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arXiv 1806.07566eess.SP
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基于序列最小优化的数据库辅助自动调制分类

Database Assisted Automatic Modulation Classification Using Sequential Minimal Optimization

K. Pavan Kumar Reddy, K. Lakhan Shiva, K. Abhilash, Y. Yoganandam

摘要:

在本文中,我们提出了一种新的算法来识别未知的输入信号的调制方案,以减轻对主用户的干扰,在认知无线电系统,这是通过使用自动调制分类(AMC)在软件定义无线电(SDR)的前端方便。在这项研究中,我们使用的模拟和数字调制属于11类的计算机模拟。基于光谱的特征已被用作序列最小优化(SMO)的输入特征。将主用户的这些特征存储在数据库中,然后将未知信号的特征与数据库中的特征进行匹配。基于最近提出的AMC,我们的新数据库方法继承了基于SMO的方法的优点,并使其在对未知信号进行分类时更具时间效率,特别是在多调制方案的情况下,以克服构建特征时的密集计算问题。在各种应用中,主用户拥有频繁的无线传输,限制了其特征大小并节省了很少的计算。基于SMO的分类方法在信噪比为15 dB时的分类准确率超过99%,在信噪比为5dB左右时的分类准确率超过95%。

英文摘要:

In this paper, we have proposed a novel algorithm for identifying the modulation scheme of an unknown incoming signal in order to mitigate the interference with primary user in Cognitive Radio systems, which is facilitated by using Automatic Modulation Classification (AMC) at the front end of Software Defined Radio (SDR). In this study, we used computer simulations of analog and digital modulations belonging to eleven classes. Spectral based features have been used as input features for Sequential Minimal Optimization (SMO). These features of primary users are stored in the database, then it matches the unknown signal's features with those in the database. Built upon recently proposed AMC, our new database approach inherits the benefits of SMO based approach and makes it much more time efficient in classifying an unknown signal, especially in the case of multiple modulation schemes to overcome the issue of intense computations in constructing features. In various applications, primary users own frequent wireless transmissions having limited their feature size and save few computations. The SMO based classification methodology proves to be over 99 \% accurate for SNR of 15 dB and accuracy of classification is over 95 \% for low SNRs around 5dB.

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