UrbanRadio3D

July 4, 2026 · View on GitHub

📡 Welcome to the RadioDiff Family

Radio map construction via generative diffusion models — UNIC Lab, Xidian University

🔷 Base Backbone

RadioDiff — The foundational diffusion model for radio map construction.   📄 Paper  |  💻 Code  |  IEEE TCCN

🔬 Physics-Informed Extensions

RadioDiff-k² — PINN-enhanced diffusion guided by the Helmholtz equation.   📄 Paper  |  💻 Code  |  IEEE JSAC

iRadioDiff — Indoor radio map construction with physical information integration.   📄 Paper  |  💻 Code  |  IEEE ICC  Best Paper

⚡ Efficiency & Dynamics

RadioDiff-Turbo — Efficiency-enhanced RadioDiff for accelerated inference.   📄 Paper  |  INFOCOM Workshop

RadioDiff-Flux — Adaptive reconstruction under dynamic environments and base station location changes.   📄 Paper  |  IEEE TCCN

🌐 Extended Scenarios

RadioDiff-3D — 3D radio map construction with the UrbanRadio3D dataset.   📄 Paper  |  💻 Code  |  IEEE TNSE

RadioDiff-FS — Few-shot learning for radio map construction with limited measurements.   📄 Paper  |  💻 Code  |  IEEE IoTJ

📶 Sparse Measurement & Localization

RadioDiff-Inverse — Sparse measurement-based radio map recovery for ISAC applications.   📄 Paper  |  💻 Code  |  IEEE TWC

RadioDiff-Loc — Sparse measurement-based NLoS localization using diffusion models.   📄 Paper  |  arXiv

📚 For a comprehensive categorized overview of radio map research, visit Awesome-Radio-Map-Categorized.

This is the demo of the dataset for UrbanRadio3D, which is accepted by IEEE TNSE.

If you have any questions, please contact me at xcwang_1@stu.xidian.edu.cn

Citation

@ARTICLE{11083758,
  author={Wang, Xiucheng and Zhang, Qiming and Cheng, Nan and Chen, Junting and Zhang, Zezhong and Li, Zan and Cui, Shuguang and Shen, Xuemin},
  journal={IEEE Transactions on Network Science and Engineering}, 
  title={RadioDiff-3D: A 3D× 3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication}, 
  year={2025},
  volume={},
  number={},
  pages={1-18},
  doi={10.1109/TNSE.2025.3590545}}

Dataset Description

All datasets used in this project can be accessed via the following cloud storage links:

  • Baidu Cloud Drive: [link]
    Extraction Code is required.

  • OneDrive: [link]
    Extraction Code is required.

  • For the Extraction Code, please click [link]

The extraction code is located at the top of the redirect page after you complete the questionnaire. If you cannot find it, you may contact us via email at xcwang_1@stu.xidian.edu.cn.

Dataset Splits

We have provided recommended splits of the dataset into training and testing sets to facilitate standardized model training and evaluation.

File Description

  • Building_Infomation.zip
    This archive contains information on building heights and their spatial distribution, which serves as critical geometric features for wireless communication environment modeling.

  • Naming Convention for RM Maps
    Each RM (Radio Map) image file follows the naming format:
    xxx_Xxxx_Yxx.png
    Where:

    • xxx indicates the building distribution map index
    • Xxxx indicates the X-coordinate of the base station
    • Yxx indicates the Y-coordinate of the base station

This naming scheme allows precise identification of the RM map's corresponding building environment and base station location, facilitating further analysis and experimental reproducibility.

For any questions regarding dataset usage or structure, please feel free to contact the project team for further assistance.