Performance Analysis for Data Compression Based Signal Classification Methods

TL;DR

This paper provides an information theoretic analysis of a blind signal classification algorithm, demonstrating its equivalence to a MAP estimator and establishing bounds on model estimation errors.

Contribution

It introduces a theoretical framework for analyzing signal classification algorithms using information theory and proves convergence properties of the estimated models.

Findings

The algorithm is equivalent to a MAP estimator based on parametric models.

A lower bound on the error exponents of model estimation is derived.

Estimated model parameters converge to true parameters with small bias.

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.