Blind Identification of LDPC Code Based on Deep Learning

Yanqin Ni, Shengliang Peng, Lin Zhou, Xi Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

In cognitive radio or military communications systems, the receiver usually needs to blindly identify which LDPC code has been adopted by the transmitter. Existing methods for blind LDPC code identification suffer from high computational complexity. This paper proposes a deep learning based LDPC code identification algorithm. According to the algorithm, the received LDPC encoded sequence is treated as a text sentence, and a special convolutional neural network (CNN), TextCNN, is utilized to understand the sequence and infer which code is adopted. Two types of LDPC codes, namely quasi-cyclic LDPC and spatially coupled LDPC, are considered. Simulation results show that, the proposed algorithm is able to accurately identify both types of LDPC codes no matterwhether an extra convolution code exists or not.

Original languageEnglish
Title of host publicationProceedings - 2019 6th International Conference on Dependable Systems and Their Applications, DSA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages460-464
Number of pages5
ISBN (Electronic)9781728160573
DOIs
StatePublished - Jan 2020
Externally publishedYes
Event6th International Conference on Dependable Systems and Their Applications, DSA 2019 - Harbin, China
Duration: 3 Jan 20206 Jan 2020

Publication series

NameProceedings - 2019 6th International Conference on Dependable Systems and Their Applications, DSA 2019

Conference

Conference6th International Conference on Dependable Systems and Their Applications, DSA 2019
Country/TerritoryChina
CityHarbin
Period3/01/206/01/20

Keywords

  • LDPC code
  • TextCNN
  • code identification

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