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Modulation Classification Based on Fourth-Order Cumulants of Superposed Signal in NOMA Systems
IEEE Transactions on Information Forensics and Security ( IF 6.8 ) Pub Date : 2021-03-22 , DOI: 10.1109/tifs.2021.3068006
Tao Li , Yongzhao Li , Octavia A. Dobre

In this paper, we study the automatic modulation classification in a non-orthogonal multiple access system. To mitigate the effect of interference, a likelihood-based algorithm and a fourth-order cumulant-based algorithm are proposed. Different from the maximum likelihood classifier for a single signal without interference, a likelihood function of the far and near users’ signals is derived. Then, a marginal probability for the far user is obtained by using the Bayesian formula. Hence, the modulation type can be determined by maximizing the marginal probability. The high computational complexity of the likelihood-based algorithm renders it impractical; accordingly, it serves as a theoretical performance bound. On the other hand, we construct a feature vector through the estimated fourth-order cumulants of the received signal including the superposed signal and noise. For each modulation pair, using the mean and covariance matrix of the estimated feature vector, its probability density function can be obtained. Then, the key is to calculate the mean and covariance matrix of the estimated feature vector. To solve this problem, the moments of the superposed signal are derived. Therefore, modulation classification can be performed by maximizing the probability density function. Extensive simulations verify that the two proposed algorithms perform well under a wide range of signal-to-noise ratios and observation lengths.

中文翻译:

NOMA系统中基于叠加信号四阶累积量的调制分类

在本文中,我们研究了非正交多路访问系统中的自动调制分类。为了减轻干扰的影响,提出了一种基于似然的算法和一种基于四阶累积量的算法。与没有干扰的单个信号的最大似然分类器不同,得出了远方和近方用户信号的似然函数。然后,使用贝叶斯公式获得远方用户的边际概率。因此,可以通过使边缘概率最大来确定调制类型。基于似然算法的高计算复杂度使其不切实际。因此,它充当了理论上的性能界限。另一方面,我们通过估计的接收信号的四阶累积量(包括叠加的信号和噪声)来构建特征向量。对于每个调制对,使用估计的特征向量的均值和协方差矩阵,可以获得其概率密度函数。然后,关键是计算估计特征向量的均值和协方差矩阵。为了解决这个问题,导出了叠加信号的矩。因此,可以通过使概率密度函数最大化来进行调制分类。大量的仿真验证了所提出的两种算法在宽范围的信噪比和观察长度范围内都能表现良好。可以得到其概率密度函数。然后,关键是计算估计特征向量的均值和协方差矩阵。为了解决这个问题,导出了叠加信号的矩。因此,可以通过使概率密度函数最大化来进行调制分类。大量的仿真验证了所提出的两种算法在宽范围的信噪比和观察长度范围内都能表现良好。可以得到其概率密度函数。然后,关键是计算估计特征向量的均值和协方差矩阵。为了解决这个问题,导出了叠加信号的矩。因此,可以通过使概率密度函数最大化来进行调制分类。大量的仿真验证了所提出的两种算法在宽范围的信噪比和观察长度范围内都能表现良好。
更新日期:2021-05-11
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