用联邦学习提升低轨卫星网络的波束管理效率
Federated Learning-driven Beam Management in LEO 6G Non-Terrestrial Networks
- 通过联邦学习让高海拔平台分布式训练波束选择模型
- 图神经网络比多层感知机在低仰角下精度更高更稳定
- 适合6G非地面网络智能波束管理研究者参考
低地球轨道（LEO）非地面网络（NTN）在动态传播条件下需要高效的波束管理。本文研究基于联邦学习（FL）的LEO卫星星座波束选择方法，利用高空平台站（HAPS）作为分布式学习节点。采用多层感知机（MLP）和图神经网络（GNN）两种模型，基于真实信道与波束成形数据进行评估。结果表明，GNN在波束预测准确性和稳定性上优于MLP，尤其在低仰角场景表现突出，为未来NTN部署提供了轻量化、智能化的波束管理方案。
原文摘要 · Abstract (English)
Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) require efficient beam management under dynamic propagation conditions. This work investigates Federated Learning (FL)-based beam selection in LEO satellite constellations, where orbital planes operate as distributed learners through the utilization of High-Altitude Platform Stations (HAPS). Two models, a Multi-Layer Perceptron (MLP) and a Graph Neural Network (GNN), are evaluated using realistic channel and beamforming data. Results demonstrate that GNN surpasses MLP in beam prediction accuracy and stability, particularly at low elevation angles, enabling lightweight and intelligent beam management for future NTN deployments.
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