论文标题
无人用的网络中基于Q网络的深度动态运动策略
Deep Q-Network Based Dynamic Movement Strategy in a UAV-Assisted Network
论文作者
论文摘要
无人驾驶飞机（UAV）辅助通信是提高未来无线网络性能的有前途解决方案，当传统的陆地基础站不可用或不足时，将无人机被部署为增强提供给地面用户的服务质量（QOS）的基站（QOS）。本文提出了一个有效的框架，以管理多个无人驾驶汽车（UAV）的动态运动，以响应地面用户的移动性，以最大程度地提高地面用户的总和数据速率。首先，我们讨论空对地面（A2G）路径损失（PL）与无人机的位置之间的关系。然后提出了基于Q-Network（DQN）的深度方法，以调整无人机的位置，以最大化用户设备（UE）的总和数据率。最后，仿真结果表明，所提出的方法能够在实时条件下调整无人机位置，以改善整个网络的QoS。
Unmanned aerial vehicle (UAV)-assisted communications is a promising solution to improve the performance of future wireless networks, where UAVs are deployed as base stations for enhancing the quality of service (QoS) provided to ground users when traditional terrestrial base stations are unavailable or not sufficient. An effective framework is proposed in this paper to manage the dynamic movement of multiple unmanned aerial vehicles (UAVs) in response to ground user mobility, with the objective to maximize the sum data rate of the ground users. First, we discuss the relationship between the air-to-ground (A2G) path loss (PL) and the location of UAVs. Then a deep Q-network (DQN) based method is proposed to adjust the locations of UAVs to maximize the sum data rate of the user equipment (UE). Finally, simulation results show that the proposed method is capable of adjusting UAV locations in a real-time condition to improve the QoS of the entire network.
