A comparison of clustering algorithms for automatic modulation classification

•Clustering algorithms are compared for Automatic Modulation Classification.•An enhanced classification method is proposed and compared to other popular methods.•The proposed method can function with a large pool of possible modulation schemes.•The proposed method is deterministic and does not rely...

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Veröffentlicht in:Expert systems with applications Jg. 151; S. 113317
Hauptverfasser: Mouton, Jacques P., Ferreira, Melvin, Helberg, Albertus S.J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.08.2020
Elsevier BV
Schlagworte:
Algorithms
Automatic modulation classification
Centroid estimation
Centroids
Classification
Cluster analysis
Clustering
Clustering algorithm
Codes
Constellation diagram
I/Q plane
Machine learning
Phase shift keying
Quadrature amplitude modulation
Random noise
Structural hierarchy
Constellation diagram
Centroid estimation
I/Q plane
Clustering algorithm
Automatic modulation classification
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•Clustering algorithms are compared for Automatic Modulation Classification.•An enhanced classification method is proposed and compared to other popular methods.•The proposed method can function with a large pool of possible modulation schemes.•The proposed method is deterministic and does not rely on machine learning.•Algorithm execution time, complexity and accuracy are evaluated. 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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.113317