Han Hui, Ren Zhiyuan, Li Lin, Zhu Zhigang
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China.
School of Electronic Engineering, Xidian University, Xi'an 710071, China.
Sensors (Basel). 2021 Mar 17;21(6):2117. doi: 10.3390/s21062117.
Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.
自动调制分类（AMC）在频谱监测和认知无线电中发挥着越来越重要的作用。随着通信和电子技术的发展，电磁环境变得日益复杂。高背景噪声水平和大动态输入已成为AMC的关键问题。本文提出了一种基于深度学习的特征融合方案，该方案试图融合输入信号不同域的特征，以获得更稳定、高效的信号调制类型表示。我们考虑特征之间的互补性，其可用于抑制背景噪声干扰和接收（截获）信号大动态范围的影响。具体而言，通过快速傅里叶变换（FFT）和韦尔奇功率谱分析将时间序列信号转换到频域，然后分别通过卷积神经网络（CNN）和堆叠自编码器（SAE）进行详细且稳定的频域特征表示。考虑到时域中的互补信息，提取瞬时幅度（相位）统计量和高阶累积量（HOC）作为融合的统计特征。基于融合后的特征，设计了概率神经网络（PNN）用于自动调制分类。仿真结果证明了所提方法的优越性能。值得注意的是，在信噪比（SNR）为0 dB的情况下，分类准确率可达99.8%。
