Advanced deep learning models for 6G: overview, opportunities and challenges
The advent of the sixth generation of mobile communications (6G) ushers in an era of
heightened demand for advanced network intelligence to tackle the challenges of an …
heightened demand for advanced network intelligence to tackle the challenges of an …
Explanation-guided backdoor attacks on model-agnostic rf fingerprinting
Despite the proven capabilities of deep neural networks (DNNs) for radio frequency (RF)
fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the …
fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the …
VIA: Establishing the link between spectrum sensor capabilities and data analytics performance
K Doke, B Okoro, A Zare… - IEEE INFOCOM 2024 …, 2024 - ieeexplore.ieee.org
Automated spectrum analytics inform critical decisions in dynamic spectrum access
networks such as (i) how to allocate network resources to clients,(ii) when to enforce …
networks such as (i) how to allocate network resources to clients,(ii) when to enforce …
Explanation-Guided Backdoor Attacks Against Model-Agnostic RF Fingerprinting Systems
Despite the proven capabilities of deep neural networks (DNNs) in identifying devices
through radio frequency (RF) fingerprinting, the security vulnerabilities of these deep …
through radio frequency (RF) fingerprinting, the security vulnerabilities of these deep …
Channel and hardware impairment data augmentation for robust modulation classification
Deep learning has achieved remarkable results in modulation classification under two
assumptions: a large amount of labeled class-balanced data is available, and the test data …
assumptions: a large amount of labeled class-balanced data is available, and the test data …
Constructing 4D Radio Map in LEO Satellite Networks with Limited Samples
Recently, Low Earth Orbit (LEO) satellite networks (ie, non-terrestrial network (NTN)), such
as Starlink, have been successfully deployed to provide broader coverage than terrestrial …
as Starlink, have been successfully deployed to provide broader coverage than terrestrial …
Sums: Sniffing Unknown Multiband Signals under Low Sampling Rates
J Peng, Z Chen, Z Lin, H Yuan, Z Fang, L Bao… - arXiv preprint arXiv …, 2024 - arxiv.org
Due to sophisticated deployments of all kinds of wireless networks (eg, 5G, Wi-Fi, Bluetooth,
LEO satellite, etc.), multiband signals distribute in a large bandwidth (eg, from 70 MHz to 8 …
LEO satellite, etc.), multiband signals distribute in a large bandwidth (eg, from 70 MHz to 8 …
Seek and Classify: End-to-end Joint Spectrum Segmentation and Classification for Multi-signal Wideband Spectrum Sensing
The rise in the use of wireless communication has led to the problem of spectrum scarcity in
licensed bands. The popularity of the Internet of Things (IoT) requires innovative solutions …
licensed bands. The popularity of the Internet of Things (IoT) requires innovative solutions …
Real-Time Spectrum Segmentation and Classification with Over-The-Air Data
Spectrum usage is increasing daily, necessitating new methods for efficient utilization.
Spectrum sharing allows the coexistence of multiple wireless communication systems in the …
Spectrum sharing allows the coexistence of multiple wireless communication systems in the …
INFORMATION ACCESS FOR INFRASTRUCTURALLY-CHALLENGED ENVIRONMENTS AND BEYOND THROUGH MUTUALLY AWARE SPECTRUM SHARING …
K Doke - 2024 - scholarsarchive.library.albany.edu
Abstract The Radio Frequency (RF) spectrum is scarce and to make it available for new
mobile wireless services, regulators are forced to re-allocate spectrum from existing services …
mobile wireless services, regulators are forced to re-allocate spectrum from existing services …