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.
arXiv:2608. 00406v1 Announce Type: cross Abstract: Accurate indoor localization is essential for emerging applications in robotic navigation and search and rescue.
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:2606. 18734v1 Announce Type: cross Abstract: Accurate, site-specific channel information is crucial for optimizing next-generation wireless networks.
arXiv:2508. 03736v2 Announce Type: replace-cross Abstract: In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations.
arXiv:2609.24253v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) provides high-fidelity scenes for large-scale embodied simulation, but constructing large-scale urban assets remains con...
arXiv:2507. 19653v2 Announce Type: replace-cross Abstract: We study the realism of Sionna v1.
The paper presents a method for enhancing coarse 5 m digital surface models (DSMs) to 0.5 m resolution by guiding a denoising diffusion process with high‑resolution spectral images. This approach transfers fine visual details—such as crisp outlines and roof structures—from the imagery into the elevation maps, yielding more accurate surface geometry than traditional interpolation or filtering. Experiments on Central European cities show that the resulting DSMs exhibit improved structural detail and overall quality.
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:2603.11252v2 Announce Type: replace Abstract: Although semantic 3D city models are internationally available and becoming increasingly detailed, the incorporation of material information remain...
arXiv:2606. 09882v1 Announce Type: cross Abstract: The paradigm of digital twin cities is shifting from coarse visual mapping toward more precise and actionable digitization of urban assets.
The paper proposes a method to enhance coarse 5 m digital surface models (DSMs) to 0.5 m resolution by guiding the super‑resolution process with high‑resolution spectral images. It uses denoising diffusion to transfer image‑visible details, such as crisp outlines and roof structures, into the elevation maps, achieving more accurate surface geometry than traditional interpolation or filtering. Experiments on Central European cities show that the approach yields high‑quality DSMs with improved structural detail.