  In this work, we investigate the feasibility and effectiveness of employing
deep learning algorithms for automatic recognition of the modulation type of
received wireless communication signals from subsampled data. Recent work
considered a GNU radio-based data set that mimics the imperfections in a real
wireless channel and uses 10 different modulation types. A Convolutional Neural
Network (CNN) architecture was then developed and shown to achieve performance
that exceeds that of expert-based approaches. Here, we continue this line of
work and investigate deep neural network architectures that deliver high
classification accuracy. We identify three architectures - namely, a
Convolutional Long Short-term Deep Neural Network (CLDNN), a Long Short-Term
Memory neural network (LSTM), and a deep Residual Network (ResNet) - that lead
to typical classification accuracy values around 90% at high SNR. We then study
algorithms to reduce the training time by minimizing the size of the training
data set, while incurring a minimal loss in classification accuracy. To this
end, we demonstrate the performance of Principal Component Analysis in
significantly reducing the training time, while maintaining good performance at
low SNR. We also investigate subsampling techniques that further reduce the
training time, and pave the way for online classification at high SNR. Finally,
we identify representative SNR values for training each of the candidate
architectures, and consequently, realize drastic reductions of the training
time, with negligible loss in classification accuracy.
