Abstract
A new blind channel estimation algorithm is presented. This algorithm comes from the well-known maximum likelihood estimation approach. However, we intentionally smooth the joint probability density function (PDF) of a finite set of observations in order to reduce the computational burden. As a result, we obtain an online clustering algorithm whose main characteristic is the constraint of symmetry among cluster centers. Computational simulations are used to evaluate this algorithm.
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