Wireless Signal Classification Based on High-Order Cumulants and Machine Learning

2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC) Pub Date : 2017-12-01 DOI:10.1109/ICCTEC.2017.00127

Yongrong Zhang, Guannan Wu, Jian Wang, Q. Tang

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引用次数: 2

Abstract

Wireless Signal Classification (WSC) is mainly based on Automatic Modulation Classification (AMC) and an intermediate process between signal detection and demodulation, which can identify the modulation from the received signal. The higher-order cumulants (HOCs) are derived from the received signals and input as features to the selected classifier. HOCs have good anti-noise performance, which can classify the modulation methods commonly used in current wireless communication systems. In this paper, the classification of {OFDM, BPSK, QPSK, GFSK, 16QAM, 64QAM} is realized by MATLAB programming based on the characteristic of HOCs. A new feature parameter is proposed according to 2th order cumulant and 6th order cumulant. The simulation results show that under the SNR of 3dB, the DT classifier's classification accuracy is over 99.4%. Those 6 kinds of signals are the main modulation signals used in wireless communication systems. Therefore, this paper has engineering application value in some degree.

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基于高阶累积量和机器学习的无线信号分类

无线信号分类(WSC)主要基于自动调制分类(AMC)，它是信号检测和解调之间的一个中间过程，可以从接收到的信号中识别出调制。高阶累积量(hoc)由接收到的信号和作为特征输入的分类器导出。hoc具有良好的抗噪声性能，可以对当前无线通信系统中常用的调制方式进行分类。本文根据hoc的特点，通过MATLAB编程实现了{OFDM、BPSK、QPSK、GFSK、16QAM、64QAM}的分类。根据2阶累积量和6阶累积量提出了新的特征参数。仿真结果表明，在信噪比为3dB的情况下，DT分类器的分类准确率达到99.4%以上。这六种信号是无线通信系统中使用的主要调制信号。因此，本文具有一定的工程应用价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC)

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