Analysis of Distribution Test-based and Feature-based Approaches toward Automatic Modulation Classification

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.

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Polar Feature Based Deep Architectures for Automatic Modulation Classification Considering Channel Fading

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) ◽

10.1109/globalsip.2018.8646375 ◽

2018 ◽

Cited By ~ 5

Author(s):

Chieh-Fang Teng ◽

Ching-Chun Liao ◽

Chun-Hsiang Chen ◽

An-Yeu Andy Wu

Keyword(s):

Modulation Classification ◽

Channel Fading ◽

Automatic Modulation Classification ◽

Feature Based

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Accuracy Analysis of Feature-Based Automatic Modulation Classification via Deep Neural Network

Sensors ◽

10.3390/s21248252 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8252

Author(s):

Zhan Ge ◽

Hongyu Jiang ◽

Youwei Guo ◽

Jie Zhou

Keyword(s):

Deep Learning ◽

Frequency Offset ◽

Classification Performance ◽

Modulation Classification ◽

Gaussian Channel ◽

Automatic Modulation Classification ◽

Phase Offset ◽

Flat Fading ◽

Feature Based ◽

Non Gaussian

A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of FB-AMC among the most commonly used features, including higher-order cumulants (HOC), features-based fuzzy c-means clustering (FCM), grid-like constellation diagram (GCD), cumulative distribution function (CDF), and raw IQ data. A novel end-to-end modulation classifier based on deep learning, named CCT classifier, which can automatically identify unknown modulation schemes from extracted features using a general architecture, was proposed. Features except GCD are first converted into two-dimensional representations. Then, each feature is fed into the CCT classifier for modulation classification. In addition, Gaussian channel, phase offset, frequency offset, non-Gaussian channel, and flat-fading channel are also introduced to compare the performance of different features. Additionally, transfer learning is introduced to reduce training time. Experimental results showed that the features HOC, raw IQ data, and GCD obtained better classification performance than CDF and FCM under Gaussian channel, while CDF and FCM were less sensitive to the given phase offset and frequency offset. Moreover, CDF was an effective feature for AMC under non-Gaussian and flat-fading channels, and the raw IQ data can be applied to different channels’ conditions. Finally, it showed that compared with the existing CNN and K-S classifiers, the proposed CCT classifier significantly improved the classification performance for MQAM at N = 512, reaching about 3.2% and 2.1% under Gaussian channel, respectively.

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