Evaluation of Machine Learning Algorithms for Automatic Modulation Recognition

  • Muhammed Abdurrahman Hazar
  • Niyazi Odabaşioğlu
  • Tolga EnsariEmail author
  • Yusuf Kavurucu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)


Automatic modulation recognition (AMR) becomes more important because of 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 tried for this problem. In this study, we evaluate performance of different machine learning algorithms for AMR. Specifically, we propose nonnegative matrix factorization (NMF) technique and additionally we evaluate performance of artificial neural networks (ANN), support vector machines (SVM), random forest tree, k-nearest neighbor (k-NN), Hoeffding tree, logistic regression and Naive Bayes methods to obtain comparative results. These are most preferred feature extraction methods in the literature and they are used for a set of modulation types for general-purpose communication. We compare their recognition performance in accuracy metric. Additionally, we prepare and donate the first data set to University of California-Machine Learning Repository related with AMR.


Automatic modulation recognition Nonnegative matrix factorization Artificial neural networks Support vector machines Random forest tree K-nearest neighbor Hoeffding tree Naive Bayes Logistic regression 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Muhammed Abdurrahman Hazar
    • 1
  • Niyazi Odabaşioğlu
    • 1
  • Tolga Ensari
    • 2
    Email author
  • Yusuf Kavurucu
    • 3
  1. 1.Electrical and Electronics EngineeringIstanbul UniversityIstanbulTurkey
  2. 2.Computer EngineeringIstanbul UniversityIstanbulTurkey
  3. 3.Computer EngineeringTurkish Naval AcademyIstanbulTurkey

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