Automatic Modulation Classification of Real Signals in AWGN Channel Based on Sixth-Order Cumulants

Automatic modulation classification (AMC) represents an important integral part of modern communication systems. While novel AMC algorithms based on complex neural network structures showed significant performance improvements, in practical applications low algorithm complexity of AMC algorithms based on higher-order cumulants still makes them very attractive. AMC algorithm based on sixth- order cumulants showed very good performance in this context, especially when it comes to distinguishing Binary Phase Shift Keying (BPSK) signals from complex constellations. Still, no further analysis of expected performance with other real constellations was presented for this algorithm so far. In this paper, the performance was explored in a wider context of real signals classification, by observing various Pulse Amplitude Modulation (PAM) constellations, whose statistical features are presented for the first time. Their classification performance was tested via Monte Carlo simulations, and explained through the presence of bias under conditions of strong additive white Gaussian noise channel, reported in this paper for real signals for the first time. One new approach in AMC is proposed, which ensures improvement in the classification of real signal constellations. Achieved improvement is confirmed in many Monte Carlo experiments, where the proposed new AMC scheme is tested versus the most popular standard higher-order cumulants-based algorithms.