Iterative Decoding on Multiple Tanner Graphs Using Random Edge Local Complementation

TL;DR

This paper introduces SPA-ELC, a local iterative decoding method that enhances sum-product algorithm performance by applying random edge local complementation on multiple Tanner graph structures, especially for small algebraic codes.

Contribution

It presents a novel local graph update-based iterative decoding approach, SPA-ELC, which outperforms standard SPA and rivals SPA-PD without global graph modifications.

Findings

Significant error rate improvement over standard SPA.

Comparable performance to SPA-PD with lower complexity.

Effective for small algebraic codes like Golay and quadratic residue codes.

Abstract

In this paper, we propose to enhance the performance of the sum-product algorithm (SPA) by interleaving SPA iterations with a random local graph update rule. This rule is known as edge local complementation (ELC), and has the effect of modifying the Tanner graph while preserving the code. We have previously shown how the ELC operation can be used to implement an iterative permutation group decoder (SPA-PD)--one of the most successful iterative soft-decision decoding strategies at small blocklengths. In this work, we exploit the fact that ELC can also give structurally distinct parity-check matrices for the same code. Our aim is to describe a simple iterative decoder, running SPA-PD on distinct structures, based entirely on random usage of the ELC operation. This is called SPA-ELC, and we focus on small blocklength codes with strong algebraic structure. In particular, we look at the extended Golay code and two extended quadratic residue codes. Both error rate performance and average decoding complexity, measured by the average total number of messages required in the decoding, significantly outperform those of the standard SPA, and compares well with SPA-PD. However, in contrast to SPA-PD, which requires a global action on the Tanner graph, we obtain a performance improvement via local action alone. Such localized algorithms are of mathematical interest in their own right, but are also suited to parallel/distributed realizations.