The paper introduces Physics-Unrolled Hybrid Neural Operator (PU‑HNO), a three‑stage cascade that transforms low‑fidelity ray‑tracing outputs and scene priors into high‑fidelity indoor radio maps by sequentially modeling reflection, diffraction, and scattering. It demonstrates that, under conditionally unbiased label noise, the model can learn stable propagation structures and surpass its own training labels. Experiments on varied floorplans show PU‑HNO outperforming image‑to‑image baselines, wireless learning models, and monolithic neural operators in both image quality and wireless deployment metrics.
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: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: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:2405. 17366v3 Announce Type: replace Abstract: We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation that is used for wireless communication simulation in 3D indoor environments.
The paper introduces GCNO, a physics‑based, variable‑rate neural operator that compresses wireless channel matrices by identifying a sample‑dependent set of dominant propagation paths instead of treating the matrix as an image. GCNO leverages receive‑transmit channel structure, a first‑order Taylor correction, and least‑squares recovery to encode path directions and strengths, and the base station reconstructs the channel analytically from these tuples. Experiments on three ray‑traced environments show GCNO outperforms neural feedback baselines in reconstruction accuracy for the same payload or achieves the same accuracy with lower payload, and it generalizes to unseen antenna counts without retraining.
arXiv:2607. 16449v1 Announce Type: new Abstract: Accurate path loss prediction is a critical component of wireless network planning.
arXiv:2607. 28994v1 Announce Type: cross Abstract: High-fidelity radio fields are typically simulated for every scene--transmitter configuration or fitted separately to each scene, failing to exploit propagation structures shared across environments.
GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels proposes a variable‑rate compressor that reports only the dominant propagation paths of a wireless channel instead of a full complex‑valued matrix. The method uses the channel’s receive‑transmit structure to locate paths, applies a first‑order Taylor correction for sub‑grid directions, and recovers path strengths via least squares, all trained without explicit path labels. Experiments on three ray‑traced environments show GCNO outperforms neural feedback baselines in reconstruction accuracy for the same payload or achieves the same accuracy with a lower payload, and it generalizes to unseen antenna counts without retraining.
arXiv:2605. 12569v2 Announce Type: replace-cross Abstract: Global navigation satellite system (GNSS) interference poses a serious threat to reliable positioning, especially in indoor and multipath-rich environments where source localization is highly challenging.
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.
arXiv:2607. 20909v1 Announce Type: cross Abstract: Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), from sparse measurements, a task that is critical for spectrum management, interference mitigation, and localization in modern wireless networks.
arXiv:2608. 16167v1 Announce Type: cross Abstract: High-precision radio map construction is essential for emerging 6G Integrated Sensing and Communication (ISAC) applications, including digital twins and intelligent transportation.