Automatic blind modulation recognition of analog and digital signals in cognitive radios

In cognitive radio networks, automatic modulation recognition (AMR) is a fundamental step to perform the classification of different modulation formats. It plays an important role both in military and civil applications. For a cognitive radio receiver, AMR is the intermediate step between signal detection and demodulation. In this work, we propose a novel blind AMR algorithm to distinguish amongst both digital and analog signals. In particular, we exploit higher order moments as well as some features of the received signals, recognizing not only the modulation formats (amplitude, phase, and frequency) but also the levels of the constellations. We can classify up to 17 different (analog and digital) modulations. The simulation results confirm the validity of our approach for blind modulation recognition in cognitive radio applications.