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Found 15 papers, 5 papers with code
Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations
We leverage our dataset to dissect the performance of a state-of-the-art sensing framework across different environments and individuals.
A$^2$-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems
For this reason, we propose a novel A$^2$-UAV framework to optimize the number of correctly executed tasks at the edge.
Terahertz Communications Can Work in Rain and Snow: Impact of Adverse Weather Conditions on Channels at 140 GHz
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
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
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.
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing
We show that BottleFit decreases power consumption and latency respectively by up to 49% and 89% with respect to (w. r. t.)
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks
Next Generation (NextG) networks are expected to support demanding tactile internet applications such as augmented reality and connected autonomous vehicles.
Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation
In this paper, we introduce Colosseum as a testbed that is for the first time open to the research community.
The Tags Are Alright: Robust Large-Scale RFID Clone Detection Through Federated Data-Augmented Radio Fingerprinting
Recent work, however, has unveiled that time-varying channel conditions can significantly decrease the accuracy of the RFP process.
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
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.
Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification
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
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
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
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
The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before.
Networking and Internet Architecture
