CAJ | 학술논문

高阶累积量是一种用于数字调制方式识别的重要特征。笔者在高斯白噪声信道下，构造了一种用于识别线性数字调制方式的二维归一化四阶累积量特征，并推导出该特征近似服从高斯分布。为了验证该模型与特征样本服从的统计模型一致，根据贝叶斯准则在二维特征平面上构造最大似然分类器，并从理论上推导出二元调制方式识别问题的平均分类正确率，它与仿真实验得到的平均分类正确率吻合得很好，证明了该方法的正确性。
고계루적량시일충용우수자조제방식식별적중요특정。필자재고사백조성신도하，구조료일충용우식별선성수자조제방식적이유귀일화사계루적량특정，병추도출해특정근사복종고사분포。위료험증해모형여특정양본복종적통계모형일치，근거패협사준칙재이유특정평면상구조최대사연분류기，병종이론상추도출이원조제방식식별문제적평균분류정학솔，타여방진실험득도적평균분류정학솔문합득흔호，증명료해방법적정학성。
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.