An efficient maximum-likelihood-decoding algorithm for linear block codes with algebraic decoder

Kaneko, T.; Nishijima, Takeshi; Inazumi, Hiroshige; Hirasawa, Shigeichi

doi:10.1109/18.312155

Cited by 131 publications

(63 citation statements)

References 5 publications

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“…Thus for this code the superset used is {xI ( x is divisible by 2 and 32 ~ x ~ 224), or (x = 0), or (x = 256)}. The simulation results of our hybrid MLSD decoding algorithm for the (256, 139) code for "'(b equal to 5.5 dB, 6.0 dB, 6.5 dB, and 7.0 dB are given in Table 4, where q is set to Table 4 attests to the fact that the complexity of our hybrid MLSD decoding algorithm is much less than that of the MLSD decoder presented in [20). Table 4, the memory requirement of our hybrid MLSD decoding algorithm is small for the (256, 139) code for "'(b greater than or equal to 5.5 dB.…”

Section: Simulation Results For the Awgn Channelmentioning

confidence: 99%

“…In order to compare our hybrid MLSD decoding algorithm with the MLSD decoder presented in (20), we also simulate our hybrid MLSD decoding algorithm for the (256, 139) binary-extended BCH code. Since we do not know the HW for the (256, 139), we use a superset for it.…”

Section: Simulation Results For the Awgn Channelmentioning

confidence: 99%

“…In [28,29,7,20,13,17,11,14] several decoding algorithms for linear block codes were proposed. These algorithms are efficient for codes of short-to-moderate lengths, but become impractical for codes of long lengths transmitted over channels with low SNR; so we still lack an efficient decoding algorithm for long linear block codes.…”

Section: This Paper Deals With Maximum-likelihood Soft-decision (Mlsdmentioning

confidence: 99%

See 2 more Smart Citations

Efficient heuristic search algorithms for soft-decision decoding of linear block codes

Shih¹,

Wulff²,

Hartmann

et al. 1998

IEEE Trans. Inform. Theory

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This paper deals with maximum-likelihood soft-decision decoding as well as suboptimal softdecision decoding of linear block codes. In this paper we present a novel and efficient hybrid decoding algorithm for ( n, k) linear block codes. This algorithm consists of three new decoding algorithms: M A*, H*, and Directed Search. It hybridizes these three algorithms to take advantage of their strengths and make the decoding more efficient. The first algorithm, MA*, is a modified Algorithm A* that conducts a heuristic search through a code tree of the transmitted code when the decoding problem is transformed into a problem of graphsearch through a code tree. M A* takes into consideration more properties of the code and is considerably more efficient than the original A* algorithm presented by Han, Hartmann, and Chen. The second algorithm, H*, is a new decoding algorithm that determines the value of every component of a minimum-cost codeword by estimating the cost of the minimum-cost codeword, which has a fixed value at one of the k most reliable, linearly independent bit positions when the decoding problem is transformed into a minimum-cost problem among all codewords of the transmitted code. The suboptimal version of this algorithm can be incorporated with other decoding algorithms to reduce the search space during the decoding process. The third algorithm, Directed Search, is a novel heuristic approach designed to enhance the performance of soft-decision decoding by searching in continuous space. This approach explores the search space between a given vector and the received vector and finds the closest codeword to the received vector in the space explored. Simulation results for this hybrid algorithm are presented for the {128, 64), the (256, 131), and the (256, 139) binaryextended BCH codes. This hybrid algorithm can efficiently decode the {128, 64) code for any signal-to-noise ratio and has near-optimal to optimal performance. Previously, no practical decoder could have decoded this code with such a performance for all ranges of signal-to-noise ratio.

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Section: Simulation Results For the Awgn Channelmentioning

confidence: 99%

Section: Simulation Results For the Awgn Channelmentioning

confidence: 99%

Section: This Paper Deals With Maximum-likelihood Soft-decision (Mlsdmentioning

confidence: 99%

See 1 more Smart Citation

Efficient heuristic search algorithms for soft-decision decoding of linear block codes

Shih¹,

Wulff²,

Hartmann

et al. 1998

IEEE Trans. Inform. Theory

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show abstract

“…Next, this proposed decoding method exceeding the BCH bound is applied to soft-decision decoding methods that apply the limit-distance decoding method multiple times, especially algorithm 2 for Chase decoding [3], the TK decoding method [9], and the KNIH decoding method [5,13]. It is shown that the number of calculations of limit-distance decoding, representing the primary computational volume, can be reduced substantially without degrading the decoding error rate of the conventional decoding method.…”

Section: Introductionmentioning

confidence: 99%

On decoding methods beyond the BCH bound and their applications to soft-decision decoding

Kobayashi

Matsushima

Hirasawa

1999

Electron. Comm. Jpn. Pt. III

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“…With the development of sampling and signal processing techniques, the soft output of the channel has become available. Many soft-decision-based decoding algorithms have been applied to the error-correcting codes [25][26][27][28][29][30] and yield better decoding performances than hard decision algorithms. Some blind frame synchronization techniques for error correcting codes also utilize the soft output of the channel to improve the synchronization performances [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Information-Dispersion-Entropy-Based Blind Recognition of Binary BCH Codes in Soft Decision Situations

Zhou

Huang

Liu

et al. 2013

Entropy

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A method of blind recognition of the coding parameters for binary Bose-Chaudhuri-Hocquenghem (BCH) codes is proposed in this paper. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary BCH codes, while the coding parameters are unknown. The problem can be addressed on the context of the non-cooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. The recognition processing includes two major procedures: code length estimation and generator polynomial reconstruction. A hard decision method has been proposed in a previous literature. In this paper we propose the recognition approach in soft decision situations with Binary-Phase-Shift-Key modulations and Additive-White-Gaussian-Noise (AWGN) channels. The code length is estimated by maximizing the root information dispersion entropy function. And then we search for the code roots to reconstruct the primitive and generator polynomials. By utilizing the soft output of the channel, the recognition performance is improved and the simulations show the efficiency of the proposed algorithm.

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An efficient maximum-likelihood-decoding algorithm for linear block codes with algebraic decoder

Cited by 131 publications

References 5 publications

Efficient heuristic search algorithms for soft-decision decoding of linear block codes

Efficient heuristic search algorithms for soft-decision decoding of linear block codes

On decoding methods beyond the BCH bound and their applications to soft-decision decoding

Information-Dispersion-Entropy-Based Blind Recognition of Binary BCH Codes in Soft Decision Situations

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