Title :
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
fDate :
July 31 2011-Aug. 4 2011
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;
Conference_Titel :
Computer Communications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on
Conference_Location :
Maui, HI
Print_ISBN :
978-1-4577-0637-0
DOI :
10.1109/ICCCN.2011.6006012
