Classification of M-ary QAM Based on Instantaneous Power Moment with Adjustable Median

Modulation classification is expected to play an important role in the recent wireless communications applications such as spectral sensing in the cognitive radios and detection of unlicensed radio stations. Various techniques for modulation classification have been proposed, and the approach based on a statistical moment is known for its low computational complexity. The major drawback of the classification based on the higher-order statistics is its performance inferior to an optimal approach based on likelihood functions. In this paper, we propose a novel modulation classification approach based on the statistical moment of instantaneous power with adjustable median assuming the knowledge of the signal-to-noise ratio (SNR) and show its effectiveness for M-ary quadrature amplitude modulations.