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

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...

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Bibliographic Details
Published inIEEE transactions on vehicular technology Vol. 69; no. 5; pp. 5688 - 5692
Main Authors Wang, Yu, Gui, Jie, Yin, Yue, Wang, Juan, Sun, Jinlong, Gui, Guan, Gacanin, Haris, Sari, Hikmet, Adachi, Fumiyuki
Format Journal Article
LanguageEnglish
Published New York IEEE 01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial neural networks
Automatic modulation classification
channel statement information
Classification
Communications systems
Computer simulation
Deep learning
Equalization
Learning theory
Machine learning
Methods
MIMO (control systems)
Modulation
multiple-input and multiple-output systems
Neural networks
zero-forcing equalization
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Summary: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.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.2981995