RadioFlow 🚀📡

Flow‑Matching for Lightning‑Fast, High‑Fidelity Radio‑Map Generation

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✨ Why RadioFlow?

RadioFlow is a lightweight, ultra-fast generative model tailored for high-fidelity radio map construction. Compared to existing baselines like diffusion-based and UNet-based methods, it delivers significantly better visual quality, drastically reduced inference time, and an exceptionally compact model size—especially with the edge-friendly RadioFlow-Lite variant. Powered by Conditional Flow Matching, spatial attention UNet, and classifier-free guidance, it achieves state-of-the-art performance with a single-step ODE solver, completely bypassing the costly iterative denoising used in diffusion models.

The framework features a modular design with:

• 🧱 Flexible UNet-based architecture and attention modules
• 🧠 A training pipeline supporting mixed precision, EMA, and real-time visualization
• ⚙️ RadioFlow can be seamlessly scaled down to a lightweight version for edge and embedded devices

▶️ Download Pretrained Checkpoints (BaiduNetDisk)

From noise to signal map in just one deterministic step. 🚀

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🚀 Quick Start

1. Dataset

• RadioMapSeer
  Download link

• RadioMap3DSeer
  Download link

2. Training

1. Open config.py and set:
   • data_dir: path to your dataset
   • training hyperparameters (e.g., learning rate, batch size, number of epochs)
2. Choose the appropriate data loader:
   • RadioUNet_c for the RadioMapSeer dataset
   • RadioMap3Dset for the RadioMap3DSeer dataset
3. Launch training:

   python train.py

3. Testing

• DRM evaluation:

  python test.py --task drm

• SRM evaluation:

  python test.py --task srm

4. Visualization

1. In config.py, configure the VizConfig class to specify visualization options.
2. Run the visualization script:

   python viz.py

📝 Reproducing Paper Results

🧪 Task	📦 Dataset	📉 NMSE ↓	🔊 PSNR ↑	📏 RMSE ↓	🧠 SSIM ↑
SRM	RadioMapSeer	0.0023	39.83 dB	0.0103	0.9249
DRM	RadioMapSeer	0.0028	39.37 dB	0.0108	0.9236
SRM	RadioMap3DSeer	0.0496	26.87 dB	0.0458	0.7377

📊 Visual Gallery

DRM Flow (ours) vs RadioUNet	SRM Flow (ours) vs RadioUNet
Fig. 1: DRM Flow comparison	Fig. 2: SRM Flow comparison

DRM Task: CFG Scale Comparison	SRM Task: CFG Scale Comparison
Fig. 3: DRM map outputs under different CFG scale settings	Fig. 4: SRM map outputs under different CFG scale settings

Fig. 5: Quantitative comparison of NMSE, PSNR, RMSE, Time,and Params for RadioFlow against other methods.