Windowed erasure decoding of LDPC Convolutional Codes

Papaleo, Marco; Iyengar, Aravind R.; Siegel, Paul H.; Wolf, J.K.; Corazza, G.E.

doi:10.1109/itwksps.2010.5503166

Cited by 44 publications

(34 citation statements)

References 7 publications

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“…In [69], Papaleo, Iyengar, Siegel, Wolf, and Corazza study the performance of windowed decoding of convolutional LDPC codes on the BEC. Such a decoder has a decoding complexity which is independent of the chain length, an important practical advantage.…”

Section: B Prior Work On Spatially Coupled Codesmentioning

confidence: 99%

Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

Kudekar

Richardson

Urbanke

2013

IEEE Trans. Inform. Theory

362

318

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Abstract-We investigate spatially coupled code ensembles. For transmission over the binary erasure channel, it was recently shown that spatial coupling increases the belief propagation threshold of the ensemble to essentially the maximum a-priori threshold of the underlying component ensemble. This explains why convolutional LDPC ensembles, originally introduced by Felström and Zigangirov, perform so well over this channel.We show that the equivalent result holds true for transmission over general binary-input memoryless output-symmetric channels. More precisely, given a desired error probability and a gap to capacity, we can construct a spatially coupled ensemble which fulfills these constraints universally on this class of channels under belief propagation decoding. In fact, most codes in that ensemble have that property. The quantifier universal refers to the single ensemble/code which is good for all channels but we assume that the channel is known at the receiver.The key technical result is a proof that under belief propagation decoding spatially coupled ensembles achieve essentially the area threshold of the underlying uncoupled ensemble.We conclude by discussing some interesting open problems.

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Section: B Prior Work On Spatially Coupled Codesmentioning

confidence: 99%

Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

Kudekar

Richardson

Urbanke

2013

IEEE Trans. Inform. Theory

362

318

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“…A different type of sliding window decoding was introduced in [7] for proving that the threshold improvement of LDPC convolutional codes does not vanish as L → ∞ (see also [2]). This type of window decoding was also investigated in [8] from a latency perspective.…”

Section: Introductionmentioning

confidence: 99%

“…We demonstrate that the window decoder considered in [8], [7] and [2] can be interpreted as a natural and easily implementable approximation of a special MPS in which superfluous node updates are omitted. Simulation results for some (3,6)-regular code examples show that with the sliding window decoder LDPC convolutional codes can outperform their block code counterparts without increasing latency and complexity.…”

Section: Introductionmentioning

confidence: 99%

Efficient message passing scheduling for terminated LDPC convolutional codes

Lentmaier

Prenda

Fettweis

2011

2011 IEEE International Symposium on Information Theory Proceedings

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Abstract-Message passing schedules that reduce the decoding complexity of terminated LDPC convolutional code ensembles are analyzed. Considering the AWGN channel, various schedules are compared by means of density evolution. The results of the analysis together with computer simulations for some (3,6)-regular codes confirm that sliding window decoding is an attractive practical solution for low-latency and low-complexity decoding.

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“…Thus, in I iterations, messages can propagate only over a window of size I constraint length. (See also the recent paper by Papaleo et al [28], which investigates further reducing the window size for codes operating on a binary erasure channel (BEC).) Another simplification is achieved by exploiting the fact that a single decoding iteration of two variable nodes that are at least m s + 1 time units apart can be performed independently, since the corresponding bits cannot participate in the same parity-check equation.…”

Section: B Implementation Aspects Of Ldpc Convolutional Codesmentioning

confidence: 99%

“…Although this does not cause problems for some applications that are not delay-sensitive (e.g., deep-space communication), for other applications that are delay-sensitive (e.g., real-time voice/video transmission), design specifications may be met by deriving LDPC convolutional codes from shorter LDPC block codes, thus sacrificing some coding gain, but reducing memory and delay requirements, or by employing a reduced window size decoder, as suggested in the recent paper by Papaleo et al [28], with a resulting reduction in the "convolutional gain. "…”

Section: Cost Of the "Convolutional Gain"mentioning

confidence: 99%

Deriving Good LDPC Convolutional Codes from LDPC Block Codes

Pusane

Smarandache

Vontobel

et al. 2011

IEEE Trans. Inform. Theory

120

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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 codes. We investigate some possible reasons for this "convolutional gain," and we also discuss the -mostly moderate -decoder cost increase that is incurred by going from LDPC block to LDPC convolutional codes.

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Windowed erasure decoding of LDPC Convolutional Codes

Cited by 44 publications

References 7 publications

Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

Efficient message passing scheduling for terminated LDPC convolutional codes

Deriving Good LDPC Convolutional Codes from LDPC Block Codes

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