Pragmatic Earth-Fixed Beam Management for 3GPP NTN Common Signaling in LEO Satellites

This work proposes a pragmatic method for the design of beam footprint layouts and beam hopping illumination patterns to efficiently broadcast 3GPP NTN common signaling to large coverage areas using EIRP-limited LEO satellites. This method minimizes the time resources required to sweep over the whole coverage while ensuring that the signal-to-interference-plus-noise ratio received by users is above a given threshold. It discusses the design of: (i) an Earth-fixed grid of beam layouts; (ii) beamforming vectors and beam power allocation; (iii) beam hopping patterns and (iv) space, time and frequency resource allocation of 3GPP common signaling. Two main beam layout solutions are proposed to significantly reduce the number of beams required to illuminate the coverage area: one based on phased array beams with low beam crossover levels and the other on widened beams. A numerical evaluation using practical system parameters showed that both solutions perform similarly, but that the best result is obtained with phased arrays beams with optimized beam cross over levels. Indeed, for the system evaluated, they allowed reducing the total number of beams from 1723 to 451, which combined with a proper beam hopping pattern and scheduling scheme allowed obtaining a coverage ratio of 100% and a common signaling efficiency (i.e. number of slots carrying common signaling over total number of slots) up to 80.6% for the most stringent common signaling periodicity of 20 ms considered by 3GPP.

Kolmogorov-Arnold Networks (KANs) for Time Series Analysis

This paper introduces a novel application of Kolmogorov-Arnold Networks (KANs) to time series forecasting, leveraging their adaptive activation functions for enhanced predictive modeling. Inspired by the Kolmogorov-Arnold representation theorem, KANs replace traditional linear weights with spline-parametrized univariate functions, allowing them to learn activation patterns dynamically. We demonstrate that KANs outperforms conventional Multi-Layer Perceptrons (MLPs) in a real-world satellite traffic forecasting task, providing more accurate results with considerably fewer number of learnable parameters. We also provide an ablation study of KAN-specific parameters impact on performance. The proposed approach opens new avenues for adaptive forecasting models, emphasizing the potential of KANs as a powerful tool in predictive analytics.

From 5G to 6G: Revolutionizing Satellite Networks through TRANTOR Foundation

5G technology will drastically change the way satellite internet providers deliver services by offering higher data speeds, massive network capacity, reduced latency, improved reliability and increased availability. A standardised 5G ecosystem will enable adapting 5G to satellite needs. The EU-funded TRANTOR project will seek to develop novel and secure satellite network management solutions that allow scaling up heterogeneous satellite traffic demands and capacities in a cost-effective and highly dynamic way. Researchers also target the development of flexible 6G non-terrestrial access architectures. The focus will be on the design of a multi-orbit and multi-band antenna for satellite user equipment (UE), as well as the development of gNodeB (gNB) and UE 5G non-terrestrial network equipment to support multi-connectivity.