Integrating UAVs into Existing Wireless Networks: A Stochastic Geometry Approach

TL;DR

This paper analyzes the integration of UAVs as base stations into existing wireless networks using stochastic geometry, exploring coverage, rate performance, and the tradeoffs involved in network heterogeneity and user mobility.

Contribution

It provides an analytical framework for evaluating UAV-based base stations within a three-tier network, considering joint and split control/data architectures for stationary and mobile users.

Findings

UAV base stations can enhance coverage and capacity.

Splitting control and data planes benefits mobile users.

Network heterogeneity impacts handover rates and capacity gains.

Abstract

The integration of unmanned aerial vehicles (UAVs) into wireless networks has opened a new horizon to meet the capacity and coverage requirements foreseen in future wireless networks. Harnessing UAVs as flying base stations (BSs) has helped to achieve a cost-effective and on-the-go wireless network that may be used in several scenarios such as to support disaster response and in temporary hotspots. Despite the extensive research on the application side of UAVs, the integration of UAVs as BSs into existing wireless networks remains an open challenge. While UAV BSs can help to increase the area spectral efficiency, the added network heterogeneity and BS densification may diminish the capacity gains due to increased handover rates. In this work, we shed some light on this tradeoff by studying a three tier network architecture consisting of macro, small, and UAV BSs and analyze its coverage and rate performance. In particular, we consider joint control/data and split architectures and express the rate performance for stationary and mobile users, using an analytical approach based on stochastic geometry. While both joint and split architectures offer similar performance for stationary users, the numerical results suggest the splitting of control and data plane for mobile users.