A maximum-likelihood soft-decision sequential decoding algorithm for binary convolutional codes

Author

Han, Yunghsiang S. ; Chen, Po-Ning ; Wu, Hong-Bin

Author_Institution

Dept. of Comput. Sci. & Inf. Eng., Nat. Chi Nan Univ., Nan Tou, Taiwan

Volume

50

Issue

2

fYear

2002

fDate

2/1/2002 12:00:00 AM

Firstpage

173

Lastpage

178

Abstract

We present a trellis-based maximum-likelihood soft-decision sequential decoding algorithm (MLSDA) for binary convolutional codes. Simulation results show that, for (2, 1, 6) and (2, 1, 16) codes antipodally transmitted over the AWGN channel, the average computational effort required by the algorithm is several orders of magnitude less than that of the Viterbi algorithm. Also shown via simulations upon the same system models is that, under moderate SNR, the algorithm is about four times faster than the conventional sequential decoding algorithm (i.e., stack algorithm with Fano metric) having comparable bit-error probability

Keywords

AWGN channels; binary codes; convolutional codes; error statistics; maximum likelihood decoding; sequential decoding; AWGN channel; Fano metric; SNR; Viterbi algorithm; binary convolutional codes; bit-error probability; maximum-likelihood soft-decision sequential decoding; simulation results; stack algorithm; trellis-based maximum likelihood decoding algorithm; AWGN channels; Additive white noise; Computational complexity; Computational modeling; Convolutional codes; Costs; Helium; Maximum likelihood decoding; Noise reduction; Viterbi algorithm;

fLanguage

English

Journal_Title

Communications, IEEE Transactions on

Publisher

ieee

ISSN

0090-6778

Type

jour

DOI

10.1109/26.983310

Filename

983310