基于认知无线电的频谱感知技术研究
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摘要
伴随着无线通信技术的不断发展,无线频谱资源的需求也相应的不断增长,导致频谱资源匮乏现象日益显现,同时目前采用的固定频谱分配机制也限制了频谱资源的充分利用,导致了频谱利用率低下。认知无线电技术的提出,为解决频谱资源匮乏和频谱利用率低的问题提供了一种新的思路,被认为是当前解决上述问题的最佳方法。频谱感知技术作为认知无线电的关键技术,是实现认知无线电应用,提高认知系统频谱利用率的首要前提。本文主要研究认知无线电中的频谱感知技术,完成的工作主要包含两个部分:
在第一部分,本文将基于协方差矩阵Cholesky分解的单用户频谱感知扩展到多用户合作频谱感知,提出了基于协方差矩阵Cholesky分解的合作频谱检测算法,充分地联合利用多认知用户的信息进行合作检测来提高检测性能。此外,本文还提出了一种基于协方差矩阵Cholesky分解的合作频谱检测的改进算法,根据认知用户接收到信号的不同相关性,选择相关性较强的认知用户参与频谱检测,有效地降低算法的运算复杂度。
在第二部分,本文基于认知OFDM系统的多载波检测模型,给出了多载波联合频谱感知算法,并将多载波联合频谱感知算法应用于能耗受限的认知OFDM系统中,提出了能耗受限的多载波联合检测算法,即在给定感知能耗约束条件下,通过建立最大化吞吐量优化模型,联合多个载波选取最优感知时间和判决门限参数,使得认知用户的总吞吐量最大化。最后的仿真结果表明,采用多载波联合频谱感知算法可获得比传统的频谱感知算法更大的吞吐量,与能耗均分频谱感知算法相比较,能耗受限的多载波联合频谱感知算法可以达到更大的吞吐量,从而能够更好的节约能耗。
全文共分为五章,第一章主要介绍了本课题的研究背景、意义、现状和论文结构安排;第二章概述了认知无线电中的频谱感知技术理论;第三章研究了基于协方差矩阵的合作频谱感知技术;第四章研究了认知OFDM系统中的频谱感知技术;第五章对全文工作的总结和展望。
With wireless communications technology development, the demands on the wireless spectrum resources are continuously increased, which leads to spectrum scarcity phenomenon looming, and the current fixed spectrum allocation mechanism also limits the full use of the spectrum resource, leading to the poor spectrum utilization. The proposed cognitive radio technology provides a new way to address the scarcity of spectrum resources and low utilization of the spectrum, is considered to be the best method to solve the above problem. As a key technology of cognitive radio, spectrum sensing is the primary premise of achieving cognitive radio applications and improving the cognitive system spectrum utilization. This thesis mainly studies on spectrum sensing technology in cognitive radio, the main completed work contains of two parts .
In part one, this thesis proposes the cooperative spectrum sensing algorithm based on the cholesky factorization of covariance matrix by extending the single user spectrum sensing to multi-user cooperative spectrum sensing spectrum. The proposed algorithm can improve the sensing performance by joint detection using multi-user information. In addition, this thesis also proposes an improved cooperative spectrum sensing algorithm based on the cholesky factorization of covariance matrix. The proposed improved algorithm can effectively reduce the computational complexity by selecting the stronger relevant signal for detection among cognitive users.
In part two, based on multi-carrier detection model for OFDM system, this thesis gives multi-carrier joint spectrum sensing algorithm. Applied to the OFDM system with limited energy consumption, the multi-carrier joint spectrum sensing algorithm under the energy-constrained for OFDM system is proposed. Both the sensing time and the decision threshold parameters of each carrier are optimized, under the case of the energy-constrained, to maximize the total throughput of the system. The simulation results illustrate that multi-carrier joint spectrum sensing algorithm can achieve more throughput than traditional spectrum sensing algorithm, comparing with energy equipartition spectrum sensing algorithm, the multi-carrier joint spectrum sensing method under the energy-constrained for OFDM system not only has higher throughput, but also save more energy.
The organization of this thesis is as follows. Chapter one briefly describes the background and significance of this theme and situation and main work of this thesis. Chapter two summarizes the spectrum sensing technology theory in cognitive radio. Chapter three studies the cooperative spectrum sensing based on the covariance matrix. Chapter four studies the spectrum sensing for OFDM cognitive radio system. The last chapter presents the summary and prospect of this thesis.
