A modulation recognition method based on enhanced data representation and convolutional neural network
Liu Depeng, XU Jian-hua, Xiang Changbo, Fang Pengfei · 2021
Signal modulation recognition is a key technique in the domain of spectrum monitoring and management. However, due to the complexity of signal modulation types and the lack of signal prior information, the performance of modulation recognition might degrade dramatically. In this paper, a modulation recognition approach based on enhanced data representation and neural network is proposed. Firstly, a deep convolutional neural network structure is designed for learning the latent features and identifies the modulation type of input signal. Then, in order to alleviate the information scarcity of received signal, the enhanced representation matrix of received spectrum data is constructed as networks inputs. Finally, the convolutional neural network is adjusted to adopt and combined with the enhanced data representation to form the presented modulation recognition scheme. The experiments results demonstrate that the proposed neural network is effective for modulation recognition and the enhanced data representation can effectively improve the performance of the neural network.