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This paper introduces a novel Gaussian Splatting for Environment-Aware Beamforming (GSBF) approach that leverages a persistent 3D Gaussian representation to model environmental scattering responses, eliminating the need for instantaneous channel state information (CSI). By utilizing bidirectional spherical Gaussian kernels and two-sided electromagnetic rasterization, GSBF synthesizes beam patterns directly from the access point and user positions, significantly reducing computational complexity and pilot overhead. Simulation results show that GSBF outperforms traditional methods like exhaustive beam alignment (EBA) in terms of latency and efficiency.
GSBF achieves beamforming without the need for instantaneous channel state information, dramatically reducing latency and complexity in MIMO systems.
Beamforming plays a key role in multiple-input-multiple-output (MIMO) communication systems. However, conventional beamforming design normally requires accurate instantaneous channel state information (CSI) and iterative optimization, which incur substantial pilot overhead and computational complexity. Recognizing that radio propagation is intrinsically governed by the physical geometry, we develop a 3D Gaussian splatting for environment-aware beamforming (GSBF) pipeline based on multi-modal data, which characterizes the environment through a persistent 3D Gaussian representation. Specifically, GSBF models the environmental scattering response with reciprocity-preserving bidirectional spherical Gaussian (Bi-SG) kernels and performs two-sided electromagnetic rasterization to render an angular propagator map. The rendered map is then aggregated through an over-complete array-manifold dictionary and projected to the constant-modulus beamformers, thereby synthesizing beams directly from the access point (AP) pose and user position without online instantaneous CSI. Simulations demonstrate that GSBF consistently outperforms baselines such as exhaustive beam alignment (EBA) with lower latency.
