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Characterizing terahertz channels for monitoring human lungs with wireless nanosensor networks

机译:利用无线纳米传感器网络表征太赫兹通道以监测人的肺

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We characterize terahertz wireless channels for extracting data from nanoscale sensors deployed within human lungs. We discover that the inhalation and exhalation of oxygen and carbon dioxide causes periodic variation of the absorption coefficient of the terahertz channel. Channel absorption drops to its minimum near the end of inhalation, providing a window of opportunity to extract data with minimum transmission power. We propose an algorithm for nanosensors to estimate the periodic channel by observing signal-to-noise ratio of the beacons transmitted from the data sink. Using real respiration data from multiple subjects, we demonstrate that the proposed algorithm can estimate the minimum absorption interval of the periodic channel with 98.5% accuracy. Our analysis shows that by confining all data collections during the estimated low-absorption window of the periodic channel, nanosensors can reduce power consumption by six orders of magnitude. Finally, we demonstrate that for wireless communications within human lungs, 0.1-0.12 THz is the least absorbing spectrum within the terahertz band.
机译:我们表征了太赫兹无线通道的特征,用于从人肺内部署的纳米级传感器中提取数据。我们发现,氧气和二氧化碳的吸入和呼出会导致太赫兹通道吸收系数的周期性变化。在吸气结束时,通道吸收降至最低,这为以最小传输功率提取数据提供了机会。我们提出了一种用于纳米传感器的算法,通过观察从数据宿传输的信标的信噪比来估计周期性信道。使用来自多个对象的真实呼吸数据,我们证明了所提出的算法可以以98.5%的精度估算周期性通道的最小吸收间隔。我们的分析表明,通过将所有数据收集限制在周期性通道的估计低吸收窗口内,纳米传感器可以将功耗降低六个数量级。最后,我们证明对于人肺内的无线通信,太赫兹频带内的吸收光谱最小为0.1-0.12 THz。

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