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Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning

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Document pages: 12 pages

Abstract: With the increasing number of acp{uav} as users of the cellular network, theresearch community faces particular challenges in providing reliable ac{uav}connectivity. A challenge that has limited research is understanding how thelocal building and ac{bs} density affects ac{uav} s connection to a cellularnetwork, that in the physical layer is related to its spectrum efficiency. Withmore acp{bs}, the ac{uav} connectivity could be negatively affected as it has ac{los} to most of them, decreasing its spectral efficiency. On the otherhand, buildings could be blocking interference from undesirable ac{bs},improving the link of the ac{uav} to the serving ac{bs}. This paper proposesa ac{rl}-based algorithm to optimise the height of a UAV, as it movesdynamically within a range of heights, with the focus of increasing the UAVspectral efficiency. We evaluate the solution for different ac{bs} andbuilding densities. Our results show that in most scenarios ac{rl} outperformsthe baselines achieving up to 125 over naive constant baseline, and up to20 over greedy approach with up front knowledge of the best height of UAV inthe next time step.

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