Analysis of the statistical model with the two-dimensional cumulant feature applying to modulation classification

被引:1
作者
Liu, Pei [1 ]
Shui, Penglang [1 ]
Guo, Yongming [2 ]
Li, Ning [2 ]
机构
[1] National Key Lab. of Radar Signal Processing, Xidian Univ.
[2] National Institute of Radio Spectrum Management
来源
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2014年 / 41卷 / 02期
关键词
Cumulants; Gaussian distribution; Maximum likelihood classifier; Modulation classification; Statistical model;
D O I
10.3969/j.issn.1001-2400.2014.02.008
中图分类号
学科分类号
摘要
Higher order cumulants are the key features for implementing digital modulation classification. However, few available literatures focus on the statistical model of cumulant features. A two-dimensional normalized fourth-order cumulant feature is proposed to classify linear digital modulation in the additive white Gaussian noise channel, and then it is derived that the two-dimensional feature asymptotically obeys Gaussian distribution. In order to show the correctness of the proposition, a maximum likelihood classifier is formed in the two-dimensional feature domain according to the Bayesian criterion. The average probability of correct classification of the binary class problem is theoretically determined, which is consistent with the result obtained by simulations, thus justifying the correctness of the proposed theoretical results.
引用
收藏
页码:44 / 50
页数:6
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