标题： 需求：基于回归和深度Q学习的智能无人机 cellular 用户到基站关联 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）正成为下一代蜂窝网络的重要用户。 由于强视距（LoS）信道（以及干扰）和基站（BS）天线错位等因素的影响，无人机用户经历的无线环境与地面用户非常不同。 因此，当无人机移动并频繁切换时，它们可能会遭受显著的服务质量下降。 解决方案是让无人机能够感知周围环境，并利用这种感知智能地连接到蜂窝网络。 本文提出了基于回归和深度Q学习的智能无人机蜂窝用户到基站关联（REQIBA），该方案允许无人机在城市地区飞行时，利用接收到的信号功率、基站位置和周围建筑拓扑信息，智能地连接到底层基站。 我们展示了与地面用户常用的启发式关联方案相比，REQIBA最多可以将总无人机吞吐量提高一倍。 我们还评估了诸如无人机高度、建筑物密度以及因切换导致的吞吐量损失等环境因素对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, 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. As a consequence, the UAVs may 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 taking advantage of this awareness. In this paper we present REgression and deep Q-learning for Intelligent UAV cellular user to Base station Association (REQIBA), a solution that allows a UAV 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.