A method to construct propagation-consistent wireless environment digital twins from sparse CSI by creating a geometry-prior Bayesian channel map and calibrating a scene-level EM property field via differentiable ray tracing.
Radioflow: Efficient radio map construction framework with flow matching
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3roles
background 1polarities
background 1representative citing papers
RadioDiff-Inv2 estimates radio maps from sparse noisy measurements under location drift by making diffusion inversion differentiable via Gaussian resampling and probability-flow ODE optimization.
Learning-based radio map construction is taxonomized as source-aware forward prediction versus source-agnostic inverse reconstruction, spanning five neural families, optics-inspired continuous fields, and a three-level physics integration framework.
citing papers explorer
-
Propagation-Consistent Wireless Environment Digital Twin Construction Under Sparse Measurements
A method to construct propagation-consistent wireless environment digital twins from sparse CSI by creating a geometry-prior Bayesian channel map and calibrating a scene-level EM property field via differentiable ray tracing.
-
RadioDiff-Inv2: Differentiable Diffusion Inversion under Location Drift from Sparse Noisy Measurements for Radio Map Estimation
RadioDiff-Inv2 estimates radio maps from sparse noisy measurements under location drift by making diffusion inversion differentiable via Gaussian resampling and probability-flow ODE optimization.
-
A Tutorial on Learning-Based Radio Map Construction: Data, Paradigms, and Physics-Awareness
Learning-based radio map construction is taxonomized as source-aware forward prediction versus source-agnostic inverse reconstruction, spanning five neural families, optics-inspired continuous fields, and a three-level physics integration framework.