论文标题

一种深厚的加强学习方法,以实现有效的无人机移动性支持

A Deep Reinforcement Learning Approach to Efficient Drone Mobility Support

论文作者

Chen, Yun, Lin, Xingqin, Khan, Talha Ahmed, Mozaffari, Mohammad

论文摘要

无人机在无数应用中的越来越多的部署依赖于无缝和可靠的无线连接来安全控制和操作无人机。蜂窝技术是为在天空中飞行无人机提供基本无线服务的关键推动力。靶向地面用法的现有蜂窝网络可以支持低空无人机用户的初始部署,但是存在诸如移动性支持之类的挑战。在本文中,我们提出了一个新型的切换框架,以提供有效的移动性支持和可靠的无线连接到陆地蜂窝网络服务的无人机。使用深度加固学习的工具,我们开发了一种深Q学习算法,以动态优化切换决策,以确保针对无人机用户的稳健连接。仿真结果表明,所提出的框架大大减少了移交数量,而牺牲了信号强度较小的相对于基线情况,无人机总是连接到提供最强接收信号强度的基站的情况。

The growing deployment of drones in a myriad of applications relies on seamless and reliable wireless connectivity for safe control and operation of drones. Cellular technology is a key enabler for providing essential wireless services to flying drones in the sky. Existing cellular networks targeting terrestrial usage can support the initial deployment of low-altitude drone users, but there are challenges such as mobility support. In this paper, we propose a novel handover framework for providing efficient mobility support and reliable wireless connectivity to drones served by a terrestrial cellular network. Using tools from deep reinforcement learning, we develop a deep Q-learning algorithm to dynamically optimize handover decisions to ensure robust connectivity for drone users. Simulation results show that the proposed framework significantly reduces the number of handovers at the expense of a small loss in signal strength relative to the baseline case where a drone always connect to a base station that provides the strongest received signal strength.

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