认知无线电中基于多节点的协作频谱检测
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摘要
认知无线电是解决频谱短缺问题的一个有效的技术,频谱检测技术是认知无线电的核心技术之一,认知用户通过对所处的环境进行检测以发现“频谱空洞”,从而在不影响授权用户的情况下提高频谱利用率。由于受到多径效应和深度衰落的影响,“隐终端”问题严重影响频谱检测的性能。协作频谱检测能够有效应对“隐终端”问题,有效地提高系统检测性能。本文从多个角度对基于多节点的协作频谱检测进行了深入的研究,针对参与检测用户数和系统吞吐量进行了参数优化,并提出了一种用于分簇协作检测的分簇算法。
本文研究了基于能量检测的频谱检测方法,分析了基于多节点的协作检测的系统模型,研究了基于不同数据融合准则下的协作检测算法,分析了信噪比和采样点数对检测概率和虚警概率的性能影响。仿真结果表明三种基于数据融合的协作频谱检测算法与基于单节点的频谱检测相比都在一定程度上提高了检测性能。
对于协作检测,参与的用户数越多,系统的频谱检测性能越好,但是随着用户数增加,系统用于检测的开销迅速增大。基于最佳用户数的协作检测研究的是如何在满足系统性能指标的前提下,折中考虑检测性能和检测开销,对参与协作检测的用户数进行优化。本文分析了基于“与”和“或”准则的协作检测的最佳用户数问题,分别在瑞利衰落信道和Nakagami信道下,对最佳用户数问题进行仿真,从而得到不同衰落信道下的最佳用户数。
本文针对认知无线电中协作频谱检测的参数给出基于协作检测的系统吞吐量的概念,并推导出系统吞吐量的数学表达式。研究了基于最优系统吞吐量的参数优化问题,通过数学推导证明存在一定的虚警概率和用户数使得系统吞吐量最大,并分别得出最优虚警概率和最优用户数的表达式。仿真结果也证明的理论推导的正确性。
最后本文对认知无线电网络采用分层结构进行检测,将网络划分为簇,簇内普通节点和簇头构成第一层网络;簇首构成第二层网络,负责数据的远距离路由转发。分层网络既可以保证原有覆盖范围内的数据通信,也可以大幅节省网络能量。本文提出了一种基于leach分簇协议的改进算法,该算法中结合了协作检测中的用户分集技术与leach分簇协议中的动态分簇技术,簇头选举过程中采用分层式的簇头选举机制,既延长了网络生存时间,又提高了网络的检测性能。仿真结果表明,基于leach改进算法的分簇频谱检测系统具有良好的性能,在保证网络整体能量有效利用的同时,提高了系统的检测性能。
With the rapid growth of wireless communications, spectrum scarcity becomes more and more serious under the low utilization of spectrum resources. Facing with the conflict, cognitive radio emerges as the solution of spectrum scarcity. It is an intelligent wireless telecommunication system allowing efficient utilization by spectrum sensing, self adaptation and dynamic spectrum sharing according to its environment sensing. Therefore, cognitive radio is recognized as an agile, intelligent and reliable communication system with ability of occupying plenty of unused spectrum under the condition that it don’t cause interference to licensed system. Spectrum sensing is the core technology of cognitive radio, Cognitive user senses the environment to find the“spectrum hole”, thus increasing the frequency spectrum utilization without affecting the licensed user. Because of the effect of multipath effect and the deep fading,“hidden terminal”problem has severely affected the performance of spectrum testing. Collaborative spectrum sensing can solve the hidden terminal problem and increase the system sensing performance. From several aspects, this paper focus on collaboration spectrum testing which is based on node and researches parameter optimization on the system sensing costs and system throughput. This thesis also puts forward the collaborative detection algorithm based on clustered network.
This thesis studies spectrum sensing based on single node. Spectrum sensing technology are comprehensively introduced, including three methods which is respectively based on matched filter、cyclostationary characteristics and energy detections. We focus on the method based on energy detection and its system model. The energy detection is simulated ,and the performance analysis of system sensing is given with curve describing detection probability、alarm probability、SNR and sampling points. We also research cooperative spectrum sensing based on multi nodes and analyse the system model. The survey of cooperative spectrum sensing using different data fusion rules is given. MATLAB simulation is presented, comparing every algorithm performance.
