  Currently, deep learning methods with stacking small size convolutional
filters are widely used for automatic modulation classification (AMC). In this
report, we find some experienced improvements by using large kernel size for
convolutional deep convolution neural network based AMC, which is more
efficient in extracting multi-scale features of the raw signal I/Q sequence
data. Also, Squeeze-and-Excitation (SE) mechanisms can significantly help AMC
networks to focus on the more important features of the signal. As a result, we
propose a multi-scale feature network with large kernel size and SE mechanism
(SE-MSFN) in this paper. SE-MSFN achieves state-of-the-art classification
performance on the public well-known RADIOML 2018.01A dataset, with average
classification accuracy of 64.50%, surpassing CLDNN by 1.42%, maximum
classification accuracy of 98.5%, and an average classification accuracy of
85.53% in the lower SNR range 0dB to 10dB, surpassing CLDNN by 2.85%. In
addition, we also verified that ensemble learning can help further improve
classification performance. We hope this report can provide some references for
developers and researchers in practical scenes.
