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:2609.00615v1 Announce Type: cross Abstract: The evolution toward sixth-generation (6G) wireless networks is driving larger antenna arrays and highly directional multi-beam transmission, making...
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.
This paper proposes incorporating channel configuration (CFG) information into Radio Foundation Model (RFM) pre-training and demonstrates that the resulting CFG-enriched embeddings serve as effective...
arXiv:2610.19015v1 Announce Type: new Abstract: Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings. Large-scale synthetic pre...
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.
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:2610.26999v1 Announce Type: new Abstract: Deep learning for radio frequency (RF) signal analysis relies on large annotated datasets, yet synthetic datasets trade physical realism for controllab...
arXiv:2606. 01862v1 Announce Type: cross Abstract: Translating user intents into physical radio signals represents the critical yet notoriously tedious final step in wireless prototyping, as it requires intricate knowledge of physical layer details and presents immense implementation challenges.
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.
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 JEPA-inspired latent field prediction model for reconstructing MIMO‑OFDM channels from sparse pilots. It maps observed CSI into latent time‑frequency tokens, predicts masked or future latent states using historical multi‑band context, and fuses these predictions with current noisy pilots to recover the full channel. Ray‑tracing experiments demonstrate that this approach reduces normalized mean square error compared to pilot‑only and direct reconstruction baselines.
arXiv:2605.08211v2 Announce Type: replace-cross Abstract: Channel-gain maps provide the channel gain between any two locations in a geographical region. They find numerous applications, from resource...