The more users engaged in cooperative spectrum detection, the better the system performance of spectrum detection. However, when more users collaborate, detection overhead increases rapidly. We put forward the cooperative spectrum detection based on optimal number of users to solve the problem how to optimize the number of users in cooperative spectrum detection under the condition that the system resource is limited. The thesis analyses the optimal number of users problem in cooperative spectrum detection which is respectively based on“and”and“or”data fusion rule. The performance of problem in Rayleigh and Nakagami fading channel is stimulated in order to find the optimal number.
The research on cooperative spectrum sensing based on 802.22 protocol is viewed from optimal system throughout. We introduce the concept and system model of 802.22 protocol. The system throughout based on cooperative spectrum sensing is proposed with the parameters of spectrum sensing. And mathematical formula is presented. In this thesis, we study the optimization of parameters based on system throughout. The mathematical deduction proves that there exists certain false alarm probability and subscribers which makes system throughout optimal. The mathematical formula is deduced.
In cognitive radio we use hierarchical networks. Users are divides into clusters:Common cluster member and cluster head constitute the first level of network; clusters make the higher level networks, which is used for long distance transmission. Hierarchical networks can save much energy while it ensures the range of data communication. In the paper, we put forward a improvement algorithm, which combines multi users diversity in cooperative spectrum and dynamic clustering in LEACH protocol. In the cluster voting process, hierarchical voting is used to enhance the system sensing performance. Simulation results show that the new algorithm has good performance. It enhances system sensing capability, ensuring the whole network’s energy use ratio.
本文研究了基于能量检测的频谱检测方法,分析了基于多节点的协作检测的系统模型,研究了基于不同数据融合准则下的协作检测算法,分析了信噪比和采样点数对检测概率和虚警概率的性能影响。仿真结果表明三种基于数据融合的协作频谱检测算法与基于单节点的频谱检测相比都在一定程度上提高了检测性能。
对于协作检测,参与的用户数越多,系统的频谱检测性能越好,但是随着用户数增加,系统用于检测的开销迅速增大。基于最佳用户数的协作检测研究的是如何在满足系统性能指标的前提下,折中考虑检测性能和检测开销,对参与协作检测的用户数进行优化。本文分析了基于“与”和“或”准则的协作检测的最佳用户数问题,分别在瑞利衰落信道和Nakagami信道下,对最佳用户数问题进行仿真,从而得到不同衰落信道下的最佳用户数。
本文针对认知无线电中协作频谱检测的参数给出基于协作检测的系统吞吐量的概念,并推导出系统吞吐量的数学表达式。研究了基于最优系统吞吐量的参数优化问题,通过数学推导证明存在一定的虚警概率和用户数使得系统吞吐量最大,并分别得出最优虚警概率和最优用户数的表达式。仿真结果也证明的理论推导的正确性。
最后本文对认知无线电网络采用分层结构进行检测,将网络划分为簇,簇内普通节点和簇头构成第一层网络;簇首构成第二层网络,负责数据的远距离路由转发。分层网络既可以保证原有覆盖范围内的数据通信,也可以大幅节省网络能量。本文提出了一种基于leach分簇协议的改进算法,该算法中结合了协作检测中的用户分集技术与leach分簇协议中的动态分簇技术,簇头选举过程中采用分层式的簇头选举机制,既延长了网络生存时间,又提高了网络的检测性能。仿真结果表明,基于leach改进算法的分簇频谱检测系统具有良好的性能,在保证网络整体能量有效利用的同时,提高了系统的检测性能。
With the rapid growth of wireless communications, spectrum scarcity becomes more and more serious under the low utilization of spectrum resources. Facing with the conflict, cognitive radio emerges as the solution of spectrum scarcity. It is an intelligent wireless telecommunication system allowing efficient utilization by spectrum sensing, self adaptation and dynamic spectrum sharing according to its environment sensing. Therefore, cognitive radio is recognized as an agile, intelligent and reliable communication system with ability of occupying plenty of unused spectrum under the condition that it don’t cause interference to licensed system. Spectrum sensing is the core technology of cognitive radio, Cognitive user senses the environment to find the“spectrum hole”, thus increasing the frequency spectrum utilization without affecting the licensed user. Because of the effect of multipath effect and the deep fading,“hidden terminal”problem has severely affected the performance of spectrum testing. Collaborative spectrum sensing can solve the hidden terminal problem and increase the system sensing performance. From several aspects, this paper focus on collaboration spectrum testing which is based on node and researches parameter optimization on the system sensing costs and system throughput. This thesis also puts forward the collaborative detection algorithm based on clustered network.