在第一部分,本文将基于协方差矩阵Cholesky分解的单用户频谱感知扩展到多用户合作频谱感知,提出了基于协方差矩阵Cholesky分解的合作频谱检测算法,充分地联合利用多认知用户的信息进行合作检测来提高检测性能。此外,本文还提出了一种基于协方差矩阵Cholesky分解的合作频谱检测的改进算法,根据认知用户接收到信号的不同相关性,选择相关性较强的认知用户参与频谱检测,有效地降低算法的运算复杂度。
在第二部分,本文基于认知OFDM系统的多载波检测模型,给出了多载波联合频谱感知算法,并将多载波联合频谱感知算法应用于能耗受限的认知OFDM系统中,提出了能耗受限的多载波联合检测算法,即在给定感知能耗约束条件下,通过建立最大化吞吐量优化模型,联合多个载波选取最优感知时间和判决门限参数,使得认知用户的总吞吐量最大化。最后的仿真结果表明,采用多载波联合频谱感知算法可获得比传统的频谱感知算法更大的吞吐量,与能耗均分频谱感知算法相比较,能耗受限的多载波联合频谱感知算法可以达到更大的吞吐量,从而能够更好的节约能耗。
全文共分为五章,第一章主要介绍了本课题的研究背景、意义、现状和论文结构安排;第二章概述了认知无线电中的频谱感知技术理论;第三章研究了基于协方差矩阵的合作频谱感知技术;第四章研究了认知OFDM系统中的频谱感知技术;第五章对全文工作的总结和展望。
With wireless communications technology development, the demands on the wireless spectrum resources are continuously increased, which leads to spectrum scarcity phenomenon looming, and the current fixed spectrum allocation mechanism also limits the full use of the spectrum resource, leading to the poor spectrum utilization. The proposed cognitive radio technology provides a new way to address the scarcity of spectrum resources and low utilization of the spectrum, is considered to be the best method to solve the above problem. As a key technology of cognitive radio, spectrum sensing is the primary premise of achieving cognitive radio applications and improving the cognitive system spectrum utilization. This thesis mainly studies on spectrum sensing technology in cognitive radio, the main completed work contains of two parts .
In part one, this thesis proposes the cooperative spectrum sensing algorithm based on the cholesky factorization of covariance matrix by extending the single user spectrum sensing to multi-user cooperative spectrum sensing spectrum. The proposed algorithm can improve the sensing performance by joint detection using multi-user information. In addition, this thesis also proposes an improved cooperative spectrum sensing algorithm based on the cholesky factorization of covariance matrix. The proposed improved algorithm can effectively reduce the computational complexity by selecting the stronger relevant signal for detection among cognitive users.
In part two, based on multi-carrier detection model for OFDM system, this thesis gives multi-carrier joint spectrum sensing algorithm. Applied to the OFDM system with limited energy consumption, the multi-carrier joint spectrum sensing algorithm under the energy-constrained for OFDM system is proposed. Both the sensing time and the decision threshold parameters of each carrier are optimized, under the case of the energy-constrained, to maximize the total throughput of the system. The simulation results illustrate that multi-carrier joint spectrum sensing algorithm can achieve more throughput than traditional spectrum sensing algorithm, comparing with energy equipartition spectrum sensing algorithm, the multi-carrier joint spectrum sensing method under the energy-constrained for OFDM system not only has higher throughput, but also save more energy.
The organization of this thesis is as follows. Chapter one briefly describes the background and significance of this theme and situation and main work of this thesis. Chapter two summarizes the spectrum sensing technology theory in cognitive radio. Chapter three studies the cooperative spectrum sensing based on the covariance matrix. Chapter four studies the spectrum sensing for OFDM cognitive radio system. The last chapter presents the summary and prospect of this thesis.
引文
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[3] Bernardo F, Perez-Romero J, Salient O. and Agusti R. Advanced spectrum management in multicell OFDMA networks enabling cognitive radio usage[J]. IEEE Wireless Commun- ications and Networking Conference, 2008,12(3):1927-1932.
