GeoGS-CE: Learning Delay--Beam Channel Priors with 3D Gaussians for High-Mobility Scenarios

TL;DR

This paper introduces GeoGS-CE, a novel two-stage channel estimation framework that leverages geometric priors and 3D Gaussian modeling to improve high-mobility wideband channel estimation with sparse pilots.

Contribution

GeoGS-CE uniquely models scene-level 3D Gaussian representations and employs a differentiable wireless rendering process to enhance channel estimation in high-mobility scenarios.

Findings

GeoGS-CE significantly outperforms pilot-only and non-geometric baselines in CFR reconstruction.

The framework effectively captures NLoS geometric scattering support using 3D Gaussian models.

Simulation results on high-speed railway channels demonstrate improved accuracy and robustness.

Abstract

Wideband channel estimation (CE) in high-mobility scenarios remains challenging because channel responses vary rapidly, while practical systems can allocate only sparse pilots to accommodate dense users. Fortunately, many high-mobility environments, such as high-speed railways, exhibit scheduled trajectories, predictable velocities, and a limited number of dominant propagation paths. These properties induce a delay--beam power spectrum that is more stable than the instantaneous complex channel frequency response (CFR), less sensitive to the random phase coherence, and rich in geometric information. To exploit such environmental properties, we propose GeoGS-CE, a two-stage channel estimation framework for sparse-pilot high-mobility scenarios. In the offline stage, GeoGS-CE jointly models: 1) a scene-level 3D Gaussian representation that captures the non-line-of-sight (NLoS) geometric scattering support, and 2) a leakage-aware differentiable wireless rendering process that maps the NLoS Gaussians, together with an explicit virtual line-of-sight (LoS) component, to the measured delay--beam power spectrum, while accounting for practical OFDM delay and array leakage effects. In the online stage, the delay--beam power spectrum is predicted for each user location and used as a strong covariance prior, enabling accurate full-band and full-array CFR reconstruction and tracking through a linear MMSE estimator. Simulations based on channels generated from a segment of the Guangshen high-speed railway show that the proposed geometric prior substantially improves CFR reconstruction over pilot-only and non-geometric baselines.