  Blind modulation classification is an important step to implement cognitive
radio networks. The multiple-input multiple-output (MIMO) technique is widely
used in military and civil communication systems. Due to the lack of prior
information about channel parameters and the overlapping of signals in the MIMO
systems, the traditional likelihood-based and feature-based approaches cannot
be applied in these scenarios directly. Hence, in this paper, to resolve the
problem of blind modulation classification in MIMO systems, the time-frequency
analysis method based on the windowed short-time Fourier transform is used to
analyse the time-frequency characteristics of time-domain modulated signals.
Then the extracted time-frequency characteristics are converted into RGB
spectrogram images, and the convolutional neural network based on transfer
learning is applied to classify the modulation types according to the RGB
spectrogram images. Finally, a decision fusion module is used to fuse the
classification results of all the receive antennas. Through simulations, we
analyse the classification performance at different signal-to-noise ratios
(SNRs), the results indicate that, for the single-input single-output (SISO)
network, our proposed scheme can achieve 92.37% and 99.12% average
classification accuracy at SNRs of -4 dB and 10 dB, respectively. For the MIMO
network, our scheme achieves 80.42% and 87.92% average classification accuracy
at -4 dB and 10 dB, respectively. This outperforms the existing classification
methods based on baseband signals.
