用平面高斯点云实现6G无线信道高精度建模
RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting
- 以平面高斯点为几何基元，从稀疏路径损耗谱重建信道
- 相比现有方法，重建精度更高且训练成本降低
- 适合6G大规模天线系统中的信道状态信息建模
在6G时代，为满足更高系统吞吐量和新兴技术需求，需采用大规模天线阵列并获取精确的空间信道状态信息（Spatial-CSI）。传统建模方法如经验模型、射线追踪和基于测量的方法在空间分辨率、效率和可扩展性方面面临挑战。基于辐射场的方法虽具潜力，但仍存在几何不准确和监督成本高的问题。本文提出RF-PGS框架，仅通过稀疏路径损耗谱即可重建高保真无线电传播路径。通过引入具有特定射频优化的平面高斯作为几何基元，RF-PGS在首个几何训练阶段实现密集且贴合表面的场景重建。在后续射频训练阶段，结合定制化的多视角损失函数，所提出的全结构化射频辐射场能准确建模无线电传播行为。与先前辐射场方法相比，RF-PGS显著提升重建精度，降低训练成本，并实现无线信道的高效表示，为可扩展的6G Spatial-CSI建模提供实用解决方案。
原文摘要 · Abstract (English)
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.
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