Digital Modulation Classification Based On BAT Swarm Optimization and Random Forest


  • Batool abd alhadi sultan College of Science, Department of computer University of Diyala, Diyala, Iraq
  • Taha Mohammed Hasan College of Science, Department of computer University of Diyala, Diyala, Iraq



Classification, Bat Swarm Optimization Algorithm, Random Forests, Automatic Modulation Identification


The applications of digitally modulated signals are still in progress and expansion. Automatic Modulation Identification (AMI) is important to classify the digitally modulated signals ..To get better results of the system suggested optimization the features to discard weak or irrelevant features in the system and keep only strong relevant features .In this work, present hybrid intelligent system for the recognition related to the digitally modulated signals where used . The proposed (AMI) had been built to classify ten most popular schemes of digitally modulated signals, namely (2ASK, , 2PSK, 4PSK, 8PSK, 8QAM,16QAM ,32QAM, 64 QAM, 128QAM, and 256QAM), with the signal to noise ratio ranging from (-2 to 13) dB. High-order cumulants (HOCs) as well as high-order moments (HOMs) were utilized. .In this thesis used , Bat Swarm Optimization (BA).The Random Forest ( RF) classifier was introduced for the first time in this work. Simulation results of the System proposed , under additive white Gaussian noise channel, show that .While algorithm ( BA Swarm Optimization ) for the modulated signals we obtained a classification accuracy of around 92% for the SNR between (-2....12 ) dB.


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How to Cite

sultan, B., & Hasan, T. (2020). Digital Modulation Classification Based On BAT Swarm Optimization and Random Forest. Journal of Kufa for Mathematics and Computer, 7(1), 26–30.

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