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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2017

29.05.2017

A Novel Sparse Classifier for Automatic Modulation Classification using Cyclostationary Features

verfasst von: Udit Satija, Barathram Ramkumar, M. Sabarimalai Manikandan

Erschienen in: Wireless Personal Communications | Ausgabe 3/2017

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Abstract

Automatic modulation classification plays a key role in cognitive radio for recognizing the modulation scheme. In this paper, we propose a new classifier based on sparse signal decomposition using an overcomplete composite dictionary (constructed using cyclostationary coefficients) for the classification of modulation format of primary user or to identify noise. The basic principle of the classifier is to classify the received signal modulation format based on reconstructed sparse coefficients after solving \(l_1\) norm minimization using the overcomplete dictionary. Then, relative energies of reconstructed sparse coefficients are compared for recognition of modulation format of the received signal. It is a promising candidate for the cognitive radio due to its robust classification ability. The performance of the proposed classifier is compared with other well known classifiers available in literature. Results show the superiority of the proposed classifier over other classifiers.
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Metadaten
Titel
A Novel Sparse Classifier for Automatic Modulation Classification using Cyclostationary Features
verfasst von
Udit Satija
Barathram Ramkumar
M. Sabarimalai Manikandan
Publikationsdatum
29.05.2017
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2017
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-4435-5

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