期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 69, 期 5, 页码 5688-5692出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2020.2981995
关键词
Automatic modulation classification; deep learning; zero-forcing equalization; channel statement information; multiple-input and; multiple-output systems
资金
- National Science and Technology, Major Project of China [TC190A3WZ-2]
- National Natural Science Foundation of China [61901228, 61671253]
- Innovation and Entrepreneurship of Jiangsu High-level Talent [CZ0010617002]
- Six Top Talents Program of Jiangsu [XYDXX-010]
- 1311 Talent Plan of Nanjing University of Posts and Telecommunications
- [RK002STP16001]
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.
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