Soft-decision decoding of linear block codes based on ordered statistics

Fossorier, M.P.C.; Lin, Shu

doi:10.1109/18.412683

Cited by 547 publications

(298 citation statements)

References 24 publications

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“…If we compare the proposed algorithm with the Ordered Statistics Decoder (OSD) presented in [21], the main similarity is that both algorithms operate on the most reliable basis (first most reliable independent bit positions), which are then used to regenerate a codeword from the received word. The main differences between the two algorithms are how the candidate words are generated and the stop criteria.…”

Section: B Proposed Algorithm Complexitymentioning

confidence: 99%

Adaptive Decoding of Binary Linear Block Codes Using Information Sets and Erasures

Godoy

Wille

Cunha

2010

2010 Third International Conference on Communication Theory, Reliability, and Quality of Service

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This paper proposes an information-set based softdecision decoding algorithm for binary linear block codes. In this new algorithm a list of symbol positions that are able to generate a valid information set is obtained, in advance, by using pre-processing. During the decoding process, the algorithm reconstructs a code-word by means of the information sets from the list. Early termination of the decoding process is based on the acceptance criterion of Barros, Godoy and Wille (BGW). If the BGW criterion was not satisfied, the candidate codeword is changed via bit exchanges (erasures) and reconstructed in order to identify the wrong bits. The performance of the proposed algorithm is quite favorable, being comparable to the maximum-likelihood decoding performance, as shown by results obtained from computer simulation. This paper also demonstrates the equivalency between the BGW criterion and the Taipale and Pursley criterion, proposed independently in 1993 and in 1989 respectively.

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Section: B Proposed Algorithm Complexitymentioning

confidence: 99%

Adaptive Decoding of Binary Linear Block Codes Using Information Sets and Erasures

Godoy

Wille

Cunha

2010

2010 Third International Conference on Communication Theory, Reliability, and Quality of Service

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“…Deducing from the properties of ordered statistics [5], [13], the probability of S M to be the minimum sufficient set decreases quickly as M increases. As a result, M shall be a relatively small value.…”

Section: A Stage 1: Minimum Sufficient Set and Estimationmentioning

confidence: 99%

“…The closer is the codeword to the optimal one, the closer the estimation to S min . Meanwhile, both the Chase-III algorithm [3] and the 1-OSD algorithm [5] are able to yield a codeword close to the optimal one with a low computational load. Putting them together, we first pick the best among all the codewords produced from the two algorithms, by choosing the one that has the minimum Euclidean distance to the received vector r. We then apply Theorem 2 on this good-quality codeword to generate a sufficient set close to S min .…”

Section: A Stage 1: Minimum Sufficient Set and Estimationmentioning

confidence: 99%

“…Based on the ordered statistics of the received data, the third set of algorithms [5], [6] achieves good decoding performance with low complexity without resorting to algebraic decoding. Ordered statistics decoding (OSD) was first introduced in [5], and the improved version of this algorithm was presented later 1 This work was partially supported by UCSC COR grant. in [6] to further reduce the decoding complexity via iterative reprocessing.…”

Section: Introductionmentioning

confidence: 99%

“…Ordered statistics decoding (OSD) was first introduced in [5], and the improved version of this algorithm was presented later 1 This work was partially supported by UCSC COR grant. in [6] to further reduce the decoding complexity via iterative reprocessing. On the other hand, the decoding complexity of ordered processing grows exponentially in the order; thus OSD may still suffer from high decoding complexity as higher-order processing is favored over low-order processing for performance consideration.…”

Section: Introductionmentioning

confidence: 99%

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A new adaptive two-stage maximum-likelihood decoding algorithm for linear block codes

Sadjadpour

Tian

2004

2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577)

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This paper presents a new maximum-likelihood (ML) decoding algorithm for linear block codes. In this algorithm, the optimal performance is achieved at low computational complexity through a two-stage processing. At the first stage, a minimum sufficient setŜ that includes the optimal solution is estimated. With the minimum sufficient set, the decoding complexity can be greatly reduced without performance degradation. At the second stage, ordered processing is performed within the estimated minimum sufficient setŜ to obtain the optimal solution. During the ordered processing,Ŝ is adaptively updated to minimize the computational complexity, and an effective stopping criterion is used to decide whether the optimal solution is found. Ordered processing not only helps to find the optimal solution quickly, but also enables simplified sub-optimal solutions with bounded block error rates. The proposed algorithm is also extended to decode Block Turbo codes. Finally, simulation results are given to show that this algorithm achieves optimal performance with a low average computational complexity.

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