  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.
