arXiv:2609.07298v2 Announce Type: replace Abstract: 3D reconstruction typically strives for geometric fidelity or visual plausibility. Radio frequency digital twins (RFDT) are instead judged by wheth...
arXiv:2511. 05522v4 Announce Type: replace-cross Abstract: Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications.
The paper proposes a measurement-driven multi-layer digital twin framework for terahertz (THz) wireless data centers. It begins with extensive tri-band channel measurements at 140, 220, and 300 GHz to calibrate a physical twin that optimizes geometry, material, antenna, and propagation models. An AI channel twin, built on a line-of-sight aware implicit neural field, learns location-dependent channel statistics to enable real‑time prediction of received power and LoS probability, which feeds into a system‑level evaluation layer that analyzes coverage and interference for AP‑to‑rack and rack‑to‑rack links. Experimental results show the AI twin achieves lower power reconstruction error than existing neural‑field baselines while maintaining real‑time inference, and ceiling‑mounted AP deployment yields over 90% coverage at a 10 dB SINR threshold.
arXiv:2609.26214v1 Announce Type: new Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. Th...
arXiv:2507. 19653v2 Announce Type: replace-cross Abstract: We study the realism of Sionna v1.
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:2608. 00406v1 Announce Type: cross Abstract: Accurate indoor localization is essential for emerging applications in robotic navigation and search and rescue.
arXiv:2608. 14599v1 Announce Type: cross Abstract: The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks.
The Vienna 4G/5G Drive-Test Dataset is a city‑scale open dataset of georeferenced LTE and 5G NR measurements collected across Vienna, Austria. It combines passive wideband scanner observations with active handset logs, offering complementary network‑side and user‑side views of deployed radio access networks. The dataset includes inferred base‑station deployment descriptors, high‑resolution building and terrain models, and is organized into scanner, handset, estimated cell information, and city‑model components to support reproducible benchmarking in environment‑aware learning, propagation modeling, coverage analysis, and ray‑tracing calibration workflows.
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:2607. 01777v1 Announce Type: cross Abstract: Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage analysis, and environment-aware wireless optimization.
arXiv:2609.07726v1 Announce Type: cross Abstract: Automatic modulation classification (AMC) models are frequently trained and validated on synthetic or channel-cabled data, leaving open the question...