Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning
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With the increasing number of uav as users of the cellular network, the research community faces particular challenges in providing reliable uav connectivity. A challenge that has limited research is understanding how the local building and bs density affects uav's connection to a cellular network, that in the physical layer is related to its spectrum efficiency. With more bs, the uav connectivity could be negatively affected as it has los to most of them, decreasing its spectral efficiency. On the other hand, buildings could be blocking interference from undesirable bs, improving the link of the uav to the serving bs. This paper proposes a 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 bs and building densities. Our results show that in most scenarios 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.
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