Readme updated

README.md

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 license: mit
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+
+# 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)
+```
+
+The antenna configurations used in the paper are:
+
+| Link end | Array type | Rows × Cols | Elements |
+|---|---|---|---|
+| Base station (Tx) | UPA | 4 × 8 | 32 |
+| User equipment (Rx) | UPA | 2 × 2 | 4 |
+
+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}
+}
+```