GNN-RSMA: An Interference Management Framework for a Large-Scale HAPS Network

2026-07-31

AI Summary

This work addresses the challenge of highly correlated channels and severe intra-resource-block interference in high-altitude platform station (HAPS) networks, caused by strong line-of-sight links and wide coverage. To tackle this issue, the paper proposes a novel approach that integrates graph neural networks (GNNs) with rate-splitting multiple access (RSMA) for the first time. By clustering users to construct a heterogeneous graph model, the method efficiently optimizes the power allocation between common and private streams in RSMA to maximize the minimum spectral efficiency. The proposed scheme significantly reduces computational complexity while ensuring user fairness and enhancing the performance of the worst-case user. It outperforms conventional multiple access schemes and achieves performance comparable to optimization based on successive convex approximation (SCA).

Abstract

Integrating non-terrestrial networks (NTN) with terrestrial infrastructure is a key enabler of next-generation wireless systems, providing ubiquitous connectivity while meeting stringent rate and latency requirements. In particular, high altitude platform stations (HAPS) can complement terrestrial networks and jointly form vertical heterogeneous networks (vHetNets), extending coverage while delivering high-capacity, reliable, and low-latency connectivity for user equipments (UEs) including ground users and uncrewed aerial vehicles (UAVs). However, the high altitude deployment of HAPS establishes strong line-of-sight (LoS) links to UEs, creating highly correlated channels among UEs. Moreover, the wide coverage footprint of HAPS enables it to serve a large number of UEs, forcing limited radio resources to be shared among many UEs and resulting in significant intra-resource block (RB) interference. To address this challenge, we propose an interference management scheme based on UE clustering and rate-splitting multiple access (RSMA). Specifically, the network is modeled as a heterogeneous graph, and a graph neural network (GNN) is developed to efficiently allocate the common and private RSMA powers, maximizing the minimum spectral efficiency (SE) in a fast and scalable manner. Simulation results demonstrate that the proposed GNN-RSMA interference management algorithm outperforms conventional multiple access schemes while achieving fairness and worst-user performance comparable to successive convex approximation (SCA)-based optimization at only a fraction of its computational cost.

Problem

• HAPS
• interference management
• rate-splitting multiple access
• non-terrestrial networks
• resource block interference

Innovation

• Graph Neural Network
• Rate-Splitting Multiple Access
• HAPS
• Interference Management
• Heterogeneous Graph