MAP decoding in channels with memory

Author

Turin, William

Author_Institution

Dept. of Commun. Res., AT&T Labs.-Res., Florham Park, NJ, USA

Volume

48

Issue

5

fYear

2000

fDate

5/1/2000 12:00:00 AM

Firstpage

757

Lastpage

763

Abstract

The expectation-maximization (EM) algorithm is popular in estimating the parameters of various statistical models. We consider applications of the EM algorithm to the maximum a posteriori (MAP) sequence decoding assuming that sources and channels are described by hidden Markov models (HMMs). The HMMs can accurately approximate a large variety of communication channels with memory and, in particular, wireless fading channels with noise. The direct maximization of the a posteriori probability (APP) is too complex. The EM algorithm allows us to obtain the MAP sequence estimation iteratively. Since each step of the EM algorithm increases the APP, the algorithm can improve the performance of any decoding procedure

Keywords

Viterbi decoding; block codes; convolutional codes; fading channels; hidden Markov models; iterative decoding; optimisation; parameter estimation; sequential decoding; EM algorithm; HMM; MAP decoding; TCM; Viterbi algorithm; a posteriori probability; block codes; communication channels; convolutional codes; decoding performance; expectation-maximization algorithm; hidden Markov models; iterative MAP sequence estimation; maximum a posteriori sequence decoding; memory; noise; parameter estimation; statistical models; trellis coded modulator; wireless fading channels; Communication channels; Fading; Gaussian processes; Hidden Markov models; Interference; Iterative algorithms; Iterative decoding; Parameter estimation; Viterbi algorithm; Wireless communication;

fLanguage

English

Journal_Title

Communications, IEEE Transactions on

Publisher

ieee

ISSN

0090-6778

Type

jour

DOI

10.1109/26.843188

Filename

843188