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

Sepehr Dehdashtian; Matin Hashemi; Saber Salehkaleybar

doi:10.1109/lwc.2021.3056631

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arXiv · 2009.07774

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

Sepehr Dehdashtian·Matin Hashemi·Saber Salehkaleybar

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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.

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BibTeX RIS

Sepehr Dehdashtian, Matin Hashemi, Saber Salehkaleybar. 2021-01-30. Deep-Learning Based Blind Recognition of Channel Code Parameters over Candidate Sets under AWGN and Multi-Path Fading Conditions. https://doi.org/10.1109/lwc.2021.3056631

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