基于时频分析的多天线系统调制盲分类
Time-Frequency Analysis based Blind Modulation Classification for Multiple-Antenna Systems
摘要：
调制盲分类是认知无线电网络实现的重要步骤。多输入多输出（MIMO）技术广泛应用于军事和民用通信系统。由于MIMO系统中信道参数先验信息的缺乏和信号的重叠，传统的基于似然和基于特征的方法不能直接应用于这些场景。为此，本文针对MIMO系统中的调制盲识别问题，采用基于加窗短时傅立叶变换的时频分析方法对时域调制信号的时频特性进行分析。然后将提取的时频特征转换成RGB谱图，并根据RGB谱图图像应用基于迁移学习的卷积神经网络对调制类型进行分类。最后，决策融合模块用于融合所有接收天线的分类结果。仿真结果表明，对于单输入单输出（SISO）网络，在信噪比为-4 dB和10 dB时，该方法的平均分类准确率分别达到92.37%和99.12%。对于MIMO网络，我们的计划达到80.42%和87.92%的平均分类精度分别为-4 dB和10 dB。这优于现有的基于基带信号的分类方法。
英文摘要：
Blind modulation classification is an important step to implement cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in the MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time-frequency analysis method based on the windowed short-time Fourier transform is used to analyse the time-frequency characteristics of time-domain modulated signals. Then the extracted time-frequency characteristics are converted into RGB spectrogram images, and the convolutional neural network based on transfer learning is applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module is used to fuse the classification results of all the receive antennas. Through simulations, we analyse the classification performance at different signal-to-noise ratios (SNRs), the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of -4 dB and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at -4 dB and 10 dB, respectively. This outperforms the existing classification methods based on baseband signals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
