无人机信道建模新视角:失视概率的长尾

ZiHan Meng, Youjia Chen, Ming Ding, D. López-Pérez
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引用次数: 3

摘要

准确的信道建模对于网络性能分析至关重要,特别是在考虑无人驾驶飞行器(uav)时。涉及无人机的测量活动表明,无人机和地面基站(BSs)之间的路径损失高度依赖于无人机高度,使得以前的视距(LoS)概率模型不准确。本文从理论上研究了基于高度依赖LoS概率的更现实信道模型的覆盖概率、平均遍历率和面积频谱效率。研究结果表明,无人机网络中LoS概率函数的长尾对BS密度较低的稀疏网络的性能有重要影响。我们的新发现还表明,无人机网络的覆盖范围可能随着无人机密度的增加而不断下降,因为LoS概率随着链路距离的增加而缓慢下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Look at UAV Channel Modeling: A Long Tail of LoS Probability
Accurate channel modelling is crucial for network performance analysis, particularly when considering unmanned aerial vehicles (UAVs). Measurement campaigns involving UAVs have shown that the path loss between UAVs and terrestrial base stations (BSs) is highly dependent on the UAV height, rendering previous line-of-sight (LoS) probability models inaccurate. In this paper, the coverage probability, average ergodic rate and area spectral efficiency are theoretically investigated based on a more realistic channel model, with height dependent LoS probabilities. Our results show that the long tail of the LoS probability function in UAV networks has an important performance impact on sparse networks where the BS density is low. Our new findings also show that the coverage of UAV-enabled networks may continuously decrease with the UAV density, since the LoS probability slowly declines with the link range.
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