论文标题

使用增强学习,用于针对细胞连接的无人机的自适应高度优化

Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning

论文作者

Fonseca, Erika, Galkin, Boris, Amer, Ramy, DaSilva, Luiz A., Dusparic, Ivana

论文摘要

与蜂窝连接的无人机提供可靠的连通性可能非常具有挑战性。它们的性能在很大程度上取决于周围环境的性质,例如地面BS的密度和高度。另一方面,高层建筑物可能会阻止地面BS的不希望的干扰信号,从而改善无人机及其服务BS之间的连通性。为了解决这种环境中无人机的连通性,本文提出了一种RL算法,以动态优化无人机在环境中移动时的高度,以提高其体验到的吞吐量或光谱效率。在两种设置中评估了所提出的解决方案:使用一系列生成的环境,在该环境中我们改变了BS和构建密度的数量,以及使用从爱尔兰都柏林的实验获得的现实世界数据的情况。结果表明,根据情况,我们提出的基于RL的解决方案将无人机QoS提高了6%至41%。我们还得出结论,当高度高于建筑物的高度飞行时,建筑物的密度变化对无人机QoS没有影响。另一方面,BS密度会对无人机QoS产生负面影响,而BSS数量越高会产生更多的干扰和无人机性能。

Providing reliable connectivity to cellular-connected UAV can be very challenging; their performance highly depends on the nature of the surrounding environment, such as density and heights of the ground BSs. On the other hand, tall buildings might block undesired interference signals from ground BSs, thereby improving the connectivity between the UAVs and their serving BSs. To address the connectivity of UAVs in such environments, this paper proposes a RL algorithm to dynamically optimise the height of a UAV as it moves through the environment, with the goal of increasing the throughput or spectrum efficiency that it experiences. The proposed solution is evaluated in two settings: using a series of generated environments where we vary the number of BS and building densities, and in a scenario using real-world data obtained from an experiment in Dublin, Ireland. Results show that our proposed RL-based solution improves UAVs QoS by 6% to 41%, depending on the scenario. We also conclude that, when flying at heights higher than the buildings, building density variation has no impact on UAV QoS. On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.

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