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首页> 外文期刊>Journal of signal processing systems for signal, image, and video technology >Introducing an Efficient Statistical Model for Automatic Modulation Classification
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Introducing an Efficient Statistical Model for Automatic Modulation Classification

机译：介绍用于自动调制分类的高效统计模型

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摘要

Nowadays, Automatic Modulation Classification (AMC) plays an important role in many applications of cooperative and non-cooperative communication such as spectrum management, cognitive radio, intelligent modems, and interference identification. In this paper, a new robust AMC algorithm based on Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is proposed. Primarily, multi-level wavelet transform is applied on the received data samples. To select the efficient statistical model for wavelet coefficient description, the statistical characteristics of these coefficients are surveyed. The proposed analysis precisely illustrates that these coefficients have heteroscedasticity property which has not been mentioned before. Subsequently, to describe the wavelet coefficients, the heteroscedastic GARCH model is employed and its parameters are extracted as the features. Finally, the obtained features are applied to support vector machine (SVM) classifier to simultaneously determine the modulation type and constellation size. Eleven different types and sizes of the digital modulation schemes in various channels such as AWGN, flat fading and multipath fading in presence of common channel impairments are examined. The experimental results reveal the superior performance of the proposed method in comparison with the previously introduced approaches.
机译：如今，自动调制分类（AMC）在协作和非协作通信的许多应用（例如频谱管理，认知无线电，智能调制解调器和干扰识别）中发挥着重要作用。提出了一种基于广义自回归条件异方差（GARCH）模型的鲁棒AMC算法。首先，将多级小波变换应用于接收到的数据样本。为了选择用于小波系数描述的有效统计模型，调查了这些系数的统计特性。所提出的分析精确地说明了这些系数具有以前没有提到的异方差性。随后，为了描述小波系数，采用了异方差GARCH模型，并提取了其参数作为特征。最后，将获得的特征应用于支持向量机（SVM）分类器，以同时确定调制类型和星座图大小。在存在公共信道损伤的情况下，检查了诸如AWGN，平坦衰落和多径衰落等各种信道中的11种不同类型和大小的数字调制方案。实验结果表明，与先前介绍的方法相比，该方法具有更好的性能。

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