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- W3044578481 abstract "With the increasing number of acp{uav} as users of the cellular network, the research community faces particular challenges in providing reliable ac{uav} connectivity. A challenge that has limited research is understanding how the local building and ac{bs} density affects ac{uav}'s connection to a cellular network, that in the physical layer is related to its spectrum efficiency. With more 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 other hand, buildings could be blocking interference from undesirable ac{bs}, improving the link of the ac{uav} to the serving ac{bs}. This paper proposes a ac{rl}-based algorithm to optimise the height of a UAV, as it moves dynamically within a range of heights, with the focus of increasing the UAV spectral efficiency. We evaluate the solution for different ac{bs} and building densities. Our results show that in most scenarios ac{rl} outperforms the baselines achieving up to 125% over naive constant baseline, and up to 20% over greedy approach with up front knowledge of the best height of UAV in the next time step." @default.
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- W3044578481 date "2020-07-27" @default.
- W3044578481 modified "2023-09-27" @default.
- W3044578481 title "Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning." @default.
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