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Neural Computing and Applications

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19-03-2018 | Original Article

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

Authors: Asad Hussain, M. F. Sohail, Sheraz Alam, Sajjad A. Ghauri, I. M. Qureshi

Published in: Neural Computing and Applications | Issue 10/2019

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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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Title: Classification of M-QAM and M-PSK signals using genetic programming (GP)
Authors: Asad Hussain
M. F. Sohail
Sheraz Alam
Sajjad A. Ghauri
I. M. Qureshi
Publication date: 19-03-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3433-1

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