Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

Abstract

Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio (SNR), and classifies all forms of primary user's signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise (AWGN) channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.