LDPC codes: comparing cluster graphs to factor graphs
This work addresses the problem of improving inference efficiency for LDPC codes, which is incremental as it compares existing graphical model representations.
The study compared cluster graph and factor graph representations for LDPC codes, finding that cluster graphs offer advantages in computational cost, convergence speed, and accuracy of marginal probabilities.
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.