Modeling error sources in digital channels

A modified Baum-Welch algorithm is developed and applied to estimating parameters of error source models that belong to the class of hidden Markov models (HMM). Such models arise in the description of bursty error statistics in communication channels. A key element used repeatedly for estimating parameters of such models is the computation of the likelihood of given sequences of observations. Several recursive methods are available for efficiently computing this likelihood. However, even recursive methods can require prohibitive amounts of computation if the observation sequences are very long. Modifications of the Baum-Welch reestimation algorithm that significantly reduces the computational requirements when the observation sequences contain long stretches of identical observations are discussed. The algorithms are used here to estimate parameters of a binary error source model using the results of computer simulation