Modulation classification in fading channels using antenna arrays

Blind modulation classification (MC) is an intermediate step between signal detection and demodulation, and plays a key role in various civilian and military applications In this paper, first we provide an overview of decision-theoretic MC approaches. Then we derive the average likelihood ratio (ALR) based classifier for linear and nonlinear modulations, in noisy channels with unknown carrier phase offset and also in Rayleigh fading channels. Since these ALR-based classifiers are complex to implement, we then develop a quasi hybrid likelihood ratio (QHLR) based classifier, where the unknown parameters are estimated using low-complexity techniques. This QHLR-based classifier is much simpler to implement and is also applicable to any fading distribution, including Rayleigh and Rice. Afterwards, we propose a generic multi-antenna classifier for linear and nonlinear modulations, using an antenna array at the receiver. This classifier has the potential to improve the performance of traditional single-antenna classifiers, including the proposed QHLR-based algorithm, via spatial diversity. Simulation results are provided to show the performance enhancement offered by the new QHLR-based multi-antenna classifier, in a variety of channel and fading conditions.