TWC 2025: WRF-GS+

Welcome to the WRF-GS+ repository, an enhanced version of the original WRF-GS that has been accepted for TWC 2025. You can access the accompanying paper, "Neural Representation for Wireless Radiation Field Reconstruction: A 3D Gaussian Splatting Approach", for more details.

Abstract: Wireless channel modeling plays a pivotal role in designing, analyzing, and optimizing wireless communication systems. Nevertheless, developing an effective channel modeling approach has been a long-standing challenge. This issue has been escalated due to denser network deployment, larger antenna arrays, and broader bandwidth in next-generation networks. To address this challenge, we put forth WRF-GS, a novel framework for channel modeling based on wireless radiation field (WRF) reconstruction using 3D Gaussian splatting (3D-GS). WRF-GS employs 3D Gaussian primitives and neural networks to capture the interactions between the environment and radio signals, enabling efficient WRF reconstruction and visualization of the propagation characteristics. The reconstructed WRF can then be used to synthesize the spatial spectrum for comprehensive wireless channel characterization. While WRF-GS demonstrates remarkable effectiveness, it faces limitations in capturing high-frequency signal variations caused by complex multipath effects. To overcome these limitations, we propose WRF-GS+, an enhanced framework that integrates electromagnetic wave physics into the neural network design. WRF-GS+ leverages deformable 3D Gaussians to model both static and dynamic components of the WRF, significantly improving its ability to characterize signal variations. In addition, WRF-GS+ enhances the splatting process by simplifying the 3D-GS modeling process and improving computational efficiency. Experimental results demonstrate that both WRF-GS and WRF-GS+ outperform baselines for spatial spectrum synthesis, including ray tracing and other deep-learning approaches.

Visualization results

Quantitative analysis results

Installation

Create the basic environment

conda env create --file environment.yml
conda activate wrfgsplus

Install some extensions

cd submodules
pip install ./simple-knn
pip install ./diff-gaussian-rasterization # or cd ./diff-gaussian-rasterization && python setup.py develop
pip install ./fused-ssim

Training & Evaluation

Due to file size limitations, a small dataset is included to help quickly verify the code, which can be executed using the following command:

python train.py

More datasets can be found here.

To-Do List

• Release more case study code.
• Optimize related code structure.

BibTex

If you find this work useful in your research, please cite:

@ARTICLE{11258087,
  author={Wen, Chaozheng and Tong, Jingwen and Hu, Yingdong and Lin, Zehong and Zhang, Jun},
  journal={IEEE Transactions on Wireless Communications}, 
  title={Neural Representation for Wireless Radiation Field Reconstruction: A 3D Gaussian Splatting Approach}, 
  year={2025},
  volume={},
  number={},
  pages={1-1},
  keywords={Wireless communication;Computational modeling;Three-dimensional displays;Adaptation models;Accuracy;Antenna arrays;Solid modeling;Optical scattering;Optical reflection;Optical diffraction;Wireless channel modeling;wireless radiation field reconstruction;3D Gaussian splatting;channel prediction},
  doi={10.1109/TWC.2025.3631663}}

Acknowledgment

Some code snippets are borrowed from 3DGS.