UAV Access Point Placement for Connectivity to a User with Unknown Location Using Deep RL
UAV Access Point Placement for Connectivity to a User with Unknown Location Using Deep RL
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DOI：
10.1109/gcwkshps45667.2019.9024644
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Enes Krijestorac;Samer S. Hanna;D. Cabric
中科院分区：
文献类型：
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作者：
Enes Krijestorac;Samer S. Hanna;D. Cabric
近年来，无人机 (UAV) 已被考虑用于电信目的，用作中继、缓存或物联网数据收集器。除了易于部署之外，它们的可操作性还允许它们调整其位置，以优化地面用户设备的链路或基站的链路的容量。之前分析此类无人机最佳放置的大部分工作至少做出以下两个假设之一：可以使用简单模型来预测信道，或者已知地面上用户的位置。在本文中，我们使用深度强化学习（深度 RL）在城市环境中以最佳方式放置为地面用户提供服务的无人机，而无需事先了解通道或用户位置。我们的算法依赖于信号与干扰加噪声比 (SINR) 测量和拓扑的 3D 地图来考虑阻塞和散射体。此外，它设计用于在任何城市环境中运行。光线追踪软件模拟条件下的结果表明，在最大迭代次数的限制下，我们的算法收敛到目标 SINR 的成功率为 90%。
In recent years, unmanned aerial vehicles (UAVs) have been considered for telecommunications purposes as relays, caches, or IoT data collectors. In addition to being easy to deploy, their maneuverability allows them to adjust their location to optimize the capacity of the link to the user equipment on the ground or of the link to the basestation. The majority of the previous work that analyzes the optimal placement of such a UAV makes at least one of two assumptions: the channel can be predicted using a simple model or the locations of the users on the ground are known. In this paper, we use deep reinforcement learning (deep RL) to optimally place a UAV serving a ground user in an urban environment, without the previous knowledge of the channel or user location. Our algorithm relies on signal-to-interference-plus- noise ratio (SINR) measurements and a 3D map of the topology to account for blockage and scatterers. Furthermore, it is designed to operate in any urban environment. Results in conditions simulated by a ray tracing software show that with the constraint on the maximum number of iterations our algorithm has a 90% success rate in converging to a target SINR.