A Scalable Graph Neural Network Decoder for Short Block Codes
This work addresses decoding challenges in communication systems, offering a scalable solution that can generalize across different code lengths and rates, though it is incremental as it builds on existing graph neural network and belief propagation methods.
The paper tackles the problem of decoding short block codes by proposing an edge-weighted graph neural network (EW-GNN) decoder, which outperforms belief propagation and deep-learning-based BP methods in decoding error rate while offering scalability with trainable parameters independent of codeword length.
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.