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Reinforcement Learning-Based Trajectory Optimization of Solar Panel-Equipped UAV BS for Energy Efficiency
Dong Uk Kim, Choong Seon Hong, Seong Bae Park, Jong Won Choi
http://doi.org/10.5626/JOK.2023.50.10.899
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.
Deploying UAV based on Reinforcement Learning for Throughput Maximization in UAV Environments
http://doi.org/10.5626/JOK.2020.47.7.700
Because of the commercialization of the 5G network, many base stations must enhance a reliable communication quality. Thus, many studies are being conducted to provide mobility and economic benefits to the UAVs-Base Station (UAVs-BS) on behalf of the ground base stations. In this paper, we propose a system to identify a location wherein multiple users can access optimal service throughput by considering users’ requirements and the Base Station(BS)’s position in UAVs communication. Based on the Air-To-Ground(A2G) Path Loss Model, the virtual communication environment is established and Max-Min Airtime Fairness is applied for equitable channel usage time distribution according to user requirements. Additionally, the Proximal Policy Optimization (PPO) algorithm is applied to set an optimal location with the maximum throughput. As a result, the proposed systems allow the UAVs to be in the locations with high service throughput for users with different demands.
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