Research on Blind Recognition Algorithm of Channel Coding Based on One-Dimensional Convolutional Neural Network Under the Low SNR Regime

Pan Deng*, Tianqi Zhang, Baoze Ma, Zeliang An

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

To solve the problem of blind identification of channel coding in signal interception and intelligent communication systems, a blind recognition algorithm of channel coding based on a convolutional neural network (CNN) is proposed. To deal better with the input characteristics of one-dimensional soft decision sequences under the low SNR regime, we design and construct a low-complexity one-dimensional CNN classifier. Firstly, the dataset of five types of channel codes is generated, including linear block codes, Turbo codes, convolutional codes, LDPC codes and Polar codes. Secondly, the one-dimensional CNN classifier is initialized and imported into the database. Thirdly, the training dataset.mat file generated in matlab is imported into the convolutional neural network, and the dataset of five types of channel codes is labeled. Finally, a one-dimensional convolutional neural network model is established, and the dataset in the model is compiled and trained, the training data tags in the model are predicted and the confusion matrix is saved. The simulation results show that the proposed algorithm expands the recognition range of existing types of channel codes, and for the first time completes the blind recognition of five types of channel codes. Compared with the traditional algorithm LSTM (Long Short Term Memory), the algorithm proposed in this paper has lower complexity, the recognition rate can be kept 92% when the SNR is 0 dB, and the performance is improved by 24%.

Original languageEnglish
JournalNeural Processing Letters
Volume55
Pages (from-to)5653-5673
Number of pages21
ISSN1370-4621
DOIs
Publication statusPublished - Oct 2023
Externally publishedYes

Bibliographical note

Funding Information:
The National Natural Science Foundation of China (Grant Nos: 61671095, 61371164, 61975171, 62201113), the Project of Key Laboratory of Signal and Information Processing of Chongqing (No. CSTC2009CA2003), the Natural Science Foundation of Chongqing (No. cstc2021jcyj-msxmX0836), the Research Project of Chongqing Educational Commission (Nos. KJ1600427, KJ1600429), Yibin University of College-level Research and Cultivation Program (2019PY35), the open project fund of Intelligent Terminal Key Laboratory of Sichuan Province of China under Grant SCITLAB-0020.

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Blind recognition
  • Channel codes
  • LDPC code
  • One-dimensional CNN
  • Polar code

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