Performance Analysis for Data Compression Based Signal Classification Methods

Abstract

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.

5 pages submitted to 2010 IEEE International Symposium on Information Theory