Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting

2025-06-15

AI Summary

To address the low accuracy of conventional methods and the slow inference and poor real-time deployability of NeRF-based approaches in wireless radiation field (WRF) modeling, this paper introduces deformable 2D Gaussian rasterization—the first such application in wireless communications—and proposes a lightweight, dynamic, high-fidelity WRF representation framework. Key contributions include: (1) the first explicit WRF representation using deformable 2D Gaussians; (2) a lightweight MLP-driven deformation modeling mechanism enabling compact dynamic reconstruction under single-side transceiver mobility; and (3) a CUDA-accelerated rasterization pipeline integrated with a spectrum-driven joint AoA/RSSI prediction framework. Evaluated on both synthetic and real indoor scenarios, our method achieves spectral reconstruction at >100,000 fps—500× faster than state-of-the-art—while significantly improving signal fidelity and localization accuracy.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Machine Learning: Matrix & Tensor Methods

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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. Code and datasets will be released.

Problem

• Modeling wireless radiance field for communication tasks
• Overcoming limited accuracy in traditional WRF methods
• Enabling real-time WRF reconstruction with Gaussian splatting

Innovation

• Deformable 2D Gaussian splatting for WRF
• CUDA-accelerated rasterization for real-time spectra
• Lightweight MLP models mobility-induced variations