| dc.description.abstract | Cellular data demand continues to increase from year to year, and to manage this rising
demand network operators adopt new technologies and designs for their cellular networks.
Among these, network densification is seen as a viable method of increasing network capacity in dense urban environments. This densification will consist of network operators deploying miniaturised base stations (referred to as small cells or picocells) at areas of particularly high user traffic (which we refer to as hotspots). These small cells have very small coverage areas compared to conventional base stations, and would supplement the existing base station networks in providing cellular service to users.
The recent proliferation of Unmanned Aerial Vehicles (UAVs) in hobbyist and commercial
fields has attracted the attention of network operators, and UAVs are beginning to see
integration into cellular networks for covering low-density areas, as well as for emergency applications. In this thesis we explore the possibility of using small, quadcopter-style UAVs which carry small cells to deliver wireless service to users in urban environments, for dealing with demand hotspots. Because of their airborne nature and their ability to intelligently move in three-dimensional space, UAVs small cells will achieve very different coverage and capacity performance, when compared to static terrestrial infrastructure. A review of the existing state-of-the-art from the wireless community on UAV operation shows that certain important aspects of UAV networks have not been adequately explored. These aspects include: the impact of inter-cell UAV interference, the impact of the wireless backhaul infrastructure design, and the impact of UAV recharging techniques. While the wireless community has adopted stochastic geometry for analysing the performance of terrestrial networks, there is currently a lack of UAV network models which capture all of the unique features of UAV networks. The purpose of this work is to analyse the performance of UAV small cell networks when they are used to serve users in dense urban environments, and to explore how the various design parameters of the UAV network impact this performance. We consider both the user access as well as the wireless backhaul links in our analysis.
Our contributions are as follows:
We provide a stochastic geometry model for UAV small cells in interference-limited urban
environments, when positioned above user hotspots. This model captures the impact of
several UAV network and environmental parameters, such as the height of the UAVs above
ground, their density per unit area, their antenna directionality, the geometry of the buildings in the area, as well as the size of user hotspots.
Using our model we demonstrate that there exists an optimum height that the UAVs should
operate at, to maximise the achieveable performance. This height mitigates the impact of
both signal blockage due to buildings, as well as interference from other UAVs. The value ofthe optimum height is shown to be a function of the other network parameters.We also demonstrate that, for sufficiently large densities of UAVs in the network, the UAVs
should be spaced out in a regular grid to mitigate interference and improve network
performance, rather than be positioned above the hotspots to minimise distance to the users.
For the wireless backhaul link we consider a dedicated network of Ground Stations (GSs)
which are deployed exclusively for UAV backhaul connectivity. We compare the backhaul
performance of the dedicated GS network against the performance when UAVs backhaul
through the existing terrestrial base station network. We demonstrate how the GS network is necessary for providing wireless backhaul connectivity unless the UAVs are equipped with high-performance antennas.
We consider sub-6GHz and millimeter-wave technologies and demonstrate that both have
their strengths and weaknesses when used for the wireless backhaul. The sub-6GHz
technology can allow for simpler GS deployment, as existing base station sites may be reused. The millimeter-wave backhaul, on the other hand, provides for superior performance due to larger bandwidths and active beam steering, which mitigates interference.
We model the battery consumption behaviour of the UAVs and demonstrate several methods
that can be used to recharge UAV batteries while ensuring that the UAV network continues
to provide service to user hotspots. We demonstrate how these methods impact the network
performance in different ways, whether it is capital expenditure, downtime, or restrictions on the range of acceptable UAV heights. We also review several upcoming developments in the field of battery technology, and demonstrate how UAV small cells may be able to operate for longer when the technologies enter use.
We conclude with a discussion of the legal restrictions on UAV flight, and how they are being gradually relaxed to accomodate commercial adoption of UAVs. We then discuss possible
research directions for our future work. | en |
