论文标题
移动蜂窝连接的无人机：天空限制的增强学习
Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits
论文作者
论文摘要
蜂窝连接的无人机（UAV）面临着有关连通性和能源效率的几个关键挑战。通过基于学习的策略，我们提出了一种一般新型的多臂匪徒（MAB）算法，以减少无人机的断开时间，移交率和能源消耗，并考虑其完成任务完成的时间。通过将问题作为无人机速度的函数提出，我们通过采用适当的相应学习参数（例如与盲策略相比，HO率降低了50％。但是，结果表明，学习参数的最佳组合在急需取决于任何特定应用以及PI的权重对最终目标函数上。
A cellular-connected unmanned aerial vehicle (UAV)faces several key challenges concerning connectivity and energy efficiency. Through a learning-based strategy, we propose a general novel multi-armed bandit (MAB) algorithm to reduce disconnectivity time, handover rate, and energy consumption of UAV by taking into account its time of task completion. By formulating the problem as a function of UAV's velocity, we show how each of these performance indicators (PIs) is improved by adopting a proper range of corresponding learning parameter, e.g. 50% reduction in HO rate as compared to a blind strategy. However, results reveal that the optimal combination of the learning parameters depends critically on any specific application and the weights of PIs on the final objective function.
