Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting

TL;DR

This paper introduces SwiftWRF, a fast and accurate Gaussian splatting-based framework for modeling wireless radiance fields, enabling real-time spectrum synthesis and improved localization tasks in communication systems.

Contribution

SwiftWRF is the first deformable 2D Gaussian splatting method for wireless radiance fields, achieving over 100,000 fps rendering and 500x speedup compared to prior neural radiance field approaches.

Findings

SwiftWRF achieves up to 500x faster spectrum reconstruction.

It improves signal quality in WRF modeling.

It enables real-time spectrum synthesis for communication tasks.

Abstract

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/.