Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning. - Marco Skocaj; Lorenzo Mario Amorosa; Giorgio Ghinamo; Giuliano Muratore; Davide Micheli; Flavio Zabini; Roberto Verdone (Comput. Commun., 2022)

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; Weronika Maria Bachan; Valerio Lieti; Enrico Buracchini; Roberto Verdone (PIMRC, 2023)

Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs. - Lorenzo Mario Amorosa; Marco Skocaj; Roberto Verdone; Deniz Gündüz (ICC, 2024)

GUMBLE: Uncertainty-Aware Conditional Mobile Data Generation Using Bayesian Learning. - Marco Skocaj; Lorenzo Mario Amorosa; Michele Lombardi 001; Roberto Verdone (IEEE Trans. Mob. Comput., 2024)

Goal-Oriented Uplink Scheduling Requests in Wireless Networks via Graph Neural Networks. - Lorenzo Mario Amorosa; Leonardo Spampinato; Chiara Buratti; Roberto Verdone (EUROCON, 2025)

Path Selection Based on Network Service Quality for Infrastructure-Assisted Automated Driving. - Andrea Giovannini; Claudia Campolo; Vittorio Todisco; Antonella Molinaro; Lorenzo Mario Amorosa; Lu Lei; Alessandro Bazzi (WCNC, 2025)

5G Architectures Enabling Remaining Useful Life Estimation for Industrial IoT: A Holistic Study. - Nicolò Longhi; Lorenzo Mario Amorosa; Sara Cavallero; Enrico Buracchini; Roberto Verdone (IEEE Open J. Commun. Soc., 2025)

Reconstruction of Sparse Urban Wireless Signals via Group Equivariant Non-Expansive Operators. - Lorenzo Mario Amorosa; Francesco Conti 004; Nicola Quercioli; Flavio Zabini; Tayebeh Lotfi Mahyari; Yiqun Ge; Patrizio Frosini (CoRR, 2025)