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README.md

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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