---
license: mit
tags:
  - underwater-acoustic
  - channel-estimation
  - denoising
  - deep-learning
---

# DSS-Net Checkpoints

Pre-trained model checkpoints for **DSS-Net: Dynamic-Static Separation Networks for UWA Channel Denoising**.

## Available Models

| Model | File | Size | Description |
|-------|------|------|-------------|
| **DSS-Net (Full)** | `dss_net_full_best.pth` | 499MB | Best performing model (NMSE: -25.27 dB) |
| Baseline U-Net | `baseline_unet_best.pth` | 355MB | Baseline for comparison (NMSE: -20.41 dB) |

## Usage

```python
import torch
from model import UNetDecomposer

# Load model
model = UNetDecomposer(
    in_channels=2,
    base_channels=64,
    depth=4,
    use_attention=True
)

# Load weights
checkpoint = torch.load('dss_net_full_best.pth', map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
```

## Citation

```bibtex
@article{yang2025dssnet,
  title={DSS-Net: Dynamic--Static Separation Networks for Physics-Inspired UWA Channel Denoising},
  author={Yang, Xiaoyu and Chen, Yinda and Tong, Feng and Zhou, Yuehai},
  journal={IEEE Transactions on Wireless Communications},
  year={2025}
}
```

## Links

- **GitHub**: https://github.com/ydchen0806/dss_net
- **Paper**: IEEE TWC 2025