基于Matern硬核点过程的无人机辅助蜂窝网络吞吐量分析

无人机与传统的地面蜂窝基站混合组网有望成为一种实现数据高速传输的重要手段。针对这一场景，提出一种针对下行双层异构网络吞吐量性能的分析框架，采用泊松点过程对地面基站的位置进行建模，同时考虑到无人机之间存在的最小安全距离约束，采用Matern硬核点过程对无人机的位置进行建模，利用随机几何工具推导得到用户平均数据速率的简易表达式。根据得到的解析表达式，进而讨论无人机高度和功率控制因子的最优参数组合。结果表明，在提出的网络模型下选择适当的功率控制因子可以在保证地面基站用户速率的同时，有效地提高无人机用户的平均数据速率。

Throughput analysis of UAV-assisted cellular networks by Matern hardcore point processes

Unmanned aerial vehicles (UAVs) have been expected to coexist with conventional terrestrial cellular networks and become an important component to support high rate transmissions. This paper presents an analytical framework for evaluating the throughput performance of a downlink two-tier heterogeneous network. The locations of terrestrial base stations (BSs) are modeled by Poisson point processes. Considering the minimum distance constraint among UAVs, Matern hardcore point processes is utilized to model the locations of UAVs. Tools of stochastic geometry are invoked to derive more tractable expressions for average data rates of users. With the analytical results, we discuss the optimal combinations of UAVs' height and power control factor. The result shows that an appropriate power control factor can effectively maximize UAV users' average data rate as well as guaranteeing the BS users' performance under our proposed model.