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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Veröffentlicht in:	Entropy (Basel, Switzerland) Jg. 24; H. 12; S. 1809
Hauptverfasser:	Song, Bixue, Feng, Yongxin, Wang, Yang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 12.12.2022 MDPI
Schlagworte:	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
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Abstract	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.
AbstractList	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.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. 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.
Audience	Academic
Author	Wang, Yang Feng, Yongxin Song, Bixue
AuthorAffiliation	School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
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Cites_doi	10.1109/PIMRC.2019.8904878 10.1109/TWC.2019.2962113 10.1002/9781119562306.ch15 10.1109/CompComm.2018.8780605 10.3115/v1/D14-1179 10.1109/ICCNC.2018.8390279 10.1109/BMSB47279.2019.8971888 10.4236/jcc.2020.87008 10.1007/978-3-319-24574-4_28 10.1109/VTCSpring.2016.7504469 10.1109/SPAWC.2019.8815542 10.1109/ACCESS.2020.2971526 10.1109/TVT.2020.3000345 10.3390/e23070863 10.1109/TIT.2009.2021379 10.1109/ICAIIC.2019.8669025 10.1109/TSP.2020.3023582 10.1109/TVT.2021.3077757 10.1109/JETCAS.2020.2995962 10.1109/TWC.2020.3022922 10.1109/ICASSP.2019.8683778 10.1109/SPAWC.2018.8445986 10.1109/ICASSP40776.2020.9053222 10.1109/ICC.2018.8422289 10.1109/TSP.2020.3040897 10.1038/323533a0 10.1109/JSSC.2020.3005763 10.1038/nature14539 10.1109/ACCESS.2020.3041290 10.1109/ACCESS.2020.3022176 10.1109/WSCE49000.2019.9040920 10.1109/LCOMM.2019.2918535 10.1109/CVPR.2016.90 10.3390/info9100254 10.1109/COMST.2020.2967127 10.1109/TCOMM.2020.3014946
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Keywords	denoising subnetwork deep learning polar codes decoding subnetwork attention mechanism DIR-Net
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SubjectTerms	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
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Title	DIR-Net: Deep Residual Polar Decoding Network Based on Information Refinement
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