Abstract
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.
Highlights
Automatic modulation classification (AMC) determines the underlying modulation type of intercepted signals from a given set of modulation schemes [1]
We describe the extraction of various features used in this comparative study, including higher-order cumulants (HOC), features-based fuzzy c-means clustering, grid-like constellation diagram (GCD), cumulative distribution function (CDF), and raw IQ data
The ideal maximum likelihood (ML) classifier is described in Section 3.3, which provides an upper bound of classification performance for the CCT classifier under ideal/non-ideal channel conditions
Summary
Automatic modulation classification (AMC) determines the underlying modulation type of intercepted signals from a given set of modulation schemes [1]. It plays an important role in many fields, such as cognitive radio, software-defined radio, interference identification, and spectrum management. The LB algorithm usually treats AMC as a problem of multiple hypothesis testing It always suffers from high computational complexity, it is the optimal classifier in the Bayesian sense. The FB algorithm usually provides sub-optimal solutions It can be executed efficiently with lower computational complexity compared with the previous algorithm [7]. The FB algorithm has been sufficiently investigated and applied under various scenarios
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