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

Radio map construction via generative diffusion models — UNIC Lab, Xidian University

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🔷 Base Backbone

RadioDiff — The foundational diffusion model for radio map construction.   📄 Paper  |  💻 Code  | 

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🔬 Physics-Informed Extensions

RadioDiff-k² — PINN-enhanced diffusion guided by the Helmholtz equation.   📄 Paper  |  💻 Code  | 

iRadioDiff — Indoor radio map construction with physical information integration.   📄 Paper  |  💻 Code  |   

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⚡ Efficiency & Dynamics

RadioDiff-Turbo — Efficiency-enhanced RadioDiff for accelerated inference.   📄 Paper  | 

RadioDiff-Flux — Adaptive reconstruction under dynamic environments and base station location changes.   📄 Paper  | 

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🌐 Extended Scenarios

RadioDiff-3D — 3D radio map construction with the UrbanRadio3D dataset.   📄 Paper  |  💻 Code  | 

RadioDiff-FS — Few-shot learning for radio map construction with limited measurements.   📄 Paper  |  💻 Code  | 

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📶 Sparse Measurement & Localization

RadioDiff-Inverse — Sparse measurement-based radio map recovery for ISAC applications.   📄 Paper  |  💻 Code  | 

RadioDiff-Loc — Sparse measurement-based NLoS localization using diffusion models.   📄 Paper  | 

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📚 For a comprehensive categorized overview of radio map research, visit 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