  Automatic modulation classification (AMC) is of crucial importance for
realizing wireless intelligence communications. Many deep learning based models
especially convolution neural networks (CNNs) have been proposed for AMC.
However, the computation cost is very high, which makes them inappropriate for
beyond the fifth generation wireless communication networks that have stringent
requirements on the classification accuracy and computing time. In order to
tackle those challenges, a novel involution enabled AMC scheme is proposed by
using the bottleneck structure of the residual networks. Involution is utilized
instead of convolution to enhance the discrimination capability and
expressiveness of the model by incorporating a self-attention mechanism.
Simulation results demonstrate that our proposed scheme achieves superior
classification performance and faster convergence speed comparing with other
benchmark schemes.
