Hierarchical Resource Management, Node Placement, and User Association in ATINs

Abstract

High-altitude platform base stations (HAP-BSs) offer capabilities comparable to those of low-Earth orbit satellites in providing communication support for next-generation wireless networks. They are particularly efficient when integrated with uncrewed aerial vehicle base stations (UAV-BSs). However, UAV-BSs encounter practical challenges, such as limited battery lifetime and the necessity for uninterrupted line-of-sight communication, which can constrain their operational efficiency. To address these challenges, this study proposes a hierarchical framework that enhances the coordination and efficiency of UAV-BSs in conjunction with HAP-BSs in the aerial-terrestrial integrated network (ATIN). First, a data-driven genetic algorithm is employed to determine the optimal locations for UAV-BS hovering and landing, considering user demand and operational constraints. This ensures that the UAV-BSs are deployed where they are most effective. Second, resource allocation between HAP-BSs and UAV-BSs is managed through a Stackelberg evolutionary game, wherein HAP-BSs define resource usage policies and UAV-BSs adapt their offloading strategies in response to changing conditions. This approach ensures adaptability to evolving demands while improving both energy efficiency and user satisfaction. The simulation results validate the framework and demonstrate significant improvements in these metrics compared with conventional UAV-BS deployment strategies.