This thesis studies spectrum sensing based on single node. Spectrum sensing technology are comprehensively introduced, including three methods which is respectively based on matched filter、cyclostationary characteristics and energy detections. We focus on the method based on energy detection and its system model. The energy detection is simulated ,and the performance analysis of system sensing is given with curve describing detection probability、alarm probability、SNR and sampling points. We also research cooperative spectrum sensing based on multi nodes and analyse the system model. The survey of cooperative spectrum sensing using different data fusion rules is given. MATLAB simulation is presented, comparing every algorithm performance.
The more users engaged in cooperative spectrum detection, the better the system performance of spectrum detection. However, when more users collaborate, detection overhead increases rapidly. We put forward the cooperative spectrum detection based on optimal number of users to solve the problem how to optimize the number of users in cooperative spectrum detection under the condition that the system resource is limited. The thesis analyses the optimal number of users problem in cooperative spectrum detection which is respectively based on“and”and“or”data fusion rule. The performance of problem in Rayleigh and Nakagami fading channel is stimulated in order to find the optimal number.
The research on cooperative spectrum sensing based on 802.22 protocol is viewed from optimal system throughout. We introduce the concept and system model of 802.22 protocol. The system throughout based on cooperative spectrum sensing is proposed with the parameters of spectrum sensing. And mathematical formula is presented. In this thesis, we study the optimization of parameters based on system throughout. The mathematical deduction proves that there exists certain false alarm probability and subscribers which makes system throughout optimal. The mathematical formula is deduced.
In cognitive radio we use hierarchical networks. Users are divides into clusters:Common cluster member and cluster head constitute the first level of network; clusters make the higher level networks, which is used for long distance transmission. Hierarchical networks can save much energy while it ensures the range of data communication. In the paper, we put forward a improvement algorithm, which combines multi users diversity in cooperative spectrum and dynamic clustering in LEACH protocol. In the cluster voting process, hierarchical voting is used to enhance the system sensing performance. Simulation results show that the new algorithm has good performance. It enhances system sensing capability, ensuring the whole network’s energy use ratio.
引文
[1]樊昌信,詹道庸,徐炳祥等.通信原理(第5版)[M].国防工业出版社,2001年5月.
[2] Federal Communications Commission, Spectrum Policy Task Force, Rep. ET Docket No.02-135[R]. Nov. 2002.
[3] Federal Communication Commission, Unlicensed operation in the TV broadcast bands and additional spectrum for unlicensed devices below 900MHz in the 3 GHz band. ET Docket No.04-186[R]. May 2004.
[4]J. Mitola, Gerald Q. Cognitive radio: Making software radios more personal[C]. IEEE Personal Communications, Aug. 1999, 6(4):13-18.
[5] Margulies A.S ,Mitola, J. Software defined radio: a technical challenge and a migration strategy[C]. In Proceedings of IEEE 5th International Symposium on Spread Spectrum Techniques and Applications, Sep. 1998, 2:551-56.
[6] IEEE Standard definitions and concepts for spectrum management and advantage radio technologies[S]. Institute of Electrical and Electronics Engineers Standards Activities Department, P1900.1, Mar. 2006.
[7] Mitola J. Software radio architecture: a mathematical perspective[J]. IEEE Journal on Selected Areas in Comms,Apr. 1999, 17(4):514-538.
[8] Haykin S. Cognitive Radio: Brain-Empowered Wireless Communications[J]. IEEE Journal on Selected Areas in Communications, Feb. 2005, 23(2):201-220.
[9] The XG Vision, Request for Comments[EB/OL].Version 2.0, XG Working Group. http://www.darpa.mil/ato/programs/XG/rfcs.html.
[10] Over DRiVE[EB/OL]. Website.http://www.ist-overdrive.org/.
[11] L Xu, et al. DRiVEing to the internet: dynamic radio for ip services in vehicular environment[C]. In Proceedings of 2005 25th Annual IEEE Conference on Local Computer Networks. 2000:281-289.
[12]周贤伟,孟潭等.认知无线电研究综述[J].电讯技术,2006(6):1-6.
