# Shared Graph Neural Network for Channel Decoding
## 2 Preliminary
* Extensions to non-AWGN channels and other modulation methods are possible. ## 6 Conclusions

This paper proposes two weight sharing methods to reduce the complexity of channel decoders based on SGNN1 and SGNN2, which have almost the same or a slightly reduced BER performance, while SGNN1 and SGNN2 decoding algorithms have fewer parameters in comparison with the GNN-based channel decoding algorithm. We have also provided a detailed algorithm flow of SGNN1 and SGNN2 algorithms. We conducted experiments on the proposed SGNN1- and SGNN2-based BCH decoders, regular LDPC decoders, and 5G NR LDPC decoders. Numerical results show that, compared with the original decoder, the decoders based on SGNN1 and SGNN2 have a comparable performance, while the proposed algorithm requires fewer memory storage resources. The proposed low-complexity decoding algorithms have advantages when applied to resource-constrained devices such as internet of things communications, but performance requirements need to be considered. Conceptualization, Y.L. and Q.W. ; Methodology, Y.L. ; Validation, Q.W. and X.C. ; Formal analysis, Q.W. ; Investigation, Q.W. ; Resources, Q.W. and Y.L. ; Data curation, Q.W. and X.C. ; Writing--original draft, Q.W. and Y.L. ; Writing--review and editing, B.K.N., C.-T.L. and Y.M. ; Visualization, Y.L. All authors have read and agreed to the published version of the manuscript. This work was funded by the research funding of the Macao Polytechnic University, Macao SAR, China (Project no. RP/FCA-01/2023). The study was conducted in accordance with the Declaration of Helsinki. Informed Consent StatementInformed consent was obtained from all subjects involved in the study. For privacy reasons, the data source of this article has not been made public. The authors declare no conflict of interest. ## References

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