Avoiding Burst-like Error Patterns in Windowed Decoding of Spatially Coupled LDPC Codes

TL;DR

This paper proposes adaptive window shift schemes for windowed decoding of spatially coupled LDPC codes, reducing complexity and burst-like errors, with practical stall detection methods validated through GPU simulations.

Contribution

It introduces adaptive window shift strategies based on BER prediction to improve decoding efficiency and error performance in windowed SC-LDPC decoding.

Findings

Reduced average decoding complexity compared to naive schemes.

Improved BER performance with adaptive window shifts.

Foresightful stall prediction is not significantly better than retrospective detection.

Abstract

In this work, we analyze efficient window shift schemes for windowed decoding of spatially coupled low-density parity-check (SC-LDPC) codes, which is known to yield close-tooptimal decoding results when compared to full belief propagation (BP) decoding. However, a drawback of windowed decoding is that either a significant amount of window updates are required leading to unnecessary high decoding complexity or the decoder suffers from sporadic burst-like error patterns, causing a decoder stall. To tackle this effect and, thus, to reduce the average decoding complexity, the basic idea is to enable adaptive window shifts based on a bit error rate (BER) prediction, which reduces the amount of unnecessary updates. As the decoder stall does not occur in analytical investigations such as the density evolution (DE), we examine different schemes on a fixed test-set and exhaustive monte-carlo simulations based on our graphic processing unit (GPU) simulation framework. As a result, we can reduce the average decoding complexity of the naive windowed decoder while improving the BER performance when compared to a non-adaptive windowed decoding scheme. Furthermore, we show that a foresightful stall prediction does not significantly outperform a retrospective stall detection which is much easier to implement in practice.