GNN-based Online Beamforming Design for HAPS-Assisted NTN

2026-05-29

AI Summary

This work addresses the low energy efficiency experienced by cell-edge users in terrestrial cellular networks, which stems from path loss, shadowing, and inter-cell interference. To mitigate these challenges, the authors propose a high-altitude platform station (HAPS)-assisted cooperative beamforming architecture that leverages line-of-sight links between HAPS and ground base stations for data relaying. An innovative online optimization framework integrating graph neural networks (GNNs) is developed to effectively model the dynamic network topology and solve the non-convex energy efficiency maximization problem in real time. Experimental results demonstrate that the proposed approach significantly improves the 5th-percentile network energy efficiency, thereby substantially enhancing quality of service for edge users.

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.

Problem

• cell-edge users
• inter-cell interference
• path loss
• shadowing
• energy efficiency

Innovation

• Graph Neural Networks
• HAPS-assisted NTN
• Online Beamforming
• Energy Efficiency Maximization
• Cell-edge Users