Convolutional Neural Networks for Automatic Cognitive Radio Waveform Recognition

Abstract

Cognitive radio technology is an important branch in the field of wireless communication, and automatic identification is a major part of cognitive radio technology. Convolutional neural network (CNN) is an advanced neural network, which is the forefront of application in the digital image recognition area. In this paper, we explore CNN in an automatic system to recognize the cognitive radio waveforms. Excitedly, it is a more effective model with high ratio of successful recognition (RSR) under high power background noise. The system can identify eight kinds of signals, including binary phase shift keying (Barker codes modulation) linear frequency modulation, Costas codes, Frank code, and polytime codes (T1, T2, T3, and T4). The recognition part includes a CNN classifier. First, we determine the appropriate architecture to make CNN effective for proposed system. Specifically, we focus on how many convolutional layers are needed, what appropriate number of hidden units is, and what the best pooling strategy is. Second, we research how to obtain the image features into CNN that based on Choi–Williams time-frequency distribution. Finally, by means of the simulations, the results of classification are demonstrated. Simulation results show the overall RSR is 93.7% when the signal-to-noise ratio is −2dB.