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Polar code construction by estimating noise using bald hawk optimized recurrent neural network model.

Sunil Yadav Kshirsagar1, Venkatrajam Marka2

  • 1Department of Mathematics, School of Advanced Sciences, VIT-AP University, Beside AP Secretariate, Amaravati, Andhra Pradesh, 522241, India.

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Summary
This summary is machine-generated.

This study introduces a novel Recurrent Neural Network (RNN)-based Decoder with Bald Hawk Optimization (BHO) for polar codes. This advanced decoder significantly reduces decoding errors in noisy communication channels.

Keywords:
Bit error rate (BER)Frame error rate (FER)Noise estimationPolar code constructionRecurrent neural network (RNN)

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Area of Science:

  • Information Theory
  • Coding Theory
  • Machine Learning

Background:

  • Polar codes approach Shannon capacity, but decoding errors persist in noisy channels.
  • Recurrent Neural Networks (RNNs) offer potential for advanced noise estimation in decoding.

Purpose of the Study:

  • To develop a robust Recurrent Neural Network (RNN)-based Decoder with Bald Hawk Optimization (BHO) for polar codes.
  • To enhance noise estimation and reduce error rates in polar code decoding.

Main Methods:

  • Integration of RNNs for noise estimation within the polar coding framework.
  • Application of the Bald Hawk Optimization (BHO) algorithm to refine decoder performance.
  • Evaluation using Bit Error Rate (BER), Binary Phase Shifting Key-BER (BPSK-BER), and Frame Error Rate (FER) metrics.

Main Results:

  • Achieved exceptionally low error rates: BER (0.0000087), BPSK-BER (0.01519), and FER (0.000182).
  • Demonstrated superior performance in a 4 dB SNR context with BER (0.0000073), BPSK-BER (0.02065), and FER (0.000108).
  • The proposed RNN-based Decoder with BHO significantly outperforms existing decoders.

Conclusions:

  • The RNN-based Decoder with BHO provides a flexible and adaptive solution for polar coding.
  • This model achieves state-of-the-art error correction performance, crucial for reliable communication systems.