[4] Blum R S, Kassam S A, Poor H V. Distributed detection with multiple sensors: part II-advanced topics[J]. Proceedings of the IEEE, 1997,85(1):64-79.
[5] Buddhikot M M. Directions in wireless networking using coordinated dynamic spectrum access[C]. WoWMOM, 2005.
[6] Chair Z, Varshney P K. Optimal data fusion in multiple sensor detection systems[J]. IEEE Trans Aerosp Electron Syst, 1986,22(1):98–101.
[7] Chen Y F. Improved energy detector for random signals in gaussian noise[J]. IEEE Transaction on Wireless Communications, 2010,9(2):558-562.
[8]褚御芝,郑宝玉,季薇.协同频谱感知中的融合策略[J].南京邮电大学学报, 2010,30(3):39-43.
[9] Fan W, Krunz M, Cui S G. Price-based spectrum management in cognitive radio networks [J]. IEEE Journal of Selected Topics in Signal Processing, 2008,2(1):74-87.
[10]范永辉. Bartlett分解与多元正态总体均值的广义推断[J].数学学报, 2010,53(2):329- 331.
[11] Farhang B B, Kempter R M. Communication techniques for spectrum sensing and commu- nication in cognitive radios[J]. IEEE Communications Magazine, 2008,46(4):80-85.
[12] Fcc. ET Docket No 02-135. Spectrum policy task force report[R]. FCC ET Docket, 2002.
[13]郭彩丽,冯春燕,曾志民.认知无线电网络技术及应用[M].北京:电子工业出版社, 2010.
[14] Han N, Shon S H, Joo J O, Kim J M. Spectral correlation based signal detection method for spectrum sensing in IEEE 802.22 WRAN systems [C]. Intern. Conf. Advanced Commu- nication Technology, (Korea), Feb. 2006. USA: IEEE, 2006.
[15] Haykin S. Cognitive radio: brain-empowered wireless communications[J]. IEEE Journal on Selected Areas in Communications, 2005,23 (2):201–220.
[16] IEEE 802 LAN/MAN Standards committee 802.2 WG [EB/OL]. (2004-01-01) .[2011-10- 19]. http://www.ieee802.org/ 22.
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[18] Johnstone I M. On the distribution of the largest eigenvalue in principle components analysis [J]. The Annals of Statistics, 2001,29(2):295-327.
[19] Kattepur A K, Hoang A T. Data and decision fusion for distributed spectrum sensing in cognitive radio networks[C]. ICICS, 2007.
[20] Krikke J, Alfonso B. In the news[J]. IEEE Intelligent Systems, 2006,21(3):4-7.
[21] Krishnamoorthy K. Handbook of statistical distributions with applications[M]. Chapman & Hall/CRC, 2006.
[22] Leaves P, Grandblaise D, Bourse D. Dynamic spectrum allocation in composite reconfi- gurable wireless networks[J]. IEEE Communications Magazine, 2004,12(5):72~81.
[23] Lehtomaki J J, Vartiainen J, Juntti M. Spectrum sensing with forward methods[C]. Military Communications Conference, MILCOM, 2006.
[24] Li Z X, Wang H, Kuang J M. A two-step spectrum sensing scheme for cognitive radio networks[J]. International Conference on Information Science and Technology, 2011,12 (3):694- 698.
[25] Liang Y C, Zeng Y H, Peh E. C. Y. Hoang. Sensing-throughput tradeoff for cognitive radio networks[J]. IEEE Trans Wireless Commun, 2008,7(4):1326-1337.
[26] Marcus M.J. Unlicensed cognitive sharing of TV spectrum: the controversy at the federal communications commission[J]. IEEE Communications Magazine, 2005,45(5):23-25.
[27] Mitola J, Maquire G Q. Cognitive radio: making software radios more personal [J]. IEEE Personal Communications, 1999,6(4):13-18.
[28] Nautilus project website[EB/OL].(2009-03-11).[2011-10-19]. http://www.cs.ucsb.edu/ht- zheng/cognitive/nautilus.html.
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[30] U.S. Frequency allocation chart as of october 2003[EB/OL].(2003-10-01).[2011-10-19]. http://www.ntia.doc.gov/osmhome/allochrt.html.
[31] Nualart D, Peccati G. Central limit theorems for sequences of multiple stochastic integrals [J]. Annals of Probability, 2005,33 (1):177-193.
