A Geometric Algebra-Informed 3DGS Framework for Wireless Channel Prediction

In this paper, we introduce Geometric Algebra–Informed 3D Gaussian Splatting (GAI-GS), a framework for wireless modeling that couples 3D Gaussian splatting with a geometric-algebra–based attention mechanism to explicitly model ray–object interactions in complex propagation environments. GAI-GS encodes joint spatial–electromagnetic (EM) relations into token representations, enabling scene-level aggregation within a unified, end-to-end neural architecture. This design renders ray tracing for wireless propagation physically grounded, with token interactions that respect EM constraints including multipath, path-dependent attenuation, and reflection/diffraction. Through extensive evaluations on on multiple real-world indoor datasets, GAI-GS consistently surpasses current baselines across various wireless tasks.