Moment Analysis of Decision Variables for MAP Decoding

Author

Kaizu, Aki ; Morishima, Yu ; Oka, Ikuo ; Ata, Shingo

Author_Institution

Grad. Sch. of Eng., Osaka City Univ., Osaka, Japan

fYear

2011

fDate

July 31 2011-Aug. 4 2011

Firstpage

1

Lastpage

5

Abstract

For the performance evaluation of convolutional codes, computer simulations are widely used, and few studies have been made for exact error probability of convolutional codes of more than 4-state. In this paper, an analytical approach is presented for moments of decision variables for a maximum a-posteriori probability (MAP) decoding. The moments are derived by recurrence relations. An application of the moments is demonstrated for error probability. In order to see a fast convergence in the moment techniques, a modified MAP is employed. The bit error probability of the modified MAP decoding is expressed by both the Gram-Charlier expansion and the maximum entropy method and the validity of the moments derivation has been confirmed.

Keywords

convolutional codes; error statistics; maximum entropy methods; maximum likelihood decoding; Gram-Charlier expansion; MAP decoding; bit error probability; computer simulation; convolutional code performance evaluation; decision variable moment analysis; maximum a posteriori probability decoding; maximum entropy method; recurrence relation; Convergence; Convolutional codes; Decoding; Entropy; Error probability; Measurement; Probability density function;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Communications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on

Conference_Location

Maui, HI

ISSN

1095-2055

Print_ISBN

978-1-4577-0637-0

Type

conf

DOI

10.1109/ICCCN.2011.6006012

Filename

6006012