Selective max-min algorithm for low-density parity-check decoding

With the growing importance of error correction in different communication systems, using an efficient and easily implementable code is always appreciated. One of the most important codes is the low-density parity-check (LDPC) code. Two main iterative decoding algorithms are usually used, namely the...

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Bibliographic Details
Published in:IET communications Vol. 7; no. 1; pp. 65 - 70
Main Authors: Hareedy, Ahmed H, Khairy, Mohamed M
Format: Journal Article
Language:English
Published: Stevenage The Institution of Engineering and Technology 01.01.2013
John Wiley & Sons, Inc
Subjects:
Algorithms
Communication systems
Complexity
Decoding
error correction code
error correction codes
Expenses
Handles
iterative decoding
iterative decoding algorithm
LDPC code
low density parity check
matrix algebra
min‐sum algorithm
MS algorithm
Parity
parity check codes
parity check matrix
Performance degradation
selective max‐min algorithm
SMM
SP algorithm
sum‐product algorithm
iterative decoding algorithm
sum-product algorithm
error correction codes
SP algorithm
selective max-min algorithm
parity check codes
MS algorithm
parity check matrix
min-sum algorithm
error correction code
low density parity check
matrix algebra
SMM
iterative decoding
LDPC code
ISSN:1751-8628, 1751-8636
Online Access:Get full text
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Summary:With the growing importance of error correction in different communication systems, using an efficient and easily implementable code is always appreciated. One of the most important codes is the low-density parity-check (LDPC) code. Two main iterative decoding algorithms are usually used, namely the sum-product (SP) algorithm (also referred to as belief propagation) and the min–sum (MS). The SP algorithm is more accurate but suffers from very high complexity. On the other hand, the MS algorithm has a much lower complexity at the expense of some performance degradation. To handle this performance degradation, many algorithms were presented in the literature as improvements for the MS, like the scaled MS and the offset MS. However, all those improved algorithms are more complex than the traditional MS. In this study, an efficient and low complexity LDPC decoding algorithm, called selective max–min (SMM), is proposed. The SMM performance is closer to SP than to MS as long as the average number of ones per column in the parity check matrix is around or less than 4 (which is the case for most of the communication systems using LDPC). On the other hand, the SMM exhibits only a minor complexity increase over traditional MS making it suitable for practical implementation.
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ISSN:1751-8628
1751-8636
DOI:10.1049/iet-com.2011.0722