Automatic Modulation Classification for MIMO Systems via Deep Learning
and Zero-Forcing Equalization
Abstract
Automatic modulation classification (AMC) is one of the most critical
technologies for non-cooperative communication systems. Recently, deep
learning (DL) based AMC (DL-AMC) methods have attracted significant
attention due to their preferable performance. However, the study of
most of DL-AMC methods are concentrated in the single-input and
single-output (SISO) systems, while there are only a few works on
DL-based AMC methods in multiple-input and multiple-output (MIMO)
systems. Therefore, we propose in this work a convolutional neural
network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC)
method for MIMO systems. Simulation results demonstrate that the
CNN/ZF-AMC method achieves better performance than the artificial neural
network (ANN) with high order cumulants (HOC)-based AMC method under the
condition of the perfect channel state information (CSI). Moreover, we
also explore the impact of the imperfect CSI on the performance of the
CNN/ZF-AMC method. Simulation results demonstrated that the
classification performance is not only influenced by the imperfect CSI,
but also associated with the number of the transmit and receive
antennas.