Multi-UAV Enabled Aerial-Ground Integrated Networks: A Stochastic Geometry Analysis
Multi-UAV Enabled Aerial-Ground Integrated Networks: A Stochastic Geometry Analysis
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DOI：
10.1109/tcomm.2022.3204662
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发表时间：
2022-10
影响因子：
8.3
通讯作者：
Shangwei Zhang;Yajie Zhu;Jiajia Liu
中科院分区：
文献类型：
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作者：
Shangwei Zhang;Yajie Zhu;Jiajia Liu
多架无人机可作为空中基站，为大规模地面设备提供灵活可靠的通信服务。考虑到实际中无人机之间的互斥关系，对这种多无人机网络进行分析是一项非常具有挑战性的任务。基于随机几何的工具，我们在本文中开发了一个理论框架，用于建模和分析无人机的空中网络遵循mat<s:1>核心点过程（MHCPP）。由于排斥点过程的可处理概率生成函数（PGFL）不可用，我们采用基于泊松点过程的近似方法来分析典型GD的累积干扰和信噪比（SIR）。同时考虑视距通信和非视距通信，得到了网络覆盖概率和平均速率的近似表达式。最后，给出了大量的仿真结果，验证了所提框架的有效性和准确性。
Multiple unmanned aerial vehicles (UAVs) can function as aerial base stations to provide flexible and reliable communication services for massive ground devices (GDs). It is quite a challenging task to analyze such multi-UAV networks when considering practical mutually exclusive relationships among UAVs. Based on the tools of stochastic geometry, we in this paper develop a theoretical framework for modeling and analyzing aerial networks with UAVs following Matérn hard-core point process (MHCPP). As the tractable probability generating functional (PGFL) of repulsive point processes is unavailable, we employ an approximate approach based on the Poisson point process to analyze the cumulative interference and the signal-to-interference ratio (SIR) of a typical GD. By considering both line-of-sight (LOS) and none-line-of-sight (NLOS) communications, we obtain the approximation expressions of the network coverage probability and average rate. Finally, extensive simulation results are presented to validate the efficiency and accuracy of our proposed framework.