Abstract
Automatic Modulation Classification of Real Signals in AWGN Channel Based on Sixth-Order Cumulants
Highlights
Automatic modulation classification (AMC) is a technique commonly connected with wireless systems and applications, standing for modulation format recognition process and further demodulation of a priori unknown signals at the receiver side
Sixth-order cumulants showed significantly better performance than fourth-order cumulants [12]. They show to be superior in low complexity, memory requirements, and inference time when compared with other up-to-date AMC algorithms, like neural networks – but requiring additional performance improvements to remain competitive with those algorithms [5]
When it comes to real signal constellations, those are quite rarely considered in research, with some exceptions: Binary Phase Shift Keying (BPSK) signals are included in many research works; Pulse Amplitude Modulation (PAM) formats from PAM-4 to PAM-64 were considered in classical work of Swami [13] under fourthorder cumulants AMC, and in [2] under complex network classifier; PAM-4 signals were considered in [14]
Summary
Automatic modulation classification (AMC) is a technique commonly connected with wireless systems and applications, standing for modulation format recognition process and further demodulation of a priori unknown signals at the receiver side. In this paper we discuss the classification performance of classical sixth-order cumulants-based AMC algorithm for a wide set of PAM constellations, present their error variances and parameters for efficiency in distinguishing particular constellations, for the first time. These parameters are compared with those corresponding with fourth-order cumulants-based AMC algorithm, under the same set of PAM constellations, and tested in Monte Carlo simulations.
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