非合作通信中调制识别算法研究进展

doi:10.3981/j.issn.1000-7857.2019.04.010

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摘要概述了非合作通信中调制识别技术的概念、内涵；介绍了经典调制识别算法和智能调制识别算法的基本原理与处理流程，分析了每类算法的优势以及存在的问题；针对现有调制识别算法存在的问题，探讨了非合作通信中调制识别算法的发展方向。

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	黄知涛
	杨杰
	王翔
	崔轩
	王永芳

关键词 ：非合作通信, 调制识别, 特征提取, 最大似然, 深度学习

Abstract：Based on a comprehensive study of the modulation recognition algorithm in the non-cooperative communication, this paper reviews the current research status of the communication signal classical modulation recognition algorithm and the intelligent modulation recognition algorithm based on the deep learning. The concept and the connotation of the modulation recognition technology in the noncooperative communication are explained, focusing on the basic principle and the processing flow of the classical modulation recognition algorithm and the intelligent modulation recognition algorithm, the advantages and disadvantages of each kind of algorithms are analyzed, and the future development direction of the modulation recognition algorithm in the non-cooperative communication are discussed.

Key words： non-cooperative communication modulation recognition feature extraction maximum likelihood deep learning

收稿日期: 2018-10-29

基金资助:国家自然科学基金项目（61401490）

作者简介: 黄知涛,教授,研究方向为通信信号处理、模式识别和人工智能,电子信箱:huangzhitao@nudt.edu.cn;杨杰,硕士研究生,研究方向为调制识别、深度学习,电子信箱:13701350272@163.com

引用本文:

黄知涛, 杨杰, 王翔, 崔轩, 王永芳. 非合作通信中调制识别算法研究进展[J]. 科技导报, 2019, 37(4): 55-62.
HUANG Zhitao, YANG Jie, WANG Xiang, CUI Xuan, WANG Yongfang. A Survey of modulation recognition algorithms in non-cooperative communication. Science & Technology Review, 2019, 37(4): 55-62.

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http://www.kjdb.org/CN/10.3981/j.issn.1000-7857.2019.04.010 或 http://www.kjdb.org/CN/Y2019/V37/I4/55

