Optimal Discriminant Functions Based On Sampled Distribution Distance for Modulation Classification

TL;DR

This paper introduces an optimal, low-complexity modulation classification method using sampled distribution distances and testpoint optimization, achieving minimal error rates based on Bayesian decision criteria.

Contribution

It develops a novel approach for modulation classification that optimally utilizes distribution distances at testpoints, improving accuracy over existing methods.

Findings

Significant reduction in classification error compared to prior methods

Optimal testpoint placement enhances classification performance

Method asymptotically achieves the minimum possible error

Abstract

In this letter, we derive the optimal discriminant functions for modulation classification based on the sampled distribution distance. The proposed method classifies various candidate constellations using a low complexity approach based on the distribution distance at specific testpoints along the cumulative distribution function. This method, based on the Bayesian decision criteria, asymptotically provides the minimum classification error possible given a set of testpoints. Testpoint locations are also optimized to improve classification performance. The method provides significant gains over existing approaches that also use the distribution of the signal features.