SIG-ALL-YOU-NEED

信号领域论文复现（不定期更新）

RML数据集生成

数据集生成代码参考：

@ARTICLE{10070586,
  author={Sathyanarayanan, Venkatesh and Gerstoft, Peter and Gamal, Aly El},
  journal={IEEE Transactions on Wireless Communications}, 
  title={RML22: Realistic Dataset Generation for Wireless Modulation Classification}, 
  year={2023},
  volume={22},
  number={11},
  pages={7663-7675},
  keywords={Modulation;Wireless communication;Mathematical models;Atmospheric modeling;Computational modeling;Benchmark testing;Ad hoc networks;Deep learning;modulation classification;benchmark dataset;GNU radio;spectrum sensing},
  doi={10.1109/TWC.2023.3254490}}

Linux环境下安装依赖库的命令 :

1. conda create --name gnuradio
2. conda activate gnuradio
3. conda install -c conda-forge gnuradio=3.8.3
4. conda install -c conda-forge scipy
5. conda install -c conda-forge matplotlib
6. git clone https://github.com/myersw12/gr-mapper.git
7. cd gr-mapper && mkdir build && cd build
8. chmod -R 777 ../../
9. conda install -c conda-forge gnuradio-build-deps
10. conda activate $CONDA_DEFAULT_ENV
11. conda install -c conda-forge cppunit
12. cmake -G Ninja -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX -DCMAKE_PREFIX_PATH=$CONDA_PREFIX -DLIB_SUFFIX="" ..
13. cmake --build .
14. cmake --build . --target install

Podcast.wav下载链接link；

下载并放到"generate_data/Podcast.wav"目录下；

RML22数据集与RML16数据集在代码生成上的主要不同之处：

samples_per_symbol=2 #RML22
samples_per_symbol=8 #RML16

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}
}

@ARTICLE{10702346,
  author={Feng, Zhixi and Chen, Shuai and Ma, Yue and Gao, Yachen and Yang, Shuyuan},
  journal={IEEE Transactions on Industrial Informatics}, 
  title={Learning Temporal–Spectral Feature Fusion Representation for Radio Signal Classification}, 
  year={2024},
  volume={},
  number={},
  pages={1-10},
  keywords={Feature extraction;Spectrogram;Wireless communication;Convolution;Wireless sensor networks;Kernel;Time-frequency analysis;Time-domain analysis;Robustness;Informatics;Feature fusion;radio signal classification (RSC);temporal–spectral feature representation},
  doi={10.1109/TII.2024.3461777}}