[32] Peng Q H, Zeng K, Wang J, Li S Q. A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio context[C]. Proc IEEE PIMRC, 2006.
[33] Richard V N. OFDM for wireless multimedia communications[M]. Boston London: Arte-ch House, 2000.
[34] Sahai A, Hoven N, Tandra R. Some fundamental limits on cognitive radio[C]. Allenton Conference on Communications. Control and Computing. Oct. 2004:66~69.
[35] Shankar S. Spectrum agile radios: utilization and sensing architecture[C]. IEEE DySPAN 2005, 2005.
[36] Shen J, Xie G, Liu S, Zeng L, Gao J and Liu Y. Soft versus hard cooperative energy detection under low SNR[J]. IEICE Thans. On Communications, 2008,91(11):3732-3735.
[37]孙丽丽.基于协方差的协作频谱感知技术研究[D].吉林:吉林大学硕士学位论文,2011.
[38] Tulino A M, Verdu S. Random matrix theory and wireless communication[M]. Boston: Now Publish Inc, 2004.
[39] Tuttlebee W著,杨小牛译.软件无线电技术与实现[M].北京:电子工作出版社, 2004.
[40] Urkowitz H. Energy detection of unknown deterministic signals[J]. Proceedings of the IEEE.1967,55(4):523-531.
[41] Visser F E, Janssen G J M, Pawelczak P. Multi-node spectrum sensing based on energy detection for dynamic spectrum access[J]. IEEE Radio Communication Magazine, 2007,42(3):20-25.
[42]王磊,郑宝玉,李雷.基于随机矩阵理论的协作频谱感知[J].电子与信息学报, 2009,31(8):1925-1929.
[43]王文博,郑侃著.宽带无线通信OFDM技术[M].北京:人民邮电出版社, 2007.
[44] Weiss T A, Jondral F K. Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency[J]. IEEE Radio Communications, 2004,42(3):8-14.
[45] Wild B, Ramchandran K. Detecting primary receivers for cognitive radio applications[C]. DySPAN 2005, First IEEE International Symposium, Nov, 2005.
[46]杨淼,安建平,陈宁,卫景宠.基于Duffing混沌系统的频谱感知算法[J].北京理工大学学报, 2011,31(3):329-332.
[47] Yang W D, Cai Y M, Xu Y Y. A fuzzy collaborative spectrum sensing scheme in cognitive radio[C]. Proceedings of 2007 International Symposium on Intelligent Signal Processing and Communication Systems, 2007.
[48] Yang X, Lei K J, Cao X Y. Blind diction for primary user based on the sample covariance matrix in cognitive radio[J]. IEEE Communications Actions Letters, 2011,15(1):40-42.
[49] Yucek T, Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys and Tutorials, 2009,11(1):116-130.
[50] Zeng Y H, Liang Y C. Covariance based signal detections for cognitive radio[C]. New Communication Conference, 2006.
[51] Zeng Y H, Liang Y C. Eigenvalue based spectrum sensing algorithms for cognitive radio[J]. IEEE Trans Commu, 2009,57(6):1784-1793.
[52] Zeng Y H, Liang Y C. Spectrum sensing algorithm for cognitive radio based on statistical covariance[J]. IEEE Transactions on Vehicular Technology, 2009,58(4):1804-1805.
[53] Zeng Y H, Liang Y C, Zhang R. Blindly combined energy detection for spectrum sensing in cognitive radio[J]. IEEE Signal Processing Letters, 2008,15:649-652.
[54]张平,冯志勇.认知无线网络[M].北京:科学出版社,2010.
[55] Zhang W, Letaief K B. Cooperative spectrum sensing with transmits and relay diversity in cognitive radio network[J]. IEEE Transactions on Wireless Communication, 2008,7(12): 4761-4766.
[56]张怡,席博,黄印,陈利民.能量和协方差检验的联合频谱感知算法研究[J].航空计算技术, 2011,41(2):113-116.
[57]张中兆,一种新的循环谱统计量频谱感知算法.北京交通大学学报, 2009,33(5):25-29.
[58]朱道元,赵桂芹. Bartlett分解定理的简便证明[J].东南大学学报, 2001,31(5):125-126.
