基于大数据的复杂环境下调制分类方法  

Modulation classification based on big data in complex environment

作　　者：师长立 韦统振[1,2] 吴理心 叶泽雨[1,2] 尹靖元 SHI Changli;WEI Tongzhen;WU Lixin;YE Zeyu;YIN Jingyuan(Institute of Electrical Engineering,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院电工研究所,北京100190 [2]中国科学院大学,北京100049

出　　处：《太赫兹科学与电子信息学报》2022年第1期16-21,28,共7页Journal of Terahertz Science and Electronic Information Technology

基　　金：国防基础科研计划资助项目(JCKY2019130C002)。

摘　　要：随着频率使用设备的激增和大数据时代的到来,频谱管理和控制面临着有效性和准确性的挑战。调制分类技术是频谱管理和控制的基础,也是其关键部分。因此,在大数据场景下进行有效的调制分类技术非常重要。本文不仅考虑了大数据背景下分类模型的有效性,还考虑了复杂电磁环境中噪声的动态性。因此,构建了一个包含不同信噪比下不同信号的大数据集,并利用大数据驱动深度学习模型,最终得到调制分类的结果。该方法只需训练一个模型即可实现调制分类,避免了以往算法中模型训练的冗余。仿真结果验证了该方法的有效性和可靠性。With the proliferation of frequency-using devices and the advent of the era of big data,spectrum management and control are faced with challenges of effectiveness and accuracy. Modulation classification technology is the foundation and key part of spectrum management and control. Therefore,the effectiveness of modulation classification technology in big data scenario is very important. This paper considers not only the validity of the classification model under the background of big data, but also the dynamics of noise in the complex electromagnetic environment. A big dataset containing different signals under different Mixed Signal-to-Noise Ratios(MSNR) is constructed, and the big data is utilized to drive the Deep Learning model, and the classification results are finally obtained. The proposed method can realize modulation classification by training just one model, which avoids the redundancy of model training in previous algorithms. The simulation results demonstrate the effectiveness and reliability of the proposed method.

关 键 词：大数据 非高斯噪声 调制分类 深度学习 

分 类 号：TN914.3[电子电信—通信与信息系统]