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pytorch implementation of "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data"

License: MIT License

Shell 0.08% Python 99.92%

semantic-communication-systems's Introduction

Deep-Learning-Enabled-Semantic-Communication-Systems-with-Task-Unaware-Transmitter-and-Dynamic-Data

pytorch implementation of "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data"

Prerequites

  • [Python 3.7]
  • [PyTorch 0.1.12]
  • [Torchvision 0.9.1]
  • [Torch 1.5.1]
  • [Numpy 1.21.2]

The folders

"semantic_extraction" and "semantic_system_with_DA" are the semantic extraction part and the data adaptation part of the proposed method in the paper.

The details are represented in the two sub-folders.

Citation

Please use the following BibTeX citation if you use this repository in your work:

@article{Deep_semantic_comm_2022,
  title={Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data},
  author={Zhang, Hongwei and Shao, Shuo and Tao, Meixia and Bi, Xiaoyan and Letaief, Khaled B},
  journal={arXiv preprint arXiv:2205.00271},
  year={2022}
}

semantic-communication-systems's People

Contributors

sjtu-mxtao avatar

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