arXiv AI

Cross-Receiver Open-Set Radio Frequency Fingerprinting via Structure-First Adaptation

arXiv:2607. 02567v2 Announce Type: replace-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 applied to a target receiver.

arXiv AI
Jul 7

CRODA-ST: Single-Target Cross-Receiver Open-Set Radio Fingerprint Recognition

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 AI
Jul 14

Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting

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
arXiv Machine Learning
Aug 10

RIS-Aided mmWave Localization Under Cross-Link Interference via Beam-Domain ML Fingerprinting

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
arXiv Machine Learning
Jun 24

WiFi-Based People Counting Using Beam-Steerable Antennas: A Test-bed Study

arXiv:2606. 23710v1 Announce Type: cross Abstract: Ubiquitous perception through RF signals is a pivotal opportunity for future technology: it enables personalized services such as smart living, remote healthcare, automated logistics or interaction through free-space gestures.

By Riccardo Bersan, Anay Ajit Deshpande, Sanaz Kianoush, Daniele Piazza, Stefano Savazzi
arXiv Machine Learning
Jul 20

Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications

arXiv:2508. 00042v2 Announce Type: replace-cross Abstract: Machine learning models deployed in non-stationary environments degrade silently, since as the input distribution drifts their accuracy decays without an error signal and without labels to reveal it.

By Athanasios Tziouvaras, Carolina Fortuna, George Floros, Kostas Kolomvatsos, Panagiotis Sarigiannidis, Marko Grobelnik, Bla\v{z} Bertalani\v{c}
arXiv AI
Jul 7

The S-ICDF Dataset: Sionna-Simulated Dynamic Interference Characterization and Direction Finding

arXiv:2607. 03411v1 Announce Type: cross Abstract: Jamming and spoofing threaten wireless and satellite navigation by disrupting or manipulating radio frequency (RF) signals, undermining availability, integrity, and trust.

By Christian Wielenberg, Lucas Heublein, Jonathan Ott, Alexander Mattick, Nisha L. Raichur, Jonas Pirkl, Lukas Schelenz, Tobias Feigl, George Yammine, Christopher Mutschler, Felix Ott