24 citations · 27 across the 12 of their papers we have counts for
12 papers
Indirect Estimation of SINR via SSB and CSI-RS RSRP in 5G NR
Leonardo Spampinato, Mahamadou Togola, Matteo Bernabè +3
Predicting user equipment (UE) performance is essential for proactive network control, resource management, and digital twin sandboxes. However, the inherent flexibility and comple…
Decentralized Multi-Agent Urban Traffic Management via Spatio-Temporal Mobility Profile Planning
Lorenzo Mario Amorosa, Lorenzo Farina, Vittorio Todisco +1
As modern cities face increasingly severe traffic congestion, connected and autonomous vehicles (CAVs) have emerged as a crucial enabling technology for next-generation intelligent…
Proactive URLLC Adaptation for Connected Vehicles Through ML-Based Channel Prediction
Andrea Giovannini, Lorenzo Mario Amorosa, Vittorio Todisco +4
Connected and automated vehicles (CAVs) are expected to increasingly rely on 5G and future 6G ultra-reliable and low-latency communication (URLLC) services to support safety-critic…
Goal-Oriented Learning at the Edge: Graph Neural Networks Over-the-Air for Blockage Prediction
Lorenzo Mario Amorosa, Zhan Gao, Tony Chahoud +4
Sixth-generation (6G) wireless networks evolve from connecting devices to connecting intelligence. The focus turns to Goal-Oriented Communications, where the effectiveness of commu…
V2N-Based Algorithm and Communication Protocol for Autonomous Non-Stop Intersections
Lorenzo Farina, Lorenzo Mario Amorosa, Marco Rapelli +3
Intersections are critical areas for road safety and traffic efficiency, accounting for a significant portion of vehicle crashes and fatalities. While connected and autonomous vehi…
Multi-Agent Meta-Advisor for UAV Fleet Trajectory Design in Vehicular Networks
Leonardo Spampinato, Lorenzo Mario Amorosa, Enrico Testi +2
Future vehicular networks require continuous connectivity to serve highly mobile users in urban environments. To mitigate the coverage limitations of fixed terrestrial macro base s…
Learning a Decentralized Medium Access Control Protocol for Shared Message Transmission
Lorenzo Mario Amorosa, Zhan Gao, Roberto Verdone +2
In large-scale Internet of things networks, efficient medium access control (MAC) is critical due to the growing number of devices competing for limited communication resources. In…
Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentation
Tony Chahoud, Lorenzo Mario Amorosa, Riccardo Marini +1
Accurate outdoor positioning in cellular networks is hindered by sparse, heterogeneous measurement collections and the high cost of exhaustive site surveys. This paper introduces a…
Reconstruction of SINR Maps from Sparse Measurements using Group Equivariant Non-Expansive Operators
Lorenzo Mario Amorosa, Francesco Conti, Nicola Quercioli +4
As sixth generation (6G) wireless networks evolve, accurate signal-to-interference-noise ratio (SINR) maps are becoming increasingly critical for effective resource management and…
Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs
Lorenzo Mario Amorosa, Marco Skocaj, Roberto Verdone +1
The ever-increasing demand for high-quality and heterogeneous wireless communication services has driven extensive research on dynamic optimization strategies in wireless networks.…
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…
Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning
Marco Skocaj, Lorenzo Mario Amorosa, Giorgio Ghinamo +4
Recent years witnessed a remarkable increase in the availability of data and computing resources in communication networks. This contributed to the rise of data-driven over model-d…