Performance Analysis of UAV Enabled Disaster Recovery Network: A Stochastic Geometric Framework based on Matern Cluster Processes

Drones will be employed by Facebook and Google for capacity off-loading in front/back hauling scenarios utilizing drone-empowered autonomous heterogeneous networks. But in another application, drone-based, post-disaster recovery of communication networks will also be of crucial importance in the design of future smart cities. So, in order to address the design issues of these latter networks, we present (from a stochastic geometric perspective) a comprehensive statistical framework for the spatial distribution of these hybrid user-centric drone/micro cellular networks. We introduce the novel idea of using a Stenien’s cell (with variable radius) to model the region over which the drones will be distributed and the drones will effectively form a Matern cluster process (MCP) across the original network space. We then employ this newly developed framework to investigate the impact of changing several parameters on the performance of the new drone small-cell clustered networks (DSCCNs) and we develop appropriate closed-form expressions that model the performance (later validated via Monte Carlo simulations).