arXiv:2608. 19881v1 Announce Type: cross Abstract: Radio frequency (RF) fingerprinting authenticates wireless devices from hardware-induced I/Q impairments, typically with deep learning feature extractors that are accurate but opaque, limiting their use in security critical settings.
By Mikhail Krasnov, Ljupcho Milosheski, Carolina Fortuna
The paper presents a calibrated radio‑frequency fingerprinting approach that handles co‑channel interference from multiple transmitters. By framing the task as a multi‑label classification problem, the authors use a 1D CNN and calibrate confidence thresholds to bound the average number of false negatives, ensuring reliable detection of spectrum violations. Experiments on the POWDER 5G testbed with Wi‑Fi, LTE, and 5G NR signals achieve up to 97% accuracy, with calibrated recall closely matching the specified false‑negative bounds even under out‑of‑distribution interference.
By Tariq Abdul-Quddoos, Xiangfang Li, Lijun Qian
The paper introduces FreqSpaNet, a neural network designed to detect hardware integrity violations in wireless devices by learning spatio-frequency polarization fingerprints (SFPFs). It employs a dual-branch architecture: a frequency branch that captures local variations across neighboring frequencies and a geometry-aware spatial branch that models directional relationships using angular information. The two learned representations are adaptively fused, and complementary pretraining is used to preserve distinct characteristics while capturing shared information, achieving a mean AUROC of 96.31%—9.05 points above the baseline—across seven hardware replacement scenarios.
By Xiaoxuan Huang, Jinlong Xu, YiZhe Wang, Meng Zhang, Xian Li, Yuying Bian
arXiv:2507.12133v2 Announce Type: replace
Abstract: Device recognition is vital for security in wireless communication systems, particularly for applications like access control. Radio Frequency Fing...
By Hanwen Liu, Yuhe Huang, Yifeng Gong, Yanjie Zhai, Jiaxuan Lu
arXiv:2608. 00796v1 Announce Type: cross Abstract: Automatic RF modulation recognition is of critical importance in spectrum monitoring, electronic warfare, and cognitive radio applications, where low signal-to-noise ratio (SNR) conditions and the growing diversity of modulation schemes limit the performance of existing methods.
By Nurettin Safak, Durdu Can Yerdeyatar, Muhammet Sefa Demirel, Alperen Marasli, Taha Eren Atmaca, Ozgun Ersoy
The paper presents a channel-informed neural network for physical-layer key generation (PKG) that extracts binary key features directly from IQ measurements while grounding the representation in the multipath channel. The multi-task recurrent network jointly learns reciprocity-preserving features and an auxiliary channel estimate, using deep metric learning and channel-informed supervision. Experiments on indoor and outdoor software-defined-radio data show lower bit disagreement for legitimate users, improved key diversity with ray-traced augmentation, and successful NIST randomness tests after SHA-3 privacy amplification.
By Jose Angel Sanchez Viloria, George Sklivanitis, Dimitris Pados, Elizabeth Serena Bentley