系统工程与电子技术 ›› 2018, Vol. 40 ›› Issue (1): 171-177.doi: 10.3969/j.issn.1001-506X.2018.01.25

• 通信与网络 • 上一篇    下一篇

基于高阶累积量和小波变换的调制识别算法

谭晓衡1,2, 褚国星2, 张雪静2, 杨扬#br#

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  1. 1. 重庆大学生物感知与智能信息处理重庆市重点实验室, 重庆 400044; 2. 重庆大学通信工程学院, 重庆 400044; 3. 安徽四创电子股份有限公司, 安徽 合肥 230001
  • 出版日期:2018-01-08 发布日期:2018-01-08

Modulation recognition algorithm based on high-order cumulants and wavelet transform

TAN Xiaoheng1,2, CHU Guoxing2, ZHANG Xuejing2, YANG Yang3#br#

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  1. 1. Chongqing Key Laboratory of Bioperception & Intelligent Information Processing, Chongqing University, Chongqing 400044, China; 2. College of Communication Engineering, Chongqing University, Chongqing 400044, China; 3. Anhui Sun Create Electronics Company Limited, Hefei 230001, China
  • Online:2018-01-08 Published:2018-01-08

摘要:

针对当前调制识别算法在低信噪比下识别率不高的问题,提出结合高阶累积量和小波变换的混合调制识别算法。该算法利用了小波变换提取的两个特征参数,以及基于四阶和六阶累积量构造出一个新的特征参数,并应用反向传播神经网络分类器对调制信号进行识别。仿真结果证明,该算法能够在信噪比低至2 dB时,识别率仍可达到98%以上,由此证明了该方法的有效性和稳健性。

Abstract: A joint method is proposed based on combination of highorder cumulants and wavelet transform for recognizing the major modulation schemes at low signal-to-noise ratios (SNRs) which are applied to concurrent communication systems. Two feature parameters using amplitude and phase of digital signals after wavelet transform and a new feature parameter are extracted from four-order and six-order cumulants and are used to identify the modulation schemes with back propagation neural network classifier. Simulation results show that the average recognition rate is more than 98% with SNR higher than 2dB, which proves the validity and robustness of the method.