Iterative Decoding Algorithms Powered by Deep Learning

In this paper, we analyze the performance of neural belief propagation (BP) decoding on the additive white Gaussian noise (AWGN) channel, compared to the traditional BP algorithm. Previous investigations have shown that assigning pre-trained weights to BP messages can significantly improve the decod...

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Veröffentlicht in:2025 12th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN) S. 1 - 6
Hauptverfasser: Jovanovic, Dimitrije, Ivanis, Predrag
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 09.06.2025
Schlagworte:
AWGN channel
AWGN channels
Belief propagation
Bit error rate
Codes
Decoding
Deep learning
Iterative decoding
MDPC codes
neural network
Neural networks
TensorFlow
Training
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Zusammenfassung:In this paper, we analyze the performance of neural belief propagation (BP) decoding on the additive white Gaussian noise (AWGN) channel, compared to the traditional BP algorithm. Previous investigations have shown that assigning pre-trained weights to BP messages can significantly improve the decoding performance in case of high-density parity-check (HDPC) codes, by reducing the negative impact of short cycles. These weights are trained by a neural network whose structure matches the trellis of the decoder. Specifically, we show that medium-density paritycheck (MDPC) codes decoded with neural BP algorithm can achieve lower bit error rate versus HDPC codes with the same codeword length and the same code rate.
DOI:10.1109/IcETRAN66854.2025.11114279