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.
Radioformer: A multiple-granularity radio map estimation trans- former with 1 \textpertenthousand spatial sampling
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Proposes Stochastic-Triggered Trajectory-Based Sampling (ST-TBS) to address sampling distribution shift in learning-based radio map estimation, reducing RMSE under trajectory observations on RadioMapSeer and SpectrumNet datasets.
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
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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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Bridging the Sampling Distribution Shift in Radio Map Estimation: A Trajectory-Aware Paradigm
Proposes Stochastic-Triggered Trajectory-Based Sampling (ST-TBS) to address sampling distribution shift in learning-based radio map estimation, reducing RMSE under trajectory observations on RadioMapSeer and SpectrumNet datasets.
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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.