认知无线电系统中频谱感知关键技术研究
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摘要
认知无线电技术作为一种智能无线通信系统,能够通过感知周围环境的变化,有效地利用空闲的频谱资源,成为解决无线频谱资源匮乏以及频谱利用率低下问题的关键技术。而认知无线电中的频谱感知技术是认知无线电系统能够正常工作的前提。本文从快速、准确的检测要求出发,针对无线区域网络的频谱感知技术展开了深入研究。
本文首先介绍了几种经典的频谱感知技术,并讨论了噪声方差不确定时对现有检测算法的影响。为克服噪声不确定度对频谱检测性能的影响,本文研究了认知无线电中的基于熵的频谱感知问题,将非均匀量化思想引入熵检测理论中,提出了非均匀量化谱熵的频谱检测方法。该方法将接收频谱序列进行非均匀量化,使接收信号在仅含噪声时能够最大化熵值,从而能够提高检测性能,仿真结果表明相同条件下,所提方法相比于均匀量化谱熵检测能够获得约3dB的检测性能增益;并且所提算法的门限确定不受噪声方差的影响,因此系统性能具有噪声不确定度鲁棒性,在噪声方差不确定的条件下具有比能量检测更好的检测性能。
本文还针对单节点检测所遇到的衰落、隐藏终端等问题,提出了基于非均匀量化谱熵的硬判决和软判决联合检测方法。联合检测能够融合多个节点的检测信息进行综合判决,比单节点检测具有更好的检测性能。本文对各个节点在相同信噪比下和不同信噪比下的合作检测算法进行仿真分析。仿真结果表明在相同信噪比条件下“K秩”融合性能最好,在信噪比为-10dB、虚警概率为0.2的条件下比单节点检测提高50%的检测概率。而在不同信噪比条件下,软判决中的最大比合并算法给不同信噪比节点分配不同权值,能够获得最好的检测性能,仿真结果表明该算法比“K秩”融合准则算法在虚警概率0.1的条件下提高约10%的检测性能。
By exploiting the existing wireless spectrum opportunistically, cognitive radio technology is developed to solve current wireless network problems resulting from the limited available spectrum and the inefficiency in spectrum usage. A cognitive radio (CR) is designed to be aware of and sensitive to the changes in its surrounding, which makes spectrum sensing an important requirement for the realization of cognitive radio networks. Spectrum sensing enables CR users to detect spectrum holes without causing interference to the primary networks. This dissertation will make a deep study of the spectrum detection in wireless regional area networks.
Firstly, several techniques of spectrum sensing are discussed and the concept of noise uncertainty is introduced. A spectrum sensing algorithm based on information theory in CR is considered to avoid the influence by noise uncertainty. By introducing the nonuniform quantization, the spectrum entropy-based detection scheme is proposed. The proposed scheme can improve quantization performance and maximize entropy value so as to improve the detective performance. The simulation results show that the scheme can obtain approximate 3dB gain in detection performance than spectrum entropy-based detection using uniform quantization. Furthermore, the simulation results verify the robustness against noise uncertainty, and show that the proposed scheme outperforms energy detection under noise uncertainty situation.
Secondly, to the fading or hidden-station problems existed in single node detection, several hard decision methods and soft decision methods based on entropy detection in cooperative spectrum sensing are proposed in the dissertation. Fusion rules of cooperative spectrum sensing are compared by computer simulation under each node with same Signal to Noise Ratio (SNR) and different SNR. Simulation results show K out of N rule obtains the best performance under same SNR condition. And it increases detection probability by 50% than single node detection for a target false-alarm probability of 0.1 and SNR of -10dB. While maximal ratio combination (MRC) rule of the soft decision is the optimal algorithm under different SNR conditions. The simulation results show that MRC rule improves detective performance by 10% than K out of N rule for a target false-alarm probability of 0.1.
