GeoUQ-GFNet reconstructs dense urban gain radio maps from sparse measurements using geometry priors and uncertainty-guided active sensing, showing consistent gains over non-adaptive sampling on the new UrbanRT-RM ray-tracing benchmark.
High-Efficiency Urban 3D Radio Map Estimation Based on Sparse Measurements
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FPN-Transformer with uncertainty head reduces RMSE for cross-height CKM prediction to 5.347 dB zero-shot and 3.518 dB few-shot on a layered aerial benchmark, outperforming 3D-RadioDiff.
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Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection
GeoUQ-GFNet reconstructs dense urban gain radio maps from sparse measurements using geometry priors and uncertainty-guided active sensing, showing consistent gains over non-adaptive sampling on the new UrbanRT-RM ray-tracing benchmark.
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Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing
FPN-Transformer with uncertainty head reduces RMSE for cross-height CKM prediction to 5.347 dB zero-shot and 3.518 dB few-shot on a layered aerial benchmark, outperforming 3D-RadioDiff.