Federated Learning-driven Beam Management in LEO 6G Non-Terrestrial Networks
It addresses efficient beam management for LEO satellite networks, which is an incremental improvement using existing methods on new data.
This work tackled beam management in LEO 6G Non-Terrestrial Networks by investigating Federated Learning-based beam selection, and the result showed that a Graph Neural Network outperformed a Multi-Layer Perceptron in accuracy and stability, especially at low elevation angles.
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.