6 papers
Dual-Graph Multi-Agent Reinforcement Learning for Handover Optimization
Matteo Salvatori, Filippo Vannella, Sebastian Macaluso +5
HandOver (HO) control in cellular networks is governed by a set of HO control parameters that are traditionally configured through rule-based heuristics. A key parameter for HO opt…
mmWave Beam Selection in Analog Beamforming Using Personalized Federated Learning
Martin Isaksson, Filippo Vannella, David Sandberg +1
Using analog beamforming in mmWave frequency bands we can focus the energy towards a receiver to achieve high throughput. However, this requires the network to quickly find the bes…
Learning Optimal Antenna Tilt Control Policies: A Contextual Linear Bandit Approach
Filippo Vannella, Alexandre Proutiere, Yassir Jedra +1
Controlling antenna tilts in cellular networks is imperative to reach an efficient trade-off between network coverage and capacity. In this paper, we devise algorithms learning opt…
A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation
Erik Aumayr, Saman Feghhi, Filippo Vannella +2
Safe interaction with the environment is one of the most challenging aspects of Reinforcement Learning (RL) when applied to real-world problems. This is particularly important when…
Remote Electrical Tilt Optimization via Safe Reinforcement Learning
Filippo Vannella, Grigorios Iakovidis, Ezeddin Al Hakim +2
Remote Electrical Tilt (RET) optimization is an efficient method for adjusting the vertical tilt angle of Base Stations (BSs) antennas in order to optimize Key Performance Indicato…
Off-policy Learning for Remote Electrical Tilt Optimization
Filippo Vannella, Jaeseong Jeong, Alexandre Proutiere
We address the problem of Remote Electrical Tilt (RET) optimization using off-policy Contextual Multi-Armed-Bandit (CMAB) techniques. The goal in RET optimization is to control the…