GNN-RSMA：一种用于大规模高海拔平台站（HAPS）网络的干扰管理框架
GNN-RSMA: An Interference Management Framework for a Large-Scale HAPS Network
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中文总结 AI 辅助
该研究针对大规模HAPS网络的RB内干扰问题，提出基于UE聚类与RSMA的GNN-RSMA干扰管理框架，仿真显示其性能优于传统方案，计算成本远低于SCA优化。
中文摘要 AI 辅助
将非地面网络（NTN）与地面基础设施融合是下一代无线系统的关键支撑，可在满足严苛速率与延迟要求的同时实现无处不在的连接。其中，高海拔平台站（HAPS）可作为地面网络的补充，共同构成垂直异构网络（vHetNets），为地面用户、无人机（UAVs）等用户设备（UEs）拓展覆盖范围，提供高容量、可靠且低延迟的连接。但HAPS的高空部署使其与UE间建立强视距（LoS）链路，导致UE间信道高度相关；同时HAPS覆盖范围广，需为大量UE共享有限的无线资源，产生显著的资源块（RB）内干扰。为解决该挑战，本文提出一种基于UE聚类和速率拆分多址接入（RSMA）的干扰管理方案：将网络建模为异构图，开发图神经网络（GNN）以高效分配RSMA的公共与私有功率，快速且可扩展地最大化最小频谱效率（SE）。仿真结果表明，所提GNN-RSMA干扰管理算法性能优于传统多址接入方案，其公平性与最差用户性能可与基于逐次凸近似（SCA）的优化相媲美，而计算成本仅为后者的一小部分。
英文摘要
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.
