Modulation Recognition Algorithm based on Digital Communication Signal Time-Frequency Image
Kuixian Li, Jibo Shi · 2021 8th International Conference on Dependable Systems and Their Applications (DSA) · 2021
Signal automatic modulation recognition stands for the modulation modes that can classify and recognize various communication signals automatically. Modulation recognition, as the midway between the detection and demodulation for signals, is critical in both military and civilian applications. Electronic countermeasures and software radio, for example. Although modulation recognition technique has advanced significantly in the past few years, there are still various challenges to be overcome as the complex engineering environment and wireless communication channel requirements become more sophisticated. This paper proposes an approach based on the Convolutional Neural Network (CNN) feature fusion to overcome the problem of numerous modulation modes failing to examine the correlation in distinct characters. Moreover, a pseudo Wigner-Ville distribution is appeared in this paper to turn a one-dimensional signal into an image, the image features are extracted by a kind of Convolutional Neural Network model, and the features extracted by CNN are combined with artificial features. The modulation recognition performance is further enhanced by the fusion feature. Simulation findings suggest the technique proposed in this paper enhances the ability especially when the SNR is low, implying this mode of building fusion features is more successful.