Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs

no code implementations • 27 Nov 2023 • Lorenzo Mario Amorosa, Marco Skocaj, Roberto Verdone, Deniz Gündüz

The ever-increasing demand for high-quality and heterogeneous wireless communication services has driven extensive research on dynamic optimization strategies in wireless networks.

Decision Making Inductive Bias +1

Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements

no code implementations • 5 Jul 2023 • Marco Skocaj, Francesca Conserva, Nicol Sarcone Grande, Andrea Orsi, Davide Micheli, Giorgio Ghinamo, Simone Bizzarri, Roberto Verdone

The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures.

Anomaly Detection Decision Making +1

Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning

no code implementations • 27 Jan 2023 • Marco Skocaj, Pedro Enrique Iturria Rivera, Roberto Verdone, Melike Erol-Kantarci

Federated Learning (FL) has emerged as a promising framework for distributed training of AI-based services, applications, and network procedures in 6G.

Federated Learning Graph Representation Learning +2