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.

翻译：低地球轨道（LEO）非地面网络（NTNs）需要在动态传播条件下实现高效的波束管理。本研究探讨了基于联邦学习（FL）的LEO卫星星座波束选择方案，其中各轨道平面通过利用高空平台站（HAPS）作为分布式学习节点。采用真实信道与波束赋形数据，对多层感知机（MLP）和图神经网络（GNN）两种模型进行了评估。结果表明，GNN在波束预测精度与稳定性方面均优于MLP，尤其在低仰角场景下表现突出，从而为未来NTN部署提供了轻量化智能波束管理方案。