Deep learning aided cyclostationary feature analysis for blind modulation recognition in massive MIMO systems

Abstract

Blind modulation recognition (BMR) has been proposed as a promising approach for massive multiple-input multiple-output (M-MIMO) systems to support massive user equipment (UE). In this work, we devise a new uplink BMR algorithm based on a deep learning (DL) aided cyclostationary feature (CF) analysis approach. Firstly, we propose the so-called minimum description length (MDL) method aided complex fast independent component analysis (CFICA) algorithm, to separate the multi-user (MU) signals subjected to arbitrary, different modulation schemes with an unknown number of transmitters. Then, we tailor a convolutional deep belief network (CDBN) to analyze the relation between CF and modulation type (MT) for generating the BMR results. As the proposed BMR process is invoked blindly, it is particularly suitable for non-cooperative MU communication scenarios. Simulation results demonstrate the effectiveness of the proposed technique.