Due to its high mobility and low cost, unmanned aerial vehicle mounted base station (UAV-BS) can be deployed in a fast and cost-efficient manner for providing wireless services in areas where traditional terrestrial infrastructures cannot be laid for technical and economic reasons. In this letter, we investigate the problem of joint three-dimensional (3D) deployment and power allocation for maximizing the system throughput in a UAV-BS system. To solve this non-convex problem, we propose a deep deterministic policy gradient (DDPG) based algorithm. The proposed algorithm allows the UAV-BS to explore in continuous state and action spaces to learn the optimal 3D hovering location and power allocation. Simulation results show that the proposed algorithm outperforms the traditional deep Q-learning-based method and genetic algorithm.

中文翻译：

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UAV-BS 的联合 3D 部署和功率分配：一种深度强化学习方法

由于其高机动性和低成本，无人机车载基站（UAV-BS）可以以快速且具有成本效益的方式部署，为因技术和经济原因无法铺设传统地面基础设施的地区提供无线服务。在这封信中，我们研究了联合三维 (3D) 部署和功率分配问题，以最大限度地提高 UAV-BS 系统中的系统吞吐量。为了解决这个非凸问题，我们提出了一种基于深度确定性策略梯度（DDPG）的算法。所提出的算法允许 UAV-BS 在连续状态和动作空间中进行探索，以学习最佳 3D 悬停位置和功率分配。仿真结果表明，该算法优于传统的基于深度Q学习的方法和遗传算法。