Low-complexity ML decoding for convolutional tail-biting codes

Author

Pai, Hung-Ta ; Han, Yunghsiang S. ; Wu, Ting-Yi ; Chen, Po-Ning ; Shieh, Shin-Lin

Author_Institution

Grad. Inst. of Commun. Eng., Nat. Taipei Univ., Taipei

Volume

12

Issue

12

fYear

2008

fDate

12/1/2008 12:00:00 AM

Firstpage

883

Lastpage

885

Abstract

Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting codes. 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 A* algorithm has over 5 times less average decoding complexity in the second phase when E_b/N₀ges 4 dB.

Keywords

Viterbi decoding; computational complexity; convolutional codes; maximum likelihood decoding; trellis codes; A* algorithm; Viterbi algorithm; convolutional tail-biting codes; low-complexity ML decoding; maximum-likelihood decoding algorithm; trellis information; Computational complexity; Convolution; Convolutional codes; Councils; Degradation; Maximum likelihood decoding; Performance loss; Signal to noise ratio; Viterbi algorithm; Viterbi algorithm, maximum-likelihood, tailbiting codes, algorithm A*;

fLanguage

English

Journal_Title

Communications Letters, IEEE

Publisher

ieee

ISSN

1089-7798

Type

jour

DOI

10.1109/LCOMM.2008.072181

Filename

4720239