂型衰减和通道的赫斯特指数远远大于0.5的平均值。这些结果表明,以身体为中心的信道可以具有长记忆或LRD特性,可以用于预测分析和智能决策,以构建未来的无线以人为中心的网络,可以自主感知和行动。我们还澄清是否存在的LRD属性是足够的可靠预测的身体为中心的渠道。

英文摘要:

We investigate the existence of 'long-memory' or long-range dependence (LRD) of the wireless body-centric channels, e.g., on-body, body-to-body (B2B), with real-life experimental dataset collected from 10 co-located wireless body area networks or BANs (people fitted with wearable sensors). We examine two different factors on that purpose such as: the pattern of the decaying autocorrelation function (ACF) and the Hurst exponent. From the experimental outcome, we show that, the ACF decay of the body-centric channels follows a power-like decay and the channels have a Hurst exponent much greater than 0.5 on average. These results indicate that the body-centric channels can possess long-memory or LRD characteristic which can be used for predictive analysis and intelligent decision making to build futuristic wireless human-centered networks that can sense and act autonomously. We also clarify whether the presence of the LRD property is sufficient for reliable prediction of the body-centric channels.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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