基于CNN算法的特征图像自动调制分类方法

Jung Ho Lee, Kwang-Yul Kim, Y. Shin
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引用次数: 38

摘要

本文提出了一种基于特征图像的调制类型自动分类方法。该方法使用卷积神经网络(CNN)作为图像分类的深度学习算法之一。为了对调制类型进行分类,在二维图像中变换各种特征,并将该图像作为CNN的输入。仿真结果表明,该方法提高了分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature Image-Based Automatic Modulation Classification Method Using CNN Algorithm
In this paper, we propose a feature image-based automatic modulation classification (AMC) method to classify modulation type. The proposed method uses a convolutional neural network (CNN) which is one of deep learning algorithms for image classification. In order to classify the modulation type, various features are transformed in a two-dimensional image and this image is used as the input of the CNN. From the simulation results, we show that the proposed method improves classification performance.
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