[59] Zhuan Y, Gokhan M, John G. Energy detection using estimation noise variance for spectrum sensing in cognitive radio networks[C]. IEEE Communications Society subject matter experts for publication in the WCNC, 2008.
[2] Bai Z D, Silverstein J W. Eigenvalues of large sample covariance matrices of spiked population models [J]. Journ. of Mult. Anal (S1095-7243), 2006,97(6):1382-1408.
[3] Bernardo F, Perez-Romero J, Salient O. and Agusti R. Advanced spectrum management in multicell OFDMA networks enabling cognitive radio usage[J]. IEEE Wireless Commun- ications and Networking Conference, 2008,12(3):1927-1932.
[4] Blum R S, Kassam S A, Poor H V. Distributed detection with multiple sensors: part II-advanced topics[J]. Proceedings of the IEEE, 1997,85(1):64-79.
[5] Buddhikot M M. Directions in wireless networking using coordinated dynamic spectrum access[C]. WoWMOM, 2005.
[6] Chair Z, Varshney P K. Optimal data fusion in multiple sensor detection systems[J]. IEEE Trans Aerosp Electron Syst, 1986,22(1):98–101.
[7] Chen Y F. Improved energy detector for random signals in gaussian noise[J]. IEEE Transaction on Wireless Communications, 2010,9(2):558-562.
[8]褚御芝,郑宝玉,季薇.协同频谱感知中的融合策略[J].南京邮电大学学报, 2010,30(3):39-43.
[9] Fan W, Krunz M, Cui S G. Price-based spectrum management in cognitive radio networks [J]. IEEE Journal of Selected Topics in Signal Processing, 2008,2(1):74-87.
[10]范永辉. Bartlett分解与多元正态总体均值的广义推断[J].数学学报, 2010,53(2):329- 331.
[11] Farhang B B, Kempter R M. Communication techniques for spectrum sensing and commu- nication in cognitive radios[J]. IEEE Communications Magazine, 2008,46(4):80-85.
[12] Fcc. ET Docket No 02-135. Spectrum policy task force report[R]. FCC ET Docket, 2002.
[13]郭彩丽,冯春燕,曾志民.认知无线电网络技术及应用[M].北京:电子工业出版社, 2010.
[14] Han N, Shon S H, Joo J O, Kim J M. Spectral correlation based signal detection method for spectrum sensing in IEEE 802.22 WRAN systems [C]. Intern. Conf. Advanced Commu- nication Technology, (Korea), Feb. 2006. USA: IEEE, 2006.
[15] Haykin S. Cognitive radio: brain-empowered wireless communications[J]. IEEE Journal on Selected Areas in Communications, 2005,23 (2):201–220.
[16] IEEE 802 LAN/MAN Standards committee 802.2 WG [EB/OL]. (2004-01-01) .[2011-10- 19]. http://www.ieee802.org/ 22.
[17]解可新,韩健,林友联.最优化方法[M].天津:天津大学出版社. 2008.
[18] Johnstone I M. On the distribution of the largest eigenvalue in principle components analysis [J]. The Annals of Statistics, 2001,29(2):295-327.
[19] Kattepur A K, Hoang A T. Data and decision fusion for distributed spectrum sensing in cognitive radio networks[C]. ICICS, 2007.
[20] Krikke J, Alfonso B. In the news[J]. IEEE Intelligent Systems, 2006,21(3):4-7.
[21] Krishnamoorthy K. Handbook of statistical distributions with applications[M]. Chapman & Hall/CRC, 2006.
[22] Leaves P, Grandblaise D, Bourse D. Dynamic spectrum allocation in composite reconfi- gurable wireless networks[J]. IEEE Communications Magazine, 2004,12(5):72~81.
[23] Lehtomaki J J, Vartiainen J, Juntti M. Spectrum sensing with forward methods[C]. Military Communications Conference, MILCOM, 2006.
[24] Li Z X, Wang H, Kuang J M. A two-step spectrum sensing scheme for cognitive radio networks[J]. International Conference on Information Science and Technology, 2011,12 (3):694- 698.
[25] Liang Y C, Zeng Y H, Peh E. C. Y. Hoang. Sensing-throughput tradeoff for cognitive radio networks[J]. IEEE Trans Wireless Commun, 2008,7(4):1326-1337.
