Asia-Pacific Network Operations and Management Symposium
Multi-UAVs Collaboration System based on Machine Learning for Throughput Maximization
Yu Min Park, Minkyung Lee, Choong Seon Hong,
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Due to commercialization of the 5G network, many base stations need to enhance a reliable communication quality. Thus, many studies have still worked to provide mobility and economic benefits to the UAVs-Base Station (UAVs-BS) on behalf of ground base stations. In this paper, we propose a system to find a location where multiple users can have an optimal service throughput by considering users' requirements in Multi-UAVs communication. Based on the Air-To-Ground Path Loss Model, the virtual communication environment is established and Airtime Fairness is applied for equitable channel usage time distribution according to user requirements. Thus, we apply a collaborative algorithm with modified K-means that can distribute users to each UAV and solve communication overload problems. In addition, 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 Multi-UAVs to be in the locations with high service throughput for users with different demands.