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Federated Learning-driven Beam Management in LEO 6G Non-Terrestrial Networks

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Bartsioka, Maria Lamprini, Bartsiokas, Ioannis A., Panagopoulos, Athanasios D., Kaklamani, Dimitra I., Venieris, Iakovos S.
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Space Physics
Accès en ligne:	Acceder al recurso
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author	Bartsioka, Maria Lamprini Bartsiokas, Ioannis A. Panagopoulos, Athanasios D. Kaklamani, Dimitra I. Venieris, Iakovos S.
author_facet	Bartsioka, Maria Lamprini Bartsiokas, Ioannis A. Panagopoulos, Athanasios D. Kaklamani, Dimitra I. Venieris, Iakovos S.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2603_10983
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Federated Learning-driven Beam Management in LEO 6G Non-Terrestrial Networks Bartsioka, Maria Lamprini Bartsiokas, Ioannis A. Panagopoulos, Athanasios D. Kaklamani, Dimitra I. Venieris, Iakovos S. Machine Learning Space Physics 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.
title	Federated Learning-driven Beam Management in LEO 6G Non-Terrestrial Networks
topic	Machine Learning Space Physics
url	https://arxiv.org/abs/2603.10983