Parallel versus sequential updating for Belief Propagation decoding

TL;DR

This paper introduces a sequential updating scheme for belief propagation decoding that halves the number of iterations needed compared to the traditional parallel scheme, reducing overall complexity while maintaining error correction performance.

Contribution

The paper proposes and demonstrates the effectiveness of a sequential updating scheme for belief propagation decoding, showing it reduces iterations and complexity without sacrificing accuracy.

Findings

SUS requires about half the iterations of PUS for similar error correction.

Both schemes have similar complexity per iteration.

SUS benefits from inter-iteration information sharing.

Abstract

sequential updating scheme (SUS) for the belief propagation algorithm is proposed, and is compared with the parallel (regular) updating scheme (PUS). Simulation results on various codes indicate that the number of iterations of the belief algorithm for the SUS is about one half of the required iterations for the PUS, where both decoding algorithms have the same error correction properties. The complexity per iteration for both schemes is similar, resulting in a lower total complexity for the SUS. The explanation of this effect is related to the inter-iteration information sharing, which is a property of only the SUS, and which increases the "correction gain" per iteration