3D Aerial Vehicle Base Station (UAV-BS) Position Planning based on Deep Q-Learning for Capacity Enhancement of Users With Different QoS Requirements

• Main Authors: Guo, Jianli, Huo, Yonghua, Shi, Xiujuan, Wu, Jiahui, Yu, Peng, Feng, Lei, Li, Wenjin
• Format: Conference Proceeding
• Language: English
• Subjects: aerial base station; Atmospheric modeling; Base stations; deep reinforcement learning; DQN; Planning; Quality of service; Reinforcement learning; Spectral efficiency; Three-dimensional displays; Unmanned aerial vehicles
• ISSN: 2376-6506
• DOI: 10.1109/IWCMC.2019.8766625

With the development of modern network, the demand of users has increased dramatically, and more data and services are required. This has caused tremendous pressure on the Macro-cellular network of infrastructure. Air access has become a new solution for the development of communications. Unmanned aerial vehicle (UAV) is used as an air node to improve coverage and capacity. Based on deep Q-Network(DQN) algorithm and considering the different quality of service requirements of different users, this paper proposes an optimal 3D location planning algorithm. The results show that the use of multiple UAVs can not only provide capacity enhancement, but also meet the different QoS requirements of different users. The average spectral efficiency of the system is increased by 15.5%, and user coverage to meet QoS requirements increased by 25.5%.