Deep Learning-Based Automatic Modulation Classification Over MIMO Keyhole Channels

Abstract

Automatic modulation classification (AMC) is a significant part of cognitive communication systems. In early researches, likelihood-based (LB) and feature-based (FB) solutions were proposed for the AMC problem. With the developments in the data-driven approaches, a third method based on deep learning (DL) has recently gained prominence among AMC researchers. It is shown that convolutional neural network based classifiers are very efficient in the AMC for both single input single output (SISO) and multiple-input multiple-output (MIMO) systems. However, for most of the works in MIMO-AMC, the channel considered is full rank. This work addresses the problem of AMC over rank deficient channels such as a keyhole channel using a DL-based classifier. The proposed DL-classifier utilizes a CNN, which does not employ pooling layers or dropouts in the convolutional layers. To further improve the classification accuracy, decision cooperation as well as feature fusion is employed. In addition to the keyhole effect, this work investigates the effect of antenna correlation on DL-based AMC. A comparative study of the proposed method and the existing FB AMC method for the MIMO keyhole channel is also presented.