Sepehr Dehdashtian, Matin Hashemi, Saber Salehkaleybar, "Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions", IEEE Wireless Communications Letters, 2021.
We consider the problem of recovering channel code parameters over a candidate set by merely analyzing the received encoded signals. We propose a deep learning-based solution that I) is capable of identifying the channel code parameters for several coding scheme (such as LDPC, Convolutional, Turbo, and Polar codes), II) is robust against channel impairments like multi-path fading, III) does not require any previous knowledge or estimation of channel state or signal-to-noise ratio (SNR), and IV) outperforms related works in terms of probability of detecting the correct code parameters.
Step 1: Download the zipped python source code: code.zip
Step 2: Download the trained models and test datasets: bcr
Step 3: Extract all above files in the same folder, and then, run the python file.
Please use the following entry to cite our work in your publications:
@article{dehdashtian2021wcl,
author = {Sepehr Dehdashtian and Matin Hashemi and Saber Salehkaleybar},
title = {Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions},
journal = {IEEE Wireless Communications Letters},
year = {2021},
}