Deep Learning-based Cooperative Automatic Modulation Classification
Method for MIMO Systems
Abstract
Automatic modulation classification (AMC) is one of the most essential
algorithms to identify the modulation types for the non-cooperative
communication systems. Recently, it has been demonstrated that deep
learning (DL)-based AMC method effectively works in the single-input
single-output (SISO) systems, but DL-based AMC method is scarcely
explored in the multiple-input multiple-output (MIMO) systems. In this
correspondence, we propose a convolutional neural network (CNN)-based
cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver
equipped with multiple antennas cooperatively recognizes the modulation
types. Specifically, each received antenna gives their recognition
sub-results via the CNN, respectively. Then, the decision maker
identifies the modulation types with the recognition sub-results and
cooperative decision rules, such as direct voting (DV), weighty voting
(WV), direct averaging (DA) and weighty averaging (WA). The simulation
results demonstrate that the Co-AMC method, based on the CNN and WA, has
the highest correct classification probability in the four cooperative
decision rules. In addition, the CNN-based Co-AMC method also performs
better than the high order cumulants (HOC)-based traditional AMC
methods, which shows the effective feature extraction and powerful
classification capabilities of the CNN.