Shared Graph Neural Network for Channel Decoding

Qingle Wu, Benjamin K. Ng, Chan Tong Lam, Xiangyu Cen, Yuanhui Liang, Yan Ma

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


With the application of graph neural network (GNN) in the communication physical layer, GNN-based channel decoding algorithms have become a research hotspot. Compared with traditional decoding algorithms, GNN-based channel decoding algorithms have a better performance. GNN has good stability and can handle large-scale problems; GNN has good inheritance and can generalize to different network settings. Compared with deep learning-based channel decoding algorithms, GNN-based channel decoding algorithms avoid a large number of multiplications between learning weights and messages. However, the aggregation edges and nodes for GNN require many parameters, which requires a large amount of memory storage resources. In this work, we propose GNN-based channel decoding algorithms with shared parameters, called shared graph neural network (SGNN). For BCH codes and LDPC codes, the SGNN decoding algorithm only needs a quarter or half of the parameters, while achieving a slightly degraded bit error ratio (BER) performance.

Original languageEnglish
Article number12657
JournalApplied Sciences (Switzerland)
Issue number23
Publication statusPublished - Dec 2023


  • BCH codes
  • channel decoding algorithm
  • graph neural network
  • LDPC codes
  • shared graph neural network


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