基于轻量型网络的单载波信号调制识别

针对当前应用深度学习实现数字信号调制识别过程中网络复杂、计算量高、硬件平台要求高的问题, 本文提出了在改进的MobileNetV3轻量级神经网络中使用信号星座图调制识别的方法. 首先, 将接收到的MPSK和MQAM信号转换成星座图像, 将其进行灰度图像提取, 灰度图像增强, 构建星座图的图像数据集, 然后将ResNet中的跨层结构引入MobileNetV3网络, 解决了随着网络层数的增加, 权重减小而导致的梯度消失现象. 最后将星座图数据集用于训练MobileNetV3的轻量型神经网络权重, 对星座图像进行识别. MobileNetV3基于深度卷积可分离和神经架构搜索(network architecture search, NAS)技术在保证识别精度的前提下, 大大降低了参数量和训练时间, 将对于简单信号的调制识别, 轻量型神经网络可以有效简化网络结构, 降低对硬件设备的要求. 仿真结果表明, 针对的调制信号(BPSK、QPSK、8PSK、16QAM、64QAM), 能实现识别率为99.76%的调制识别, 相较于传统应用深度学习实现调制识别的网络, 网络参数量和计算量明显减小.

Modulation Recognition of Single Carrier Signal Based on Lightweight Network

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