Abstract:Wireless channel modeling is a key building block for next-generation wireless systems. Predicting the channel state information (CSI) across different transmitter locations can substantially reduce the pilot and feedback overhead of conventional channel estimation. We propose P-WRFGS, an efficient wireless radiance field modeling framework built upon 3D Gaussian splatting. P-WRFGS introduces a learnable mask for each 3D Gaussian primitive to indicate its importance, which guides the pruning of less significant primitives for more efficient rendering. The model is trained using a weighted combination of rendering and regularization losses, allowing a flexible trade-off between rendering quality and efficiency. Numerical results on the $\text{NeRF}^2$ dataset demonstrate that P-WRFGS achieves up to 100$\times$ storage reduction and 7$\times$ rendering speed-up with only mild degradation in SSIM and the achievable rate. Moreover, initializing the Gaussian primitives from a 3D point cloud of the scene further improves the entire quality-efficiency trade-off.
From: Chenghong Bian [view email]
[v1]
Thu, 14 May 2026 18:38:29 UTC (1,780 KB)
[v2]
Sun, 23 Aug 2026 08:15:52 UTC (1,819 KB)
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