认知无线电中非高斯噪声下数字调制信号识别方法

doi:10.3969/j.issn.1000-436x.2014.01.010

Abstract

Abstract:

In cognitive radio system,the traditional methods of digital modulation signals recognition with Alpha stable distribution noise have the problems of poor performance and high computation complexity.A novel recognition method based on correlation coefficient of fractional lower order cyclic spectrum was proposed to solve this problem.The method extracts the recognition characteristic parameters which are five correlation coefficients of the fractional lower order cyclic spectrum’s section and frequency spectrum’s section and projection planes of both,and then decision tree was used as a classifier to achieve digital modulation signals recognition.Simulation results show that the proposed method not only has higher recognition rates and better robustness but also has lower computation complexity in an Alpha stable distribution noise environment,which is more suitable for cognitive radio system.

Key words: cognitive radio, modulation recognition, Alpha stable distribution, fractional lower order cyclic spectrum, correlation coefficient

Ming-qian LIU,Bing-bing LI,Chao-feng CAO,Zhao LI. Recognition method of digital modulation signals over non-Gaussian noise in cognitive radio[J]. Journal on Communications, 2014, 35(1): 82-88.

Figures/Tables 8

计算复杂度	本文方法	文献[8]方法	文献[9]方法
加法次数	N lbN+(M?1)N+2N?1 (M ＞＞1,M=2ⁿ)	N lbN+(M?1)N+N (M ＞＞1,M=2ⁿ)	N lbN+(M?1)N+N(N?1) (M ＞＞1,M=2ⁿ)
乘法次数	$N^{2} M + \frac{N}{2} 1 b N + 3 N + 2$	$N^{2} M + \frac{N}{2} 1 b N + 3 N + N^{2}$	$N^{2} M + \frac{N}{2} 1 b N + N^{2}$
	(M ＞＞1,M=2ⁿ)	(M ＞＞1,M=2ⁿ)	(M ＞＞1,M=2ⁿ)
比较次数	0	$\frac{N^{2} (N - 1)}{2}$	0

References 12

[1]	MITOLA J , MAGUIRE G Q . Cognitive radio:making software radios more personal[J]. IEEE Personal Communications, 1999,6(4): 13-18.
[2]	SHI Q H , KARASAWA Y . Automatic modulation identification based on the probability density function of signal phase[J]. IEEE Transaction on Communications, 2012,60(4): 1033-1044.
[3]	TSIHRINTZIS G A , NIKIAS C L . Fast estimation of the parameters of alpha-stable impulsive interference[J]. IEEE Transaction on Signal Processing, 1996,44(6): 1492-1503.
[4]	MA X Y , NIKIAS C L . Parameter estimation and blind channel identifications in impulsive signal environments[J]. IEEE Transaction on Signal Processing, 1995,43(12): 2884-2997.
[5]	WANG F G , WANG X D . Fast and robust modulation classification via kolmogorov-smirnov test[J]. IEEE Transaction on Communications, 2010,58(8): 2324-2332.
[6]	杨伟超, 赵春晖, 成宝芝 . Alpha 稳定分布噪声下的通信信号识别[J]. 应用科学学报, 2010,28(2): 111-114. YANG W C , ZHAO C H , CHENG B Z . Recognition of communication dignals in noise with Alpha dtable distribution[J]. Journal of Applied Science, 2010,28(2): 111-114.
[7]	贺涛 . 数字通信信号调制识别若干新问题研究[D]. 电子科技大学, 2007. HE T . Study on Some Novel Issues of Digital Communication Signal Modulation Classification[D]. University of Electronic Science and Technology of China, 2007.
[8]	赵春晖, 杨伟超, 杜宇 . 采用分数低阶循环谱相干系数的调制识别[J]. 应用科学学报, 2011,29(6): 565-570. ZHAO C H , YANG W C , DU Y . Modulation recognition using fractional low-order cyclic spectrum coherence coefficient[J]. Journal of Applied Science, 2011,29(6): 565-570.
[9]	赵春晖, 杨伟超, 马爽 . 基于广义二阶循环统计量的通信信号调制识别研究[J]. 通信学报, 2011,32(1): 144-150. ZHAO C H , YANG W C , MA S . Research on communication signal modulation recognition based on the generalized second-order cyclic statistics[J]. Journal on Communications, 2011,32(1): 144-150.
[10]	MA X Y , NIKIAS C L . Joint estimation of time delay and frequency delay in impulsive noise using fractional lower order statistics[J]. IEEE Transactions on Signal Processing, 1996,44(11): 2669-2687.
[11]	LIU Y , QIU T S . Exploitation of cyclostationarity using fractional lower-order cyclic statistics[A]. 2011 7th International Conference on Wireless Communications,Networking and Mobile Computing (IEEE WiCOM)[C]. Wuhan,China, 2011. 1-4.
[12]	WANG J , KURUOGLU E E , ZHOU T . Alpha-stable channel capacity[J]. IEEE Communications Letters, 2011,15(10): 1107-1109.

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调制信号	ρ₁	ρ₂	ρ₃	ρ₄	ρ₅
BPSK	0.056 3～0.105 3	0.809 3～0.810 4	0.728 0～0.769 8	0.317 3～0.400 4	0.399 5～0.503 7
QPSK	0.257 3～0.588 5	0.897 8～0.995 2	0.399 0～0.684 5	0.506 6～0.579 6	0.361 5～0.608 2
2FSK	0.143 6～0.418 4	0.819 1～0.910 7	0.735 4～0.812 4	0.289 6～0.506 4	0.423 7～0.538 2
4FSK	0.229 7～0.437 4	0.827 1～0.979 5	0.746 4～0.848 7	0.277 9～0.563 7	0.579 3～0.643 4
MSK	0.271 6～0.568 5	0.915 8～0.996 0	0.401 1～0.709 1	0.388 4～0.461 5	0.373 7～0.556 3

调制信号		本文方法			文献[8]方法			文献[9]方法
调制信号	0 dB		5 dB	0 dB		5 dB	0 dB		5 dB
2FSK	100		100	100		100	84.5		90
4FSK	93		96.5	90		92.5	78.5		85
MSK	94		98	94		97	87		92
BPSK	100		100	75		97.5	100		100
QPSK	99.5		100	95		98	98		100

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Recognition method of digital modulation signals over non-Gaussian noise in cognitive radio

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