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Deep-Learning-Based blind recognition of channel code parameters over candidate sets under awgn and multi-path fading conditions

Dehdashtian, S ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1109/LWC.2021.3056631
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
  4. Abstract:
  5. 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. © 2012 IEEE
  6. Keywords:
  7. Channel coding ; Codes (symbols) ; Multipath fading ; Signal to noise ratio ; Blind recognition ; Candidate sets ; Channel impairment ; Channel state ; Code parameters ; Coding scheme ; Encoded signals ; Related works ; Deep learning
  8. Source: IEEE Wireless Communications Letters ; Volume 10, Issue 5 , 2021 , Pages 1041-1045 ; 21622337 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9344644