论文标题

表征5G毫米波网络中首次派生的多径组件:TOA,AOA和非视线偏差

Characterizing the First-Arriving Multipath Component in 5G Millimeter Wave Networks: TOA, AOA, and Non-Line-of-Sight Bias

论文作者

O'Lone, Christopher E., Dhillon, Harpreet S., Buehrer, R. Michael

论文摘要

本文介绍了基于随机几何学的5G毫米波(MM-WAVE)细胞的传播统计分析。特别是,得出了第一个派生多径成分(MPC)的到达时间(TOA)和到达角度(AOA)分布。这些统计数据在许多应用程序中找到了它们的实用性,例如基于蜂窝的本地化,通道建模和MM波初始访问(IA)的链接建立。利用随机几何形状的工具,使用布尔模型来统计表征反射器的随机位置,方向和尺寸,例如建筑物。假设非线(NLOS)传播是由于一阶(即单杆)反射引起的,并且反射器可以促进或阻止反射,则得出了首次放置MPC的路径长度(即绝对时间延迟)的分布。然后使用此结果来获得本地化文献中的第一个NLOS偏置分布,该分布基于第一个派生MPC的绝对延迟用于户外飞行时间(TOF)范围测量。该分布显示出与文献中通常假定的伽玛和指数NLOS偏置模型的匹配,这些分布仅通过启发式或间接方法才符合文献中的指数偏差模型。在这个分析框架下继续,得出了第一次派系MPC的AOA分布,从而使环境障碍如何影响AOA,并代表了布尔模型下的第一个AOA分布。

This paper presents a stochastic geometry-based analysis of propagation statistics for 5G millimeter wave (mm-wave) cellular. In particular, the time-of-arrival (TOA) and angle-of-arrival (AOA) distributions of the first-arriving multipath component (MPC) are derived. These statistics find their utility in many applications such as cellular-based localization, channel modeling, and link establishment for mm-wave initial access (IA). Leveraging tools from stochastic geometry, a Boolean model is used to statistically characterize the random locations, orientations, and sizes of reflectors, e.g., buildings. Assuming non-line-of-sight (NLOS) propagation is due to first-order (i.e., single-bounce) reflections, and that reflectors can either facilitate or block reflections, the distribution of the path length (i.e., absolute time delay) of the first-arriving MPC is derived. This result is then used to obtain the first NLOS bias distribution in the localization literature that is based on the absolute delay of the first-arriving MPC for outdoor time-of-flight (TOF) range measurements. This distribution is shown to match exceptionally well with commonly assumed gamma and exponential NLOS bias models in the literature, which were only attainted previously through heuristic or indirect methods. Continuing under this analytical framework, the AOA distribution of the first-arriving MPC is derived, which gives novel insight into how environmental obstacles affect the AOA and also represents the first AOA distribution derived under the Boolean Model.

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