  We survey the latest advances in machine learning with deep neural networks
by applying them to the task of radio modulation recognition. Results show that
radio modulation recognition is not limited by network depth and further work
should focus on improving learned synchronization and equalization. Advances in
these areas will likely come from novel architectures designed for these tasks
or through novel training methods.
