arXiv:2607. 02567v1 Announce Type: cross Abstract: Radio frequency fingerprint identification (RFFI) provides a physical-layer credential for Internet of Things devices, but open-set decisions become fragile when a threshold calibrated on a source receiver is transferred to a target receiver.
By Fengchong Yao, Jianbing Li, Qing Liu, Kefeng Song, Haitao Li, Song Wang, Feixiang Wang
arXiv:2607. 09760v1 Announce Type: cross Abstract: Radio frequency fingerprint identification (RFFI) uses transmitter-specific hardware imperfections as a physicallayer identity cue for Internet of Things (IoT) devices, but deep RFFI models often degrade when the acquisition environment changes.
By Fengchong Yao, Jianbing Li, Qing Liu, Qikun Liu, Kefeng Song, Haitao Li, Song Wang
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: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
arXiv:2608. 07444v1 Announce Type: cross Abstract: Accurate user equipment (UE) localization is critical for beam management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) based sixth-generation (6G) networks, especially if the direct base-station-UE links are unavailable.
By Md Tarek Hassan, Dmitry Zelenchuk, Muhammad Ali Babar Abbasi
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. We propose Polar Monotonic Kolmogorov-Arnold Networks (Polar MKAN), a block partitioned monotonic encoder on polar inputs in which each latent dimension depends exclusively on magnitude or phase, yielding channel separation and monotone responses by construction.