[26] Marcus M.J. Unlicensed cognitive sharing of TV spectrum: the controversy at the federal communications commission[J]. IEEE Communications Magazine, 2005,45(5):23-25.
[27] Mitola J, Maquire G Q. Cognitive radio: making software radios more personal [J]. IEEE Personal Communications, 1999,6(4):13-18.
[28] Nautilus project website[EB/OL].(2009-03-11).[2011-10-19]. http://www.cs.ucsb.edu/ht- zheng/cognitive/nautilus.html.
[29] Neihart N M, Roy S, Allstot D J. Multi-resolution sensing technique for multiple antenna cognitive radio[C]. Circuits and System,2007. ISCAS2007.IEEE International Symposium on,2007.
[30] U.S. Frequency allocation chart as of october 2003[EB/OL].(2003-10-01).[2011-10-19]. http://www.ntia.doc.gov/osmhome/allochrt.html.
[31] Nualart D, Peccati G. Central limit theorems for sequences of multiple stochastic integrals [J]. Annals of Probability, 2005,33 (1):177-193.
[32] Peng Q H, Zeng K, Wang J, Li S Q. A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio context[C]. Proc IEEE PIMRC, 2006.
[33] Richard V N. OFDM for wireless multimedia communications[M]. Boston London: Arte-ch House, 2000.
[34] Sahai A, Hoven N, Tandra R. Some fundamental limits on cognitive radio[C]. Allenton Conference on Communications. Control and Computing. Oct. 2004:66~69.
[35] Shankar S. Spectrum agile radios: utilization and sensing architecture[C]. IEEE DySPAN 2005, 2005.
[36] Shen J, Xie G, Liu S, Zeng L, Gao J and Liu Y. Soft versus hard cooperative energy detection under low SNR[J]. IEICE Thans. On Communications, 2008,91(11):3732-3735.
[37]孙丽丽.基于协方差的协作频谱感知技术研究[D].吉林:吉林大学硕士学位论文,2011.
[38] Tulino A M, Verdu S. Random matrix theory and wireless communication[M]. Boston: Now Publish Inc, 2004.
[39] Tuttlebee W著,杨小牛译.软件无线电技术与实现[M].北京:电子工作出版社, 2004.
[40] Urkowitz H. Energy detection of unknown deterministic signals[J]. Proceedings of the IEEE.1967,55(4):523-531.
[41] Visser F E, Janssen G J M, Pawelczak P. Multi-node spectrum sensing based on energy detection for dynamic spectrum access[J]. IEEE Radio Communication Magazine, 2007,42(3):20-25.
[42]王磊,郑宝玉,李雷.基于随机矩阵理论的协作频谱感知[J].电子与信息学报, 2009,31(8):1925-1929.
[43]王文博,郑侃著.宽带无线通信OFDM技术[M].北京:人民邮电出版社, 2007.
[44] Weiss T A, Jondral F K. Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency[J]. IEEE Radio Communications, 2004,42(3):8-14.
[45] Wild B, Ramchandran K. Detecting primary receivers for cognitive radio applications[C]. DySPAN 2005, First IEEE International Symposium, Nov, 2005.
[46]杨淼,安建平,陈宁,卫景宠.基于Duffing混沌系统的频谱感知算法[J].北京理工大学学报, 2011,31(3):329-332.
[47] Yang W D, Cai Y M, Xu Y Y. A fuzzy collaborative spectrum sensing scheme in cognitive radio[C]. Proceedings of 2007 International Symposium on Intelligent Signal Processing and Communication Systems, 2007.
[48] Yang X, Lei K J, Cao X Y. Blind diction for primary user based on the sample covariance matrix in cognitive radio[J]. IEEE Communications Actions Letters, 2011,15(1):40-42.
[49] Yucek T, Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys and Tutorials, 2009,11(1):116-130.
[50] Zeng Y H, Liang Y C. Covariance based signal detections for cognitive radio[C]. New Communication Conference, 2006.
[51] Zeng Y H, Liang Y C. Eigenvalue based spectrum sensing algorithms for cognitive radio[J]. IEEE Trans Commu, 2009,57(6):1784-1793.
[52] Zeng Y H, Liang Y C. Spectrum sensing algorithm for cognitive radio based on statistical covariance[J]. IEEE Transactions on Vehicular Technology, 2009,58(4):1804-1805.
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