A log-likelihood function-based algorithm for QAM signal classification

Abstract

In this paper we derive a log-likelihood function-based classification algorithm for classifying quadrature amplitude modulation (QAM) signals buried in additive white Gaussian noise. We derive the amplitude density functions of received QAM signals first, then develop the required statistics for signal classification based on the maximum a posteriori probability criterion and demonstrate a schematic structure of classifier for M-ary QAM signals. The resultant structure of this proposed classifier is shown to be flexible and easy to expand. Both the theoretical approach and the numerical approach are employed to evaluate the performance that is expressed in terms of the probability of successful classification. We also provide an example to show the capabilities of the developed classifier. It is illustrated that two approaches have consistent results and the successful classification rate reaches 100% for SNR⩾12 dB.