arXiv AI
Jun 19
On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments
arXiv:2507. 19653v2 Announce Type: replace-cross Abstract: We study the realism of Sionna v1.
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.
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arXiv AI
Jul 2
Meta-Transfer Learning for mmWave Beam Alignment
arXiv:2607. 00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging.
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.
arXiv Machine Learning
Jul 7
Physics-Informed Domain-Invariant Feature Learning with Autoencoder-Driven Gaussian Clustering for Robust Non-line-of-Sight Scenarios
arXiv:2607. 02537v1 Announce Type: cross Abstract: Jamming and spoofing pose significant threats to wireless and satellite navigation by disrupting radio-frequency (RF) signals and compromising availability and integrity.
arXiv Machine Learning
5d ago
Self-Localizing MIMO Beam Mapping for Intelligent Open RAN with Continuously Evolving Channel Memory
arXiv:2511. 17007v2 Announce Type: replace-cross Abstract: Open and intelligent radio access networks (RANs) envisioned for 6G require accurate and reusable wireless channel knowledge for intelligent inference and control.
arXiv Machine Learning
Jun 29
Temporal Convolutional Autoencoder for Interference Mitigation in FMCW Radar Altimeters
arXiv:2505. 22783v2 Announce Type: replace-cross Abstract: Reliable altitude estimation with frequency-modulated continuous wave (FMCW) radar altimeters is increasingly a challenge due to in-band interference from modern communication systems.
arXiv AI
Aug 11
GLocFM: A Geometry-Aware Foundation Model for 3D Indoor Wireless Localization
arXiv:2608. 09285v1 Announce Type: cross Abstract: Learning-based wireless localizers often fail to utilize geometric information about the propagation environment, limiting their ability to exploit non-line-of-sight (NLoS) propagation and generalize across scenes.
arXiv AI
Jul 8
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 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.
arXiv Machine Learning
Jul 10
RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization
arXiv:2607. 08045v1 Announce Type: cross Abstract: Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks.
arXiv AI
Jun 6
LatentWave: JEPA Pretraining for Wireless Foundation Models
arXiv:2606. 06373v1 Announce Type: cross Abstract: Wireless foundation models have emerged as a promising alternative to building separate models for each wireless task.
arXiv Machine Learning
Aug 7
EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver
arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.