Efficient message passing scheduling for terminated LDPC convolutional codes

Lentmaier, Michael; Prenda, Maria Mellado; Fettweis, Gerhard

doi:10.1109/isit.2011.6033865

Cited by 62 publications

(41 citation statements)

References 9 publications

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“…As noted previously, under tight latency constraints, the WD threshold can be improved by using small w; in fact, excellent WD performance has been shown for binary SC-LDPC code ensembles using repeated edges in the protograph and w = 1 [29], [30]. In the case of qary SC-LDPC code ensembles, we have also found that the case w = 1, i.e., the set of edge…”

Section: Code Ensemble Constructionsupporting

confidence: 71%

“…We are interested in searching for q-ary SC-LDPC code ensembles for which a small window size W can provide WD with a good threshold, which implies that the coupling width w should be kept small. Indeed, our results for q-ary SC-LDPC codes together with those in the literature for binary SC-LDPC codes [29], [30] show that ensembles with w = 1 provide the best latency-constrained performance with WD.…”

Section: B Windowed Decoding (Wd)supporting

confidence: 67%

“…Employing FSD for SC-LDPC codes can result in large latency, since a large coupling length L is needed to achieve near-capacity thresholds [25]. To resolve this issue, a more efficient technique, called windowed decoding (WD), was proposed in [29], [30] for binary SC-LDPC codes. Compared to FSD, WD exploits the convolutional nature of the SC parity-check matrix to localize decoding and thereby reduce latency.…”

Section: Index Termsmentioning

confidence: 99%

“…As previously mentioned, compared to FSD, where iterative decoding is carried out on the entire parity-check matrix, WD of SC-LDPC code ensembles takes advantage of the convolutional structure of the parity-check matrix and localizes the decoding process to a small portion of the matrix, i.e., the BP algorithm is carried out only for those checks and variables covered by a "window". Consequently, WD is an efficient way to reduce the memory and latency requirements of SC-LDPC codes [29], [30]. The WD algorithm can be described as follows (see [29] for further details):…”

Section: B Windowed Decoding (Wd)mentioning

confidence: 99%

“…• At each time instant/window position, the BP algorithm runs until a fixed number of iterations has been performed or some stopping rule [29], [30], [32] is satisfied, after which the window shifts c column blocks and those c column block symbols shifted out of the window are decoded. The first c column blocks in a window are called the target symbols.…”

Section: B Windowed Decoding (Wd)mentioning

confidence: 99%

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Design of Spatially Coupled LDPC Codes Over GF $(q)$ for Windowed Decoding

Wei

Mitchell

Fuja

et al. 2016

IEEE Trans. Inform. Theory

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In this paper we consider the generalization of binary spatially coupled low-density parity-check (SC-LDPC) codes to finite fields GF(q), q ≥ 2, and develop design rules for q-ary SC-LDPC code ensembles based on their iterative belief propagation (BP) decoding thresholds, with particular emphasis on low-latency windowed decoding (WD). We consider transmission over both the binary erasure channel (BEC) and the binary-input additive white Gaussian noise channel (BIAWGNC) and present results for a variety of (J, K)-regular SC-LDPC code ensembles constructed over GF(q) using protographs.Thresholds are calculated using protograph versions of q-ary density evolution (for the BEC) and qary extrinsic information transfer analysis (for the BIAWGNC). We show that WD of q-ary SC-LDPC codes provides significant threshold gains compared to corresponding (uncoupled) q-ary LDPC block code (LDPC-BC) ensembles when the window size W is large enough and that these gains increase as the finite field size q = 2 m increases. Moreover, we demonstrate that the new design rules provide WD thresholds that are close to capacity, even when both m and W are relatively small (thereby reducing decoding complexity and latency). The analysis further shows that, compared to standard flooding-schedule decoding, WD of q-ary SC-LDPC code ensembles results in significant reductions in both decoding complexity and decoding latency, and that these reductions increase as m increases.For applications with a near-threshold performance requirement and a constraint on decoding latency, we show that using q-ary SC-LDPC code ensembles, with moderate q > 2, instead of their binary counterparts results in reduced decoding complexity.

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Section: Code Ensemble Constructionsupporting

confidence: 71%

Section: B Windowed Decoding (Wd)supporting

confidence: 67%

Section: Index Termsmentioning

confidence: 99%

Section: B Windowed Decoding (Wd)mentioning

confidence: 99%

Section: B Windowed Decoding (Wd)mentioning

confidence: 99%

See 3 more Smart Citations

Design of Spatially Coupled LDPC Codes Over GF $(q)$ for Windowed Decoding

Wei

Mitchell

Fuja

et al. 2016

IEEE Trans. Inform. Theory

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Enabling Technologies for High Spectral‐Efficiency Coherent Optical Communication Networks

Zhou¹,

Xie²

2016

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Forward error correction (FEC) in optical communications has been first demonstrated in 1988 [1]. Since then, coding technology has evolved significantly. This pertains not only to the codes but also to encoder and decoder architectures. Modern high-speed optical communication systems require high-performing FEC engines that support throughputs of 100 GBit/s or multiples thereof, that have low power consumption, that realize net coding gains (NCGs) close to the theoretical limits at a target bit error rate (BER) of below 10 −15 , and that are preferably adapted to the peculiarities of the optical channel.Forward error correction coding is based on deterministically adding redundant bits to a source information bit sequence. After transmission over a noisy channel, a decoding system tries to exploit the redundant information for fully recovering the source information. Several methods for generating the redundant bit sequence from the source information bits are known. Transmission systems with 100 GBit/s and 400 GBit/s today typically use one of two coding schemes to generate the redundant information: Block-Turbo Codes (BTCs) or Low-Density Parity-Check (LDPC) codes. In coherent systems, so-called soft information is usually ready available and can be used in high performing systems within a soft-decision decoder architecture. Soft-decision information means that no binary 0/1 decision is made before entering the forward error correction decoder. Instead, the (quantized) samples are used together with their statistics to get improved estimates of the original bit sequence. This chapter will focus on soft-decision decoding of LDPC codes and the evolving spatially coupled LDPC codes.

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