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Modulation classification, recognized as the intermediate step between signaldetection and demodulation, is widely deployed in several modern wirelesscommunication systems. Although many approaches have been studied in the lastdecades for identifying the modulation format of an incoming signal, they oftenreveal the obstacle of learning radio characteristics for most traditionalmachine learning algorithms. To overcome this drawback, we propose an accuratemodulation classification method by exploiting deep learning for beingcompatible with constellation diagram. Particularly, a convolutional neuralnetwork is developed for proficiently learning the most relevant radiocharacteristics of gray-scale constellation image. The deep network isspecified by multiple processing blocks, where several grouped and asymmetricconvolutional layers in each block are organized by a flow-in-flow structurefor feature enrichment. These blocks are connected via skip-connection toprevent the vanishing gradient problem while effectively preserving theinformation identify throughout the network. Regarding several intensivesimulations on the constellation image dataset of eight digital modulations,the proposed deep network achieves the remarkable classification accuracy ofapproximately 87% at 0 dB signal-to-noise ratio (SNR) under a multipathRayleigh fading channel and further outperforms some state-of-the-art deepmodels of constellation-based modulation classification.

Learning Constellation Map with Deep CNN for Accurate Modulation Recognition