  Modulation classification, recognized as the intermediate step between signal
detection and demodulation, is widely deployed in several modern wireless
communication systems. Although many approaches have been studied in the last
decades for identifying the modulation format of an incoming signal, they often
reveal the obstacle of learning radio characteristics for most traditional
machine learning algorithms. To overcome this drawback, we propose an accurate
modulation classification method by exploiting deep learning for being
compatible with constellation diagram. Particularly, a convolutional neural
network is developed for proficiently learning the most relevant radio
characteristics of gray-scale constellation image. The deep network is
specified by multiple processing blocks, where several grouped and asymmetric
convolutional layers in each block are organized by a flow-in-flow structure
for feature enrichment. These blocks are connected via skip-connection to
prevent the vanishing gradient problem while effectively preserving the
information identify throughout the network. Regarding several intensive
simulations on the constellation image dataset of eight digital modulations,
the proposed deep network achieves the remarkable classification accuracy of
approximately 87% at 0 dB signal-to-noise ratio (SNR) under a multipath
Rayleigh fading channel and further outperforms some state-of-the-art deep
models of constellation-based modulation classification.
