数据驱动的RF信号深度调制识别方法

基于卷积结构的信号调制识别神经网络的识别性能受信号调制类型种类限制。例如，在12 dB信噪比条件下，同时对24种信号调制类型进行识别，其识别准确率仅为80%。若需要进一步提高识别性能，则要求更复杂的网络模型，导致网络训练所需数据集规模和硬件资源成本增大。鉴于此，针对无线电信号特征，设计一种适用于无线电信号调制识别的紧致残差神经网络，将其作为信号调制类型特征学习和特征提取工具，实现从原始I、Q数据到信号调制类型的端到端识别。利用迁移学习降低网络重新训练所需样本数，增强在无线信道响应发生变化时的环境适应能力，降低训练阶段所需的硬件资源和训练数据集规模。研究表明，当信道脉冲响应改变时，所提的信号调制识别神经网络在信噪比为12 dB条件下的识别性能达到95%，多个对比实验验证本文所设计神经网络的识别性能具有优势。

A Data-driven Deep Modulation Identification Method for RF Signals

The identification performance with convolutional neural network (CNN) is limited with the types of signal modulation identification. For instance, the identification accuracy is just only 80% when 24 kinds of modulation waveforms presented at SNR=12 dB. If a better recognition performance wants to be obtained, more complicated network must be required. It directly enlarges the requirement of the data set size and the cost of hardware calculation resources is also increased. Therefore, a compact residual neural network for radio signal modulation identification is designed in the paper, which can be used to extract the characteristics of signal modulation. The end-to-end identification is accomplished from the baseband in-phase and quadrature components. By using transfer learning, the number of samples in the network retraining stage is reduced dramatically and the adaptive ability of the proposed network is enhanced. The test results show that the identification performance with the proposed neural network approaches 95% when the SNR is 12 dB even though the wireless channel impulse response verified. Several comparative experiments illustrate the advantages of the proposed neural network.