Abstract
The expectation-maximization (EM) algorithm was first introduced in the statistics literature as an iterative procedure that under some conditions produces maximum-likelihood (hit) parameter estimates. In this paper we investigate the application of the EM algorithm to sequence estimation in the presence of random disturbances and additive white Gaussian noise. As examples of the use of the EM algorithm, we look at the random-phase and fading channels, and show that a formulation of the sequence estimation problem based on the EM algorithm can provide a means of obtaining ML sequence estimates, a task that has been previously too complex to perform.
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