[1] Weaver C S, Cole C A, Krumland R B, et al. The automatic classification of modulation types by pattern recognition[R]. Palo Alto:Stanford University California Stanford Electronics Labs, 1969.
[2] Dobre O A, Abdi A, Bar-Ness Y, et al. Survey of automatic modulation classification techniques:Classical approaches and new trends[J]. IET Communications, 2007, 1(2):137-156.
[3] Avci E, Avci D. A novel approach for digital radio signal classification:Wavelet packet energy-multiclass support vector machine (WPE-MSVM)[J]. Expert Systems with Applications, 2008, 34(3):2140-2147.
[4] Xu J L, Su W, Zhou M. Likelihood-ratio approaches to automatic modulation classification[J]. IEEE Transactions on Systems Man & Cybernetics Part C, 2011, 41(4):455-469.
[5] Wu H C, Saquib M, Yun Z. Novel automatic modulation classification using cumulant features for communications via multipath channels[J]. IEEE Transactions on Wireless Communications, 2008, 7(8):3098-3105.
[6] Xu J L, Su W, Zhou M C. Software-defined radio equipped with rapid modulation recognition[J]. IEEE Transactions on Vehicular Technology, 2010, 59(4):1659-1667.
[7] Ramkumar B. Automatic modulation classification for cognitive radios using cyclic feature detection[M]. IEEE Press, 2009.
[8] Ali A, Yangyu F, Liu S. Automatic modulation classification of digital modulation signals with stacked autoencoders[J]. Digital Signal Processing, 2017, 71:108-116.
[9] Zhou Q, Lu H, Jia L, et al. Automatic modulation classification with genetic backpropagation neural network[C]//Evolutionary Computation. Piscataway NJ:IEEE, 2016:4626-4633.
[10] Xiang F, Li J. A hierarchical digital modulation classification algorithm for adaptive wireless communication systems[J]. Wireless Personal Communications, 2006, 39(3):321-326.
[11] Liang W U, Hua J, Cui W L. Modulation classification using cyclostationarity test and support vector machine[J]. Journal of Applied Sciences, 2013, 31(6):593-600.
[12] 罗利春. 无线电侦察信号分析与处理[M]. 北京:国防工业出版社, 2003. Luo Lichun. Radio reconnaissance signal analysis and processing[M]. Beijing:National Defense Industry Press, 2003.
[13] Azzouz E E, Nandi A K. Automatic identification of digital modulation types[J]. Signal Processing, 1995, 47(1):55-69.
[14] Swami A, Sadler B M. Hierarchical digital modulation classification using cumulants[J]. IEEE Transactions on communications, 2000, 48(3):416-429.
[15] Panagiotou P, Anastasopoulos A, Polydoros A. Likelihood ratio tests for modulation classification[C]//Century Military Communications Conference Proceedings. Piscataway NJ:IEEE, 2000(2):670-674.
[16] Huan C Y, Polydoros A. Likelihood methods for MPSK modulation classification[J]. IEEE Transactions on Communications, 1995, 43(234):1493-1504.
[17] Wei W, Mendel J M. Maximum-likelihood classification for digital amplitude-phase modulations[J]. IEEE Transactions on Communications, 2000, 48(2):189-193.
[18] Hameed F, Dobre O A, Popescu D C. On the likelihoodbased approach to modulation classification[J]. IEEE Transactions on Wireless Communications, 2009, 8(12):5884-5892.
[19] Liedtek F F. Computer simulation of an automatic classification procedure for digitally modulated communication signals with unknown parameters[J]. Signal Processing, 1984, 6(4):311-323.
[20] Nandi A K, Azzouz E E. Automatic analogue modulation recognition[J]. Signal Processing 1995, 46(2):211-222.
[21] Nandi A K, Azzouz E E. Automatic identification of digital modulation types[J]. Signal Processing, 1995, 47(1):55-69.
[22] Nandi A K, Azzouz E E. Algorithms for automatic modulation recognition of communication signals[J]. IEEE Transactions on Communications, 1998, 46(4):431-436.
[23] 谭正骄, 施继红, 胡继峰. 基于随机森林的低阶数字调制识别算法研究[J]. 通信技术, 2018, 51(3):527-532. Tan Zhengjiao, Shi Jihong, Hu Jifeng. Low-order digital modulation recognition algorithm based on random forest[J]. Communications Technology, 2018, 51(3):527-532.
[24] Gardner W, Franks L. Characterization of cyclostationary random signal processes[J]. IEEE Transactions on Information Theory, 1975, 21(1):4-14.
[25] Gardner W, Brown W, Chen C K. Spectral correlation of modulated signals:Digital modulation(Part Ⅱ)[J]. IEEE Transactions on Communications, 1987, 35(6):595-601.
[26] Gardner W A, Spooner C M. Cyclic spectral analysis for signal detection and modulation recognition[C]//Military Communications Conference. Piscataway NJ:IEEE, 1988:419-424.
