UAV-Assisted Networks Through a Tunable Dependent Model
Ziyi Chen, Hongtao Zhang
Abstract
In existing works of performance analysis of unmanned aerial vehicle (UAV) assisted networks, the location dependence between macro base stations (MBSs) and UAVs has not been considered. Considering UAV's particular agility, this letter proposes a tunable model for dependent deployment, and analyzes the performance of the setup via stochastic geometry. Specifically, to avoid the strong mutual interference between UAVs and MBSs, UAVs are only deployed in the macro cell boundaries, of which area fraction can be adjusted. In addition, we obtain a lower bound on the coverage probabilities (CPs) of MBS users and CP expression of UAV users. The results show that the CPs of UAV users in cell boundaries are significantly increased about 51% by the selective addition of UAVs, when signal to interference ratio (SIR) threshold equals 0dB, compared with the scenario of a single MBS tier network.