基于瞬时特征参数和功率谱熵的联合调制识别

doi:10.16180/j.cnki.issn1007-7820.2022.11.015

Abstract

Abstract:

To solve the problem that the traditional instantaneous characteristic parameter recognition method has few signal types and low recognition rate under low SNR, a modulation recognition method based on the combination of instantaneous characteristic parameter and power spectrum entropy is proposed in this study. The improved instantaneous amplitude and phase characteristic parameters are used to identify the modulation signals, and the power spectrum entropy characteristic parameters are introduced to further realize the in-class recognition of more signals. The decision tree classification method is used to identify and classify the 9 common digital modulation signals {ASK, 4ASK, 2FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM} with appropriate threshold values. Monte Carlo experiment results show that compared with the existing recognition methods, the proposed method increases the number of signal types, and improves the signal recognition accuracy in the case of low SNR.

Key words: modulation recognition, instantaneous eigenvalue, feature parameters, power spectrum entropy

CLC Number:

TN911.72

XIE Aiping,ZHANG Yusheng,LIU Ying,HE Ziang,GAO Rui. Joint Modulation Recognition Based on Instantaneous Feature and Power Spectrum Entropy[J].Electronic Science and Technology, 2022, 35(11): 104-110.

Figures/Tables 7

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References 22

[1]	Õner M. On the classification of binary space shift keying modulation[J]. IEEE Communications Letters, 2018, 22(8):1584-1587. doi: 10.1109/LCOMM.2018.2840147
[2]	Su W. Feature space analysis of modulation classification using very high-order statistics[J]. IEEE Communications Letters, 2013, 17(9):1688-1691. doi: 10.1109/LCOMM.2013.080613.130070
[3]	冯晓东, 曾军. 基于决策论的数字调制信号识别方法[J]. 电子科技, 2015, 28(4):124-127.
	Feng Xiaodong, Zeng Jun. A method for digital modulation recognition based on decision theory[J]. Electronic Science and Technology, 2015, 28(4):124-127.
[4]	Xu J L, Su W, Zhou M. Likelihood ratio approaches to automatic modulation classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2011, 41(4):455-469. doi: 10.1109/TSMCC.2010.2076347
[5]	Hameed F, Dobre O A, Popescu D C. On the likelihood based approach to modulation classifcation[J]. IEEE Transactions on Wireless Communications, 2009, 8(12):5884-5892. doi: 10.1109/TWC.2009.12.080883
[6]	陈卫东. 数字通信信号调制识别算法研究[D]. 西安: 西安电子科技大学, 2001.
	Chen Weidong. Research on digital communication signal modulation recognition algorithm[D]. Xi'an: Xidian University, 2001.
[7]	Zhang J, Cabric D, Wang F, et al. Cooperative modulation classification for multipath fading channels via expectation-maximization[J]. IEEE Transactions on Wireless Communications, 2017, 16(10):6698-6711. doi: 10.1109/TWC.2017.2728530
[8]	Ozdemir O, Wimalajeewa T, Dulek B, et al. Asynchronous linear modulation classification with multiple sensors via generalized EM algorithm[J]. IEEE Transactionson Wireless Communications, 2015, 14(11):6389-6400.
[9]	Nandi A K, Azzouz E E. Automatic analogue modulation recognition[J]. Signal Processing, 1995, 46(2):211-222. doi: 10.1016/0165-1684(95)00083-P
[10]	Azzouz E E, Nandi A K. Automatic identification of digital modulation types[J]. Signal Processing, 1995, 47(1):55-69. doi: 10.1016/0165-1684(95)00099-2
[11]	于洋, 李孝严, 张晓春. BPSK,QPSK,UQPSK,64QAM信号自动调制识别[J]. 电子科技, 2015, 28(1):49-52.
	Yu Yang, Li Xiaoyan, Zhang Xiaochun. Automatic modulation recognition of BPSK QPSK,UQPSK,64QAM signal[J]. Electronic Science and Technology, 2015, 28(1):49-52.
[12]	李一兵, 葛娟, 林云. 基于熵特征和支持向量机的调制识别方法[J]. 系统工程与电子技术, 2012, 34(8):1691-1695.
	Li Yibing, Ge Juan, Lin Yun. Modulation recognition using entropy features and SVM[J]. Systems Engineering and Electronics, 2012, 34(8):1691-1695.
[13]	Hu Y Q, Juan L, Tan X. Digital modulation recognition based on instantaneous information[J]. The Journal of China Universities of Posts and Telecommunications, 2010, 17(3):52-90.
[14]	阙隆树, 冯全源. 数字调制信号识别中的相位去卷叠门限研究[J]. 信号处理, 2010, 26(1):56-59.
	Que Longshu, Feng Quanyuan. Study on phase unwrapping threshold of digital signal identification[J]. Signal Processing, 2010, 26(1):56-59.
[15]	Dai W, Wang Y, Wang J. Joint power estimation and modulation classification using second and higher statistics[C]. Orlando: Proceedings of the IEEE Wireless Communications & Networking Conference, 2002.
[16]	周围, 周正中, 张德民. 一种基于四阶累积量的空间特征盲估计方法[J]. 电子与信息学报, 2007, 29(1):101-104.
	Zhou Wei, Zhou Zhengzhong, Zhang Demin. A blind spatial signature estimation algorithm based on the fourth order cumulant[J]. Journal of Electronics & Information Technology, 2007, 29(1):101-104.
[17]	张利, 李青. 基于高阶累积量的调制识别方法的研究[J]. 信息工程大学学报, 2017, 18(4):23-28.
	Zhang Li, Li Qing. Research on modulation recognition method based on high-order cumulants[J]. Journal of Information Engineering University, 2017, 18(4):23-28.
[18]	张达敏, 王旭. 对基于决策论的数字信号调制识别方法的改进[J]. 计算机应用, 2009, 29(12):3227-3230. doi: 10.3724/SP.J.1087.2009.03227
	Zhang Damin, Wang Xu. Improved method of digital signals modulation identification based on decision theory[J]. Journal of Computer Applications, 2009, 29(12):3227-3230. doi: 10.3724/SP.J.1087.2009.03227
[19]	Li Y B, Ge J, Lin Y, et al. Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting[J]. Journal of Central South University, 2014, 21(11):4254-4260. doi: 10.1007/s11771-014-2422-5
[20]	赵凌伟. 一种新的通信信号识别方法研究[J]. 通讯世界, 2016(7):84-85.
	Zhao Lingwei. Research on a new method of communication signal recognition[J]. Communication World, 2016(7):84-85.
[21]	Yang Y Q, Zhang X Q. A modified method for digital modulation recognition based on instantaneous signal features[C]. Xiamen: Proceedings of the Third International Conference on Electronic Information Technology and Computer Engineering, 2019.
[22]	徐泽琨, 黄明, 丁照雨, 等. 基于信号特征值的判决调制模式识别设计[J]. 工业技术创新, 2019, 6(1):54-58.
	Xu Zekun, Huang Ming, Ding Zhaoyu, et al. Design of decision modulation pattern recognition based on signal eigenvalues[J]. Industrial Technology Innovation, 2019, 6(1):54-58.

特征参数	功率谱熵值1	功率谱熵值2
门限值1	2.3	3.05
门限值2	2.4	2.95
门限值3	2.5	3.00

特征参数	门限值
T₁	0.310
T₂	0.223
T₃	0.315
T₄	0.225
T₅	2.400
T₆	2.950
T₇	0.440
T₈	0.380

Joint Modulation Recognition Based on Instantaneous Feature and Power Spectrum Entropy

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