Enhanced modulation classification algorithm based on Kolmogorov-Smirnov test

Abstract

We propose an enhanced automatic modulation classification algorithm based on Kolmogorov-Smirnov test. The proposed classifier employs the real and imaginary components extracted from the received signal as separate decision statistics. Also, unlike the conventional K-S test based algorithm, mean square error (MSE) between the empirical cumulative distribution and the hypothesized distribution for each modulation candidate is evaluated in the proposed algorithm. Simulation results show that the proposed algorithm provides better classification performance than the conventional K-S test based algorithm in an additive white Gaussian noise (AWGN)