arXiv:2608. 15314v1 Announce Type: new Abstract: Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an outage threshold.
arXiv:2605. 18931v2 Announce Type: replace-cross Abstract: Heavy-tailed distributions are prevalent in performance evaluation, network traffic, and risk modeling.
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.
arXiv:2607. 11429v1 Announce Type: new Abstract: TR 38.
arXiv:2608. 11544v1 Announce Type: cross Abstract: We propose CVaR-penalized Generative Particle Algorithm (CVaR-GPA), a robust, tail-agnostic algorithm for fine-tuning generative models to learn heavy-tailed distributions and capture extreme events, requiring no prior knowledge or estimation of the target's tail characteristics.
The paper investigates why reconstruction quality in latent generative models does not always predict generative performance, attributing the issue to a mismatch between encoder-induced and generation-time latent distributions. It introduces Generation‑Aware Reconstruction (GAR), a method that perturbs encoder latents with noise and denoises them through the generative model before decoding, creating a continuous trajectory that reveals how the decoder behaves across latent spaces. The resulting GAR‑FID metric correlates strongly with generation FID, and using intermediate GAR latents for decoder adaptation consistently improves generative quality across different model scales.
arXiv:2609.00773v1 Announce Type: cross Abstract: High-dimensional clustering is challenging when component distributions are both heavy-tailed and directionally asymmetric. We propose a deep skew-$t...
The paper investigates adaptive UAV sampling for constructing radio environment maps (REMs) from non‑Gaussian IoT observations using Integrated Nested Laplace Approximation (INLA) to update a Matérn Gaussian Markov random field posterior. Waypoints are chosen via entropy pre‑screening and a Sherman–Morrison lookahead, and the study finds that widening the prior shadowing standard deviation consistently improves NMSE and credible‑interval coverage across multiple ray‑traced windows, while online width estimation from UAV counts does not recover this gain. Comparative analyses show that posterior‑informed sampling outperforms random and lawnmower strategies, but differences among informative acquisition rules remain unresolved.
arXiv:2608. 11951v1 Announce Type: cross Abstract: Extreme events in air transport, such as severe arrival delays and abnormal air times, cause cascading network disruptions with substantial operational, economic, and safety costs.
arXiv:2609.24088v2 Announce Type: replace Abstract: In latent generative models, reconstruction quality is often assumed to correlate with generative performance. However, reconstruction FID (rFID) c...
arXiv:2606. 03074v1 Announce Type: new Abstract: Diffusion models achieve high-fidelity radio map construction through iterative denoising, yet their sampling cost limits practicality in dynamic wireless systems where radio maps must be refreshed repeatedly.
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.