Open AccessKNN BASED CLASSIFICATION OF DIGITAL MODULATED SIGNALS
Author(s) -
Sajjad A. Ghauri
Publication year - 2016
Publication title -
iium engineering journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.141
H-Index - 6
eISSN - 2289-7860
pISSN - 1511-788X
DOI - 10.31436/iiumej.v17i2.641
Subject(s) - phase shift keying , quadrature amplitude modulation , demodulation , qam , amplitude and phase shift keying , computer science , modulation (music) , binary offset carrier modulation , electronic engineering , analog transmission , pattern recognition (psychology) , artificial intelligence , telecommunications , engineering , digital signal , bit error rate , digital signal processing , physics , channel (broadcasting) , acoustics
Demodulation process without the knowledge of modulation scheme requires Automatic Modulation Classification (AMC). When receiver has limited information about received signal then AMC become essential process. AMC finds important place in the field many civil and military fields such as modern electronic warfare, interfering source recognition, frequency management, link adaptation etc. In this paper we explore the use of K-nearest neighbor (KNN) for modulation classification with different distance measurement methods. Five modulation schemes are used for classification purpose which is Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), Quadrature Amplitude Modulation (QAM), 16-QAM and 64-QAM. Higher order cummulants (HOC) are used as an input feature set to the classifier. Simulation results shows that proposed classification method provides better results for the considered modulation formats.