Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This is the code for the paper "RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction", IEEE TCCN.
| Date | Stars |
|---|---|
| 2026-07-31 | 315 |
| 2026-08-03 | 316 |
| 2026-08-12 | 317 |
| 2026-08-27 | 317 |
| 2026-09-05 | 318 |
| 2026-09-06 | 319 |
| 2026-09-11 | 319 |
| 2026-09-14 | 320 |
| 2026-09-16 | 321 |
| 2026-09-20 | 321 |
Today
— stars today
This week
+2 stars this week
This month
+4 stars this month
Momentum
0.0
growth rate 0.63%/day
# RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction --- ## 📡 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](https://ieeexplore.ieee.org/document/10764739) | 💻 [Code](https://github.com/UNIC-Lab/RadioDiff) |  --- ### 🔬 Physics-Informed Extensions **RadioDiff-k²** — *PINN-enhanced diffusion guided by the Helmholtz equation.* 📄 [Paper](https://ieeexplore.ieee.org/document/11278649) | 💻 [Code](https://github.com/UNIC-Lab/RadioDiff-k) |  **iRadioDiff** — *Indoor radio map construction with physical information integration.* 📄 [Paper](https://arxiv.org/abs/2511.20015) | 💻 [Code](https://github.com/UNIC-Lab/iRadioDiff) |   --- ### ⚡ Efficiency & Dynamics **RadioDiff-Turbo** — *Efficiency-enhanced RadioDiff for accelerated inference.* 📄 [Paper](https://ieeexplore.ieee.org/abstract/document/11152929/) |  **RadioDiff-Flux** — *Adaptive reconstruction under dynamic environments and base station location changes.* 📄 [Paper](https://ieeexplore.ieee.org/document/11282987/) |  --- ### 🌐 Extended Scenarios **RadioDiff-3D** — *3D radio map construction with the UrbanRadio3D dataset.* 📄 [Paper](https://ieeexplore.ieee.org/document/11083758) | 💻 [Code](https://github.com/UNIC-Lab/UrbanRadio3D) |  **RadioDiff-FS** — *Few-shot learning for radio map construction with limited measurements.* 📄 [Paper](https://ieeexplore.ieee.org/document/11577136) | 💻 [Code](https://github.com/UNIC-Lab/RadioDiff-FS) |  --- ### 📶 Sparse Measurement & Localization **RadioDiff-Inverse** — *Sparse measurement-based radio map recovery for ISAC applications.* 📄 [Paper](https://arxiv.org/abs/2504.14298) | 💻 [Code](https://github.com/UNIC-Lab/radiodiff-inverse) |  **RadioDiff-Loc** — *Sparse measurement-based NLoS localization using diffusion models.* 📄 [Paper](https://www.arxiv.org/abs/2509.01875) |  --- > 📚 For a comprehensive categorized overview of radio map research, visit [**Awesome-Radio-Map-Categorized**](https://github.com/UNIC-Lab/Awesome-Radio-Map-Categorized). --- This is the code for the paper "RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction", IEEE TCCN. Pre-trained Weight: Please contact me at [email protected] [Paper](https://ieeexplore.ieee.org/document/10764739) #### 🔥🔥🔥 News - **2024-7:** This repo is constructed. - **2024-11:** Our paper has been accepted by IEEE TCCN. - **2024-12:** The code has been released! 🎉🎉🎉 --- > **Abstract:** Radio map (RM) is a promising technology that can obtain pathloss based on only location, which is significant for 6G network applications to reduce the communication costs for pathloss estimation. However, the construction of RM in traditional is either computationally intensive or depends on costly sampling-based pathloss measuremen
Excerpt of 7,824 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ecd158d5e9bb3aae, topic:diffusion-models, desc:diffusion model