本文首先介绍了几种经典的频谱感知技术,并讨论了噪声方差不确定时对现有检测算法的影响。为克服噪声不确定度对频谱检测性能的影响,本文研究了认知无线电中的基于熵的频谱感知问题,将非均匀量化思想引入熵检测理论中,提出了非均匀量化谱熵的频谱检测方法。该方法将接收频谱序列进行非均匀量化,使接收信号在仅含噪声时能够最大化熵值,从而能够提高检测性能,仿真结果表明相同条件下,所提方法相比于均匀量化谱熵检测能够获得约3dB的检测性能增益;并且所提算法的门限确定不受噪声方差的影响,因此系统性能具有噪声不确定度鲁棒性,在噪声方差不确定的条件下具有比能量检测更好的检测性能。
本文还针对单节点检测所遇到的衰落、隐藏终端等问题,提出了基于非均匀量化谱熵的硬判决和软判决联合检测方法。联合检测能够融合多个节点的检测信息进行综合判决,比单节点检测具有更好的检测性能。本文对各个节点在相同信噪比下和不同信噪比下的合作检测算法进行仿真分析。仿真结果表明在相同信噪比条件下“K秩”融合性能最好,在信噪比为-10dB、虚警概率为0.2的条件下比单节点检测提高50%的检测概率。而在不同信噪比条件下,软判决中的最大比合并算法给不同信噪比节点分配不同权值,能够获得最好的检测性能,仿真结果表明该算法比“K秩”融合准则算法在虚警概率0.1的条件下提高约10%的检测性能。
By exploiting the existing wireless spectrum opportunistically, cognitive radio technology is developed to solve current wireless network problems resulting from the limited available spectrum and the inefficiency in spectrum usage. A cognitive radio (CR) is designed to be aware of and sensitive to the changes in its surrounding, which makes spectrum sensing an important requirement for the realization of cognitive radio networks. Spectrum sensing enables CR users to detect spectrum holes without causing interference to the primary networks. This dissertation will make a deep study of the spectrum detection in wireless regional area networks.
Firstly, several techniques of spectrum sensing are discussed and the concept of noise uncertainty is introduced. A spectrum sensing algorithm based on information theory in CR is considered to avoid the influence by noise uncertainty. By introducing the nonuniform quantization, the spectrum entropy-based detection scheme is proposed. The proposed scheme can improve quantization performance and maximize entropy value so as to improve the detective performance. The simulation results show that the scheme can obtain approximate 3dB gain in detection performance than spectrum entropy-based detection using uniform quantization. Furthermore, the simulation results verify the robustness against noise uncertainty, and show that the proposed scheme outperforms energy detection under noise uncertainty situation.
Secondly, to the fading or hidden-station problems existed in single node detection, several hard decision methods and soft decision methods based on entropy detection in cooperative spectrum sensing are proposed in the dissertation. Fusion rules of cooperative spectrum sensing are compared by computer simulation under each node with same Signal to Noise Ratio (SNR) and different SNR. Simulation results show K out of N rule obtains the best performance under same SNR condition. And it increases detection probability by 50% than single node detection for a target false-alarm probability of 0.1 and SNR of -10dB. While maximal ratio combination (MRC) rule of the soft decision is the optimal algorithm under different SNR conditions. The simulation results show that MRC rule improves detective performance by 10% than K out of N rule for a target false-alarm probability of 0.1.
引文
[1] G. Staple, K. Werbach. The End of Spectrum Scarcity [Spectrum Allocation and Utilization]. IEEE Spectrum. Mar. 2004, 41(3): 48-52.
[2] Federal Communication Commission. Facilitating Opportunities for Flexible, Efficient, and Reliable Spectrum Use Employing Cognitive Radio Technologies. NPRM & Order, ET Docket No.03-108, FCC 03-322, 2003.
[3] M. McHenry. Report on Spectrum Occupancy Measurements. Shared Spectrum Company. http://www.sharedspectrum.com/papers/spectrum-reports/.
[4] J. Mitola. Software Radios: Survey, Critical Evaluation and Future Directions. IEEE Aerospace and Electronic Systems Magazine. Apr. 1993, 8(4): 25-36.
[5] J. Mitola. Software Radio Architecture: A Mathematical Perspective. IEEE Journal on Selected Areas in Communications. Apr. 1999, 17(4): 514-538.
[6] Federal Communication Commission. Revision of Part 15 of the Commussion’s Rules Regarding Ultra-Wideband Transmission Systems. Fisrt Report and Order, ET Docket No. 98-153, FCC 02-48, 2002.
[7] J. Mitola, Jr. G. Q. Maguire. Cognitive Radios: Making Software Radios More Personal. IEEE Personal Communications. Aug. 1999: 6(4): 13-18.
[8] S. Haykin. Cognitive Radio: Brain-EmpoWered Wireless Communications. IEEE Journal on Selected Areas in Communications. Feb. 2005, 23(2): 201-220.
[9] J. Mitola. Cognitive radio for flexible mobile multimedia communications. IEEE International Workshop on Mobile Multimedia Communications, Nov. 1999: 3-10.
[10] Federal Communication Commission. Notice of Proposed Rule Making and Order. ET Docket No. 03-322, 2003.
[11] P. Kolodzy. Next Generation Communications: Kickoff Meeting. in Proceedings of the Defense Advanced Research Projects Agency. Haykin, Oct. 2001.
[12] R. W. Brodersen, A. Wolisz, D. Cabric, et al. CORVUS: A Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum. White Paper, Berkeley Wireless Research Center. UC Berkeley, Jul. 2004.
