RadSplatter: Extending 3D Gaussian Splatting to Radio Frequencies for Wireless Radiomap Extrapolation

TL;DR

RadSplatter is a novel framework that extends 3D Gaussian Splatting to radio frequencies, enabling efficient and accurate extrapolation of wireless radiomaps from sparse measurements, which is vital for scalable network applications.

Contribution

It introduces RadSplatter, combining environmental modeling, a relaxed-mean reparameterization, and a camera-free projection to improve radiomap extrapolation from limited data.

Findings

Achieves state-of-the-art accuracy in radiomap extrapolation.

Demonstrates fast execution suitable for real-world deployment.

Provides robust generalization across synthetic and real data.

Abstract

A radiomap represents the spatial distribution of wireless signal strength, critical for applications like network optimization and autonomous driving. However, constructing radiomap relies on measuring radio signal power across the entire system, which is costly in outdoor environments due to large network scales. We present RadSplatter, a framework that extends 3D Gaussian Splatting (3DGS) to radio frequencies for efficient and accurate radiomap extrapolation from sparse measurements. RadSplatter models environmental scatterers and radio paths using 3D Gaussians, capturing key factors of radio wave propagation. It employs a relaxed-mean (RM) scheme to reparameterize the positions of 3D Gaussians from noisy and dense 3D point clouds. A camera-free 3DGS-based projection is proposed to map 3D Gaussians onto 2D radio beam patterns. Furthermore, a regularized loss function and recursive fine-tuning using highly structured sparse measurements in real-world settings are applied to ensure robust generalization. Experiments on synthetic and real-world data show state-of-the-art extrapolation accuracy and execution speed.