Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers

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@Article{Abdelmutalab:2016:PC,
  author =       "Ameen Abdelmutalab and Khaled Assaleh and 
                 Mohamed El-Tarhuni",
  title =        "Automatic modulation classification based on high
                 order cumulants and hierarchical polynomial
                 classifiers",
  journal =      "Physical Communication",
  volume =       "21",
  pages =        "10--18",
  year =         "2016",
  ISSN =         "1874-4907",
  DOI =          "doi:10.1016/j.phycom.2016.08.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1874490716301094",
  abstract =     "In this paper, a Hierarchical Polynomial (HP)
                 classifier is proposed to automatically classify M-PSK
                 and M-QAM signals in Additive White Gaussian Noise
                 (AWGN) and slow flat fading environments. The system
                 uses higher order cumulants (HOCs) of the received
                 signal to distinguish between the different modulation
                 types. The proposed system divides the overall
                 modulation classification problem into several
                 hierarchical binary sub-classifications. In each binary
                 sub-classification, the HOCs are expanded into a higher
                 dimensional space in which the two classes are linearly
                 separable. It is shown that there is a significant
                 improvement when using the proposed Hierarchical
                 polynomial structure compared to the conventional
                 polynomial classifier. Moreover, simulation results are
                 shown for different block lengths (number of received
                 symbols) and at different SNR values. The proposed
                 system showed an overall improvement in the probability
                 of correct classification that reaches 100percent using
                 only 512 received symbols at 20 dB compared to
                 98percent and 98.33percent when using more complicated
                 systems like Genetic Programming with KNN classifier
                 (GP-KNN) and Support Vector Machines (SVM) classifiers,
                 respectively.",
  keywords =     "genetic algorithms, genetic programming, Modulation
                 classification, Hierarchical polynomial classifiers,
                 High order cumulants, Adaptive modulation",
}

Genetic Programming entries for Ameen Abdelmutalab Khaled Assaleh Mohamed El-Tarhuni

Citations