Joint 3D Deployment and Power Allocation for UAV-BS: A Deep Reinforcement Learning Approach

• Published in: IEEE wireless communications letters Vol. 10; no. 10; pp. 2309 - 2312
• Main Authors: Zhang, Meng, Fu, Shu, Fan, Qilin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D deployment; Algorithms; Deep learning; deep reinforcement learning; Genetic algorithms; Hovering; Machine learning; Optimization; power allocation; Resource management; Simulation; Three-dimensional displays; Throughput; Unmanned aerial vehicle; Unmanned aerial vehicles; Wireless communication
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2021.3100388

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.