Modeling the wireless radiance field (WRF) is fundamental to modern communication systems, enabling key tasks such as localization, sensing, and channel estimation. Traditional approaches, which rely on empirical formulas or physical simulations, often suffer from limited accuracy or require strong scene priors. Recent neural radiance field (NeRF-based) methods improve reconstruction fidelity through differentiable volumetric rendering, but their reliance on computationally expensive multilayer perceptron (MLP) queries hinders real-time deployment. To overcome these challenges, we introduce Gaussian splatting (GS) to the wireless domain, leveraging its efficiency in modeling optical radiance fields to enable compact and accurate WRF reconstruction. Specifically, we propose SwiftWRF, a deformable 2D Gaussian splatting framework that synthesizes WRF spectra at arbitrary positions under single-sided transceiver mobility. SwiftWRF employs CUDA-accelerated rasterization to render spectra at over 100000 fps and uses a lightweight MLP to model the deformation of 2D Gaussians, effectively capturing mobility-induced WRF variations. In addition to novel spectrum synthesis, the efficacy of SwiftWRF is further underscored in its applications in angle-of-arrival (AoA) and received signal strength indicator (RSSI) prediction. Experiments conducted on both real-world and synthetic indoor scenes demonstrate that SwiftWRF can reconstruct WRF spectra up to 500x faster than existing state-of-the-art methods, while significantly enhancing its signal quality. The project page is https://evan-sudo.github.io/swiftwrf/.

翻译：无线辐射场建模是现代通信系统的基础，能够实现定位、感知和信道估计等关键任务。传统方法依赖经验公式或物理仿真，通常存在精度有限或需要强场景先验的问题。近期基于神经辐射场的方法通过可微分体渲染提升了重建保真度，但其对计算密集型多层感知机查询的依赖阻碍了实时部署。为克服这些挑战，我们将高斯泼溅技术引入无线领域，利用其在光学辐射场建模中的高效性，实现紧凑且精确的无线辐射场重建。具体而言，我们提出了SwiftWRF——一个可变形二维高斯泼溅框架，能够在单侧收发器移动条件下合成任意位置的无线辐射场频谱。SwiftWRF采用CUDA加速的栅格化技术，以超过100000帧/秒的速度渲染频谱，并利用轻量级多层感知机建模二维高斯的形变，有效捕捉移动性引起的无线辐射场变化。除新颖的频谱合成能力外，SwiftWRF在到达角与接收信号强度指示预测等应用中的效能进一步凸显了其优势。在真实场景与合成室内场景上的实验表明，SwiftWRF能以比现有先进方法快500倍的速度重建无线辐射场频谱，同时显著提升信号质量。项目页面详见https://evan-sudo.github.io/swiftwrf/。