Modulation recognition using side information and hybrid learning

Recent applications of machine learning to modulation recognition have demonstrated the potential of deep learning to achieve state-of-the-art performance. We propose to further extend this approach by using flexible time-space decompositions that are more in line with the actual learning task, as well as integrate side-information, such as higher order moments, directly into the training process. Our promising preliminary results suggest that there are many more benefits to be reaped from such approaches.