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iRadioDiff

Welcome to the RadioDiff family

Base BackBone, Paper Link: RadioDiff, Code Link: GitHub

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

Efficiency Enhanced RadioDiff, Paper Link: RadioDiff-Turbo

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

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

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

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

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

This is the code of "iRadioDiff: Physics Informed Diffusion Model for Effective Indoor Radio Map Construction and Localization" accepted by the IEEE ICC 2026.

☀️ Before Starting

  1. install torch

We have verified that the project can run with Python 3.10, PyTorch 2.2.0, torchvision 0.17.0, torchaudio 2.2.0, and CUDA 12.1.

conda create -n radiodiff python=3.10
conda activate radiodiff
conda install pytorch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 pytorch-cuda=12.1 -c pytorch -c nvidia
  1. install other packages.
pip install -r requirement.txt
  1. prepare accelerate config.
accelerate config # HOW MANY GPUs YOU WANG TO USE.

🎇 Prepare Data

We used the Indoor Radio Map Dataset dataset for model training and testing.

  • Before training or inference, you should first generate the boundary maps and place them under BoundaryMaps in the dataset root directory. This step is required because the dataloader reads boundary files from $ICASSP2025_Dataset/BoundaryMaps with filenames in the format boundary_<original_input_filename>.png.

  • You can generate them with generate_boundary.py :

python generate_boundary.py --input-dir ./ICASSP2025_Dataset/Inputs/Task_1_ICASSP --positions-dir ./ICASSP2025_Dataset/Positions --output-dir ./ICASSP2025_Dataset/BoundaryMaps
  • The data structure should look like:
|-- $ICASSP2025_Dataset
|   |-- Input
|   |-- |-- Task_1_ICASSP
|   |-- |-- |-- B1_Ant1_f1_S0.PNG
|   |-- |-- |-- B1_Ant1_f1_S1.PNG
|   ...
|   |-- Positions
|   |-- |-- Positions_B1_Ant1_f1.csv
|   |-- |-- Positions_B1_Ant1_f2.csv
|   ...
|   |-- BoundaryMaps
|   |-- |-- boundary_B1_Ant1_f1_S0.png
|   |-- |-- boundary_B1_Ant1_f1_S1.png
|   ...
|   |-- Output
|   |-- |-- Task_1_ICASSP
|   |-- |-- |-- B1_Ant1_f1_S0.PNG
|   |-- |-- |-- B1_Ant1_f1_S1.PNG
|	...

🎉 Training

accelerate launch train_cond_dpm.py --cfg ./configs/ICA_dm.yaml

V. Inference.

make sure your model weight path is added in the config file ./configs/ICA_dm.yaml (line 66), and run:

python sample_cond_dpm.py --cfg ./configs/ICA_dm.yaml

Note that you can modify the sampling_timesteps (line 7) to control the inference speed.

Thanks

Thanks to the base code DDM-Public.