用通用模型合成任意位置的射频信号方向谱，无需针对每场景重训练。
Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis
- 基于邻近发射机信号谱的插值理论，构建跨场景通用射频场模型。
- 在未见场景布局上达到当前最优性能，单场景基准也优于现有方法。
- 适合需要快速部署、泛化能力强的无线系统设计与优化场景。
我们提出GRaF（通用射频辐射场），一种建模射频信号传播以合成任意发射机或接收机位置空间谱的框架。每个空间谱测量接收机周围所有方向上的信号功率。不同于需为每场景单独训练的传统方法，GRaF具备跨场景泛化能力。我们证明了射频域中的插值理论：某发射机的空间谱可由地理邻近发射机的谱近似。基于此，GRaF包含两部分：(i) 几何感知的Transformer编码器，利用邻近发射机信息学习场景无关的隐式射频辐射场；(ii) 神经射线追踪算法，用于估计接收机处的谱接收。实验表明，GRaF在单场景基准上优于现有方法，并在未见场景布局上达到当前最优性能。
原文摘要 · Abstract (English)
We present GRaF, Generalizable Radio-Frequency (RF) Radiance Fields, a framework that models RF signal propagation to synthesize spatial spectra at arbitrary transmitter or receiver locations, where each spectrum measures signal power across all surrounding directions at the receiver. Unlike state-of-the-art methods that adapt vanilla Neural Radiance Fields (NeRF) to the RF domain with scene-specific training, GRaF generalizes across scenes to synthesize spectra. To enable this, we prove an interpolation theory in the RF domain: the spatial spectrum from a transmitter can be approximated using spectra from geographically proximate transmitters. Building on this theory, GRaF comprises two components: (i) a geometry-aware Transformer encoder that captures spatial correlations from neighboring transmitters to learn a scene-independent latent RF radiance field, and (ii) a neural ray tracing algorithm that estimates spectrum reception at the receiver. Experimental results demonstrate that GRaF outperforms existing methods on single-scene benchmarks and achieves state-of-the-art performance on unseen scene layouts.
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