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.
Radiodiff-loc: Diffusion model enhanced scattering congnition for nlos localization with sparse radio map estimation
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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.
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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.
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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.