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DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes.

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  • 1School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China.

Entropy (Basel, Switzerland)
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Summary

This study introduces a novel Double Long Short-Term Memory (DLSTM) network to improve error correction for short Polar codes used in 5G control channels. The DLSTM decoder significantly enhances performance over existing methods.

Keywords:
5GLSTMSC decodingbit flippingpolar codesrobustness

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Area of Science:

  • Telecommunications Engineering
  • Information Theory
  • Machine Learning for Signal Processing

Background:

  • Polar codes are the 5G control channel coding standard.
  • Existing successive cancellation flipping (SC flipping) algorithms show poor performance with short polar codes.
  • There is a need for improved decoding methods for short polar codes.

Purpose of the Study:

  • To propose a novel decoding algorithm for short polar codes.
  • To enhance the performance of polar code decoding, particularly for short block lengths.
  • To address the limitations of the SC flipping algorithm in 5G control channels.

Main Methods:

  • A Double Long Short-Term Memory (DLSTM) neural network is proposed to identify the first error bit.
  • Frozen bits are clipped in the DLSTM output layer to improve prediction accuracy.
  • Gaussian approximation and multi-bit flipping strategies are employed to enhance channel reliability assessment and error correction.
  • Padding and masking techniques ensure compatibility with various block lengths.

Main Results:

  • The proposed DLSTM-based algorithm demonstrates competitive error-correction performance compared to the CA-SCL algorithm.
  • The DLSTM decoder significantly outperforms the machine learning-based multi-bit flipping SC (ML-MSCF) decoder.
  • Superior performance is achieved over the dynamic SC flipping (DSCF) decoder for short polar codes.

Conclusions:

  • The DLSTM neural network offers a robust and effective solution for decoding short polar codes.
  • This approach significantly improves error-correction capabilities in 5G control channels.
  • The proposed method represents a substantial advancement over current machine learning-based and dynamic flipping decoders.