Open AccessDynamic deployment of multi‐UAV base stations with deep reinforcement learning
Author(s) -
Wu Guanhan,
Jia Weimin,
Zhao Jianwei
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12205
Subject(s) - software deployment , reinforcement learning , base station , computer science , real time computing , base (topology) , simulation , artificial intelligence , telecommunications , mathematical analysis , mathematics , operating system
Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (BSs) to provide auxiliary communication services. In this letter, we propose a deep reinforcement learning (DRL)‐based dynamic deployment method for multi‐UAV communications. The phasic policy gradient (PPG) is designed to improve the sample efficiency and the attention of the multi‐UAV deployment. Simulation results are provided to verify the effectiveness of the proposed method.