Sensing-Assisted High Reliable Communication: A Transformer-Based Beamforming Approach

This repository contains the implementation of our research on multimodal learning-based beamforming. This work is based on the research paper: Sensing-Assisted High Reliable Communication: A Transformer-Based Beamforming Approach.

📂 Repository Structure

• Data_Augmentation/ - Implements data augmentation techniques for improving model generalization.
• Data_Preprocessing/ - Scripts for preprocessing raw dataset before training.
• Dataset/ - Directory to store the dataset.
• log/test/ - Stores log files.
• args.txt - Stores argument configurations for training and testing.
• config_seq.py - Configuration file for model and training hyperparameters.
• data.py - Data loading and processing utilities.
• main.py - Main script for training and evaluating the model.
• model.py - Defines the neural network architecture.
• scheduler.py - Learning rate scheduler for training.

📥 Dataset Download

The dataset required for training and evaluation can be downloaded from the following link:

🔗 Dataset Download

After downloading, extract and place the dataset inside the Dataset/ folder.

📌 Notes

• Ensure the dataset is correctly placed in the Dataset/ folder before running the scripts.
• Logs and training results will be stored in the log/test/ directory.

📜 Citation

If you find this work useful, please consider citing our paper:

@ARTICLE{10539181,
  author={Cui, Yuanhao and Nie, Jiali and Cao, Xiaowen and Yu, Tiankuo and Zou, Jiaqi and Mu, Junsheng and Jing, Xiaojun},
  journal={IEEE Journal of Selected Topics in Signal Processing}, 
  title={Sensing-Assisted High Reliable Communication: A Transformer-Based Beamforming Approach}, 
  year={2024},
  doi={10.1109/JSTSP.2024.3405859}
}