Search Results for author:
Found 22 papers, 6 papers with code
Securing O-RAN Open Interfaces
The next generation of cellular networks will be characterized by openness, intelligence, virtualization, and distributed computing.
SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction
In this paper, we introduce SeizNet, a closed-loop system for predicting epileptic seizures through the use of Deep Learning (DL) method and implantable sensor networks.
Modeling Interference for the Coexistence of 6G Networks and Passive Sensing Systems
In this paper, we provide the first, fundamental analysis of Radio Frequency Interference (RFI) that large-scale terrestrial deployments introduce in different satellite sensing systems now orbiting the Earth.
SignCRF: Scalable Channel-agnostic Data-driven Radio Authentication System
Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven IoT authentication technique that leverages the unique hardware-level manufacturing imperfections associated with a particular device to recognize (fingerprint) the device based on variations introduced in the transmitted waveform.
Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures
This is obtained through custom RAN control applications (i. e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network.
Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym
Softwarization, programmable network control and the use of all-encompassing controllers acting at different timescales are heralded as the key drivers for the evolution to next-generation cellular networks.
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.
OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms
In this paper we present OpenRAN Gym, a unified, open, and O-RAN-compliant experimental toolbox for data collection, design, prototyping and testing of end-to-end data-driven control solutions for next generation Open RAN systems.
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices
Here, ML blocks replace the individual processing blocks of an OFDM receiver, and we specifically describe this swapping for the legacy channel estimation, symbol demapping, and decoding blocks with Neural Networks (NNs).
Deep neural network goes lighter: A case study of deep compression techniques on automatic RF modulation recognition for Beyond 5G networks
Automatic RF modulation recognition is a primary signal intelligence (SIGINT) technique that serves as a physical layer authentication enabler and automated signal processing scheme for the beyond 5G and military networks.
OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN
The next generation of cellular networks will be characterized by softwarized, open, and disaggregated architectures exposing analytics and control knobs to enable network intelligence.
ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms
In this paper, we address these challenges by proposing practical solutions and software pipelines for the design, training, testing, and experimental evaluation of DRL-based closed-loop control in the Open RAN.
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.
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
Streaming from the Air: Enabling Drone-sourced Video Streaming Applications on 5G Open-RAN Architectures
This may result in uplink inter-cell interference and uplink performance degradation for the neighboring ground UEs when drones transmit at high data-rates (e. g., video streaming).
Networking and Internet Architecture Signal Processing
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
Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks
Next Generation (NextG) cellular networks will be natively cloud-based and built upon programmable, virtualized, and disaggregated architectures.
Toward End-to-End, Full-Stack 6G Terahertz Networks
Recent evolutions in semiconductors have brought the terahertz band in the spotlight as an enabler for terabit-per-second communications in 6G networks.
Networking and Internet Architecture Signal Processing
Redefining Wireless Communication for 6G: Signal Processing Meets Deep Learning with Deep Unfolding
In this position paper, we motivate the need to redesign iterative signal processing algorithms by leveraging deep unfolding techniques to fulfill the physical layer requirements for 6G networks.
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
