A Scalable Graph Neural Network Decoder for Short Block Codes
Abstract
In this work, we propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN). The EW-GNN decoder operates on the Tanner graph with an iterative message-passing structure, which algorithmically aligns with the conventional belief propagation (BP) decoding method. In each iteration, the "weight" on the message passed along each edge is obtained from a fully connected neural network that has the reliability information from nodes/edges as its input. Compared to existing deep-learning-based decoding schemes, the EW-GNN decoder is characterised by its scalability, meaning that 1) the number of trainable parameters is independent of the codeword length, and 2) an EW-GNN decoder trained with shorter/simple codes can be directly used for longer/sophisticated codes of different code rates. Furthermore, simulation results show that the EW-GNN decoder outperforms the BP and deep-learning-based BP methods from the literature in terms of the decoding error rate.
Cite
@article{arxiv.2211.06962,
title = {A Scalable Graph Neural Network Decoder for Short Block Codes},
author = {Kou Tian and Chentao Yue and Changyang She and Yonghui Li and Branka Vucetic},
journal= {arXiv preprint arXiv:2211.06962},
year = {2022}
}
Comments
Submitted to IEEE conference for possible publication