Likelihood-Based Algorithms for Linear Digital Modulation Classification in Fading Channels

Blind modulation classification (MC) is an intermediate step between signal detection and demodulation, with both military and civilian applications. MC is a challenging task, especially in a non-cooperative environment, as no prior information on the incoming signal is available at the receiver. In this paper, we investigate classification of linear digital modulations in slowly varying flat fading channels. With unknown channel amplitude, phase and noise power at the receive-side, we derive hybrid likelihood ratio test (HLRT) and quasiHLRT (QHLRT) - based classifiers, and discuss their performance versus computational complexity. It is shown that the QHLRT algorithm provides a low computational complexity solution, yet yielding performance close to the HLRT algorithm