6 papers
Meta-Learning-Based Handover Management in NextG O-RAN
Michail Kalntis, George Iosifidis, José Suárez-Varela +2
While traditional handovers (THOs) have served as a backbone for mobile connectivity, they increasingly suffer from failures and delays, especially in dense deployments and high-fr…
Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question
Stefanos Bakirtzis, Paul Almasan, José Suárez-Varela +5
Differentiable ray tracing has recently challenged the status quo in radio propagation modelling and digital twinning. Promising unprecedented speed and the ability to learn from r…
CHOMET: Conditional Handovers via Meta-Learning
Michail Kalntis, Fernando A. Kuipers, George Iosifidis
Handovers (HOs) are the cornerstone of modern cellular networks for enabling seamless connectivity to a vast and diverse number of mobile users. However, as mobile networks become…
Smooth Handovers via Smoothed Online Learning
Michail Kalntis, Andra Lutu, Jesús Omaña Iglesias +2
With users demanding seamless connectivity, handovers (HOs) have become a fundamental element of cellular networks. However, optimizing HOs is a challenging problem, further exacer…
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning
Michail Kalntis, George Iosifidis, Fernando A. Kuipers
Open Radio Access Network systems, with their virtualized base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interope…
Through the Telco Lens: A Countrywide Empirical Study of Cellular Handovers
Michail Kalntis, José Suárez-Varela, Jesús Omaña Iglesias +4
Cellular networks rely on handovers (HOs) as a fundamental element to enable seamless connectivity for mobile users. A comprehensive analysis of HOs can be achieved through data fr…