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Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network.

Fan Mei1, Hong Chen1, Yingke Lei1

  • 1School of Electronic Countermeasures, National University of Defense Technology, Hefei 230000, China.

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|July 2, 2021
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Summary
This summary is machine-generated.

This study introduces a novel Recurrent Neural Network (RNN) method for identifying Forward Error Correction (FEC) code types in non-cooperative communication without prior information. The algorithm achieves a 99% recognition rate for various FEC codes, demonstrating high accuracy and practicality.

Keywords:
blind recognitionforward error correction codesnon-cooperative systemparameter initializationrecurrent neural network

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Information Theory

Background:

  • Forward Error Correction (FEC) coding is crucial for reliable communication.
  • Recognizing FEC code types is vital in non-cooperative communication systems.
  • Current methods often require prior knowledge of code types, limiting their application.

Purpose of the Study:

  • To develop a novel method for identifying FEC code types without prior information in non-cooperative communication.
  • To classify input data into specific FEC code categories: Bose-Chaudhuri-Hocquenghem (BCH), Low-density Parity-check (LDPC), Turbo, and convolutional codes.
  • To enhance Recurrent Neural Network (RNN) model performance through optimized weight initialization.

Main Methods:

  • Utilizing a Recurrent Neural Network (RNN) for FEC code type identification.
  • Implementing an optimized weight initialization method to improve RNN model training.
  • Classifying data into BCH, LDPC, Turbo, and convolutional codes.

Main Results:

  • The proposed RNN-based method achieves an average recognition rate of 99% for FEC code types.
  • High accuracy is maintained across a signal-to-noise ratio (SNR) range of 0 dB to 10 dB.
  • Experimental comparisons validate the effectiveness and practicality of the algorithm.

Conclusions:

  • The developed algorithm effectively identifies FEC code types in non-cooperative communication scenarios.
  • The method meets engineering practice requirements for accuracy and reliability.
  • The optimized RNN approach offers a robust solution for blind FEC code recognition.