GNN-based Online Beamforming Design for HAPS-Assisted NTN
arXiv:2606.00244
The paper proposes using a high‑altitude platform station (HAPS) to relay data for cell‑edge users and designs beamforming vectors at both the terrestrial base station and HAPS via an online graph neural network framework to maximize energy efficiency.
Abstract
In terrestrial networks, especially in urban areas, cell-edge users often face significant capacity limitations due to high path loss, shadowing, and inter-cell interference (ICI). This paper proposes integrating a high-altitude platform station (HAPS) into terrestrial networks, where terrestrial base stations (BS) can alleviate these issues by relaying data intended for cell-edge users via HAPS, thereby leveraging line-of-sight (LoS) links. We formulate an energy-efficiency (EE) maximization problem to jointly design beamforming vectors at the BS and HAPS with the goal of improving cell-edge user performance. Since the resulting problem is non-convex, we develop an online optimization framework based on a graph neural networks (GNN), which effectively captures the network topology. Numerical results show that the proposed HAPS-assisted architecture improves network performance, particularly by increasing the 5th-percentile EE, thereby enhancing service for cell-edge users.
7 pages, 6 figures, Accepted for publication in the IEEE 104th Vehicular Technology Conference (VTC2026-Fall)