Maria Lamprini Bartsioka

arXiv2026

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

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.

cs.LG↗

arXiv2026

Interoperable rApp/xApp Control over O-RAN for Mobility-aware Dynamic Spectrum Allocation

Open Radio Access Networks (O-RAN) enable the disaggregation of radio access functions and the deployment of control applications across different timescales. However, designing interoperable control schemes that jointly exploit long-term traffic awareness and near-real-time radio resource optimization remains a challenging problem, particularly under dense multi-cell interference and heterogeneous service demands. This paper proposes an interoperable rApp/xApp-driven dynamic spectrum allocation (DSA) framework for O-RAN, based on a graph-theoretic formulation of physical resource block (PRB) assignment. The proposed architecture leverages a non-real-time radio intelligent controller (Non-RT RIC) rApp to predict aggregated traffic evolution and generate high-level spectrum policies at the minutes timescale, while a near-real-time RIC (Near-RT RIC) xApp constructs a user-centric conflict graph and performs fairness-aware PRB allocation at sub-second timescales. To mitigate persistent user starvation, a conflict-aware modified proportional fair (MPF) scheduling mechanism is applied, enabling controlled interference-free PRB time-sharing. Extensive simulation results demonstrate that the proposed framework significantly improves the PRB assignment success rate (above 90%) and service-share fairness (above 85%) across different channel configurations and user demands, while maintaining architectural separation and rApp/xApp interoperability in accordance with O-RAN principles.

cs.NI↗