Tracking performance of online large margin semi-supervised classifiers in automatic modulation classification

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 new method to evaluate the tracking performance, and classification by using semi-supervised online passive-aggressive classifier. This classifier employs a self-training approach for tracking performance evaluation in AWGN channels with unknown signal to noise ratios. Simulation results shows that adding unlabeled input samples to the training set, improve the tracking capacity of the presented system. 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.