The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs). However, the complexity, mobility, and heterogeneity of NTN entities and resources present various challenges from system design to deployment. This paper proposes a novel approach to designing a heterogeneous network consisting of HAPSs and unmanned aerial vehicles (UAVs) being LAPS entities. Our approach involves jointly optimizing the three-dimensional trajectory and channel allocation for aerial base stations, with a focus on ensuring fairness and the provision of quality of service (QoS) to ground users. Furthermore, we consider the load on base stations and incorporate this information into the optimization problem. The proposed approach utilizes a combination of deep reinforcement learning and fixed-point iteration techniques to determine the UAV locations and channel allocation strategies. Simulation results reveal that our proposed deep learning-based approach significantly outperforms learning-based and conventional benchmark models.
Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity
Department of Electrical Engineering and Computer Science, York University（约克大学电气工程与计算机科学系）；Samsung Research America（三星美国研究院）；Department of Software and IT Engineering, École de technologie supérieure (ÉTS), University of Quebec（魁北克大学软件与信息技术工程系，École de technologie supérieure）；Non-Terrestrial Networks (Carleton-NTN) Lab and the Department of Systems and Computer Engineering, Carleton University（非地面网络（Carleton-NTN）实验室和系统与计算机工程系，卡尔顿大学）
Slot-hopping Enabled Loiter Guidance and Automation for Fixed-wing UAV Corridors
American Institute of Aeronautics and Astronautics, Inc.（美国航空航天学会）；Department of Mechatronics Engineering（机械电子工程系）；Department of Aerospace Engineering（航空航天工程系）
