24 citations · 45 across the 8 of their papers we have counts for
4 papers · 1 filter
An End-To-End Analysis of Deep Learning-Based Remaining Useful Life Algorithms for Satefy-Critical 5G-Enabled IIoT Networks
Lorenzo Mario Amorosa, Nicolò Longhi, Giampaolo Cuozzo +4
Remaining Useful Life (RUL) prediction is a critical task that aims to estimate the amount of time until a system fails, where the latter is formed by three main components, that i…
Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements
Marco Skocaj, Francesca Conserva, Nicol Sarcone Grande +5
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…
A New Scheduler for URLLC in 5G NR IIoT Networks with Spatio-Temporal Traffic Correlations
Sara Cavallero, Nicole Sarcone Grande, Francesco Pase +4
This paper explores the issue of enabling Ultra-Reliable Low-Latency Communications (URLLC) in view of the spatio-temporal correlations that characterize real 5th generation (5G) I…
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning
Marco Skocaj, Pedro Enrique Iturria Rivera, Roberto Verdone +1
Federated Learning (FL) has emerged as a promising framework for distributed training of AI-based services, applications, and network procedures in 6G. One of the major challenges…