Multiple hypothesis modulation classification based on cyclic cumulants of different orders

A multiple hypothesismodulation QAM classification task is adressed in this paper. The classifier is designed within the rigorous framework of decision theory. A characteristic feature is extracted from the signal, and is compared to the possible theoretical features in the maximum likelihood sense. This feature is composed of a combination between fourth-order and squared second-ordercyclic temporal cumulants. No assumption about the power of the signal is made. It is shown that this uncertainty about the power of the signal does not affect the decision rule. As an application, we present simulated performance in the context of 4-QAM vs 16-QAM vs 64-QAM classification.