  Belief propagation (BP) decoding of low-density parity-check (LDPC) codes
with various dynamic decoding schedules have been proposed to improve the
efficiency of the conventional flooding schedule. As the ultimate goal of an
ideal LDPC code decoder is to have correct bit decisions, a dynamic decoding
schedule should be variable node (VN)-centric and be able to find the VNs with
probable incorrect decisions and having a good chance to be corrected if chosen
for update. We propose a novel and effective metric called conditional
innovation (CI) which serves this design goal well. To make the most of dynamic
scheduling which produces high-reliability bit decisions, we limit our search
for the candidate VNs to those related to the latest updated nodes only.
  Based on the CI metric and the new search guideline separately or in
combination, we develop several highly efficient decoding schedules. To reduce
decoding latency, we introduce multi-edge updating versions which offer extra
latency-performance tradeoffs. Numerical results show that both single-edge and
multi-edge algorithms provide better decoding performance against most dynamic
schedules and the CI-based algorithms are particularly impressive at the first
few decoding iterations.
