Online Large Margin Semi-supervised Algorithm for Automatic Classification of Digital Modulations

Automatic classification of modulation type in detected signals is an intermediate step between signal detection and demodulation, and is also an essential task for an intelligent receiver in various civil and military applications. In this paper, we propose a semi-supervised online passive-aggressive classifier that uses self-training approach for AWGN channels with unknown or variable signal to noise ratios to classify the modulated signals. Simulation results shows that adding unlabeled input samples to the training set, improve the generalization capacity of the presented classifier. In addition to the online properties which are suitable for time variated systems, this algorithm requires less numbers of signal samples (thus is fast) to convergence to the correct answer and can be further to adapt to the input SNR. The selection of appropriate features helps the general system to work for a set of initial sample of each class. The simulation results show that the employing this learning method increase the accuracy level.