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

pip install -r requirements.txt

Training

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:

@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 file for details.