Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks

Abstract : An important element in many wireless communication activities is distinguishing between different radio signals. In this paper, we address some important problems within radio communication signal classification, one of which is detection of unknown signal formats. To tackle some of these problems, we propose a combined classifier, consisting of two different neural network types, and evaluate its performance on a variety of semi-realistic radio communication signals. Experimental results indicate that the proposed classifier can exploit the individual strengths of the neural networks and achieve both good discrimination between known, and reliable detection of unknown signal formats. We also argue that combining classifiers may be beneficial in terms of adapting to changing requirements.