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GenAI_Channel_Modeling_Models
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---
license: mit
---

# 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](TBC)]

---

## 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).

---

## 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/` |

---

## 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.

---

## Downloading the checkpoints

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

---

## Running inference

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

```bash
# cDDIM — LoS
python infer_cDDIM_LoS.py generate

# cFMM — LoS
python infer_cFMM_LoS.py generate
```

Full instructions are in the [code repository](https://github.com/Telefonica-Scientific-Research/GenAI_Channel_Modeling/tree/main/cDDIM_and_cFMM).

---

## 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 |

---

## Datasets

The corresponding channel datasets are available at:  
**[https://huggingface.co/datasets/PaulAlm/GenAI_Channel_Modeling_Datasets](https://huggingface.co/datasets/PaulAlm/GenAI_Channel_Modeling_Datasets)**

---

## Related resources

- **Code repository:** [GenAI_Channel_Modeling](https://github.com/Telefonica-Scientific-Research/GenAI_Channel_Modeling)
- **Datasets:** [GenAI_Channel_Modeling_Datasets](https://huggingface.co/datasets/PaulAlm/GenAI_Channel_Modeling_Datasets)

---

## Citation

If you use these models, please cite:

```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}
}
```