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Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization

Authors:
Wang, Y. ,  Gui, J. ,  Yin, Y. ,  Wang, J. ,  Sun, J. ,  Gui, G. ,  Gacanin, H. ,  Sari, H. ,  Adachi, F.
Journal:
IEEE Transactions on Vehicular Technology
Volume:
69
Page(s):
5688-5692
number:
5
Date:
May. 2020
ISSN:
1939-9359
DOI:
10.1109/TVT.2020.2981995
Language:
English

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., keywords=Automatic modulation classification;deep learning;zero-forcing equalization;channel statement information;multiple-input and multiple-output systems

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