[13]周贤伟,王建萍,王春江.认知无线电[M].国防工业出版社,2008.
[14]C. Cordeiro, et al. IEEE 802.22: The first world-wide wireless standard based on cognitive radios[C]. Baltimore, USA:in Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks, 2005:328-337.
[15] F.Capar, I.Martoryo, T.A.Weiss. Analysis of coexistence strategies for cellular and wireless Local area networks[C].VTC2003, 2003:1812-1816.
[16]D. Cabric, et al. Brodersen. Spectrum sharing radios[J]. IEEE Circuits Syst. Mag, 2005, 6:30-45.
[17] Leaves P, Ghaheri-Niri S, Tafazolli R. Dynamic spectrum allocation in a multi-radio environment: Concept and algorithm[C]. In Proceedings of 2001 Second International Conference on 3G Mobile Communication Technologies, IEEE Computer Society, 2001:53-57.
[18] Ian F. Akyildiz, et al. Next generation dynamic spectrum access Cognitive radio wireless network: A survey[J]. Computer Networks 2006, 50:2127-2159.
[19] S. M. Kay. Fundamentals of Statistical Signal Processing: Detection Theory[M]. Englewood Cliffs, NJ: Prentice-Hall, 1998.
[20] S. Enserink, D. Cochran. A cyclostationary feature detector[C]. In Porc. 28th Asilomar Conference on Signals, Systems, and Computers, Monterey, C.A, Oct. 2004:806-810.
[21] Urkowitz H. Energy detection of unknown detection signals[J]. Proceedings of IEEE, 1967, 55(4):523-231.
[22] Kaziska David, Srivastava Anuj. Cyclostationary processes on shape spaces for gait-based recognition[J]. Lecture Notes in Computer Science, 2006, 12(1):442-453.
[23] J. Hillenbrand, T. Weiss, and F. K. Jondral. Calculation of detection and false alarm probabilities inspectrum pooling systems[J].IEEE Commun. Lett, 2005, 9(4):349-351.
[24] A. Ghasemi, E.S. Sousa. Collaborative spectrum sensing for opportunistic access in fading environment[C]. 2005 First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Nov. 2005, 12:131-136.
[25] A. Sahai, N. Hoven, R. Tandra. Some fundamental limits in cognitive radio[C]. Allerton Conference on Communication, Control and Computing 2004, Oct. 2004:1-5.
[26]韩崇昭,朱鸿艳,段战胜等.多源信息融合[M].北京:清华大学出版社,2006年.
[27] Jayakrishnan Unnikrishnan, Venugopal V. Veeravalli. Cooperative Sensing for Primary Detection in Cognitive Radio[J]. IEEE journal of selected topics in signal processing, Feb. 2008, 2(1):18-27.
[28] J. Lunden, et al. Censoring for Collaborative Spectrum sensing in Cognitive Radios[C]. Conference Record of the Forty-First Asilomar Conference on Signal, Systems and Computers, Nov. 2007:772-776.
[29] A.K. Kattepur, et al. Data and Decision Fusion for Distributed Spectrum Sensing in Cognitive Radio Networks[C]. International conference on Information, Communication and Signal Processing, Dec. 2007:1-5.
[30] J. Kittler, F.M. Alkoot. Sum versus Vote Fusion in Multiple Classifier Systems[J]. IEEE transactions on pattern analysis and machine intelligence, Jan. 2003, 25(1):110-115.
[31] Z.Chair and P.K. Varshney,Optimal data fusion in multiple sensor detection systems[J]. IEEE Trans.on Aerospace and Elect.Syst., Jan.1986, 22:98-101.
[32]S. M. Mishra, A. Sahai, and R. Brodersen. Cooperative sensing among cognitive radios[C]. In Proc. IEEE Int. Conf. Commun. Jun. 2006, 4:1658-1663.
[33] Peh E,Ying-Chang Liang. Optimization for cooperative sensing in cognitive radio networks[C]. In: Proceedings of 2007 IEEE on Wireless Communication and Networking Conference Kowloon, 2007:27-32.
[34] Jayakrishnan Unnikrishnan, Venugopal V. Veeravalli. Cooperative Sensing for Primary Detection in Cognitive Radio[J]. IEEE Journal of selected topics in signal processing, Feb. 2008, 2(1):18-27.
