An Iterative Decoding Algorithm with Backtracking to Lower the Error-Floors of LDPC Codes

Kang, Jingyu; Huang, Qin; Lin, Shu; Abdel-Ghaffar, Khaled

doi:10.1109/tcomm.2010.101210.090628

Cited by 43 publications

(52 citation statements)

References 15 publications

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“…The second code considered is the n = 1056 and R = 1/2 quasi-cyclic LDPC (QC-LDPC) code from the IEEE 802.16e WiMAX standard [22]. Here, augmentation is compared to the second backtracking method presented in [9]. As is shown in Fig.…”

Section: Quasi-regular Mackaymentioning

confidence: 99%

“…The non-uniqueness of the code's parity-check matrix (and hence its graph) is utilized in [8] where BP is employed in parallel on a set of equivalent graphs. Backtracking was proposed in [9], which involves the iterative sign flipping of a BP message *Correspondence: alex.rigby@utas.edu.au 1 College of Sciences and Engineering, University of Tasmania, Hobart, Australia Full list of author information is available at the end of the article associated with a non-satisfied check node. Limiting the magnitudes of messages passed in BP can also successfully reduce the error floor [10].…”

Section: Introductionmentioning

confidence: 99%

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Augmented decoders for LDPC codes

Rigby

Olivier

Myburgh

et al. 2018

J Wireless Com Network

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The performance of a belief propagation decoder for low-density parity-check codes is limited by the presence of trapping sets in the code's graph. This leads to an error floor at higher signal-to-noise ratios. We propose the use of an augmented decoder which operates by iteratively decoding on a set of graphs which have a subset of repeated check nodes. We compare the augmented decoder to other modified belief propagation decoders that have been presented in the literature. We show that for all the codes considered, the augmented decoder yields the best frame error rate in the error floor region.

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Section: Quasi-regular Mackaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Augmented decoders for LDPC codes

Rigby

Olivier

Myburgh

et al. 2018

J Wireless Com Network

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show abstract

“…Decoder 2 is only applied to those sequences that caused a decoding error under decoder 1. Therefore, it is desirable to have a less computationally [7].…”

Section: Error Profile Analysismentioning

confidence: 99%

Quantization, absorbing regions and practical message passing decoders

Amiri

Srinivasa

Dolecek

2012

2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

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Low-density parity-check (LDPC) codes and accom panying message passing decoding algorithms are a popular choice for data encoding and decoding in modem communica tions and storage systems. To reduce implementation complexity, the messages in a practical message passing decoder are necessar ily quantized. It is well known that the performance of practical, quantized message passing decoders in the high-reliability regime is governed by non-codeword decoding errors, typically described via trapping/absorbing sets. Absorbing regions act as "decoding regions" around absorbing sets. In this work, we take a closer look at the interplay between quantization and absorbing regions.We provide a study of a range of quantization choices, describe the impact of quantization on the candidate absorbing regions, and derive guidelines for practical finite-precision decoders.In particular, we show that, depending on the choice of the quantization allocation, different absorbing sets emerge as dom inant: even though the overall performance of two quantization schemes can be similar, the distribution of decoding errors across possible absorbing sets can be substantially different. We take the advantage of disjointness of error profiles of two carefully chosen quantized decoders to design a decoder that is a series of these two decoders. The result is a performance improvement of at least an order magnitude relative to constituent decoders without increase in complexity.

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“…Then, if multiple decoding attempts based on multiple distinct decoding schedules can be applied to a single received frame, schedule diversity can be achieved, and the probability of falling into trapping sets should be even lower. The simulation results show that, when the proposed schedule diversity is implemented, a more significant improvement in the errorfloor performance can be achieved compared to that of both the backtracking algorithm [25] and the IDS decoders [11]. In addition, adopting the schedule designed using the M 2 I 2 -based algorithm in the first decoding attempt, and combining it with schedule diversity for the following decoding attempts, both the convergence speed in the waterfall region and the error rate in the error-floor region can be improved.…”

Section: Introductionmentioning

confidence: 99%

“…The backtracking algorithm [25], [26] is a trial-and-error strategy, which mitigates the effect of trapping sets by flipping any suspicious bits, and re-executing the decoding process. The error floor can also be lowered by using the IDS decoders proposed in [11], since the decoding messages to be updated are dynamically adjusted.…”

Section: Introductionmentioning

confidence: 99%

LDPC Decoding Scheduling for Faster Convergence and Lower Error Floor

Lee

Ueng

2014

IEEE Trans. Commun.

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This paper presents a maximum mutual information increase (M 2 I 2 )-based algorithm that can be used to arrange low-density parity-check (LDPC) decoding schedules for faster convergence, where the increase is used to guide the arrangement of the fixed decoding schedule. The predicted mutual information for the messages to be updated is used in the calculation of the increase. By looking ahead for several decoding stages, a high-order prediction can be realized, which can then be used to devise a schedule with an even faster convergence. For a single received frame, different decoding results can be obtained using different schedules, and, hence, schedule diversity, that lowers the error floor resultant from the dominant trapping sets, is proposed. By adopting the M 2 I 2 -based schedule together with the schedule diversity, both the convergence speed in the waterfall region and the error-rate in the floor region can be improved.

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An Iterative Decoding Algorithm with Backtracking to Lower the Error-Floors of LDPC Codes

Cited by 43 publications

References 15 publications

Augmented decoders for LDPC codes

Augmented decoders for LDPC codes

Quantization, absorbing regions and practical message passing decoders

LDPC Decoding Scheduling for Faster Convergence and Lower Error Floor

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