  In this paper, we present an information theoretic analysis of the blind
signal classification algorithm. We show that the algorithm is equivalent to a
Maximum A Posteriori (MAP) estimator based on estimated parametric probability
models. We prove a lower bound on the error exponents of the parametric model
estimation. It is shown that the estimated model parameters converge in
probability to the true model parameters except some small bias terms.
