Deep Learning Enabled Semantic Communication Systems

Huiqiang Xie, Zhijin Qin, Geoffrey Ye Li, and Biing-Hwang Juang

This is the implementation of Deep learning enabled semantic communication systems.

Requirements

tensorflow-gpu==2.1.0
bert4keras==0.4.2

Preprocess

Preprocess the raw text to create the input sequences.

mkdir data
wget http://www.statmt.org/europarl/v7/europarl.tgz
tar zxvf europarl.tgz
python dataset/preprocess_text.py

Train

The train option parameters are listed below

Parameters	Help
--train-snr	Set the training SNR
--train-with-mine	Train with the MINE model to maximize mutual information
--channel	Set the training channel
--bs	The training batch size
--lr	Set the training learning rate
--checkpoint-path	The path to save model

Example:

python main.py --bs=64 --train-snr=6 --channel=AWGN --train-with-mine --checkpoint-path=./checkpoint

Evaluation

The eval option parameters are listed below

Parameters	Help
--test-snr	Set the test SNR
--channel	Set the test channel
--bs	The training batch size
--checkpoint-path	The saved model

Example:

python evaluation.py --bs=256 --test-snr=6 --channel=AWGN --checkpoint-path=./checkpoint

Notes

If you want to compute the sentence similarity, please download the BERT model.

Bibtex

@article{xie2021deep,
  author={H. {Xie} and Z. {Qin} and G. Y. {Li} and B. -H. {Juang}},
  journal={IEEE Transactions on Signal Processing}, 
  title={Deep Learning Enabled Semantic Communication Systems}, 
  year={Apr. 2021},
  volume={69},
  pages={2663-2675}}

HQXie0910 / The-implementations-of-DeepSC