RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting
This addresses the need for scalable spatial channel state information modeling in 6G wireless networks, representing a novel method for a known bottleneck.
The paper tackles the challenge of accurately modeling spatial wireless channels for 6G systems by proposing RF-PGS, a framework that reconstructs high-fidelity radio propagation paths from sparse path loss spectra. The result is significantly improved reconstruction accuracy and reduced training costs compared to prior radiance field methods.
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.