Carrier frequency estimation and automatic digital modulation classification using improved zero-crossing method and statistical features

Automatic modulation classification is an intermediate step between signal detection and demodulation, and is also considered as one of the main parts of modern telecommunication receivers. It can play a vital role in many civil and military applications. In most previous studies, it has been assumed that the carrier frequency is known. It may be far from the realistic condition. In this study, it is assumed that there is no information in the receiver about the central frequencies of the received signals and the purpose is to estimate the carrier frequency and classify the type of digital modulation. For this purpose, a two-step algorithm is proposed. In the first step, the carrier frequency is initially estimated based on zero-crossing method. Then, in the second step, a gradient descent algorithm is used to maximize the mean of I/Q demodulator output with the initial point achieved by zero-crossing method. For the classification process, efficient statistical features are first extracted based on moments and cumulants for all modulated signals. After that, the classical linear discriminant analysis (LDA) and principle component analysis (PCA) methods are used to reduce the features dimension and then the modulated signals are classified. The simulation results show that the proposed algorithm estimates the carrier frequency of the signals with much less error than the ZC benchmark algorithm. In addition, it performs well in classifying modulated signals.