Low-complexity ML decoding for convolutional tail-biting codes

Hung Ta Pai; Yungh Siang Han; Ting Yi Wu; Po-Ning Chen; Shin Lin Shieh

doi:10.1109/LCOMM.2008.072181

Low-complexity ML decoding for convolutional tail-biting codes

Hung Ta Pai^*, Yungh Siang Han, Ting Yi Wu, Po-Ning Chen, Shin Lin Shieh

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

26 Scopus citations

Abstract

Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting codes [1]. The first phase applies the Viterbi algorithm to obtain the trellis information, and then the second phase employs the algorithm A* to find the ML solution. In this work, we improve the complexity of the algorithm A* by using a new evaluation function. Simulations showed that the improved Av algorithm has over 5 times less average decoding complexity in the second phase when E_b/N₀≥ 4 dB.

Original language	English
Pages (from-to)	883-885
Number of pages	3
Journal	IEEE Communications Letters
Volume	12
Issue number	12
DOIs	https://doi.org/10.1109/LCOMM.2008.072181
State	Published - 2008

Keywords

Algorithm A*
Maximum-likelihood
Tailbiting codes
Viterbi algorithm

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10.1109/LCOMM.2008.072181

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title = "Low-complexity ML decoding for convolutional tail-biting codes",

abstract = "Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting codes [1]. The first phase applies the Viterbi algorithm to obtain the trellis information, and then the second phase employs the algorithm A* to find the ML solution. In this work, we improve the complexity of the algorithm A* by using a new evaluation function. Simulations showed that the improved Av algorithm has over 5 times less average decoding complexity in the second phase when Eb/N0≥ 4 dB.",

keywords = "Algorithm A*, Maximum-likelihood, Tailbiting codes, Viterbi algorithm",

author = "Pai, {Hung Ta} and Han, {Yungh Siang} and Wu, {Ting Yi} and Po-Ning Chen and Shieh, {Shin Lin}",

year = "2008",

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language = "English",

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T1 - Low-complexity ML decoding for convolutional tail-biting codes

AU - Pai, Hung Ta

AU - Han, Yungh Siang

AU - Wu, Ting Yi

AU - Chen, Po-Ning

AU - Shieh, Shin Lin

PY - 2008

Y1 - 2008

N2 - Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting codes [1]. The first phase applies the Viterbi algorithm to obtain the trellis information, and then the second phase employs the algorithm A* to find the ML solution. In this work, we improve the complexity of the algorithm A* by using a new evaluation function. Simulations showed that the improved Av algorithm has over 5 times less average decoding complexity in the second phase when Eb/N0≥ 4 dB.

AB - Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting codes [1]. The first phase applies the Viterbi algorithm to obtain the trellis information, and then the second phase employs the algorithm A* to find the ML solution. In this work, we improve the complexity of the algorithm A* by using a new evaluation function. Simulations showed that the improved Av algorithm has over 5 times less average decoding complexity in the second phase when Eb/N0≥ 4 dB.

KW - Algorithm A

KW - Maximum-likelihood

KW - Tailbiting codes

KW - Viterbi algorithm

UR - http://www.scopus.com/inward/record.url?scp=58149496626&partnerID=8YFLogxK

U2 - 10.1109/LCOMM.2008.072181

DO - 10.1109/LCOMM.2008.072181

M3 - Article

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SN - 1089-7798

VL - 12

SP - 883

EP - 885

JO - IEEE Communications Letters

JF - IEEE Communications Letters

IS - 12

ER -

Low-complexity ML decoding for convolutional tail-biting codes

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Keywords

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