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---
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
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