Dong Uk Kim  Choong Seon Hong  Seong Bae Park  Jong Won Choi

10.5626/JOK.2023.50.10.899

UAV base station Wireless Communication Reinforcement Learning Energy Harvesting

5G and B5G wireless communication systems use new bands, such as millimeter-wave, to meet user requirements. However, these new bands have limitations such as lower diffraction, lower transmittance, and stronger straightness than traditional frequency bands. To address these limitations, a cellular communication paradigm supported by Unmanned Aerial Vehicle (UAV), makes communication services more flexible than existing ground base stations. However, UAVs have limited battery capacity, which affects the life of telecommunications services. To address this problem, this paper considers UAVs equipped with solar panels. Movement toward energy generation and altitude for user data rate maximization due to solar power of UAVs can consume a lot of energy. Energy generation, data rate maximization, and energy consumption have a trade-off relationship. Therefore, in this study, we proposed a system to locate UAVs that could optimize the above trade-off relationship using agents learned using a reinforcement learning algorithm called "Proximal Policy Optimization (PPO)" and compare the system proposed in this paper.