An Efficient Chase Decoder for Turbo Product Codes

Argon, C.; McLaughlin, S.W.

doi:10.1109/tcomm.2004.829556

Cited by 51 publications

(23 citation statements)

References 7 publications

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“…For a single conventional SDD operation, the total number of computations required is 2  , while it is ( − ) + 2 + 2  ( − ) for the ULD. Thus, the relative complexity   , which is the ratio of the two terms, can be simplif ed to Table I presents   of the ULD as well as [6] for some of the codes reported in [3], and for typical values of . As it can be noted from the results, the achieved complexity reduction depends on the code used and on .…”

Section: B Uld Complexitymentioning

confidence: 99%

“…Moreover, Table I shows that the ULD substantially outperforms [3], while [6] slightly outperforms the ULD, however, HDD is still required in [6]. Therefore, the ULD and [6] require almost the same number of ED operations, yet [6] requires most of the HDD operations which has to be performed 2  times for each row/column decoding.…”

Section: B Uld Complexitymentioning

confidence: 99%

“…The authors in [5] proposed a low complexity TPC decoder by modifying the HIHO decoder at the expense of a signif cant coding gain reduction. An eff cient TPC decoder [6] is developed by utilizing a low complexity algorithm to compute the syndromes within the HDD process, which is required to generate the candidate codewords. Computing the syndromes recursively enabled reducing the number of operations required to compute the Euclidean distance (ED).…”

Section: Introductionmentioning

confidence: 99%

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Ultra-Light Decoder for Turbo Product Codes

et al. 2018

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Abstract-This work presents a novel low complexity decoder for turbo product codes (TPCs). The new decoder, denoted as ultralight decoder (ULD), can perform soft decision decoding without an algebraic hard decision decoder, which is the core of conventional soft decision decoders of block codes. Moreover, the unique structure of the ULD enables the design of a new approach to compute the minimum Euclidean distance (ED) at each decoding iteration. Therefore, the ULD offers significant complexity and delay reduction as compared to conventional TPC decoders. Reducing the complexity and delay will enable using codes with high code rates to improve the system spectral efficiency, or use powerful codes with low code rates to reduce the transmission power. The system bit error rate (BER) is presented for binary and -ary modulation schemes over additive white Gaussian noise (AWGN) and Rayleigh fading channels. The obtained numerical results show that the ULD offers coding gain that is comparable to conventional TPC decoders under various system and channel conditions, but with significantly lower complexity.

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Section: B Uld Complexitymentioning

confidence: 99%

Section: B Uld Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ultra-Light Decoder for Turbo Product Codes

et al. 2018

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“…In [2], test vector decoding is done in Gray code order, which reduces its complexity to 1 vectormatrix multiplication plus 2 p − 1 vector-vector multiplications in GF (2). In [3], dot product is applied instead of Euclidean distance. Only different bits between a valid code and Y contribute to the metric.…”

Section: Low Complexity Chase Decoding Algorithmmentioning

confidence: 99%

Flexible and high-efficiency turbo product code decoder design

Zhou

Liu

Zhang

2012

IEICE Electron. Express

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This paper presents a flexible and high-efficiency decoder for turbo product code using extended Hamming code. The supported component code ranges from (8, 4) to (128, 120) to provide enough flexibility for various communication standards. A novel Chase decoder architecture is developed with high efficiency using a low complexity algorithm. Moreover, a conflict free interleave memory access model for variable length is provided. A 90 nm standard cell technology shows that the decoder sustains a maximum throughput of 5.6 Gbps and consumes 300 k gates.

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“…For the Chase decoding in Chase-MP algorithm, as suggested in [1], we set its weight factor to be 1/2, and reliability factor to be 1 for every iteration. The number of least reliable positions (LRPs) for the Chase-II algorithm is respectively selected as p = 3, 4, 5.…”

Section: B Simulation Performance With Comparisons To That Of Trellimentioning

confidence: 99%

Improved Iterative Decoding with Analysis for Generalized Low-Density Codes

He¹,

Zhou²,

2006

2006 IEEE Information Theory Workshop - ITW '06 Chengdu

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In this paper, we propose an improved graph-based Chase-MP algorithm for iterative decoding of generalized low-density (GLD) codes, and also present analysis of the iterative decoding by using the Gaussian approximation. The simulation results on the additive white Gaussian noise (AWGN) channel show that the proposed algorithm can achieve good performance. During the iterative decoding analysis, we get a dynamic mathematical model of the Gaussian mean on iterations. As an application of the model derived, we compute decoding thresholds for several extended Bose-Chaudhuri-Hocquengnem (eBCH)-component GLD codes.

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An Efficient Chase Decoder for Turbo Product Codes

Cited by 51 publications

References 7 publications

Ultra-Light Decoder for Turbo Product Codes

Ultra-Light Decoder for Turbo Product Codes

Flexible and high-efficiency turbo product code decoder design

Improved Iterative Decoding with Analysis for Generalized Low-Density Codes

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