Search Results for author: Francesco Restuccia

Found 15 papers, 5 papers with code

Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations

1 code implementation2 Feb 2023 Marco Cominelli, Francesco Gringoli, Francesco Restuccia

We leverage our dataset to dissect the performance of a state-of-the-art sensing framework across different environments and individuals.

Terahertz Communications Can Work in Rain and Snow: Impact of Adverse Weather Conditions on Channels at 140 GHz

no code implementations29 Aug 2022 Priyangshu Sen, Jacob Hall, Michele Polese, Vitaly Petrov, Duschia Bodet, Francesco Restuccia, Tommaso Melodia, Josep M. Jornet

Next-generation wireless networks will leverage the spectrum above 100 GHz to enable ultra-high data rate communications over multi-GHz-wide bandwidths.

DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning

1 code implementation15 Apr 2022 Francesca Meneghello, Michele Rossi, Francesco Restuccia

We present DeepCSI, a novel approach to Wi-Fi radio fingerprinting (RFP) which leverages standard-compliant beamforming feedback matrices to authenticate MU-MIMO Wi-Fi devices on the move.

SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection

no code implementations11 Jan 2022 Davide Callegaro, Francesco Restuccia, Marco Levorato

We extensively evaluate SmartDet on a real-world testbed composed of a JetSon Nano as mobile device and a GTX 980 Ti as edge server, connected through a Wi-Fi link.

Edge-computing object-detection +2

Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks

1 code implementation7 Dec 2021 Peyman Tehrani, Francesco Restuccia, Marco Levorato

Next Generation (NextG) networks are expected to support demanding tactile internet applications such as augmented reality and connected autonomous vehicles.

Autonomous Vehicles Federated Learning +2

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

no code implementations8 Mar 2021 Yoshitomo Matsubara, Marco Levorato, Francesco Restuccia

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others.

Autonomous Vehicles Image Classification +2

Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification

no code implementations5 Mar 2021 Salvatore D'Oro, Francesco Restuccia, Tommaso Melodia

Results show that Chares increases the accuracy up to 4. 1x when no waveform synthesis is performed, by 1. 9x with respect to existing work, and can compute new actions within 41us.

SteaLTE: Private 5G Cellular Connectivity as a Service with Full-stack Wireless Steganography

no code implementations10 Feb 2021 Leonardo Bonati, Salvatore D'Oro, Francesco Restuccia, Stefano Basagni, Tommaso Melodia

Differently from previous work, however, it takes a full-stack approach to steganography, contributing an LTE-compliant steganographic protocol stack for PCCaaS-based communications, and packet schedulers and operations to embed covert data streams on top of traditional cellular traffic (primary traffic).

Networking and Internet Architecture Cryptography and Security

DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks

1 code implementation28 Dec 2020 Michele Polese, Francesco Restuccia, Tommaso Melodia

To do so, existing solutions mostly rely on explicit coordination between the transmitter (TX) and the receiver (RX), which significantly reduces the airtime available for communication and further complicates the network protocol design.

Networking and Internet Architecture Information Theory Information Theory

Big Data Goes Small: Real-Time Spectrum-Driven Embedded Wireless Networking Through Deep Learning in the RF Loop

no code implementations12 Mar 2019 Francesco Restuccia, Tommaso Melodia

RFLearn provides (i) a complete hardware/software architecture where the CPU, radio transceiver and learning/actuation circuits are tightly connected for maximum performance; and (ii) a learning circuit design framework where the latency vs. hardware resource consumption trade-off can explored.

Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey

no code implementations23 Jan 2019 Jithin Jagannath, Nicholas Polosky, Anu Jagannath, Francesco Restuccia, Tommaso Melodia

The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before.

Networking and Internet Architecture

Cannot find the paper you are looking for? You can Submit a new open access paper.