5 papers
Learn More by Using Less: Distributed Learning with Energy-Constrained Devices
Roberto Pereira, Cristian J. Vaca-Rubio, Luis Blanco
Federated Learning (FL) has emerged as a solution for distributed model training across decentralized, privacy-preserving devices, but the different energy capacities of participat…
A Primer on Kolmogorov-Arnold Networks (KANs) for Probabilistic Time Series Forecasting
Cristian J. Vaca-Rubio, Roberto Pereira, Luis Blanco +2
This work introduces Probabilistic Kolmogorov-Arnold Network (P-KAN), a novel probabilistic extension of Kolmogorov-Arnold Networks (KANs) for time series forecasting. By replacing…
On the Architectural Split and Radio Intelligence Controller Placement in Integrated O-RAN-enabled Non-Terrestrial Networks
Jorge Baranda, Marius Caus, Luis Blanco +5
The integration of Terrestrial Networks (TNs) with Non-Terrestrial Networks (NTNs) poses unique architectural and functional challenges due to heterogeneous propagation conditions,…
Probabilistic Forecasting for Network Resource Analysis in Integrated Terrestrial and Non-Terrestrial Networks
Cristian J. Vaca-Rubio, Vaishnavi Kasuluru, Engin Zeydan +4
Efficient resource management is critical for Non-Terrestrial Networks (NTNs) to provide consistent, high-quality service in remote and under-served regions. While traditional sing…
Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction
Engin Zeydan, Cristian J. Vaca-Rubio, Luis Blanco +3
Non-Terrestrial Networks (NTNs) are becoming a critical component of modern communication infrastructures, especially with the advent of Low Earth Orbit (LEO) satellite systems. Tr…