Knn based classification of digital modulated signals

Abstract

Demodulation process without knowledge of the modulation scheme requires Automatic Modulation Classification (AMC). When the receiver has limited information about the received signal, AMC becomes an essential process. AMC has an important place in 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 purposes which are Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), and Quadrature Amplitude Modulation (QAM) as both 16-QAM and 64-QAM. Higher order cumulants (HOC) are used as an input feature set to the classifier. Simulation results show that the proposed classification method provides better results for the considered modulation formats.