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RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting
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RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting
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In the 6G era, the demand for higher system throughput and the implementation of emerging 6G technologies require large-scale antenna arrays and accurate spatial channel state information (Spatial-CSI). Traditional channel modeling approaches, such as empirical models, ray tracing, and measurement-based methods, face challenges in spatial resolution, efficiency, and scalability. Radiance field-based methods have emerged as promising alternatives but still suffer from geometric inaccuracy and costly supervision. This paper proposes RF-PGS, a novel framework that reconstructs high-fidelity radio propagation paths from only sparse path loss spectra. By introducing Planar Gaussians as geometry primitives with certain RF-specific optimizations, RF-PGS achieves dense, surface-aligned scene reconstruction in the first geometry training stage. In the subsequent Radio Frequency (RF) training stage, the proposed fully-structured radio radiance, combined with a tailored multi-view loss, accurately models radio propagation behavior. Compared to prior radiance field methods, RF-PGS significantly improves reconstruction accuracy, reduces training costs, and enables efficient representation of wireless channels, offering a practical solution for scalable 6G Spatial-CSI modeling.
Forward citations
Cited by 5 Pith papers
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Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction
BiSplat-WRF applies 2D planar Gaussians rendered on angular domains plus a bilinear spatial transformer to capture electromagnetic interactions, outperforming prior NeRF and GS methods on SSIM for wireless radiance fi...
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Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis
GRaF learns a scene-independent latent RF radiance field from proximate transmitters via an interpolation theory, then uses neural ray tracing to synthesize spectra at new transmitter or receiver positions.
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Construction and Dynamic Update of Channel Gain Maps via 3D Gaussian Splatting
A 3D Gaussian-splatting model decomposes grid-averaged channel gain into direct and scattered paths, reconstructs static channel gain maps, and incrementally updates them from sparse new measurements.
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TeRFS: Temporal-Evolving Radio Field Synthesis
TeRFS models dynamic radio fields via anisotropic spherical Gaussians bound to analytical temporal envelopes that enable explicit multipath birth-and-death, delivering 11.5% lower MSE and 6.9x faster training than baselines.
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Mip-NeWRF: Enhanced Wireless Radiance Field with Hybrid Encoding for Channel Prediction
Mip-NeWRF predicts indoor channel frequency responses from sparse measurements using scale-normalized hybrid positional encoding and Fresnel-aware synthesis, beating NeWRF by 14.3 dB NMSE in ray-traced simulations.
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