Title :
MAP decoding in channels with memory
Author_Institution :
Dept. of Commun. Res., AT&T Labs.-Res., Florham Park, NJ, USA
fDate :
5/1/2000 12:00:00 AM
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;
Journal_Title :
Communications, IEEE Transactions on
