PC-TGS: Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting

Ye Xue, Yiheng Wang, Xinhua Shao, Qi Yan, Shutao Zhang, Tsung-Hui Chang

PC-TGS is the first framework to extrapolate channel angular power spectrum (APS) to unmeasured outdoor locations by integrating sparse RSRP measurements with dense LiDAR geometry. Published at IEEE Transactions on Wireless Communications, 2026.

For details, full performance comparisons, and BibTeX, see the Project Page.

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Quick Start

Environment

# Python >= 3.8, CUDA-enabled GPU
pip install numpy scipy h5py pyyaml tqdm einops matplotlib
pip install torch torchvision torchaudio  # match your CUDA version

Note: The training dataset is proprietary. To use this code with your own data, prepare files matching the format expected by datasets_aps_new.py.

Training

python train_radsplatter_new.py \
  --config ./radsplatter_setting_new.yml \
  --gpu 0 \
  --mode train \
  --num_scatters 2000 \
  --world_size 1 \
  --num_max_angles 800 \
  --sh_up_iter 500

Evaluation

python train_radsplatter_new.py \
  --config ./radsplatter_setting_new.yml \
  --gpu 0 \
  --mode test \
  --num_scatters 2000 \
  --world_size 1 \
  --num_max_angles 800

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Repository Structure

├── README.md
├── radsplatter_setting_new.yml         # Training and optimizer configuration
├── train_radsplatter_new.py            # Main training/testing entrance
├── radsplatter_model.py                # PC-TGS model (RM, SH coefficients, scatterer attributes)
├── radsplatter_render.py               # Tangent-plane projection + electromagnetic splatting
├── datasets_aps_new.py                 # Dataset loaders for RSRP and APS data
├── projection_utils.py                 # 3D-to-angular projection + Jacobian computation
├── complex_sh_utils_new.py             # Complex spherical harmonic evaluation
├── sh_utils.py                         # Real spherical harmonic utilities
├── pdf_utils.py                        # Gaussian PDF computation
├── prune_utils.py                      # Mahalanobis-based Gaussian filtering
├── loss_utils.py                       # Loss functions (L1, L2, SSIM, SmoothL1)
├── data_painter.py                     # APS visualization and data processing
└── utils.py                            # General tensor and rotation utilities

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Citation

If you find this work helpful, please cite:

@article{xue2026point,
  title={Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting},
  author={Xue, Ye and Wang, Yiheng and Shao, Xinhua and Yan, Qi and Zhang, Shutao and Chang, Tsung-Hui},
  journal={IEEE Transactions on Wireless Communications},
  volume={25},
  pages={17816--17830},
  year={2026},
  publisher={IEEE},
  doi={10.1109/TWC.2026.3696997}
}

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License

This project is licensed under the MIT License.