Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions
This addresses the challenge of identifying coding schemes in communication systems under noisy and fading conditions, representing an incremental improvement over prior work.
The paper tackles the problem of blind recognition of channel code parameters from received signals without prior channel knowledge, achieving superior detection probability compared to existing methods.
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.