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A CNN based end to end communication systems

Updated: 07/02/2019.
This repository contains source code necessary to reproduce the results presented in the following paper:
A CNN-Based End-to-End Learning Framework Towards Intelligent Communication Systems
by Nan Wu, Xudong Wang, Bin Lin, and Kaiyao Zhang, accepted to IEEE access.

Dependency

  • Python (3.7.0)
  • Numpy (1.15.4)
  • Keras (2.2.4)
  • Tensorflow (1.13.1)

AWGN channel

  • use model_LBC_AWGN.py to train model at a fixed Eb/N0
  • use test_model_LBC_AWGN.py to test the model at a range of Eb/N0

Rayleigh fading channel

  • use model_LBC_Rayleigh.py to train model at a fixed Eb/N0
  • use test_model_LBC_Rayleigh.py to test the model at a range of Eb/N0

Bursty AWGN channel

  • use model_LBC_Bursty_AWGN.py to train model at a fixed Eb/N0
  • use test_model_LBC_Bursty_AWGN.py to test the model at a range of Eb/N0

Differential Version

  • use model_DLBC_Rayleigh.py to train model at a fixed Eb/N0
  • use test_model_DLBC_Rayleigh.py to test the model at a range of Eb/N0
    The differential version currently only supports n=1, adding n involves complex multiplication in high-dimensional space，and is under construction

Questions?

if you have any questions, please e-mail(zky2682810462@163.com).