Performance analysis and improvement of machine learning algorithms for automatic modulation recognition over Rayleigh fading channels

HAZAR M. A. , Odabasioglu N., Ensari T., Kavurucu Y., Sayan O. F.

NEURAL COMPUTING & APPLICATIONS, vol.29, no.9, pp.351-360, 2018 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 9
  • Publication Date: 2018
  • Doi Number: 10.1007/s00521-017-3040-6
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.351-360

Abstract

Automatic modulation recognition (AMR) is becoming more important because it is usable in advanced general-purpose communication such as, cognitive radio, as well as, specific applications. Therefore, developments should be made for widely used modulation types; machine learning techniques should be employed for this problem. In this study, we have evaluated performances of different machine learning algorithms for AMR. Specifically, we have evaluated performances of artificial neural networks, support vector machines, random forest tree, k-nearest neighbor, Hoeffding tree, logistic regression, Naive Bayes and Gradient Boosted Regression Tree methods to obtain comparative results. The most preferred feature extraction methods in the literature have been used for a set of modulation types for general-purpose communication. We have considered AWGN and Rayleigh channel models evaluating their recognition performance as well as having made recognition performance improvement over Rayleigh for low SNR values using the reception diversity technique. We have compared their recognition performance in the accuracy metric, and plotted them as well. Furthermore, we have served confusion matrices for some particular experiments.

Automatic modulation recognition (AMR) is becoming more important because it is usable in advanced general-purpose communication such as, cognitive radio, as well as, specific applications. Therefore, developments should be made for widely used modulation types; machine learning techniques should be employed for this problem. In this study, we have evaluated performances of different machine learning algorithms for AMR. Specifically, we have evaluated performances of artificial neural networks, support vector machines, random forest tree, k-nearest neighbor, Hoeffding tree, logistic regression, Naive Bayes and Gradient Boosted Regression Tree methods to obtain comparative results. The most preferred feature extraction methods in the literature have been used for a set of modulation types for general-purpose communication. We have considered AWGN and Rayleigh channel models evaluating their recognition performance as well as having made recognition performance improvement over Rayleigh for low SNR values using the reception diversity technique. We have compared their recognition performance in the accuracy metric, and plotted them as well. Furthermore, we have served confusion matrices for some particular experiments.