[27] Gardner W. Spectral correlation of modulated signals:Part I:Analog modulation[J]. IEEE Transactions on Communications, 1987, 35(6):584-594.
[28] 朱德君. 谱相关理论在电子侦察中的应用[J]. 电子对抗, 1995, 1(2):43-57. Zhu Dejun. Application of spectral correlation theory in electronic reconnaissance[J]. Electronic Warfare, 1995, 1(2):43-57.
[29] 韩国栋, 蔡斌, 邬江兴. 调制分析与识别的谱相关方法[J]. 系统工程与电子技术, 2001, 23(3):34-36. Han Guodong, Cai Bin, Wu Jiangxing. Spectral correlation method for modulation recognition[J]. Systems Engineering and Electronics, 2001, 23(3):34-36.
[30] 吕杰, 张胜付, 张克, 等. 移动通信信号自动调制识别的谱相关方法[J]. 电讯技术, 1999(2):9-14. Lv Jie, Zhang Shengfu, Zhang Ke, et al. Automatic modulation recognition of mobile communication signals using spectral correlation approach[J]. Telecommunication Engineering, 1999(2):9-14.
[31] Delprat N, Escudié B, Guillemain P, et al. Asymptotic wavelet and Gabor analysis:Extraction of instantaneous frequencies[J]. IEEE transactions on Information Theory, 1992, 38(2):644-664.
[32] Ho K C, Prokopiw W, Chan Y T. Modulation identification of digital signals by the wavelet transform[J]. IEEE ProceedingsRadar, Sonar and Navigation, 2000, 147(4):169-176.
[33] Zhao Z J, Shang J N. A new method for modulation type recognition based on the time frequency representations[C]//The 6th International Conference on Signal Processing. Piscataway NJ:IEEE, 2002, 1:208-211.
[34] 范海波, 杨志俊, 曹志刚. 卫星通信常用调制方式的自动识别[J]. 通信学报, 2004, 25(1):140-149. Fan Haibo, Yang Zhijun, Cao Zhigang. Automatic recognition for common used modulations in satellite communication[J]. Journal of China Institute of Communications, 2004, 25(1):140-149.
[35] Swami A, Sadler B M. Hierarchical digital modulation classification using cumulants[J]. IEEE Transactions on communications, 2000, 48(3):416-429.
[36] Dobre O A, Bar-Ness Y, Su W. Higher-order cyclic cumulants for high order modulation classification[C]//Military Communications Conference. Piscataway NJ:IEEE, 2003, 1:112-117.
[37] Dai W, Wang Y, Wang J. Joint power estimation and modulation classification using second-and higher statistics[C]//Wireless Communications and Networking Conference. Piscataway NJ:IEEE, 2002, 1:155-158.
[38] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436.
[39] 胡宗恺, 熊刚. 一种改进的认知无线电调制识别方法研究[J]. 通信技术, 2018, 51(5):1036-1040. Hu Zongkai, Xiong Gang. Modified modulation recognition method for cognitive radio[J]. Communications Technology, 2018, 51(5):1036-1040.
[40] O'Shea T J, Corgan J, Clancy T C. Convolutional radio modulation recognition networks[C]//International conference on engineering applications of neural networks. Aberdeen:Springer, 2016:213-226.
[41] Migliori B, Zeller-Townson R, Grady D, et al. Biologically inspired radio signal feature extraction with sparse denoising autoencoders[J/OL].[2018-08-31]. https://arxiv.org/pdf/1605.05239.pdf
[42] 张永乐. 基于CNN架构的通信信号调制模式分类识别研究[D]. 沈阳:沈阳航空航天大学, 2018. Zhang Yongle. Research on classification and recognition of communication signals modulation mode based on CNN architecture[D]. Shenyang:Shenyang Aerospace University, 2018.
[43] 裴禹豪, 曲毅, 李锦明, 等. 基于AlexNet网络的MPSK与MQAM类信号的调制识别[J]. 激光杂志, 2018, 39(10):75-78. Pei Yuhao, Qu Yi, Li Jinming, et al. Modulation recognition of MPSK and MQAM signals based on AlexNet[J]. Laser Journal, 2018(10):75-78.
[44] Zhang M, Diao M, Guo L. Convolutional neural networks for automatic cognitive radio waveform recognition[J]. IEEE Access, 2017, 5:11074-11082.
[45] Zhang H, Sun H. Automatic modulation classification using stacked sparse auto-encoders[C]//2016 IEEE 13th International Conference on Signal Processing. Piscataway NJ:IEEE, 2017:248-252.
[46] Li J, Qi L, Lin Y. Research on modulation identification of digital signals based on deep learning[C]//IEEE International Conference on Electronic Information and Communication Technology. Piscataway NJ:IEEE, 2017:402-405.
[47] West N E, ÓShea T. Deep architectures for modulation recognition[C]//IEEE International Symposium on Dynamic Spectrum Access Networks. Piscataway NJ:IEEE, 2017:1-6.
[48] Fu J, Zhao C, Li B, et al. Deep learning based digital signal modulation recognition[C]//The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems. Hohhot:Springer, 2015:955-964.
[49] Yang G Q, Yang G Q. Modulation classification based on extensible neural networks[J]. Mathematical Problems in Engineering, 2017, 2017:1-10.