论文标题
使用可概括的深钢筋学习方法，交通觉醒的无人机位置
Traffic-Aware UAV Placement Using a Generalizable Deep Reinforcement Learning Methodology
论文作者
论文摘要
在极端情况下，使用充当飞行接入点（FAP）的无人飞行器（UAV）可提供按需无线连接。尽管进行了持续的研究，但根据动态用户的流量需求，无人机的头寸优化仍然具有挑战性。我们提出了流量意识的无人机放置算法（TUPA），该算法将无人机根据用户的流量需求定位为FAP，以最大程度地提高网络实用程序。使用DRL方法使FAP能够自主学习并适应网络方案的动态条件和要求。此外，拟议的DRL方法使TUPA可以将培训期间获得的知识概括为用户职位和交通需求的未知组合，而没有额外的培训。使用网络模拟器NS-3和NS3-GYM框架对TUPA进行了训练和评估。结果表明，与基线解决方案相比，TUPA增加了网络公用事业，在具有异质交通需求的情况下，平均网络实用程序最高为4倍。
Unmanned Aerial Vehicles (UAVs) acting as Flying Access Points (FAPs) are being used to provide on-demand wireless connectivity in extreme scenarios. Despite ongoing research, the optimization of UAVs' positions according to dynamic users' traffic demands remains challenging. We propose the Traffic-aware UAV Placement Algorithm (TUPA), which positions a UAV acting as FAP according to the users' traffic demands, in order to maximize the network utility. Using a DRL approach enables the FAP to autonomously learn and adapt to dynamic conditions and requirements of networking scenarios. Moreover, the proposed DRL methodology allows TUPA to generalize knowledge acquired during training to unknown combinations of users' positions and traffic demands, with no additional training. TUPA is trained and evaluated using network simulator ns-3 and ns3-gym framework. The results demonstrate that TUPA increases the network utility, compared to baseline solutions, increasing the average network utility up to 4x in scenarios with heterogeneous traffic demands.
