  Automatic modulation classification enables intelligent communications and it
is of crucial importance in today's and future wireless communication networks.
Although many automatic modulation classification schemes have been proposed,
they cannot tackle the intra-class diversity problem caused by the dynamic
changes of the wireless communication environment. In order to overcome this
problem, inspired by face recognition, a novel automatic modulation
classification scheme is proposed by using the multi-scale network in this
paper. Moreover, a novel loss function that combines the center loss and the
cross entropy loss is exploited to learn both discriminative and separable
features in order to further improve the classification performance. Extensive
simulation results demonstrate that our proposed automatic modulation
classification scheme can achieve better performance than the benchmark schemes
in terms of the classification accuracy. The influence of the network
parameters and the loss function with the two-stage training strategy on the
classification accuracy of our proposed scheme are investigated.
