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Neural Computing and Applications

, Volume 31, Issue 10, pp 6141–6149 | Cite as

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

  • Asad Hussain
  • M. F. Sohail
  • Sheraz Alam
  • Sajjad A. GhauriEmail author
  • I. M. Qureshi
Original Article

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.

Keywords

Genetic Programming Higher-order cumulants K-nearest neighbor M-QAM M-PSK 

Notes

Compliance with ethical standards

Conflict of interest

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

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Asad Hussain
    • 1
  • M. F. Sohail
    • 1
  • Sheraz Alam
    • 1
    • 2
  • Sajjad A. Ghauri
    • 4
    Email author
  • I. M. Qureshi
    • 3
  1. 1.National University of Modern LanguagesIslamabadPakistan
  2. 2.International Islamic UniversityIslamabadPakistan
  3. 3.AIR UniversityIslamabadPakistan
  4. 4.Department of electrical engineeringISRA UniversityIslamabadPakistan

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