Avoiding Burst-like Error Pattern in Window Decoding of Spatially Coupled LDPC Codes

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 closeto- optimal decoding results when compared to full belief propagation (BP) decoding. However, the significant drawback of window decoding is that either a significant amount of window updates are not 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 naiv window decoder by X % while improving the BER performance by 0.X dB when compared to a non-adaptive window 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.