基于卷积神经网络与循环谱图的调制识别方法

Modulation recognition method based on convolutional neural network and cyclic spectrum images

摘要:

为提高调制分类识别精确度，降低计算复杂度，提出了一种基于卷积神经网络(CNN)与红绿蓝(RGB)循环谱二维图的智能调制识别方法。基于循环谱特征可识别调制类型的机理，为了降低计算复杂度，将三维的循环谱转换为二维平面的RGB循环谱图，并将其用于构建数据集；将一种计算复杂度较低的CNN作为调制类型分类识别器。仿真结果表明，所提出的智能调制识别方法能够以较低的计算复杂度，获得更高的分类精确度。

Abstract:

An intelligent modulation recognition method based on the Convolutional Neural Network(CNN) and two-dimensional Red-Green-Blue(RGB) cyclic spectrum images is proposed in order to improve the modulation recognition accuracy and reduce the computational complexity. The cyclic spectrum can be employed to identify the modulation type. The three-dimensional cyclic spectra are converted to two-dimensional RGB cyclic spectra to reduce the computational complexity, which are then taken to build the data set. Moreover, a CNN based modulation classifier with low computational complexity is proposed. Simulation results show that the proposed intelligent modulation recognition algorithm can achieve higher classification accuracy with lower computational complexity.