A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

doi:10.1088/1674-1056/23/12/128401

中国物理B ›› 2014, Vol. 23 ›› Issue (12): 128401-128401.doi: 10.1088/1674-1056/23/12/128401

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇下一篇

A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

齐佩汉, 李赞, 司江勃, 高锐

State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China

收稿日期:2014-04-17 修回日期:2014-06-26 出版日期:2014-12-15 发布日期:2014-12-15
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Program Foundation of the Ministry of Education, China (Grant No. 20110203110011), and the 111 Project, China (Grant No. B08038).

A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

Qi Pei-Han (齐佩汉), Li Zan (李赞), Si Jiang-Bo (司江勃), Gao Rui (高锐)

State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China

Received:2014-04-17 Revised:2014-06-26 Online:2014-12-15 Published:2014-12-15
Contact: Qi Pei-Han E-mail:qipeihan@126.com
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 61301179), the Doctorial Program Foundation of the Ministry of Education, China (Grant No. 20110203110011), and the 111 Project, China (Grant No. B08038).

摘要/Abstract

摘要：

Spectrum sensing is an essential component to realize the cognitive radio, and the requirement for real-time spectrum sensing in the case of lacking prior information, fading channel, and noise uncertainty, indeed poses a major challenge to the classical spectrum sensing algorithms. Based on the stochastic properties of scalar transformation of power spectral density (PSD), a novel spectrum sensing algorithm, referred to as the power spectral density split cancellation method (PSC), is proposed in this paper. The PSC makes use of a scalar value as a test statistic, which is the ratio of each subband power to the full band power. Besides, by exploiting the asymptotic normality and independence of Fourier transform, the distribution of the ratio and the mathematical expressions for the probabilities of false alarm and detection in different channel models are derived. Further, the exact closed-form expression of decision threshold is calculated in accordance with Neyman–Pearson criterion. Analytical and simulation results show that the PSC is invulnerable to noise uncertainty, and can achive excellent detection performance without prior knowledge in additive white Gaussian noise and flat slow fading channels. In addition, the PSC benefits from a low computational cost, which can be completed in microseconds.

关键词: spectrum sensing, power spectral density, noise uncertainty, real time

Abstract:

Key words: spectrum sensing, power spectral density, noise uncertainty, real time

中图分类号: (Telecommunications: signal transmission and processing; communication satellites)

84.40.Ua

齐佩汉, 李赞, 司江勃, 高锐. A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems[J]. 中国物理B, 2014, 23(12): 128401-128401.

Qi Pei-Han (齐佩汉), Li Zan (李赞), Si Jiang-Bo (司江勃), Gao Rui (高锐). A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems[J]. Chin. Phys. B, 2014, 23(12): 128401-128401.

参考文献 0


[1]	Akyildiz I F, Lee W Y, Vuran M C and Mohanty S 2006 Comput. Networks 50 2127 doi: 10.1016/j.comnet.2006.05.001

[2]	Zheng S, Kam P, Liang Y and Zeng Y 2013 IEEE Trans. Wireless Commun. 12 1774 doi: 10.1109/TWC.2013.030413.120735

[3]	Haykin S 2005 IEEE Select. Areas Commun. 23 201 doi: 10.1109/JSAC.2004.839380

[4]	Zu Y X, Zhou J and Zeng C C 2010 Chin. Phys. B 19 119501

[5]	Zu Y X and Zhou J 2012 Chin. Phys. B 21 019501

[6]	De P and Liang Y C 2008 IEEE Trans. Veh. Technol. 57 2834 doi: 10.1109/TVT.2008.915520

[7]	Axell E, Leus G, Larsson E G and Poor H V 2012 IEEE Signal Process. Mag. 29 101 doi: 10.1109/MSP.2012.2183771

[8]	Liu Y, Peng Q C, Shao H Z, Peng Q H and Wang L 2013 Acta Phys. Sin. 62 078406 (in Chinese)

[9]	Zheng S L, Lou C Y and Yang X N 2010 Acta Phys. Sin. 59 3611 (in Chinese)

[10]	Cordeiro C, Challapali K, Birru D and Sai S N 2005 First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, November 8-11, 2005, p. 328

[11]	Shen L, Wang H, Zhang W and Zhao Z 2011 IEEE Trans. Wireless Commun. 10 1721 doi: 10.1109/TWC.2011.040511.101559

[12]	Sahai A, Hoven N and Tandra R 2004 Proc. Allerton Conf. Commun., Control, Comput., Monticello, October 3-7, 2004, p. 11

[13]	Lundén J, Koivunen V, Huttunen A and Poor H V 2009 IEEE Trans. Signal Process. 57 4182 doi: 10.1109/TSP.2009.2025152

[14]	Zeng Y H and Liang Y C 2009 IEEE Trans. Commun. 57 1784 doi: 10.1109/TCOMM.2009.06.070402

[15]	Zeng Y H and Liang Y C 2009 IEEE Trans. Veh. Technol. 58 1804 doi: 10.1109/TVT.2008.2005267

[16]	Bianchi P, Debbah M, Maida M and Najim J 2011 IEEE Trans. Inf. Theory 57 2400 doi: 10.1109/TIT.2011.2111710

[17]	Kortun A, Ratnarajah T, Sellathurai M, Zhong C J and Papadias C B 2011 IEEE J. Select. Topics Signal Process. 5 49 doi: 10.1109/JSTSP.2010.2066957

[18]	Penna F, Garello R, Figliloli D and Spirito M A 2009 Proc. 4th Int. Conf. Cognitive Radio Oriented Wireless Netw. Commun., June 16-18, 2009, Hannover, Germany, p. 1

[19]	Urkowitz H 1967 Proc. IEEE 55 523 doi: 10.1109/PROC.1967.5573

[20]	Axell E and Larsson G E 2011 IEEE J. Select. Areas Commun. 29 290 doi: 10.1109/JSAC.2011.110203

[21]	Gismalla E H 2011 IEEE Trans. Signal Process. 59 3712 doi: 10.1109/TSP.2011.2153849

[22]	Friman O, Volosyak I and Graser A 2007 IEEE Trans. Biomed. Eng. 54 742 doi: 10.1109/TBME.2006.889160

[23]	Quan Z, Cui S, Sayed A H and Poor H V 2009 IEEE Trans. Signal Process. 57 1128 doi: 10.1109/TSP.2008.2008540

[24]	Qi P H, Li Z, Si J B and Gao R 2013 Journal of Xidian University 40 19 (in Chinese)

[25]	Tandra R and Sahai A 2008 IEEE J. Select. Topics Signal Process. 2 4 doi: 10.1109/JSTSP.2007.914879

[26]	Altera

[27]	Gradshteyn I and Ryzhik I 2007 Table of Integrals, Series, and Products, 7th edn. (New York: Academic Press) pp. 276-278

A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

A robust power spectrum split cancellation-based spectrum sensing method for cognitive radio systems

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