UNIC-Lab/RadioDiff
This is the code for the paper "RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction", IEEE TCCN.
This tool helps wireless network planners and researchers efficiently create radio maps, which are visualizations of signal strength and path loss in an area. You input environmental data (like building layouts or terrain) and it outputs high-quality, detailed radio maps without needing extensive, costly physical signal measurements. This is ideal for anyone designing or optimizing 6G wireless networks, especially in dynamic or complex environments.
306 stars.
Use this if you need to quickly and accurately generate radio maps for wireless network planning and optimization, especially when traditional measurement-based methods are too time-consuming or expensive.
Not ideal if you require highly simplified, low-fidelity radio maps or are working with systems where precise path loss estimation is not a critical factor.
Stars
306
Forks
23
Language
Python
License
—
Category
Last pushed
Dec 06, 2025
Commits (30d)
0
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