Syndrome based block decoding of convolutional codes

Geldmacher, Jan; Hueske, Klaus; Götze, J.

doi:10.1109/iswcs.2008.4726115

Cited by 4 publications

(7 citation statements)

References 13 publications

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“…In other words, upon increasing the number of iterations or SNR, the syndrome exhibits longer sequences of zeros, which indicates error-free transmission. This fact can be exploited to partition the received block into error-free and erroneous segments, as proposed in [4], [5]. This is achieved by heuristically choosing a design parameter, L min = (L start + L end + 1), which is the minimum number of consecutive zero syndromes after which the subblock is deemed to be error-free.…”

Section: Syndrome-based Blockingmentioning

confidence: 99%

“…L start = (L min − 1)/2 symbols are appended to the following erroneous block [4], [5]. Only the remaining (L 0 − L min + 1) symbols are considered error-free.…”

Section: Syndrome-based Blockingmentioning

confidence: 99%

“…More explicitly, the constituent TCM codes, which can be optimally designed using EXtrinsic Information Transfer (EXIT) charts [3], are concatenated in a parallel fashion and iterative decoding is invoked at the receiver for exchanging extrinsic information between the pair of TCM decoders. In order to reduce its decoding complexity, we propose to reduce the effective number of decoding iterations by appropriately adapting the syndromebased block decoding approach of [4], [5] for TTCM.…”

Section: Introductionmentioning

confidence: 99%

“…This underlying property of syndrome decoding has been exploited in [4] for developing a Block Syndrome Decoder (BSD) for convolutional codes, which divides the received sequence into erroneous and error-free parts based on the syndrome. More specifically, the BSD only decodes the erroneous blocks, with the initial and final states of the trellis initialized to zero.…”

Section: Introductionmentioning

confidence: 99%

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Reduced-Complexity Syndrome-Based TTCM Decoding

Babar

Hanzo

2013

IEEE Commun. Lett.

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Abstract-The iterative decoder of Turbo Trellis Coded Modulation (TTCM) exchanges extrinsic information between the constituent TCM decoders, which imposes a high computational complexity at the receiver. Therefore we conceive the syndrome-based block decoding of TTCM, which is capable of reducing the decoding complexity by disabling the decoder, when syndrome becomes zero. Quantitatively, we demonstrate that a decoding complexity reduction of at least 17% is attained at high SNRs, with at least 20% and 45% reduction in the 5 th and 6 th iterations, respectively.Index Terms-Syndrome decoding, TTCM, error trellis, block syndrome decoding, iterative decoding.

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Section: Syndrome-based Blockingmentioning

confidence: 99%

“…L start = (L min − 1)/2 symbols are appended to the following erroneous block [4], [5]. Only the remaining (L 0 − L min + 1) symbols are considered error-free.…”

Section: Syndrome-based Blockingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reduced-Complexity Syndrome-Based TTCM Decoding

Babar

Hanzo

2013

IEEE Commun. Lett.

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“…In this work another approach to adaptive decoding, based on the block syndrome decoder (BSD), which was previously considered for efficient parallel decoding [4], is presented. Compared to the T-algorithm, this approach can reduce its effort to almost zero in the best case and can use a standard implementation of the VA.…”

Section: Introductionmentioning

confidence: 99%

An adaptive and complexity reduced decoding algorithm for convolutional codes and its application to digital broadcasting systems

Geldmacher

Hueske

Götze

2009

2009 International Conference on Ultra Modern Telecommunications &Amp; Workshops

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Convolutional codes are still prevalent in digital broadcasting systems, like DVB-T, DVB-H, DAB, DMB and DRM. This paper presents an adaptive and complexity reduced decoding algorithm for CCs. The proposed algorithm is based on the earlier proposed block syndrome decoder (BSD) and features a significant reduction of decoding effort depending on the current channel conditions. Additionally it is capable of adapting its output bit error rate to a given threshold to achieve a further reduction of decoding complexity. For this purpose, an SNR estimation is incorporated to achieve an adaption of decoding performance to a given bit error rate threshold in dynamic SNR environments. This is especially useful for the widely used serially concatenated coding schemes, which is demonstrated by means of the terrestrial DVB system.

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