基于多重分形特征的通信调制方式识别研究

陈红; 蔡晓霞; 徐云; 刘文涛

doi:10.11999/JEIT150703

基于多重分形特征的通信调制方式识别研究

doi: 10.11999/JEIT150703

1.
(解放军电子工程学院合肥 230037) ②(解放军95915部队天津 301612)

基金项目:

国家自然科学基金(61571446)，安徽省自然科学基金(KY13C152)

计量
- 文章访问数: 1596
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- 被引次数: 0
出版历程
- 收稿日期: 2015-06-08
- 修回日期: 2015-12-11
- 刊出日期: 2016-04-19

Communication Modulation Recognition Based on Multi-fractal Dimension Characteristics

1.
(PLA Electronic Engineering Institute, Hefei 230037, China)
2.
(Unit 95915 of PLA, Tianjin 301612, China)

Funds:

The National Natural Science Foundation of China (61571446), The Natural Science Foundation of Anhui Province (KY13C152)

摘要

摘要: 该文提出一种基于多重分形特征的数字通信信号调制方式识别新方法。对接收信号进行去噪预处理，基于2维数据阵列求取信号的广义维数和多重分形谱；详细讨论了权重因子q对多重分形谱的影响，提取了13个多重分形特征参数；设计了基于多重分形特征的支持向量机分类器对不同信号进行调制方式识别。仿真结果表明，该算法在低信噪比情况下具有很好的识别性能。
- 调制方式识别 /
- 多重分形特征 /
- 广义维数 /
- 支持向量机
Abstract: A new modulation recognition algorithm of digital communication signals based on the multi-fractal dimension characteristics is proposed. Employing preprocessing the received signal, the generalized dimension and the multi-fractal spectrum can be calculated by 2D data array. The effect of multi-fractal spectrum due to the weighted factorsq is discussed in detail, the 13 multi-fractal characteristic parameters are extracted. The Support Vector Machine (SVM) classifier based on the multi-fractal dimension characteristics is designed for recognition of different modulation signals. Simulation results show that the proposed method has a good recognition performance under low SNR.
- Modulation recognition /
- Multi-fractal dimension characteristics /
- Generalized dimension /
- Support Vector Machine (SVM)

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参考文献(32)

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