5 citations · 5 across the 3 of their papers we have counts for
4 papers
DRL-driven RAN Slicing Management: A V2X-oriented Approach In Multi-service Scenarios
Daniel E. Garcia-Fernandez, Pablo Vera-Soto, Sergio Fortes +6
The integration of Vehicle-to-Everything (V2X) communications is driving a profound transformation in vehicular connectivity, expected to significantly enhance traffic efficiency a…
A DRL-Driven Optimization of RAN Slice Resource Partitioning for V2X SLA Compliance in 5G Networks
M. Martínez, I. de-la-Bandera, D. E. García +6
Vehicle-to-Everything (V2X) communications impose very demanding requirements in terms of latency and reliability, which must be met in scenarios where multiple services with diver…
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization
Adriano Mendo, Jose Outes-Carnero, Yak Ng-Molina +1
This paper presents a method for optimizing wireless networks by adjusting cell parameters that affect both the performance of the cell being optimized and the surrounding cells. T…
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning
Maxime Bouton, Jaeseong Jeong, Jose Outes +2
Future generations of mobile networks are expected to contain more and more antennas with growing complexity and more parameters. Optimizing these parameters is necessary for ensur…