The Performance Analysis of Signal Recognition Using Attention Based CNN Method

Modulation recognition has always been an important task in the development of the cognitive radio. At present, there are two main application methods for signal data, namely, directly using the signal sequence and using some conversions such as constellation diagram. In this paper, the converted contour stella images are adopted as data source for research. The deep learning method has been proposed, which is called Image-based CNN with Attention Model (ICAM). ICAM is based on Residual Neural Network (ResNet). To evaluate the performance of ICAM, we generate a dataset which contains contour stella images involving of 8 kinds of modulation types with signal to noise ratio (SNR) from −6dB to 20dB. Compared with other state-of-the-art image-based methods, which are including those using constellation diagram and contour stellar images, ICAM makes more satisfactory performance. Besides, the Grad-Cam technology is applied to visualize and prove the effectiveness of ICAM.