Deep Learning Based Signal Modulation Recognition

Modulation recognition(MR)is a technology between signal detection and signal modulation in the communication system.The main purpose is to analyze the signal modulation mode through the received signal in order to carry out subsequent research and processing on the signal.Modulation recognition technology is extremely important in the fields of electronic countermeasures and radio management,especially in non-cooperative communication.After decades of rapid development,modulation recognition technology has been very mature in theory,but with the increasing complexity of the wireless communication environment,there are still many areas for improvement in practical engineering applications.In recent years,with the outstanding performance of deep learning in many fields,researchers have begun to pay attention to the combination and application of deep learning and wireless communication related technologies.The modulation signal recognition algorithm based on deep learning can automatically learn deeper feature expressions from different types of modulated signal data,so this thesis is mainly based on deep learning methods to improve the accuracy of signal modulation recognition.In this thesis,we first studied the network structure,optimization algorithm and training methods in deep learning.Then,the mathematical models of 11 kinds of modulation signals(including 3 kinds of analog modulation signals and 8 kinds of digital modulation signals)are given,and the complex signals of these 11 kinds of modulation signals are analyzed to obtain two signal representation based on high-order cumulants and time-frequency analysis.In addition,for the problem of low accuracy of modulation recognition in low signal-to-noise ratio environment,this paper combines the time-frequency analysis with the noise reduction method based on conditional generation adversarial network to reduce the noise on the modulated signal images.Finally,this paper compares the proposed image noise reduction method with the existing algorithms on public data sets,verifies that the signal representation method can effectively express the characteristics of different modulated signals,and the proposed noise reduction model can greatly improve the recognition accuracy under low signal-to noise radio environment.