  Low-Density Parity-Check (LDPC) codes are usually decoded by running an
iterative belief-propagation, or message-passing, algorithm over the factor
graph of the code. The traditional message-passing schedule consists of
updating all the variable nodes in the graph, using the same pre-update
information, followed by updating all the check nodes of the graph, again,
using the same pre-update information. Recently several studies show that
sequential scheduling, in which messages are generated using the latest
available information, significantly improves the convergence speed in terms of
number of iterations. Sequential scheduling raises the problem of finding the
best sequence of message updates. This paper presents practical scheduling
strategies that use the value of the messages in the graph to find the next
message to be updated. Simulation results show that these informed update
sequences require significantly fewer iterations than standard sequential
schedules. Furthermore, the paper shows that informed scheduling solves some
standard trapping set errors. Therefore, it also outperforms traditional
scheduling for a large numbers of iterations. Complexity and implementability
issues are also addressed.
