# GSBF: Gaussian Splatting for Environment-Aware Beamforming

arXiv: https://arxiv.org/abs/2608.05896 · HTML: https://mlsanity.com/paper/2608.05896/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [RF-3DGS: Wireless Channel Modeling with Radio Radiance Field and 3D Gaussian Splatting](https://mlsanity.com/paper/2411.19420/) (2024)
- [Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming](https://mlsanity.com/paper/2209.02258/) (2022)
- [BiGS: Bidirectional Gaussian Primitives for Relightable 3D Gaussian Splatting](https://mlsanity.com/paper/2408.13370/) (2024)
- [SG-Splatting: Accelerating 3D Gaussian Splatting with Spherical Gaussians](https://mlsanity.com/paper/2501.00342/) (2024)
- [Deep learning for location based beamforming with NLOS channels](https://mlsanity.com/paper/2201.01386/) (2021)
- [Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction](https://mlsanity.com/paper/2301.05837/) (2023)
- [Environment-Aware and Training-Free Beam Alignment for mmWave Massive MIMO via Channel Knowledge Map](https://mlsanity.com/paper/2102.09871/) (2021)
- [Bridging Visual and Wireless Sensing via a Unified Radiation Field for 3D Radio Map Construction](https://mlsanity.com/paper/2601.19216/) (2026)
- [Efficient Angle-Domain Processing for FDD-based Cell-free Massive MIMO Systems](https://mlsanity.com/paper/2001.07438/) (2020)
- [Multi-Modal Environment-Aware Beam Management for Massive MIMO: A Geometry-Driven Virtual Base Station Framework](https://mlsanity.com/paper/2606.26567/) (2026)

## Built on by (most cited first, 2 of 2)

- [AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC](https://mlsanity.com/paper/2609.39964/) (2026)
- [RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations](https://mlsanity.com/paper/2610.16656/) (2026)

## Abstract (arXiv)

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.