  Modulation recognition is a challenging task while performing spectrum
sensing in a cognitive radio setup. Recently, the use of deep convolutional
neural networks (CNNs) has shown to achieve state-of-the-art accuracy for
modulation recognition \cite{survey}. However, a wireless channel distorts the
signal and CNNs are not explicitly designed to undo these artifacts. To improve
the performance of CNN-based recognition schemes we propose a signal distortion
correction module (CM) and show that this CM+CNN scheme achieves accuracy
better than the existing schemes. The proposed CM is also based on a neural
network that estimates the random carrier frequency and phase offset introduced
by the channel and feeds it to a part that undoes this distortion right before
CNN-based modulation recognition. Its output is differentiable with respect to
its weights, which allows it to be trained end-to-end with the modulation
recognition CNN based on the received signal. For supervision, only the
modulation scheme label is used and the knowledge of true frequency or phase
offset is not required.
