2011
DOI: 10.1109/tit.2010.2095211
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Deriving Good LDPC Convolutional Codes from LDPC Block Codes

Abstract: Abstract-Low-density parity-check (LDPC) convolutional codes are capable of achieving excellent performance with low encoding and decoding complexity. In this paper we discuss several graph-cover-based methods for deriving families of timeinvariant and time-varying LDPC convolutional codes from LDPC block codes and show how earlier proposed LDPC convolutional code constructions can be presented within this framework.Some of the constructed convolutional codes significantly outperform the underlying LDPC block … Show more

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Cited by 120 publications
(81 citation statements)
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“…First, there are 2 k (l+1) branches in the trellis of (2k, k, l) convolutional codes. A branch has a codeword and so we can obtain a 2 k (l+1) ×1 matrix in i 0 i 1 …i l-1 i l ascending sort by 2 k -ary: C=[C 00...00 C 00...01 … C 00...0K C 00...10 … C KK...KK ] T (6) where C 00...00~C00...0K corresponds to the 2 k branches of state node S 00…0 , that can be derived from (3). Each element is 1×2k vector in (6), which can be converted into 2 k (l+1) ×2k matrix: 3 1 2 7 5 3 17 13 4 27 31 5 75 53 Since C is a constant matrix that can be obtained and converted to a bipolar code beforehand and can be stored in a "code word generator."…”
Section: The Add-compare-selectmentioning
confidence: 99%
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“…First, there are 2 k (l+1) branches in the trellis of (2k, k, l) convolutional codes. A branch has a codeword and so we can obtain a 2 k (l+1) ×1 matrix in i 0 i 1 …i l-1 i l ascending sort by 2 k -ary: C=[C 00...00 C 00...01 … C 00...0K C 00...10 … C KK...KK ] T (6) where C 00...00~C00...0K corresponds to the 2 k branches of state node S 00…0 , that can be derived from (3). Each element is 1×2k vector in (6), which can be converted into 2 k (l+1) ×2k matrix: 3 1 2 7 5 3 17 13 4 27 31 5 75 53 Since C is a constant matrix that can be obtained and converted to a bipolar code beforehand and can be stored in a "code word generator."…”
Section: The Add-compare-selectmentioning
confidence: 99%
“…The first task is to verify the validity of the max module, and to determine a suitable memory depth. Take the (6,3,3) convolutional code as an example where 10 different memory depths are selected based on integer times of kl=9. When the Eb/No is respectively1.5db, 2db, and 2.5db, the BER is shown with or without the max module separately in Fig.…”
Section: Fig 3 Convergence Of Ber Performance For Memory Depthmentioning
confidence: 99%
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