A Scalable Graph Neural Network Decoder for Short Block Codes

TL;DR

This paper introduces a scalable graph neural network decoder for short block codes that outperforms traditional belief propagation methods and generalizes across code lengths.

Contribution

The paper presents a novel edge-weighted GNN decoder that is scalable and transferable across different code lengths and rates, improving decoding performance.

Findings

Outperforms belief propagation in error rate

Scalable with code length and rate

Trained on short codes, works on longer 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.