Paper Info
Title
Performance Analysis of Iterative Decoding Algorithms with Memory over Memoryless Channels.
Abstract
Density evolution is often used to determine the performance of an ensemble of low-density parity-check (LDPC) codes under iterative message-passing algorithms. Conventional density evolution techniques over memoryless channels are based on the assumption that messages at iteration l are only a function of the messages at iteration l - 1 and possibly the channel output. This assumption is valid for many algorithms such as standard belief propagation (BP) and min-sum (MS) algorithms. However, there are other important iterative algorithms such as successive relaxation (SR) versions of BP and MS, and differential decoding with binary message passing (DD-BMP) algorithm of Mobini et al., for which this assumption is not valid. The reason is the introduction of memory in these algorithms. In this work, we propose a model for iterative decoding algorithms with memory which covers SR and DD-BMP algorithms as special cases. Based on this model, we derive a Bayesian network for iterative algorithms with memory over memoryless channels and use this representation to analyze the performance of the algorithms using density evolution. The density evolution technique is developed based on truncating the memory of the decoding process and approximating it with a finite order Markov process, and can be implemented efficiently. As an example, we apply our technique to analyze the performance of DD-BMP on regular LDPC code ensembles, and make a number of interesting observations with regard to the performance/complexity tradeoff of DD-BMP in comparison with BP and MS algorithms.The model presented in this paper is based on certain simplifying assumptions about the memory structure of iterative algorithms such as the existence of memory only at the output of variable nodes in the code's Tanner graph rather than at both outputs of variable and check nodes. The Bayesian network framework introduced here however, can still be used to analyze the more general scenarios.
Year
DOI
Venue
2012
10.1109/TCOMM.2012.082812.110838
IEEE Transactions on Communications
Keywords
Field
DocType
Markov processes,iterative decoding,parity check codes,Bayesian network,LDPC,Tanner graph,binary message passing algorithm,density evolution techniques,differential decoding,finite order Markov process,iterative algorithms,iterative decoding algorithms,iterative message-passing algorithms,low-density parity-check codes,memory over memoryless channels,min-sum algorithms,standard belief propagation,successive relaxation,Bayesian networks,Iterative coding schemes,belief propagation (BP),density evolution,differential decoding with binary message passing (DD-BMP),iterative decoding algorithms with memory,low-density parity-check (LDPC) codes,memoryless channels,min-sum (MS)
Sequential decoding,Markov process,Computer science,Low-density parity-check code,Algorithm,Theoretical computer science,Bayesian network,Tanner graph,Decoding methods,Message passing,Belief propagation
Journal
Volume
Issue
ISSN
60
12
0090-6778
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Emil Janulewicz100.34
Amir H. Banihashemi249054.61