115 citations · 256 across the 9 of their papers we have counts for
9 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…
Uncovering Issues in the Radio Access Network by Looking at the Neighbors
José Suárez-Varela, Andra Lutu
Mobile network operators (MNOs) manage Radio Access Networks (RANs) with massive amounts of cells over multiple radio generations (2G-5G). To handle such complexity, operations tea…
MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering
Guillermo Bernárdez, José Suárez-Varela, Albert López +5
Current trends in networking propose the use of Machine Learning (ML) for a wide variety of network optimization tasks. As such, many efforts have been made to produce ML-based sol…
RouteNet-Fermi: Network Modeling with Graph Neural Networks
Miquel Ferriol-Galmés, Jordi Paillisse, José Suárez-Varela +6
Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complex…
Fast Traffic Engineering by Gradient Descent with Learned Differentiable Routing
Krzysztof Rusek, Paul Almasan, José Suárez-Varela +3
Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewis…
Network Digital Twin: Context, Enabling Technologies and Opportunities
Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13
The proliferation of emergent network applications (e.g., telesurgery, metaverse) is increasing the difficulty of managing modern communication networks. These applications entail…
RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation
Miquel Ferriol-Galmés, Krzysztof Rusek, José Suárez-Varela +4
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that…
Digital Twin Network: Opportunities and Challenges
Paul Almasan, Miquel Ferriol-Galmés, Jordi Paillisse +13
The proliferation of emergent network applications (e.g., AR/VR, telesurgery, real-time communications) is increasing the difficulty of managing modern communication networks. Thes…
Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities
José Suárez-Varela, Paul Almasan, Miquel Ferriol-Galmés +8
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems).…