The impact when neural min-sum variant meets ordered statistics decoding of LDPC codes

TL;DR

This paper proposes a hybrid decoding framework for LDPC codes that combines neural min-sum variants with ordered statistics decoding, achieving improved performance and efficiency through innovative design and implementation strategies.

Contribution

It introduces a neural min-sum variant design methodology, efficient ordered statistics decoding schemes, and iteration diversity techniques for high-iteration LDPC decoding.

Findings

Neural min-sum variants show similar performance, simplifying hybrid design.

Two efficient ordered statistics decoding implementations are proposed.

Simulation results demonstrate improved decoding performance across various code lengths.

Abstract

This paper introduces three key initiatives in the pursuit of a hybrid decoding framework characterized by superior decoding performance, high throughput, low complexity, and independence from channel noise variance. Firstly, adopting a graphical neural network perspective, we propose a design methodology for a family of neural min-sum variants. Our exploration delves into the frame error rates associated with different decoding variants and the consequential impact of decoding failures on subsequent ordered statistics decoding. Notably, these neural min-sum variants exhibit generally indistinguishable performance, hence the simplest member is chosen as the constituent of the hybrid decoding. Secondly, to address computational complexities arising from exhaustive searches for authentic error patterns in cases of decoding failure, two alternatives for ordered statistics decoding implementation are proposed. The first approach involves uniformly grouping test error patterns, while the second scheme dynamically generates qualified searching test error patterns with varied sizes for each group. In both methods, group priorities are determined empirically. Thirdly, iteration diversity is highlighted in the case of LDPC codes requiring high maximum iterations of decoding. This is achieved by segmenting the long iterative decoding trajectory of a decoding failure into shorter segments, which are then independently fed to small models to enhance the chances of acquiring the authentic error pattern. These ideas are substantiated through extensive simulation results covering the codes with block lengths ranging from one hundred to several hundreds.