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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. 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.
The paper introduces RadioDecomp, a method for radiomap blind prediction that corrects a prior‑guided predictor using deterministic residual refinement. It identifies the conditional‑mean radiomap as the optimal target under squared loss and decomposes domain risk into approximation error and irreducible uncertainty. Experiments show that the RadioLSR variant excels in cross‑configuration generalization and outperforms a monolithic baseline in cross‑environment scenarios.
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.
The paper introduces a method to enrich Radio Foundation Models (RFMs) by incorporating channel configuration (CFG) information during pre‑training. These CFG‑enriched embeddings are shown to act as effective inductive priors for downstream tasks, especially when labeled data are scarce. Experiments across three data regimes and multiple tasks—LoS/NLoS classification, timing advance estimation, and beam selection—demonstrate that the new embeddings consistently outperform or match baseline models in both in‑distribution and out‑of‑distribution configuration settings.
WiNeRF is a neural field framework that learns a spatially continuous, complex-valued wireless channel representation from sparse channel state information collected by commodity WiFi devices. It incorporates system constraints such as antenna geometry, limited spatial resolution, and phase uncertainty through a 3D conical wave sampling model, a multi-resolution implicit scene representation, and a differentiable optimization framework. In diverse indoor environments with non‑line‑of‑sight regions, WiNeRF achieves a median prediction SNR of 5.3 dB, outperforming prior neural baselines by 4.9 dB on average, and produces a task‑agnostic channel representation that can be reused in standard signal‑processing pipelines without hardware or protocol changes.