Terahertz Spatial Wireless Channel Modeling with Radio Radiance Field
John Song, Lihao Zhang, Feng Ye, Haijian Sun
TL;DR
The paper addresses THz spatial channel modeling for 6G by leveraging a Radio Radiance Field (RRF) to reconstruct Spatial-CSI from scene geometry and sparse THz measurements. It extends the RF-3DGS framework to RF-3DGS+ by explicitly modeling full propagation paths and ToF, addressing THz-specific scattering and ultra-wide bandwidth challenges. A two-stage training pipeline first recovers geometry from visual data and then learns full path information, including $l_{prev}$, AoD, and cumulative gains, enabling unbiased path loss. Demonstrations in a high-fidelity indoor THz scenario show RF-3DGS+ outperforms baselines with sparse data, enabling faster inference and robust Spatial-CSI reconstruction for practical 6G THz systems.
Abstract
Terahertz (THz) communication is a key enabler for 6G systems, offering ultra-wide bandwidth and unprecedented data rates. However, THz signal propagation differs significantly from lower-frequency bands due to severe free space path loss, minimal diffraction and specular reflection, and prominent scattering, making conventional channel modeling and pilot-based estimation approaches inefficient. In this work, we investigate the feasibility of applying radio radiance field (RRF) framework to the THz band. This method reconstructs a continuous RRF using visual-based geometry and sparse THz RF measurements, enabling efficient spatial channel state information (Spatial-CSI) modeling without dense sampling. We first build a fine simulated THz scenario, then we reconstruct the RRF and evaluate the performance in terms of both reconstruction quality and effectiveness in THz communication, showing that the reconstructed RRF captures key propagation paths with sparse training samples. Our findings demonstrate that RRF modeling remains effective in the THz regime and provides a promising direction for scalable, low-cost spatial channel reconstruction in future 6G networks.