MAMCA

A method for Automatic Modulation Classification using Mamba structure

A project employing the Selective State Space Model (Mamba) method for Automatic Modulation Classification (AMC) in a scenario of extended signal length.

The increased sequence length complicates the learning process and diminishes accuracy, while simultaneously escalating memories and reduces timeliness. This issue brings the following impacts:

if our codes helped your reasearch, please consider citing the corresponding submission

@article{zhang2024mamca,
  title={MAMCA -- Optimal on Accuracy and Efficiency for Automatic Modulation Classification with Extended Signal Length},
  author={Yezhuo Zhang and Zinan Zhou and Yichao Cao and Guangyu Li and Xuanpeng Li},
  year={2024},
  journal={arXiv preprint arXiv:2405.11263},
}

We utilize a denosing unit for better accuracy performance under noise interference, while using Mamba as the backbone for low GPU occupancy and training/inference time.

• MAMCA structure

To related AMC works, as well as sorce code:

• Deep Learning Based Automatic Modulation Recognition: Models, Datasets, and Challenges

  AMR-Benchmark

To the denosing method employed in our work, as well as source code:

• IEEE Transactions on Industrial Informatics 2020

  Deep Residual Shrinkage Networks for Fault Diagnosis

• Deep-Residual-Shrinkage-Networks-for-intelligent-fault-diagnosis-DRSN

  pytorch-Deep-Residual-Shrinkage-Networks (DRSN)

To the Mamba method employed in our work, as well as source code:

• arXiv preprint arXiv:2312.00752

  Mamba: Linear-Time Sequence Modeling with Selective State Spaces

• mamba

  mamba

Requirements

pip install -r requirements.txt

Training

cd into code/script and do

bash RML2016.10a.sh

Contact

If you have any problem with our code or any suggestions, including discussion on SEI, please feel free to contact

• Yezhuo Zhang (zhang_yezhuo@seu.edu.cn | zhang_yezhuo@outlook.com)
• Zinan Zhou (zhouzinan919@seu.edu.cn)
• Xuanpeng Li (li_xuanpeng@seu.edu.cn)