Modulation Classification Based on Kullback-Leibler Divergence

Abstract

This paper proposes a modulation classification method based on Kullback-Leibler divergence (KLD). The proposed method involves computation of the empirical probability mass functions (PMFs) of the decision statistics and their subsequent comparison with the theoretical PMFs of the decision statistics under each candidate modulation scheme by using KLD. We use quadrature components of the received signal as the decision statistics to compute the PMFs and consider the classification of linear digital modulation schemes such as the phase shift keying and quadrature amplitude modulation schemes in an additive white Gaussian noise channel. Through computer simulations, we show that the proposed KLD-based method outperforms conventional Kolmogorov-Smirnov test-based methods in classification performance.