loading page

Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization
  • +6
  • G G ,
  • Yu Wang ,
  • Yue Yin ,
  • Juan Wang ,
  • Jinlong Sun ,
  • Hikmet Sari ,
  • Jie Gui ,
  • Fumiyuki Adachi ,
  • Haris Gacanin
Nanjing University of Posts and Telecommunications

Corresponding Author:[email protected]

Author Profile
Juan Wang
Author Profile
Jinlong Sun
Author Profile
Hikmet Sari
Author Profile
Fumiyuki Adachi
Author Profile
Haris Gacanin
Author Profile


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.