Implementation aspects of LDPC convolutional codes

Pusane, Alí Emre; Feltstrom, A.J.; Sridharan, Arvind; Lentmaier, Michael; Zigangirov, Kamil Sh.; Costello, Daniel J.

doi:10.1109/tcomm.2008.050519

Cited by 125 publications

(81 citation statements)

References 12 publications

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“…8, the LDPC convolutional codes employed a As a result of the extended graph of LDPC convolutional codes, stopping rules are not as obvious as for LDPC block codes. One such stopping rule has been proposed, however, in [12], where the average number of iterations can be reduced without affecting the error performance. Using this rule, the independent processors in the pipeline decoder can sometimes be put into a "sleep" mode in order to save computations.…”

Section: Comparison Of Block and Convolutional Codes Built Frommentioning

confidence: 99%

A Comparison of ARA- and Protograph-Based LDPC Block and Convolutional Codes

Costello

Pusane

Jones

et al. 2007

2007 Information Theory and Applications Workshop

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Abstract-ARA-and protograph-based LDPC codes are capable of achieving error performance similar to randomly constructed codes while enjoying several implementation advantages as a result of their structure. LDPC convolutional codes can be derived from these codes through an unwrapping process. In this paper, we review the unwrapping process as well as the pipeline decoder that allows continuous decoding of LDPC convolutional codes. Computer simulations are then used to demonstrate that the unwrapped convolutional codes achieve a "convolutional gain" in error performance. We conjecture that this is due to the concatenation of many constraint lengths worth of received symbols in the pipeline decoding process. The consequences of this improved performance are examined in terms of factors related to decoder implementation: processor size, memory requirements, and decoding delay (latency). Finally, given identical protograph kernels, we compare derived block and convolutional codes based on the above measures.

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Section: Comparison Of Block and Convolutional Codes Built Frommentioning

confidence: 99%

A Comparison of ARA- and Protograph-Based LDPC Block and Convolutional Codes

Costello

Pusane

Jones

et al. 2007

2007 Information Theory and Applications Workshop

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“…Much less is known about their finite-length behavior in the waterfall region [5]. Even though there are multiple recent papers reporting simulation results for different classes of finite-length SC-LDPC codes [11], [12], [13], [14], [15], to date we lack analytical models to relate the BP block error probability of finite-length SC-LDPC codes to their structural parameters. The present paper addresses this problem by extending existing analytical results to analyze finite-length LDPC codes [16], [17] to a particular family of SC-LDPC codes.…”

Section: Introductionmentioning

confidence: 99%

A Scaling Law to Predict the Finite-Length Performance of Spatially-Coupled LDPC Codes

Olmos

Urbanke

2015

IEEE Trans. Inform. Theory

172

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Abstract-Spatially-coupled LDPC codes are known to have excellent asymptotic properties. Much less is known regarding their finite-length performance. We propose a scaling law to predict the error probability of finite-length spatially-coupled code ensembles when transmission takes place over the binary erasure channel. We discuss how the parameters of the scaling law are connected to fundamental quantities appearing in the asymptotic analysis of these ensembles and we verify that the predictions of the scaling law fit well to the data derived from simulations over a wide range of parameters. The ultimate goal of this line of research is to develop analytic tools for the design of spatially-coupled LDPC codes under practical constraints.

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“…This ability to balance the processor load and decoding speed dynamically is especially desirable where very large LDPC convolutional codes must be decoded with limited available on-chip memory. Further discussion on the implementation aspects of the pipeline decoder can be found in [30].…”

Section: B Implementation Aspects Of Ldpc Convolutional Codesmentioning

confidence: 99%

“…In particular, we consider the computational complexity, hardware complexity, decoder memory requirements, and decoding delay. More details on the various comparisons described in this section can be found in [30], [40], [41].…”

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

119

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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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Implementation aspects of LDPC convolutional codes

Cited by 125 publications

References 12 publications

A Comparison of ARA- and Protograph-Based LDPC Block and Convolutional Codes

A Comparison of ARA- and Protograph-Based LDPC Block and Convolutional Codes

A Scaling Law to Predict the Finite-Length Performance of Spatially-Coupled LDPC Codes

Deriving Good LDPC Convolutional Codes from LDPC Block Codes

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