Informed Dynamic Scheduling for Belief-Propagation Decoding of LDPC Codes

TL;DR

This paper introduces informed dynamic scheduling strategies for belief-propagation decoding of LDPC codes, significantly reducing iterations and improving error correction by using message values to guide update sequences.

Contribution

It proposes practical, message-informed scheduling methods that enhance convergence speed and error correction in LDPC decoding over traditional sequential approaches.

Findings

Informed schedules reduce the number of decoding iterations.

They outperform standard schedules in error correction, especially for trapping set errors.

The methods are practical and address complexity concerns.

Abstract

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.