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.

翻译：在本文中,我们提出了一个用于确定未知接收信号调制方案的新算法,以减少对认知无线电系统主要用户的干扰,这是通过软件定义无线电(SDR)前端使用自动调控分类(AMC)促进的。在这项研究中,我们使用了属于11个等级的模拟和数字调制的计算机模拟。光谱特征已被作为按顺序最优化(SMO)的输入功能使用。这些主要用户的特征存储在数据库中,然后与数据库中的特征相匹配。根据最近提出的AMC,我们的新数据库方法继承了基于软件的SMO方法的好处,并使它在对未知信号进行分类方面更有时间效率,特别是在多个调制方案的情况下,以克服构造特征的密集计算问题。在各种应用中,主要用户拥有频率无线传输,其特征大小有限,但少有计算。基于SMO的分类方法证明SNR(SNR)的精确度超过99 + + 5 dB, 低 RIS的精确度超过95。