[35] Wild B, Ramchandran K. Detecting primary receivers for cognitive radio application[C]. In: New frontiers in dynamic spectrum access network, Batltimore:2005:124-130.
[36]G. Ganesan and Y. G. Li.Cooperative spectrum sensing in cognitive radio networks[C]. Baltimore, USA: in Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN’05), 2005: 131-136.
[37]A. Ghasemi and E. S. Sousa.Collaborative spectrum sensing for opportunistic access in fading environments[C]. Baltimore, USA: in Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN’05), 2005: 131-136.
[38]S. M. Mishra, A. Sahai, and R. Brodersen. Cooperative sensing among cognitive radios[C]. Turkey :in Proc. IEEE Int. Conf. Commun, 2006:1658-1663.
[39]C. Cordeiro, et al. IEEE 802.22: The first world-wide wireless standard based on cognitive radios[C].Baltimore, USA:in Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks, 2005:328-337.
[40] Ying-Chang Liang, et al. Sensing-Throughout Tradeoff for Cognitive Radio Networks[J]. IEEE Transactions on Wireless Communications,2008,7(4):1326-1337.
[41] W.B. Heinzelman, A. P. Chandrakasan, H. Balakrishnan, An Application-Specific Protocol Architecture for Wireless Microsensor Networks[J]. IEEE Transaction on Wireless Communication, 2002, 4(1):656-660.
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[44] G. Ganesan and Y. G Li. Agility improvement through cooperation diversity in cognitive radio[C]. in Proc. IEEE Global Communications Conference, Nov. 2005, 5:2505-2509.
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[47]C. Sun, W. Zhang, and K. B. Letaief. Cluster-based cooperative spectrum sensing for cognitive radio systems[C]. Glasgow, Scotland, UK : in Proc. IEEE Int. Conf. Commun, 2007: 2511-2515.
[48] Lin YP, et al. A distributed data-centric clustering hierarachical routing algorithm for sensor networks[J]. Acta Elecronic Silica. 2004, 32(11):1801-1805.
[2] Federal Communications Commission, Spectrum Policy Task Force, Rep. ET Docket No.02-135[R]. Nov. 2002.
[3] Federal Communication Commission, Unlicensed operation in the TV broadcast bands and additional spectrum for unlicensed devices below 900MHz in the 3 GHz band. ET Docket No.04-186[R]. May 2004.
[4]J. Mitola, Gerald Q. Cognitive radio: Making software radios more personal[C]. IEEE Personal Communications, Aug. 1999, 6(4):13-18.
[5] Margulies A.S ,Mitola, J. Software defined radio: a technical challenge and a migration strategy[C]. In Proceedings of IEEE 5th International Symposium on Spread Spectrum Techniques and Applications, Sep. 1998, 2:551-56.
[6] IEEE Standard definitions and concepts for spectrum management and advantage radio technologies[S]. Institute of Electrical and Electronics Engineers Standards Activities Department, P1900.1, Mar. 2006.
[7] Mitola J. Software radio architecture: a mathematical perspective[J]. IEEE Journal on Selected Areas in Comms,Apr. 1999, 17(4):514-538.
[8] Haykin S. Cognitive Radio: Brain-Empowered Wireless Communications[J]. IEEE Journal on Selected Areas in Communications, Feb. 2005, 23(2):201-220.
[9] The XG Vision, Request for Comments[EB/OL].Version 2.0, XG Working Group. http://www.darpa.mil/ato/programs/XG/rfcs.html.
[10] Over DRiVE[EB/OL]. Website.http://www.ist-overdrive.org/.
[11] L Xu, et al. DRiVEing to the internet: dynamic radio for ip services in vehicular environment[C]. In Proceedings of 2005 25th Annual IEEE Conference on Local Computer Networks. 2000:281-289.
[12]周贤伟,孟潭等.认知无线电研究综述[J].电讯技术,2006(6):1-6.
[13]周贤伟,王建萍,王春江.认知无线电[M].国防工业出版社,2008.
[14]C. Cordeiro, et al. IEEE 802.22: The first world-wide wireless standard based on cognitive radios[C]. Baltimore, USA:in Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks, 2005:328-337.
[15] F.Capar, I.Martoryo, T.A.Weiss. Analysis of coexistence strategies for cellular and wireless Local area networks[C].VTC2003, 2003:1812-1816.
