标题： REQIBA：用于智能无人机蜂窝用户到基站关联的回归和深度Q学习 显示英文标题

标题： REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association

摘要： 无人机（UAV）正在成为下一代蜂窝网络的重要用户。 通过在空中运行，这些UAV用户由于诸如强视距（LoS）信道（和干扰）以及基站（BS）天线不对准等因素，经历了与地面用户非常不同的无线条件。 结果是，当UAV移动并且频繁切换时，它们的接收服务质量会显著下降。 解决方案是让UAV了解其周围环境，并利用这种感知智能地连接到蜂窝网络。 在本文中，我们提出了用于智能UAV蜂窝用户到基站关联的回归和深度Q学习方法（REQIBA），以允许在城市区域上空飞行的UAV使用接收到的信号功率、BS位置和周围建筑拓扑信息来智能地连接到底层BS。 我们展示了与通常由地面用户使用的启发式关联方案类似的方案相比，REQIBA可以使总UAV吞吐量翻倍。 我们还评估了环境因素如UAV高度、建筑密度以及由于切换导致的吞吐量损失对我们的解决方案性能的影响。 显示英文摘要

摘要： Unmanned Aerial Vehicles (UAVs) are emerging as important users of next-generation cellular networks. By operating in the sky, these UAV users experience very different radio conditions than terrestrial users, due to factors such as strong Line-of-Sight (LoS) channels (and interference) and Base Station (BS) antenna misalignment. The consequence of this is that the UAVs experience significant degradation to their received quality of service, particularly when they are moving and are subject to frequent handovers. The solution is to allow the UAV to be aware of its surrounding environment, and intelligently connect into the cellular network using this awareness. In this paper we present REgression and deep Q-learning for Intelligent UAV cellular user to Base station Association (REQIBA) to allow a UAV which is flying over an urban area to intelligently connect to underlying BSs, using information about the received signal powers, the BS locations, and the surrounding building topology. We demonstrate how REQIBA can as much as double the total UAV throughput, when compared to heuristic association schemes similar to those commonly used by terrestrial users. We also evaluate how environmental factors such as UAV height, building density, and throughput loss due to handovers impact the performance of our solution.