Belief-Propagation Decoding of LDPC Codes with Variable Node-Centric Dynamic Schedules

TL;DR

This paper introduces a novel metric called conditional innovation for variable node-centric dynamic scheduling in LDPC belief propagation decoding, significantly improving early iteration performance and reducing latency.

Contribution

It proposes the CI metric and new search guidelines for dynamic scheduling, enhancing decoding efficiency and early iteration accuracy in LDPC decoders.

Findings

CI-based schedules outperform most existing dynamic schedules

Multi-edge updating reduces decoding latency with minimal performance loss

Early iteration decoding performance is notably improved

Abstract

Belief propagation (BP) decoding of low-density parity-check (LDPC) codes with various dynamic decoding schedules have been proposed to improve the efficiency of the conventional flooding schedule. As the ultimate goal of an ideal LDPC code decoder is to have correct bit decisions, a dynamic decoding schedule should be variable node (VN)-centric and be able to find the VNs with probable incorrect decisions and having a good chance to be corrected if chosen for update. We propose a novel and effective metric called conditional innovation (CI) which serves this design goal well. To make the most of dynamic scheduling which produces high-reliability bit decisions, we limit our search for the candidate VNs to those related to the latest updated nodes only. Based on the CI metric and the new search guideline separately or in combination, we develop several highly efficient decoding schedules. To reduce decoding latency, we introduce multi-edge updating versions which offer extra latency-performance tradeoffs. Numerical results show that both single-edge and multi-edge algorithms provide better decoding performance against most dynamic schedules and the CI-based algorithms are particularly impressive at the first few decoding iterations.