LDPC codes: comparing cluster graphs to factor graphs
Information Theory
2023-10-03 v2 Machine Learning
math.IT
Abstract
We present a comparison study between a cluster and factor graph representation of LDPC codes. In probabilistic graphical models, cluster graphs retain useful dependence between random variables during inference, which are advantageous in terms of computational cost, convergence speed, and accuracy of marginal probabilities. This study investigates these benefits in the context of LDPC codes and shows that a cluster graph representation outperforms the traditional factor graph representation.
Keywords
Cite
@article{arxiv.2204.06350,
title = {LDPC codes: comparing cluster graphs to factor graphs},
author = {J du Toit and J du Preez and R Wolhuter},
journal= {arXiv preprint arXiv:2204.06350},
year = {2023}
}
Comments
8 pages, 6 figures