Search Results for author: Marco Giordani

Found 15 papers, 2 papers with code

A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT

no code implementations21 Nov 2023 Francesco Pase, Marco Giordani, Sara Cavallero, Malte Schellmann, Josef Eichinger, Roberto Verdone, Michele Zorzi

Industrial Internet of Things (IIoT) networks will provide Ultra-Reliable Low-Latency Communication (URLLC) to support critical processes underlying the production chains.

Scheduling Thompson Sampling

Downlink Clustering-Based Scheduling of IRS-Assisted Communications With Reconfiguration Constraints

no code implementations23 May 2023 Alberto Rech, Matteo Pagin, Leonardo Badia, Stefano Tomasin, Marco Giordani, Jonathan Gambini, Michele Zorzi

Intelligent reflecting surfaces (IRSs) are being widely investigated as a potential low-cost and energy-efficient alternative to active relays for improving coverage in next-generation cellular networks.

Clustering Quantization +1

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)

Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach

no code implementations22 Nov 2022 Francesco Pase, Marco Giordani, Giampaolo Cuozzo, Sara Cavallero, Joseph Eichinger, Roberto Verdone, Michele Zorzi

This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things (IIoT) networks.

Scheduling

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)

Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison

no code implementations1 Feb 2022 Francesco Nardo, Davide Peressoni, Paolo Testolina, Marco Giordani, Andrea Zanella

The worldwide commercialization of fifth generation (5G) wireless networks and the exciting possibilities offered by connected and autonomous vehicles (CAVs) are pushing toward the deployment of heterogeneous sensors for tracking dynamic objects in the automotive environment.

Autonomous Vehicles object-detection +1

Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systems

no code implementations20 Sep 2021 Mate Boban, Marco Giordani, Michele Zorzi

Recent advances in software, hardware, computing and control have fueled significant progress in the field of autonomous systems.

On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks

no code implementations23 Apr 2021 Valentina Rossi, Paolo Testolina, Marco Giordani, Michele Zorzi

In this paper, we evaluate how using a combination of different sensors affects the detection of the environment in which the vehicles move and operate.

Autonomous Driving object-detection +2

On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI

no code implementations1 Apr 2021 Francesco Pase, Marco Giordani, Michele Zorzi

Federated learning (FL) has recently emerged as an attractive decentralized solution for wireless networks to collaboratively train a shared model while keeping data localized.

Federated Learning

Hybrid Point Cloud Semantic Compression for Automotive Sensors: A Performance Evaluation

no code implementations5 Mar 2021 Andrea Varischio, Francesco Mandruzzato, Marcello Bullo, Marco Giordani, Paolo Testolina, Michele Zorzi

In a fully autonomous driving framework, where vehicles operate without human intervention, information sharing plays a fundamental role.

Autonomous Driving Quantization Networking and Internet Architecture

Accuracy vs. Complexity for mmWave Ray-Tracing: A Full Stack Perspective

2 code implementations14 Jul 2020 Mattia Lecci, Paolo Testolina, Michele Polese, Marco Giordani, Michele Zorzi

The millimeter wave (mmWave) band will provide multi-gigabits-per-second connectivity in the radio access of future wireless systems.

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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