Search Results for author: Matteo Drago

Found 4 papers, 1 papers with code

Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks

no code implementations22 Feb 2023 Filippo Bragato, Tommaso Lotta, Gianmaria Ventura, Matteo Drago, Federico Mason, Marco Giordani, Michele Zorzi

To ensure safety in teleoperated driving scenarios, communication between vehicles and remote drivers must satisfy strict latency and reliability requirements.

Federated Learning Reinforcement Learning (RL)

Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3

no code implementations10 Mar 2022 Matteo Drago, Tommaso Zugno, Federico Mason, Marco Giordani, Mate Boban, Michele Zorzi

Artificial intelligence (AI) techniques have emerged as a powerful approach to make wireless networks more efficient and adaptable.

Reinforcement Learning (RL)

A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario

no code implementations4 Feb 2022 Federico Mason, Matteo Drago, Tommaso Zugno, Marco Giordani, Mate Boban, Michele Zorzi

In recent years, autonomous networks have been designed with Predictive Quality of Service (PQoS) in mind, as a means for applications operating in the industrial and/or automotive sectors to predict unanticipated Quality of Service (QoS) changes and react accordingly.

reinforcement-learning Reinforcement Learning (RL)

MilliCar -- An ns-3 Module for mmWave NR V2X Networks

1 code implementation24 Feb 2020 Matteo Drago, Tommaso Zugno, Michele Polese, Marco Giordani, Michele Zorzi

Vehicle-to-vehicle (V2V) communications have opened the way towards cooperative automated driving as a means to guarantee improved road safety and traffic efficiency.

Networking and Internet Architecture

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