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Official code for 'A Generative Self-supervised Framework for Cognitive Radio Leveraging Time-Frequency Features and Attention-based Fusion'

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A Generative Self-supervised Framework for Cognitive Radio Leveraging Time-Frequency Features and Attention-based Fusion

This repository provides the implementation of the generative self-supervised framework described in the following paper:

Chen, Shuai; Feng, Zhixi; Yang, Shuyuan; Ma, Yue; Liu, Jun; Qi, Zhuoyue
A Generative Self-supervised Framework for Cognitive Radio Leveraging Time-Frequency Features and Attention-based Fusion.
IEEE Transactions on Wireless Communications, 2024.
DOI: 10.1109/TWC.2024.3513980


Setup

Step 1: Install Dependencies

Ensure you have Python installed (version >= 3.8 is recommended). Install the required dependencies using the provided requirements.txt file:

Step 2: Prepare the Datasets

Follow the dataset processing instructions provided in the paper. The datasets used in the experiments are:

  • RadioML2016.10a
  • RadioML2016.10b
  • RML2016.04c
  • ADS-B short dataset

Step 3: Run Pretraining

Execute the pretrain.py script to perform pretraining:

python pretrain.py

This script will:

  • Load the datasets from the data/ directory.
  • Perform self-supervised pretraining using the settings described in the paper.

Citation

If you find this repository useful in your work, please consider citing the following paper:

@ARTICLE{10804099,
  author={Chen, Shuai and Feng, Zhixi and Yang, Shuyuan and Ma, Yue and Liu, Jun and Qi, Zhuoyue},
  journal={IEEE Transactions on Wireless Communications}, 
  title={A Generative Self-supervised Framework for Cognitive Radio Leveraging Time-Frequency Features and Attention-based Fusion}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  keywords={Feature extraction;Time-frequency analysis;Data mining;Spectrogram;Transformers;Modulation;Radio communication;Noise reduction;Cognitive radio;Time-domain analysis;Generative framework;self-supervised learning (SSL);cognitive radio technology (CRT)},
  doi={10.1109/TWC.2024.3513980}
}

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Official code for 'A Generative Self-supervised Framework for Cognitive Radio Leveraging Time-Frequency Features and Attention-based Fusion'

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