  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.
