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--- |
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language: |
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- en |
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tags: |
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- pytorch |
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- transformer |
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- channel-estimation |
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- ofdm |
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- wireless |
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- adaptive |
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license: mit |
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datasets: |
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- custom |
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metrics: |
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- mse |
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--- |
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# AdaFortiTran: Adaptive Transformer Model for Robust OFDM Channel Estimation |
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## Model Description |
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AdaFortiTran is a novel adaptive transformer-based model for OFDM channel estimation that dynamically adapts to varying channel conditions (SNR, delay spread, Doppler shift). The model combines the power of transformer architectures with channel-aware adaptation mechanisms to achieve robust performance across diverse wireless environments. |
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## Key Features |
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- **🔄 Adaptive Architecture**: Dynamically adapts to channel conditions using meta-information |
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- **⚡ High Performance**: State-of-the-art results on OFDM channel estimation tasks |
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- **🧠 Transformer-Based**: Leverages attention mechanisms for long-range dependencies |
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- **🎯 Robust**: Maintains performance across varying SNR, delay spread, and Doppler conditions |
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- **🚀 Production Ready**: Comprehensive training pipeline with advanced features |
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## Architecture |
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The project implements three model variants: |
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1. **Linear Estimator**: Simple learned linear transformation baseline |
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2. **FortiTran**: Fixed transformer-based channel estimator |
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3. **AdaFortiTran**: Adaptive transformer with channel condition awareness |
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## Usage |
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### Installation |
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```bash |
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pip install -r requirements.txt |
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``` |
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### Training |
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```bash |
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python src/main.py --model_name adafortitran --system_config_path config/system_config.yaml --model_config_path config/adafortitran.yaml --train_set data/train --val_set data/val --test_set data/test --exp_id my_experiment |
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``` |
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## Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@misc{guler2025adafortitranadaptivetransformermodel, |
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title={AdaFortiTran: An Adaptive Transformer Model for Robust OFDM Channel Estimation}, |
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author={Berkay Guler and Hamid Jafarkhani}, |
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year={2025}, |
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eprint={2505.09076}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG}, |
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url={https://arxiv.org/abs/2505.09076}, |
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} |
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``` |
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## License |
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. |
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