2003Unpublished venueRequires access

MAP decoding using the EM algorithm

William Turin

Open publisher page 1 citations

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.

About this research paper

What this paper is about

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.

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available 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.

Key concepts: Maximum a posteriori estimation, Expectation–maximization algorithm, Decoding methods, Hidden Markov model, Fading, Algorithm, Computer science, A priori and a posteriori

Related papers

Back to paper searchBrowse research topicsOriginal source
MAP decoding using the EM algorithm — Research Paper | ScholarLens