UrbanRadio3D

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Welcome to the RadioDiff family

Base BackBone, Paper Link: RadioDiff, Code Link: GitHub, IEEE TCCN, 2025

PINN Enhanced with Helmholtz Equation, Paper Link: RadioDiff-$k^2$, Code Link: GitHub, IEEE JSAC, 2026

Efficiency Enhanced RadioDiff, Paper Link: RadioDiff-Turbo, IEEE INFOCOM wksp, 2025

Dynamic Environment or BS Location Change, Paper Link: RadioDiff-Flux, IEEE TCCN, 2026

Few-Shot Learning, Paper Link: RadioDiff-FS, Code Link: GitHub

Indoor RM Construction with Physical Information, Paper Link: iRadioDiff, Code Link: GitHub, IEEE ICC, 2026

3D RM with DataSet, Paper Link: RadioDiff-3D, Code Link: GitHub, IEEE TNSE, 2025

Sparse Measurement for RM ISAC, Paper Link: RadioDiff-Inverse, IEEE TWC, 2026

Sparse Measurement for NLoS Localization, Paper Link: RadioDiff-Loc

For more RM information, please visit the repo of Awesome-Radio-Map-Categorized

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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.

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