Sparse graph neural network aided efficient decoder for polar codes under bursty interference
In this paper, a sparse graph neural network-aided (SGNN-aided) decoder is proposed for improving the decoding performance of polar codes under bursty interference. Firstly, a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding. To further i...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864823001773 |
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| author | Shengyu Zhang Zhongxiu Feng Zhe Peng Lixia Xiao Tao Jiang |
| author_facet | Shengyu Zhang Zhongxiu Feng Zhe Peng Lixia Xiao Tao Jiang |
| author_sort | Shengyu Zhang |
| collection | DOAJ |
| description | In this paper, a sparse graph neural network-aided (SGNN-aided) decoder is proposed for improving the decoding performance of polar codes under bursty interference. Firstly, a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding. To further improve the decoding performance, a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network. This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks. Finally, predictions are generated by feeding the embedding vectors into a readout module. Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality. |
| format | Article |
| id | doaj-art-2050700322704f62b29d21d2c14f3b5e |
| institution | DOAJ |
| issn | 2352-8648 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-2050700322704f62b29d21d2c14f3b5e2025-08-20T03:09:12ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482025-04-0111235936410.1016/j.dcan.2023.12.002Sparse graph neural network aided efficient decoder for polar codes under bursty interferenceShengyu Zhang0Zhongxiu Feng1Zhe Peng2Lixia Xiao3Tao Jiang4Research Center of 6G Mobile Communications, School of Cyber Science and Engineering, and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, ChinaResearch Center of 6G Mobile Communications, School of Cyber Science and Engineering, and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, ChinaResearch Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, ChinaResearch Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, ChinaResearch Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Corresponding author.In this paper, a sparse graph neural network-aided (SGNN-aided) decoder is proposed for improving the decoding performance of polar codes under bursty interference. Firstly, a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding. To further improve the decoding performance, a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network. This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks. Finally, predictions are generated by feeding the embedding vectors into a readout module. Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.http://www.sciencedirect.com/science/article/pii/S2352864823001773Sparse graph neural networkPolar codesBursty interferenceSparse factor graphMessage passing neural network |
| spellingShingle | Shengyu Zhang Zhongxiu Feng Zhe Peng Lixia Xiao Tao Jiang Sparse graph neural network aided efficient decoder for polar codes under bursty interference Digital Communications and Networks Sparse graph neural network Polar codes Bursty interference Sparse factor graph Message passing neural network |
| title | Sparse graph neural network aided efficient decoder for polar codes under bursty interference |
| title_full | Sparse graph neural network aided efficient decoder for polar codes under bursty interference |
| title_fullStr | Sparse graph neural network aided efficient decoder for polar codes under bursty interference |
| title_full_unstemmed | Sparse graph neural network aided efficient decoder for polar codes under bursty interference |
| title_short | Sparse graph neural network aided efficient decoder for polar codes under bursty interference |
| title_sort | sparse graph neural network aided efficient decoder for polar codes under bursty interference |
| topic | Sparse graph neural network Polar codes Bursty interference Sparse factor graph Message passing neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2352864823001773 |
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