DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement

Polar codes are closer to the Shannon limit with lower complexity in coding and decoding. As traditional decoding techniques suffer from high latency and low throughput, with the development of deep learning technology, some deep learning-based decoding methods have been proposed to solve these prob...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 24; no. 12; p. 1809
Main Authors: Song, Bixue, Feng, Yongxin, Wang, Yang
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 12.12.2022
MDPI
Subjects:
Algorithms
Artificial neural networks
attention mechanism
Bit error rate
Channels
Codes
Communication
Computational linguistics
Computer simulation
Decoding
decoding subnetwork
Deep learning
denoising subnetwork
DIR-Net
Error correction & detection
Evaluation
Fading channels
Information retrieval
Language processing
Machine learning
Methods
Natural language interfaces
Neural networks
polar codes
denoising subnetwork
deep learning
polar codes
decoding subnetwork
attention mechanism
DIR-Net
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary:Polar codes are closer to the Shannon limit with lower complexity in coding and decoding. As traditional decoding techniques suffer from high latency and low throughput, with the development of deep learning technology, some deep learning-based decoding methods have been proposed to solve these problems. Usually, the deep neural network is treated as a black box and learns to map the polar codes with noise to the original information code directly. In fact, it is difficult for the network to distinguish between valid and interfering information, which leads to limited BER performance. In this paper, a deep residual network based on information refinement (DIR-NET) is proposed for decoding polar-coded short packets. The proposed method works to fully distinguish the effective and interference information in the codewords, thus obtaining a lower bit error rate. To achieve this goal, we design a two-stage decoding network, including a denoising subnetwork and decoding subnetwork. This structure can further improve the accuracy of the decoding method. Furthermore, we construct the whole network solely on the basis of the attention mechanism. It has a stronger information extraction ability than the traditional neural network structure. Benefiting from cascaded attention modules, information can be filtered and refined step-by-step, thus obtaining a low bit error rate. The simulation results show that DIR-Net outperforms existing decoding methods in terms of BER performance under both AWGN channels and flat fading channels.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e24121809