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Performance Analysis of Iterative De...

Janulewicz, Emil.

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Performance Analysis of Iterative Decoding Algorithms with Memory.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Performance Analysis of Iterative Decoding Algorithms with Memory./
Author:	Janulewicz, Emil.
Description:	103 p.
Notes:	Source: Masters Abstracts International, Volume: 49-03, page: .
Contained By:	Masters Abstracts International49-03.
Subject:	Applied Mechanics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR68626
ISBN:	9780494686263

Performance Analysis of Iterative Decoding Algorithms with Memory.
Janulewicz, Emil.

Performance Analysis of Iterative Decoding Algorithms with Memory. - 103 p.

Source: Masters Abstracts International, Volume: 49-03, page: .

Thesis (M.A.Sc.)--Carleton University (Canada), 2010.

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 independence assumption amongst all the processed messages at variable and check nodes. 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 dependence created among messages for these algorithms is due to the introduction of memory in the iterative algorithm. 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 trade off of DD-BMP in comparison with BP and MS algorithms.

ISBN: 9780494686263Subjects--Topical Terms:

1018410
Applied Mechanics.

Performance Analysis of Iterative Decoding Algorithms with Memory.
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245 1 0 $a Performance Analysis of Iterative Decoding Algorithms with Memory.
300 $a 103 p.
500 $a Source: Masters Abstracts International, Volume: 49-03, page: .
502 $a Thesis (M.A.Sc.)--Carleton University (Canada), 2010.
520 $a 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 independence assumption amongst all the processed messages at variable and check nodes. 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 dependence created among messages for these algorithms is due to the introduction of memory in the iterative algorithm. 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 trade off of DD-BMP in comparison with BP and MS algorithms.
590 $a School code: 0040.
650 4 $a Applied Mechanics. $3 1018410
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Artificial Intelligence. $3 769149
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710 2 $a Carleton University (Canada). $3 1018407
773 0 $t Masters Abstracts International $g 49-03.
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