用于调制识别的二维累积量特征统计模型

doi:10.3969/j.issn.1001-2400.2014.02.008

Abstract

Abstract:

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.

Key words: cumulants, modulation classification, statistical model, Gaussian distribution, maximum likelihood classifier

CLC Number:

TN929.5

LIU Pei;SHUI Penglang;GUO Yongming;LI Ning. Analysis of the statistical model with the two-dimensional cumulant feature applying to modulation classification[J].J4, 2014, 41(2): 44-50.

References

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

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