PaulAlm/GenAI_Channel_Modeling_Models

Model Card — GenAI Channel Modeling Models

Pre-trained checkpoints for 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
[TBC]

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Model overview

Two conditional generative model architectures are provided:

Abbreviation	Full name	Inference mechanism
cDDIM	Conditional Denoising Diffusion Implicit Model	Reverse diffusion, n_T = 150 steps
cFMM	Conditional Flow Matching Model	Euler integration, steps = 10

Both share the same Context U-Net backbone (~15.6 M parameters, n_feat = 256) and are conditioned on 3-dimensional UE coordinates (n_classes = 3). Channels are represented in beamspace as two-channel real tensors of shape (2, 4, 32) (real and imaginary parts; 4 Rx × 32 Tx beams for a 2×2 Rx UPA and 4×8 Tx UPA).

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Available checkpoints

Checkpoints are organised under logs/ using the naming convention:

{MODEL}_{dataset}_{freq}_{scenario}_{guide_w}_{N_train}_{date}/

where N_train is the number of real training samples used.

cDDIM — 3.5 GHz, LoS

N_train	Directory
200	logs/CDDIM_sina_dataset_3.5GHz_LoS_0.0_200_14_05_2026_10_19/
500	logs/CDDIM_sina_dataset_3.5GHz_LoS_0.0_500_19_05_2026_09_32/
1 000	logs/CDDIM_sina_dataset_3.5GHz_LoS_0.0_1000_19_05_2026_09_33/
2 000	logs/CDDIM_sina_dataset_3.5GHz_LoS_0.0_2000_19_05_2026_09_46/
5 000	logs/CDDIM_sina_dataset_3.5GHz_LoS_0.0_5000_19_05_2026_10_00/
10 000	logs/CDDIM_sina_dataset_3.5GHz_LoS_0.0_10000_20_05_2026_09_55/

cDDIM — 3.5 GHz, NLoS + LoS

N_train	Directory
200	logs/CDDIM_sina_dataset_3.5GHz_NLoS+LoS_0.0_200_15_05_2026_14_55/
500	logs/CDDIM_sina_dataset_3.5GHz_NLoS+LoS_0.0_500_19_05_2026_11_51/
1 000	logs/CDDIM_sina_dataset_3.5GHz_NLoS+LoS_0.0_1000_19_05_2026_11_57/
2 000	logs/CDDIM_sina_dataset_3.5GHz_NLoS+LoS_0.0_2000_19_05_2026_11_57/
5 000	logs/CDDIM_sina_dataset_3.5GHz_NLoS+LoS_0.0_5000_19_05_2026_11_58/
10 000	logs/CDDIM_sina_dataset_3.5GHz_NLoS+LoS_0.0_10000_19_05_2026_11_58/

cDDIM — 28 GHz, LoS

N_train	Directory
200	logs/CDDIM_sina_dataset_28GHz_LoS_0.0_200_13_05_2026_15_07/
500	logs/CDDIM_sina_dataset_28GHz_LoS_0.0_500_28_05_2026_09_33/
1 000	logs/CDDIM_sina_dataset_28GHz_LoS_0.0_1000_27_05_2026_08_52/

cFMM — 3.5 GHz, LoS

N_train	Directory
200	logs/FMM_sina_dataset_3.5GHz_LoS_0.0_200_14_05_2026_10_21/
500	logs/FMM_sina_dataset_3.5GHz_LoS_0.0_500_19_05_2026_12_22/
1 000	logs/FMM_sina_dataset_3.5GHz_LoS_0.0_1000_19_05_2026_12_23/
2 000	logs/FMM_sina_dataset_3.5GHz_LoS_0.0_2000_19_05_2026_12_23/
5 000	logs/FMM_sina_dataset_3.5GHz_LoS_0.0_5000_19_05_2026_13_10/
10 000	logs/FMM_sina_dataset_3.5GHz_LoS_0.0_10000_20_05_2026_09_57/

cFMM — 3.5 GHz, NLoS + LoS

N_train	Directory
200	logs/FMM_sina_dataset_3.5GHz_NLoS+LoS_0.0_200_15_05_2026_14_57/
500	logs/FMM_sina_dataset_3.5GHz_NLoS+LoS_0.0_500_19_05_2026_14_28/
1 000	logs/FMM_sina_dataset_3.5GHz_NLoS+LoS_0.0_1000_19_05_2026_14_28/
2 000	logs/FMM_sina_dataset_3.5GHz_NLoS+LoS_0.0_2000_19_05_2026_14_29/
5 000	logs/FMM_sina_dataset_3.5GHz_NLoS+LoS_0.0_5000_19_05_2026_14_29/
10 000	logs/FMM_sina_dataset_3.5GHz_NLoS+LoS_0.0_10000_19_05_2026_14_30/

cFMM — 28 GHz, LoS

N_train	Directory
200	logs/FMM_sina_dataset_28GHz_LoS_0.0_200_13_05_2026_15_08/
500	logs/FMM_sina_dataset_28GHz_LoS_0.0_500_28_05_2026_09_34/
1 000	logs/FMM_sina_dataset_28GHz_LoS_0.0_1000_27_05_2026_08_55/

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Checkpoint contents

Each model directory contains:

File	Description
model.pth	PyTorch state-dict of the trained model
training_config.txt	Hyperparameters used during training
training_log.txt	Loss curves and validation metrics logged during training
indices.npy	Shuffled dataset indices defining the train/val/test split
train.npy / val.npy / test.npy	Pre-processed channel arrays for each split
train_coords.npy / val_coords.npy / test_coords.npy	Corresponding UE coordinates

Important: The indices.npy file fixes the data split. cFMM checkpoints reuse the indices from the corresponding cDDIM run to ensure identical splits across both models.

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Downloading the checkpoints

git clone https://huggingface.co/PaulAlm/GenAI_Channel_Modeling_Models
cd GenAI_Channel_Modeling_Models
unzip logs.zip

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Running inference

After downloading, set the save_dir variable in the inference script to the desired model directory and run:

# cDDIM — LoS
python infer_cDDIM_LoS.py generate

# cFMM — LoS
python infer_cFMM_LoS.py generate

Full instructions are in the code repository.

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Training details

Hyperparameter	cDDIM	cFMM
Epochs	3 000	2 000
Batch size	100	100
Learning rate	1 × 10⁻⁴	1 × 10⁻⁴
Inference steps	150 (DDIM)	10 (Euler)
Conditioning	3D UE coordinates	3D UE coordinates
Guidance weight	0.0	0.0
Model parameters	~15.6 M	~15.6 M

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Datasets

The corresponding channel datasets are available at:
https://huggingface.co/datasets/PaulAlm/GenAI_Channel_Modeling_Datasets

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Related resources

• Code repository: GenAI_Channel_Modeling
• Datasets: GenAI_Channel_Modeling_Datasets

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Citation

If you use these models, please cite:

@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}
}