从海平面到无人机高度的太赫兹波段透射率表征

A. Saeed, Ammar Saleem, O. Gurbuz, M. Akkaş
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引用次数: 3

摘要

太赫兹(THz)通信被认为是下一代网络的候选技术,因为太赫兹频段提供了大带宽和数据速率,解决了频谱稀缺的问题。然而,由于水蒸气分子的存在,太赫兹波段的传播受大气吸收的影响很大,而且传播损失也很大。对吸收损耗的建模对于建立一个真实的封闭太赫兹路径损耗模型至关重要,该模型可用于链路级分析和计算。为此,在本文中,我们利用逐行辐射传输模型(LBLRTM)工具获得的数据,考虑通过充水选择的可用频率通道、从海平面到无人机高度的高度以及各种传输范围,表征太赫兹透射率,即吸收增益。本文采用多项式模型、指数模型和高斯模型等不同的统计模型,分析了透光率与(1)频率、(2)距离和(3)海拔的关系。数值结果表明,利用指数模型和多项式模型分别将透光率建模为距离和海拔的函数是可行的方法。这项工作可以扩展到表征整个太赫兹波段上所有频率的透射率,也可以用于更高的高度和更长的范围。
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Characterization of Terahertz Band Transmittance from Sea-Level to Drone Altitudes
Terahertz (THz) communications has been recognized as a candidate technology for the next generation networks as the THz band offers large bandwidth and data rates, catering for the problem of spectrum scarcity. However, THz band propagation is highly affected by atmospheric absorption due to water vapor molecules, in addition to the high spread loss. Modeling of the absorption loss is essential for a realistic closed form THz path loss model, which can be employed in link level analysis and formulations. For this purpose, in this paper, we characterize the THz transmittance i.e., absorption gain using the data obtained from Line-by-Line Radiative Transfer Model (LBLRTM) tool, considering the available frequency channels selected via water-filling, altitudes from sea-level to drone altitudes and various transmission ranges. We analyze the modeling of transmittance as a function of: (1) Frequency, (2) Distance and (3) Altitude, using different statistical models including, Polynomial, Exponential and Gaussian models. Numerical results depict that modeling transmittance as a function of distance and altitude are feasible approaches using the exponential and the polynomial models, respectively. This work can be extended to characterize the transmittance for all frequencies over the entire THz band, and also for higher altitudes and longer ranges.
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Optimal Precoding and Deployment of Millimeter-Wave Drone Base Stations Fading Modeling in Indoor THz Wireless Systems [BalkanCom 2021 Front cover] An ensemble learning framework for distributed resource allocation in inteference channels: The two user case Edge Computing: System Overview and Fusion with Wireless Power Transfer
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