AUTOMATIC MODULATION CLASSIFIER: REVIEW

Abstract

The automatic modulation classification (AMC) is highly important to develop intelligent receivers in different military and civilian applications including signal intelligence, spectrum management, surveillance, signal confirmation, monitoring, interference identification, as well as counter channel jamming. Clearly, without knowing much information related to transmitted data and various indefinite parameters at receiver, like timing information, carrier frequency, signal power, phase offsets, and so on, the modulation’s blind identification has been a hard task in the real world situations with multi-path fading, frequency-selective in addition to the time-varying channels. There are 2 methods could be utilized to decide the classification signal technique: Feature-based (FB) approach and the Maximum likelihood functions (LB) method. With regard to the FB (referred to as pattern-recognition) classification method used in the study. In the presented work, thorough study is provided to find easy method to identify and classify the digital modulation signals at low SNRs. Spectral-based features, high-order statistic features, wavelet-based features, also cyclic features on the basis of cyclostationary typically utilized to determine and discriminate modulation types have been examined. The number of the classifiers which have been utilized in the process of discrimination have been studied thoroughly and compared for helping researchers in determining and finding the drawbacks with pattern-recognition according to past works. The presented study serving as guide with regard to studies of AMC for determining adequate algorithms and features.

Keywords

• Linear classification schemes, Genetic algorithm, Automatic modulation classification; k-nearest neighbor; feature-based; likelihood-based; artificial neural networks; higher-order statistical; support vector machine; linear regression; continuous wavelet transform; genetic programming; fast Fourier transform.Linear classification schemes, Genetic algorithm, Automatic modulation classification; k-nearest neighbor; feature-based; likelihood-based; artificial neural networks; higher-order statistical; support vector machine; linear regression; continuous wavelet transform; genetic programming; fast Fourier transform.