Asymptotic Properties of Likelihood Based Linear Modulation Classification Systems

TL;DR

This paper analyzes the asymptotic behavior of likelihood-based linear modulation classifiers, showing that hybrid likelihood ratio tests achieve vanishing error probabilities in single-sensor settings, with conditions for multi-sensor fusion also derived.

Contribution

It provides the first asymptotic analysis of HLRT and ALRT classifiers in both single and multi-sensor settings, establishing conditions for error probability decay.

Findings

HLRT achieves asymptotically zero error probability in single-sensor systems.

Conditions are derived for error probability vanishing in multi-sensor fusion.

Asymptotic analysis of independent sensor decision fusion is presented.

Abstract

The problem of linear modulation classification using likelihood based methods is considered. Asymptotic properties of most commonly used classifiers in the literature are derived. These classifiers are based on hybrid likelihood ratio test (HLRT) and average likelihood ratio test (ALRT), respectively. Both a single-sensor setting and a multi-sensor setting that uses a distributed decision fusion approach are analyzed. For a modulation classification system using a single sensor, it is shown that HLRT achieves asymptotically vanishing probability of error (Pe) whereas the same result cannot be proven for ALRT. In a multi-sensor setting using soft decision fusion, conditions are derived under which Pe vanishes asymptotically. Furthermore, the asymptotic analysis of the fusion rule that assumes independent sensor decisions is carried out.