[16]D. Cabric, et al. Brodersen. Spectrum sharing radios[J]. IEEE Circuits Syst. Mag, 2005, 6:30-45.
[17] Leaves P, Ghaheri-Niri S, Tafazolli R. Dynamic spectrum allocation in a multi-radio environment: Concept and algorithm[C]. In Proceedings of 2001 Second International Conference on 3G Mobile Communication Technologies, IEEE Computer Society, 2001:53-57.
[18] Ian F. Akyildiz, et al. Next generation dynamic spectrum access Cognitive radio wireless network: A survey[J]. Computer Networks 2006, 50:2127-2159.
[19] S. M. Kay. Fundamentals of Statistical Signal Processing: Detection Theory[M]. Englewood Cliffs, NJ: Prentice-Hall, 1998.
[20] S. Enserink, D. Cochran. A cyclostationary feature detector[C]. In Porc. 28th Asilomar Conference on Signals, Systems, and Computers, Monterey, C.A, Oct. 2004:806-810.
[21] Urkowitz H. Energy detection of unknown detection signals[J]. Proceedings of IEEE, 1967, 55(4):523-231.
[22] Kaziska David, Srivastava Anuj. Cyclostationary processes on shape spaces for gait-based recognition[J]. Lecture Notes in Computer Science, 2006, 12(1):442-453.
[23] J. Hillenbrand, T. Weiss, and F. K. Jondral. Calculation of detection and false alarm probabilities inspectrum pooling systems[J].IEEE Commun. Lett, 2005, 9(4):349-351.
[24] A. Ghasemi, E.S. Sousa. Collaborative spectrum sensing for opportunistic access in fading environment[C]. 2005 First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Nov. 2005, 12:131-136.
[25] A. Sahai, N. Hoven, R. Tandra. Some fundamental limits in cognitive radio[C]. Allerton Conference on Communication, Control and Computing 2004, Oct. 2004:1-5.
[26]韩崇昭,朱鸿艳,段战胜等.多源信息融合[M].北京:清华大学出版社,2006年.
[27] Jayakrishnan Unnikrishnan, Venugopal V. Veeravalli. Cooperative Sensing for Primary Detection in Cognitive Radio[J]. IEEE journal of selected topics in signal processing, Feb. 2008, 2(1):18-27.
[28] J. Lunden, et al. Censoring for Collaborative Spectrum sensing in Cognitive Radios[C]. Conference Record of the Forty-First Asilomar Conference on Signal, Systems and Computers, Nov. 2007:772-776.
[29] A.K. Kattepur, et al. Data and Decision Fusion for Distributed Spectrum Sensing in Cognitive Radio Networks[C]. International conference on Information, Communication and Signal Processing, Dec. 2007:1-5.
[30] J. Kittler, F.M. Alkoot. Sum versus Vote Fusion in Multiple Classifier Systems[J]. IEEE transactions on pattern analysis and machine intelligence, Jan. 2003, 25(1):110-115.
[31] Z.Chair and P.K. Varshney,Optimal data fusion in multiple sensor detection systems[J]. IEEE Trans.on Aerospace and Elect.Syst., Jan.1986, 22:98-101.
[32]S. M. Mishra, A. Sahai, and R. Brodersen. Cooperative sensing among cognitive radios[C]. In Proc. IEEE Int. Conf. Commun. Jun. 2006, 4:1658-1663.
[33] Peh E,Ying-Chang Liang. Optimization for cooperative sensing in cognitive radio networks[C]. In: Proceedings of 2007 IEEE on Wireless Communication and Networking Conference Kowloon, 2007:27-32.
[34] Jayakrishnan Unnikrishnan, Venugopal V. Veeravalli. Cooperative Sensing for Primary Detection in Cognitive Radio[J]. IEEE Journal of selected topics in signal processing, Feb. 2008, 2(1):18-27.
[35] Wild B, Ramchandran K. Detecting primary receivers for cognitive radio application[C]. In: New frontiers in dynamic spectrum access network, Batltimore:2005:124-130.
[36]G. Ganesan and Y. G. Li.Cooperative spectrum sensing in cognitive radio networks[C]. Baltimore, USA: in Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN’05), 2005: 131-136.
[37]A. Ghasemi and E. S. Sousa.Collaborative spectrum sensing for opportunistic access in fading environments[C]. Baltimore, USA: in Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN’05), 2005: 131-136.
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