A comparison of clustering algorithms for automatic modulation classification

Abstract

In this paper, the k-means, k-medoids, fuzzy c-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points To Identify the Clustering Structure (OPTICS), and hierarchical clustering algorithms (with the addition of the elbow method) are examined for the purpose of Automatic Modulation Classification (AMC). This study compares these algorithms in terms of classification accuracy and execution time for either estimating the modulation order, determining centroid locations, or both. The best performing algorithms are combined to provide a simple AMC method which is then evaluated in an Additive White Gaussian Noise (AWGN) channel with M-Quadrature Amplitude Modulation (QAM) and M-Phase Shift Keying (PSK). Such an AMC method does not rely on any thresholds to be set by a human or machine learning algorithm, resulting in a highly flexible system. The proposed method can be configured to not give false positives, making it suitable for applications such as spectrum monitoring and regulatory enforcement.