WiSER introduces a transmitter-conditioned sparse 3D scene encoder queried by a ray-corridor decoder for radiomaps and a DETR-style set decoder for variable-cardinality CIR taps, trained on co-registered ScanNet++ and Sionna data.
Newrf: A deep learning framework for wireless radiation field reconstruction and channel prediction
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 5representative citing papers
RadTwin conditions a neural radio-propagation model on scene point clouds via physics-informed sparse attention, achieving 0.846 SSIM and 0.023 LPIPS on dynamic indoor scenes without retraining.
RFIR framework embeds RF-aware BSDF into Gaussian splatting for decoupled RF scene modeling, generalizing RCS synthesis, RSSI prediction, and wireless scene editability with performance gains.
RadioFormer3D proposes a dual-stream generative architecture with Fourier sampling and a multi-level integrity loss to reconstruct 3D radio maps from sparse horizontal measurements under weak vertical supervision.
RF-CMG synthesizes high-quality mmWave and RFID signals from WiFi using a diffusion model with Modality-Guided Embedding for high-frequency details and Low-Frequency Modality Consistency to preserve physical structure.
citing papers explorer
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WiSER: A Wireless Scene Encoder for Geometry-Grounded Multi-View Wireless Prediction
WiSER introduces a transmitter-conditioned sparse 3D scene encoder queried by a ray-corridor decoder for radiomaps and a DETR-style set decoder for variable-cardinality CIR taps, trained on co-registered ScanNet++ and Sionna data.
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RadTwin: Generalizable Wireless Digital Twin for Dynamic Environments
RadTwin conditions a neural radio-propagation model on scene point clouds via physics-informed sparse attention, achieving 0.846 SSIM and 0.023 LPIPS on dynamic indoor scenes without retraining.
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Radio-Frequency Inverse Rendering for Wireless Environment Modeling
RFIR framework embeds RF-aware BSDF into Gaussian splatting for decoupled RF scene modeling, generalizing RCS synthesis, RSSI prediction, and wireless scene editability with performance gains.
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RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling
RadioFormer3D proposes a dual-stream generative architecture with Fourier sampling and a multi-level integrity loss to reconstruct 3D radio maps from sparse horizontal measurements under weak vertical supervision.
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Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing
RF-CMG synthesizes high-quality mmWave and RFID signals from WiFi using a diffusion model with Modality-Guided Embedding for high-frequency details and Low-Frequency Modality Consistency to preserve physical structure.