RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Construction

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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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Before Starting

1. install torch

conda create -n radiodiff python=3.9
conda avtivate radiodiff
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

2. install other packages.

pip install -r requirement.txt

3. prepare accelerate config.

accelerate config # HOW MANY GPUs YOU WANG TO USE.

Prepare Data

We used the RadioMapSeer dataset for model training and testing.

• The data structure should look like:

|-- $RadioMapSeer
|   |-- gain
|   |-- |-- carsDPM
|   |-- |-- |-- XXX_XX.PNG
|   |-- |-- |-- XXX_XX.PNG
|   ...
|   |-- png
|   |-- |-- buildings_complete
|   |-- |-- |-- XXX_XX.PNG
|   |-- |-- |-- XXX_XX.PNG
|	...

🎉 Training

1. train the first stage model (AutoEncoder):

accelerate launch train_vae.py --cfg ./configs/first_radio.yaml

2. train latent diffusion-edge model:

accelerate launch train_cond_ldm.py --cfg ./configs/radio_train.yaml

3. fine-tune the pretrained model on IRT4 few-shot data:

accelerate launch train_cond_ldm_finetune.py --cfg ./configs/radio_train_irt4_finetune.yaml

V. Inference.

python sample_cond_ldm.py --cfg ./configs/radio_test_finetune.yaml