PaulAlm/GenAI_Channel_Modeling_Datasets

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]

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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	—

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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:

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)

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Generation

The datasets were generated with NVIDIA Sionna RT, 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.

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Downloading

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.

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Related resources

• Code repository: GenAI_Channel_Modeling
• Pre-trained models: GenAI_Channel_Modeling_Models

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Citation

@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}
}