A Unified Spatial Framework for Clustered UAV Networks Based on Stochastic Geometry

Abstract

To evaluate the system-level performance of clustered unmanned aerial vehicle (UAV) networks, we apply stochastic geometry in order to establish a tractable analytical framework. The users' locations are modeled as Poisson cluster processes (PCPs) and the UAVs are assumed to hover above cluster centers at a fixed altitude. In order to enhance the generality of the analysis, two typical patterns of PCPs, namely Thomas cluster process and Matern cluster process, are analyzed. Based on this spatial framework, a unified expression for the coverage probability is derived, for both millimeter wave (mmWave) and sub-6 GHz scenarios. Theoretical and numerical results demonstrate that mmWave outperforms sub-6 GHz, and an optimal altitude of UAVs exists, which maximizes the coverage probability. This result indicates that in most cases UAVs perform better than traditional terrestrial base stations, due to their mobility.