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.

翻译：我们提出了GRaF（可泛化的射频辐射场）框架，该框架通过建模射频信号传播，能够在任意发射机或接收机位置合成空间频谱，其中每个频谱测量接收机在所有周围方向上的信号功率。与现有最先进方法将原始神经辐射场（NeRF）通过场景特定训练适配至射频领域不同，GRaF能够跨场景泛化以合成频谱。为实现此目标，我们证明了射频领域中的插值理论：发射机的空间频谱可以通过地理邻近发射机的频谱进行近似。基于该理论，GRaF包含两个组件：（i）一个几何感知的Transformer编码器，通过捕捉邻近发射机的空间相关性来学习场景无关的潜在射频辐射场；（ii）一种神经光线追踪算法，用于估计接收机处的频谱接收。实验结果表明，GRaF在单场景基准测试中优于现有方法，并在未见过的场景布局上实现了最先进的性能。