---
license: mit
---

# Dataset Card — GenAI Channel Modeling Datasets

Ray-traced site-specific MIMO channel datasets used in the paper:

> **Site-Specific MIMO Channel Generation via Diffusion and Flow Matching: Fidelity, Efficiency, and Downstream Utility**  
> Sina Beyraghi, Masoud Sadeghian, Firdous Bin Ismail, Angel Lozano, Paul Almasan, and Giovanni Geraci  
> [[arXiv:2510.10190](https://arxiv.org/abs/2510.10190)]

---

## Files

| File | Frequency | Scenario | Size |
|---|---|---|---|
| `Final_Single_Scene_Channel_Sionna_V1_3_5GHz_LoS.npz` | 3.5 GHz | LoS only | — |
| `Final_Single_Scene_Channel_Sionna_V1_3_5GHz_NLoS.npz` | 3.5 GHz | NLoS only | — |
| `Final_Single_Scene_Channel_Sionna_V1_28GHz_LoS.npz` | 28 GHz | LoS only | — |

---

## Data format

Each `.npz` file contains a single array under the key `combined_array`:

```
shape: (N, N_rx, 1, N_tx, 1, 1, 4)
dtype: complex64

last dimension:
  [0] — complex channel coefficient H
  [1] — UE x-coordinate (metres)
  [2] — UE y-coordinate (metres)
  [3] — UE z-coordinate (metres)
```

To extract the channel matrix and UE coordinates from a file:

```python
import numpy as np

npz = np.load("Final_Single_Scene_Channel_Sionna_V1_3_5GHz_LoS.npz")
data = npz["combined_array"][:, :, 0, :, 0, 0, :]  # (N, N_rx, N_tx, 4)

H      = data[:, :, :, 0]   # complex channel matrices, shape (N, N_rx, N_tx)
coords = data[:, 0, 0, 1:]  # UE (x, y, z) positions,  shape (N, 3)
```

---

## Generation

The datasets were generated with [NVIDIA Sionna RT](https://nvlabs.github.io/sionna/), a GPU-accelerated ray tracing engine for wireless channel simulation, over a single outdoor urban scene. Generation scripts and instructions are available in the [code repository](https://github.com/Telefonica-Scientific-Research/GenAI_Channel_Modeling/tree/main/Channel_Sionna_RT_Github).

---

## Downloading

```bash
git clone https://huggingface.co/datasets/PaulAlm/GenAI_Channel_Modeling_Datasets
```

Due to file size this may take several minutes. Individual files can also be downloaded manually from the Hugging Face web interface.

---

## Related resources

- **Code repository:** [GenAI_Channel_Modeling](https://github.com/Telefonica-Scientific-Research/GenAI_Channel_Modeling)
- **Pre-trained models:** [GenAI_Channel_Modeling_Models](https://huggingface.co/PaulAlm/GenAI_Channel_Modeling_Models)

---

## Citation

```bibtex
@article{beyraghi2025sitespecific,
  title   = {Site-Specific MIMO Channel Generation via Diffusion and Flow Matching:
             Fidelity, Efficiency, and Downstream Utility},
  author  = {Beyraghi, Sina and Sadeghian, Masoud and Bin Ismail, Firdous and
             Lozano, Angel and Almasan, Paul and Geraci, Giovanni},
  journal = {arXiv preprint arXiv:2510.10190},
  year    = {2025}
}
```