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