AI RESEARCH
Federated Learning-driven Beam Management in LEO 6G Non-Terrestrial Networks
arXiv CS.LG
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ArXi:2603.10983v1 Announce Type: new 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.