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Abstract
Recent advancements in drone technology and base station miniaturization, together with an urgent need to reduce site rental costs, have created the unique opportunity to deploy cellular networks on a platform of mobile drones. This new development is redefining the wireless networks, as drone base stations can autonomously move in space to improve coverage and capacity of the network, tremendously enhancing the Quality of Service for conventional cell-edge users.
In this research, we explore the benefit of constantly moving drone base stations in the air to reduce the distance between the base stations and the mobile user equipments, thereby improving the performance of the cellular networks.
In particular, this thesis makes three fundamental contributions.
First, we analyse drone manoeuvrability using theory, emulation and real field experiments to find the relationship between flying speed, turning agility and energy consumption. Under the control of our developed Android program, we reveal some practical manoeuvrability factors that must be considered for the applications that require frequent changes of direction for the drone. Second, we propose drone mobility control algorithms to decide on drones' moving directions in order to improve the performance of drone base stations in the network area. As the optimal problem is NP-hard, we propose a range of practically realizable heuristics with varying complexity and performance. The proposed algorithms are evaluated taking the practical drones' limitations into account for micro hotspots scenario where many hotspots exist next to each other and a drone is deployed over each hotspot area. We show that our proposed heuristic algorithms can readily improve spectral efficiency by 34% and the 5th-percentile packet throughput by 50% compared to the scenario where drones hover over fixed locations. Third, we consider macro hotspot scenario, where users and drones can move freely in a large area. Particular challenges such as user association and physical collision among drones are addressed. We show that our proposed algorithms can achieve a significant 67\% packet throughput and 343% 5th-percentile packet throughput improvement for macro hotspot scenario. We further demonstrate that our proposed algorithms are robust against the various drone base station and user densities in the network area, and huge improvement can be achieved.
We believe that our findings in this thesis shed new light on the fundamental benefits of drone base stations in the next generation cellular networks.