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

We consider the problem of recovering channel code parameters over a candidate set by merely analyzing received encoded signals. propose deep learning-based solution that I) is capable identifying for any coding scheme (such as LDPC, Convolutional, Turbo, and Polar codes), II) robust against impairments like multi-path fading, III) does not require previous knowledge or estimation state signal-to-noise ratio (SNR), IV) outperforms related works in terms probability detecting correct parameters.