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In this work, we analyze efficient window shift schemes for windowed decodingof spatially coupled low-density parity-check (SC-LDPC) codes, which is knownto yield close-tooptimal decoding results when compared to full beliefpropagation (BP) decoding. However, a drawback of windowed decoding is thateither a significant amount of window updates are required leading tounnecessary high decoding complexity or the decoder suffers from sporadicburst-like error patterns, causing a decoder stall. To tackle this effect and,thus, to reduce the average decoding complexity, the basic idea is to enableadaptive window shifts based on a bit error rate (BER) prediction, whichreduces the amount of unnecessary updates. As the decoder stall does not occurin analytical investigations such as the density evolution (DE), we examinedifferent schemes on a fixed test-set and exhaustive monte-carlo simulationsbased on our graphic processing unit (GPU) simulation framework. As a result,we can reduce the average decoding complexity of the naive windowed decoderwhile improving the BER performance when compared to a non-adaptive windoweddecoding scheme. Furthermore, we show that a foresightful stall prediction doesnot significantly outperform a retrospective stall detection which is mucheasier to implement in practice.

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