Convolutional neural network-based modulation recognition technique for communication signals

Abstract

The last decade has seen breakthroughs in communication technology. The increasingly complex signal transmission environment has placed higher demands on signal modulation recognition. Traditional modulation recognition approaches cannot guarantee satisfactory recognition accuracy. Fortunately, with the continuous advancement of deep learning algorithms, convolutional neural network-based communication signal modulation recognition techniques have become the mainstream of current research. Therefore, this paper first reviews the development history of signal modulation recognition techniques and introduces the concepts of signal modulation theory. It includes ASK, PSK and FSK modulation methods, which are common today. Subsequently, I analyze the principles of signal modulation recognition and the implementation method of CNN in modulation recognition. To further explore the shortcomings of CNNs, I propose two optimized models, the residual network model and the CLDNN model. After comparing the performance, the former has higher performance, but its computational complexity is higher while the latter takes into account the high recognition accuracy while still reducing the network parameters as much as possible to keep the complexity at a low level.