This paper formulates the channel equalization problem in the framework of constrained maximum-likelihood estimation. This allows us to highlight key issues including the need to summarize past data and to apply a finite alphabet constraint over a sliding optimization window. The approach adopted here leads to embellishments of the usual (nonadaptive) decision-feedback equalizer and its multistep extensions. It includes a provision for degrees of belief in past estimates, which addresses the problem of error propagation.
IEEE Transactions on Communications Vol. 55, Issue 11, p. 2092-2103