Abstract
In this paper, a Quadrature Amplitude Modulation (QAM) signal recognition algorithm is proposed based on amplitude distribution of the signal. The algorithm uses envelop amplitude distribution information extracted by wavelet analysis to do modulation classification. It provides robustness for symbol rate determination. Simulation shows that it is more effective and convenient than the recognition algorithm of likelihood function at moderate Signal-to-Noise Ratio (SNR).
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References
Yawpo Yang, Ching-Hwa Liu, and Ta-Wei Soong. A log-likelihood function-based algorithm for QAM signal classification. Signal Processing, 70(1998), 61–71.
M. L. Dennis Wonga and Asoke K. Nandib. Semi-blind algorithms for automatic classification of digital modulation schemes. Digital Signal Processing, 18 (2008), 209–227.
Ya-feng Zhan, Zhi-gang Cao, and Zheng-xin Ma. Modulation classification of M-QAM signals. Journal on Communications, 25(2004)2, 68–74 (in Chinese). 詹亚锋, 曹志刚, 马正新. M-QAM 信号的调制制式识别. 通信学报, 25(2004)2, 68–74..
Li Chao and Kuo Yong-hong. QAM signals recognition based on fractal research of constellation. Information Technoloyg, 3(2005), 23–25 (in Chinese). 李超, 阔永红. 基于对星座图分形的QAM 信号识别. 信息技术, 3(2005), 23–25.
Hanwen Cheng, Hua Han, Lenan Wu, and Liang Chen. A 1-dimension structure adaptive self-organizing neural network for QAM signal classification. Third International Conference on Natural Computation, Haikou, China, Aug. 2007, 53–57.
L. C. Freitas, C. Cardoso, F. C. B. F. Muller, J. W. A. Costa, and A. Klautau. Automatic modulation classification for cognitive radio systems: results for the symbol and waveform domains. IEEE Latin-American Conference on Communications, Medellin, Colombia, Sept. 2009, 1–6.
T. A. Drumright and Zhi Ding. A new algorithm for QAM signal classification in AWGN channels. IEEE International Symposium on Circuits and Systems, Arizona, USA, May 2002, Vol. 1, 849–852.
H. Hadinejad-Mahram, and A. O. III Hero. Robust QAM modulation classification via moment matrices. The 11th IEEE International Symposium on Personal, Indoor And Mobile Radio Communications, London, U. K., Sept. 2000, Vol. 1, 133–137.
Ning An, Bingbing Li, and Min Huang. Modulation classification of higher order M-QAM signals using mixed-order moments and Fisher criterion. The 2nd International Conference on Computer and Automation Engineering, Singapore, Feb. 2010, Vol. 3, 150–153.
Domenico Grimaldi, Sergio Rapuano, and Luca De Vito. An automatic digital modulation classifier for measurement on telecommunication networks. IEEE Transaction on Instrumentation and Measurement, 56(2007)5, 1711–1720.
Zhang Xian-da and Bao Zheng. Non-stationary Signal Analysis and Processing. Beijing, National Defence Industry Press, 1998, 224–284 (in Chinese). 张贤达, 保铮. 非平稳信号分析与处理, 北京, 国防工业出版社, 1998, 224–284.
S. G. Mallat. Multifrequency channel decomposition of images and wavelet models. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(1989)12, 2091–2110.
Yang Shu-ying. Pattern Recognition and Intelligent Computing-Matlab Technology Realization. Beijing, Electronic Industry Press, 2008, 47–139 (in Chinese). 杨淑营. 模式识别与智能计算Matlab 技术实现, 北京, 电子工业出版社, 2008, 47–139.
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Communication author: Fu Yusheng, born in 1973, male, Ph.D.. University of Electronic Science and Technology of China, Chengdu 611731, China.
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Yusheng, F., Chunhui, R. & Wei, H. QAM signals recognition based on Amplitude distribution. J. Electron.(China) 28, 58–63 (2011). https://doi.org/10.1007/s11767-011-0469-0
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DOI: https://doi.org/10.1007/s11767-011-0469-0