AUTOMATIC MODULATION CLASSIFIER: A REVIEW

Abstract

The automatic modulation classification (AMC) is highly important to develop intelligent receivers indifferent military and civilian applications including signal intelligence, spectrum management, surveillance, signalconfirmation, monitoring, interference identification, as well as counter channel jamming. Clearly, without knowingmuch information related to transmitted data and various indefinite parameters at receiver, like timing information,carrier frequency, signal power, phase offsets, and so on, the modulations blind identification has been a hard taskin 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) approachand 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 methodto identify and classify the digital modulation signals at low SNRs. Spectral-based features, high-order statisticfeatures, wavelet-based features, also cyclic features on the basis of cyclostationary typically utilized to determineand discriminate modulation types have been examined. The number of the classifiers which have been utilized inthe process of discrimination have been studied thoroughly and compared for helping researchers in determiningand finding the drawbacks with pattern-recognition according to past works. The presented study serving as guidewith regard to studies of AMC for determining adequate algorithms and features.