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Classification of M-QAM and M-PSK signals using genetic programming (GP)

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Abstract

With the popularity of software-defined radio and cognitive radio-technologies in wireless communication, radio frequency devices have to adapt to changing conditions and adjust its transmitting parameters such as transmitting power, operating frequency, and modulation schemes. Thus, automatic modulation classification becomes an essential feature for such scenarios where the receiver has a little or no knowledge about the transmitter parameters. This paper presents kth nearest neighbor (KNN)-based classification of M-QAM and M-PSK modulation schemes using higher-order cumulants as input features set. Genetic programming is used to enhance the performance of the KNN classifier by creating super features from the data set. Simulation result shows improved accuracy at comparatively lower signal-to-noise ratio for all the considered modulations.

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Authors and Affiliations

  1. National University of Modern Languages, Islamabad, 44000, Pakistan

    Asad Hussain, M. F. Sohail & Sheraz Alam

  2. International Islamic University, Islamabad, 44000, Pakistan

    Sheraz Alam

  3. AIR University, Islamabad, 44000, Pakistan

    I. M. Qureshi

  4. Department of electrical engineering, ISRA University, Islamabad, Pakistan

    Sajjad A. Ghauri

Authors
  1. Asad Hussain
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  2. M. F. Sohail
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  3. Sheraz Alam
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  4. Sajjad A. Ghauri
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  5. I. M. Qureshi
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Correspondence to Sajjad A. Ghauri.

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The authors declare that there is no conflict of interests regarding the publication of this paper.

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Hussain, A., Sohail, M.F., Alam, S. et al. Classification of M-QAM and M-PSK signals using genetic programming (GP). Neural Comput & Applic 31, 6141–6149 (2019). https://doi.org/10.1007/s00521-018-3433-1

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  • DOI: https://doi.org/10.1007/s00521-018-3433-1

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