Automatic modulation classification in time-varying channels based on deep learning

Abstract

Automatic modulation classification (AMC) is an important technology in military signal reconnaissance and civilian communications such as cognitive radios. Most of the existing works focused on the AMC in additional white Gaussian noise channels, but the AMC in time-varying wireless channels is more practical and challenging. In this article, we investigate the AMC in time-varying channels by using the deep learning method for high classification accuracy. Specifically, we take the modulation constellation diagram (CD) as the key feature and propose a slotted constellation diagram (slotted-CD) scheme in order to extract the feature of the time-evolution of the CD due to channel variation. We then develop an advanced neural network for modulation classification, where the output sub-images from the slotted-CD feature extractor are first processed separately by a number of parallel convolutional neural networks and then further processed by a recurrent neural network for exploring their time relationship. Experimental results show that the proposed AMC scheme achieves higher classification accuracy in both slow and fast fading channels when compared with the traditional deep learning based AMC schemes. Such performance improvement can be clearly illustrated by visualizing the outputs of the convolutional layers of the classifier. We also show that visualization can help optimize the parameters of the AMC neural networks.