Wideband RF Radiance Field Modeling Using Frequency-embedded 3D Gaussian Splatting

TL;DR

This paper introduces a novel wideband RF radiance field modeling method using frequency-embedded 3D Gaussian Splatting, enabling accurate reconstruction across multiple frequencies in indoor environments.

Contribution

The paper proposes a unified 3D Gaussian Splatting algorithm with a frequency-embedded EM feature network for wideband RF modeling, capable of reconstructing RF fields at unseen frequencies.

Findings

Achieved SSIM of 0.922 on PAS dataset, outperforming single-frequency models.

Developed a large-scale dataset with 50,000 samples from 1 to 94 GHz.

Demonstrated effective modeling of RF fields across diverse indoor environments.

Abstract

Indoor environments typically contain diverse RF signals distributed across multiple frequency bands, including NB-IoT, Wi-Fi, and millimeter-wave. Consequently, wideband RF modeling is essential for practical applications such as joint deployment of heterogeneous RF systems, cross-band communication, and distributed RF sensing. Although 3D Gaussian Splatting (3DGS) techniques effectively reconstruct RF radiance fields at a single frequency, they cannot model fields at arbitrary or unknown frequencies across a wide range. In this paper, we present a novel 3DGS algorithm for unified wideband RF radiance field modeling. RF wave propagation depends on signal frequency and the 3D spatial environment, including geometry and material electromagnetic (EM) properties. To address these factors, we introduce a frequency-embedded EM feature network that utilizes 3D Gaussian spheres at each spatial location to learn the relationship between frequency and transmission characteristics, such as attenuation and radiance intensity. With a dataset containing sparse frequency samples in a specific 3D environment, our model can efficiently reconstruct RF radiance fields at arbitrary and unseen frequencies. To assess our approach, we introduce a large-scale power angular spectrum (PAS) dataset with 50,000 samples spanning 1 to 94 GHz across six indoor environments. Experimental results show that the proposed model trained on multiple frequencies achieves a Structural Similarity Index Measure (SSIM) of 0.922 for PAS reconstruction, surpassing state-of-the-art single-frequency 3DGS models with SSIM of 0.863.