DAWFNN: An Automatic Modulation Recognition Method Based on Multi Feature Fusion

Nowadays, automatic modulation recognition (AMR) technology has become an important component of civil and military wireless communication systems. Especially in noncooperative communication scenarios, modulation recognition plays a decisive role in the acquisition of subsequent data information. In order to fully combine the advantages of modulation recognition technology based on feature extraction and deep learning, we propose to use in-phase component and quadrature component (IQ) and a circulant feature matrix (CFM) composed of traditional feature parameters as the input data of the neural network, and design a feature extraction module for the CFM. We design a dynamic adaptive weighted feature fusion module for the intermediate feature parameters of IQ data and CFM after the neural network, and realize feature fusion that is more conducive to modulation recognition. Experimental results show that our method has advantages in recognition accuracy compared with other network models.