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SemantIC: Semantic Interference Cancellation Towards 6G Wireless Communications

Example codes for the paper “SemantIC: Semantic Interference Cancellation Towards 6G Wireless Communications”.

arXiv: https://arxiv.org/abs/2310.12768

Instruction

Tested with

python 3.7.16
pytorch 1.13.0

Steps

Run with the pre-trained semantic neural network:

Directly run “SemantIC.py” with the pre-trained semantic neural network “semantic_coder.pkl” to test the semantic interference cancellation systems.

Or training from the beginning:

Run “googlenet_train.py” to obtain neural network for classifier.
Run “ENC_DEC_train.py” to obtain neural network for semantic encoder and decoder.
Run “SemantIC.py” to test the semantic interference cancellation systems.

Notes

The source codes of LDPC are revised from the codes in: https://github.com/hichamjanati/pyldpc

The source codes of example semantic neural network, “googlenet_train.py” and “ENC_DEC_train.py”, are revised from the codes in: https://github.com/SJTU-mxtao/Semantic-Communication-Systems

This framework can be adaptive to other semantic neural network by revising the class “SemanticNN” in “SemantIC.py”.

Citation

BibTeX infomation:

@article{lin2023SemantIC,
  title={{SemantIC}: {Semantic} Interference Cancellation Towards {6G} Wireless Communications},
  author={Wensheng Lin and Yuna Yan and Lixin Li and Zhu Han and Tad Matsumoto},
  journal={arXiv preprint arXiv:2310.12768},
  year={2023}
}

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