MAP decoding using the EM algorithm
William Turin
Abstract
William Turin
Abstract
The expectation-maximization (EM) algorithm is popular in estimating parameters of various statistical models. We consider applications of the EM algorithm to the maximum a posteriori (MAP) sequence decoding in channels described by hidden Markov models (HMMs). It is well known that HMMs can accurately approximate large variety of communication channels with memory and in particular, wireless fading channels with noise. The direct maximization of the a posteriori probability is too complex. The EM algorithm allows us to obtain the MAP sequence estimation iteratively.
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The expectation-maximization (EM) algorithm is popular in estimating parameters of various statistical models. We consider applications of the EM algorithm to the maximum a posteriori (MAP) sequence decoding in channels described by hidden Markov models (HMMs). It is well known that HMMs can accurately approximate large variety of communication channels with memory and in particular, wireless fading channels with noise. The direct maximization of the a posteriori probability is too complex. The EM algorithm allows us to obtain the MAP sequence estimation iteratively.
Key concepts: Maximum a posteriori estimation, Expectation–maximization algorithm, Decoding methods, Hidden Markov model, Fading, Algorithm, Computer science, A priori and a posteriori