Article

Informed Dynamic Scheduling for Belief-Propagation Decoding of LDPC Codes

03/2007;
Source: arXiv

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.

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Available from: Andres I. Vila Casado, Oct 17, 2014
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    • "The kernel steps of sequential updating algorithms focus on finding the order of message updating which converges fastest. To our knowledge, the best decoding algorithms in the sense of performance is informed dynamic scheduling (IDS) [18] [19] [20] [21] [22] [23] [24] algorithms, which update messages dynamically. A metric called residual [18] is used in IDS which decides the updating order of propagated messages . "
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    ABSTRACT: Low-density parity-check (LDPC) codes can be applied in a lot of different scenarios such as video broadcasting and satellite communications. LDPC codes are commonly decoded by an iterative algorithm called belief propagation (BP) over the corresponding Tanner graph. The original BP updates all the variable-nodes simultaneously, followed by all the check-nodes simultaneously as well. We propose a sequential scheduling algorithm based on weighted bit-flipping (WBF) algorithm for the sake of improving the convergence speed. Notoriously, WBF is a low-complexity and simple algorithm. We combine it with BP to obtain advantages of these two algorithms. Flipping function used in WBF is borrowed to determine the priority of scheduling. Simulation results show that it can provide a good tradeoff between FER performance and computation complexity for short-length LDPC codes.
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    • "In this area, we note [14] proposes several such techniques, [15] slows decoder convergence using averaged decoding, [16] selectively biases the messages from check nodes, [17] employs informed scheduling of nodes for updating, and [18] adds check equations to the parity check matrix. This paper makes no changes to the standard SPA decoding aside from fixing common numerical problems. "
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    ABSTRACT: This paper addresses the prediction of error floors of variable-regular Low Density Parity Check (LDPC) codes in the Additive White Gaussian Noise (AWGN) channel. Specifically, we focus on the Sum-Product Algorithm (SPA) decoder in the log-domain at high SNRs. We hypothesize that several published error floor levels are due to numerical saturation within their decoders when handling high SNRs. We take care to develop a log-domain SPA decoder that does not saturate near-certain messages and find the error rates of our decoder to be lower by at least several orders of magnitude. We study the behavior of near-codewords / trapping sets that dominate the reported error floors. J. Sun, in his Ph.D. thesis, used a linear system model to show that error floors due to elementary trapping sets don't exist under certain conditions, assuming that the SPA decoder is non-saturating [1]. We develop a refined linear model which we find to be capable of predicting the error floors caused by elementary trapping sets for saturating decoders. Performance results of several codes at several levels of decoder saturation are presented.
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    • "The current state of the messages in the graph can be used to dynamically update the schedule, producing what we call Informed Dynamic Scheduling (IDS). We presented IDS in [12] and first published it in [13]. To our knowledge, these and the simultaneous work in [14] are the first works on the subject of dynamic scheduling for LDPC decoding. "
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    ABSTRACT: Low-Density Parity-Check (LDPC) codes are usually decoded by running an iterative belief-propagation (BP), or message-passing, algorithm over the factor graph of the code. The traditional message-passing scheduling, called flooding, 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 introduces the problem of finding the best sequence of message updates. We propose Informed Dynamic Scheduling (IDS) strategies that select the message-passing schedule according to the observed rate of change of the messages. In general, IDS strategies require computation to select the message to update but converge in fewer message updates because they focus on the part of the graph that has not converged. Moreover, IDS yields a lower error-rate performance than either flooding or sequential scheduling because IDS strategies overcome traditional trapping-set errors. This paper presents IDS strategies that address several issues including performance for short-blocklength codes, complexity, and implementability.
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