Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions

TL;DR

This paper introduces a deep learning method for blind recognition of channel code parameters across various coding schemes, robust to channel impairments and without prior channel knowledge, outperforming existing methods.

Contribution

The proposed deep learning approach can identify channel code parameters for multiple coding schemes without prior channel information, handling AWGN and multi-path fading effectively.

Findings

Successfully identifies code parameters across multiple coding schemes.

Demonstrates robustness against multi-path fading and noise.

Outperforms existing methods in detection probability.

Abstract

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 any 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.