Research on Modulation Recognition Method of Single Carrier Signal Based on Lightweight Network

Abstract

Aiming at the problems of network complexity, large computation amount and high hardware platform requirements in the current process of applying deep learning to realize digital signal modulation recognition, this paper proposes a method of using signal constellation diagram modulation recognition in the improved MobileNetV3 lightweight neural network, and introduces the cross-layer structure of Resnet into the MobileNetV3 network. The phenomenon of vanishing gradient caused by decreasing weight with increasing network layers is solved. Firstly, the received MPSK and MQAM signals are converted into constellation diagrams, which are extracted from gray images and enhanced. The image data set of constellation diagram is constructed, which is used to train the lightweight neural network weight of MobileNetV3, and then the constellation diagram is recognized. MobileNetV3 is based on deep convolution separable and Network Architecture Search (NAS) technology, which greatly reduces the number of parameters and training time on the premise of ensuring the recognition accuracy. For the modulation recognition of simple signals, the lightweight neural Network can effectively simplify the Network structure caused to reduce hardware requirements. The simulation results show that the modulated signals (BPSK, QPSK, 8PSK, 16QAM, 64QAM) can achieve the recognition rate of 99.76%. Compared with the traditional network using deep learning to realize the modulation recognition, the number of network parameters and the computational cost are significantly reduced.