[13] Project Summary. NeTS-ProWIN: High Performance Cognitive Radio Platform with Integrated Physical and Network Layer Capabilities. WINLAB, Rutgers University.
[14] Project Summary. NeTS-ProWIN: Cognitive Radios for Open Access to Spectrum. WINLAB, Rutgers University.
[15] IEEE 802 LAN/MAN Standards Committee 802.22 WG. http://www.ieee802.org /22/.
[16] IEEE 802.16’s License-Exempt (LE) TaskGroup. http://www.ieee802.org/16/le/.
[17] IEEE P1900 Working Group. IEEE P1901.1TM/D01. http://grouper.ieee.org/ groups/emc/emc /1900/index.html.
[18] D. Cabric, S.M. Mishra, R. W. Brodersen. Implementation Issues in Spectrum Sensing for Cognitive Radios. Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers. Nov. 2004, 1: 772-776.
[19] V. D. Chakravarthy, A. K. Shaw. Cognitive Radio-An Adaptive Waveform with Spectral Sharing Capability. IEEE Wireless Communications and Networking Conference. Mar. 2005, 2: 724-729.
[20] R.S. Carl, C. Gerald. Draft Standard for Wireless Regional Area Networks Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Polices and Procedures for Operation in The TV Bands. http://www.ieee802.org/22.
[21] J. Benko, S. Y. Chang, Y. Cheong, et al. Draft PHY/MAC Specification for IEEE 802.22. IEEE 802.22-06/0069r2, May 2006.
[22] J. Benko, Y. Cheong, C. Cordeiro, et al. A PHY/MAC Proposal for IEEE 802.22 WRAN Systems. IEEE 802.22-06/0003r3. Mar. 2006.
[23] K. CHallapali. Spectrum Agile Radios: Real-Time Measurements. Cognitive Radio Conference. Washington DC, Oct. 2004.
[24] E. Larsson, M. Skoglund. Cognitive Radio in a Frequency-Planned Environment: Some Basic Limits. IEEE Transactions on Wireless Communications. Dec. 2008, 7(12): 4800-4806.
[25]赵知劲,郑仕链,孔宪正。认知无线电中频谱感知技术。现代雷达。2008, 30(5): 65-69。
[26] A. Ghasemi, E. S. Sousa. Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Nov. 2005: 131-136.
[27]赵树杰,赵建勋。信号检测与估计理论。北京:清华大学出版社,2005。
[28] H. Urkowitz. Energy Detection of Unknown Deterministic Signals. Proceedings of the IEEE. Apr. 1967, 55(4): 523–231.
[29] H. Y. Tang. Some Physical Layer Issues of Wide-band Cognitive Radio Systems. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Baltimore, MD, Nov. 2005: 151-159.
[30] R. Tandra, A. Sahai. Fundamental Limit on Detection in Low SNR under Noise Uncertainty. International Conference on Wireless Networks, Communications and Moble Computing. Jun. 2005, 1: 464-469.
[31] A. Sonnenschein, P. M. Fishman. Radiometric Detection of Spread-Spectrum Signals in Noise of Uncertain Power. IEEE Transactions on Aerospace and Electronic Systems. Jul.1992, 28(3): 654-660.
[32] R. Tandra, A. Sahai. SNR Walls for Signal Detection. IEEE Journal of Selected Topics in Signal Processing. Feb. 2008, 2(1): 4-17.
[33] W. A. Gardner. Spectral Correlation of Modulated Signals: Part I - Analog Modulation. IEEE Transactions on Communications. June 1987, 35(6): 584-594.
[34] V. Amod, B. Dandawate, B. Georgios. Statistical Tests for Presence of Cyclostationarity. IEEE Transactions on Signal Processing. Sep. 1994, 42(9):652355-2369.
[35] V. Turunen, M. Kosunen, A. Huttunen, et al. Implementation of Cyclostationary Feature Detector for Cognitive Radios. 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications. Jun. 2009: 1-4.
[36] P. J. Kolodzy. Interference Temperature: A Metric for Dynamic Spectrum Utilization. International Journal of Network Management. 2006, 16: 103-113.
[37]周小飞,张宏纲。认知无线电原理及应用。北京:北京邮电大学出版社,2007年3月。
[38] B. Wild, K. Ramchandrm. Detecting Primary Receivers for Cognitive Radio Applications. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Nov. 2005: 124-130.
[39]科弗(T. M. Cover),托马斯(J. A. Thomas)著;阮吉寿,张华译。信息论基础。北京:机械工业出版社。2007年11月。
[40] J.-F. Bercher, C. Vignat. Estimating the Entropy of a Signal with Applications. IEEE Transactions on Signal Processing. 2000, 48(6): 1687-1694.
[41] Y. Zhang, Q. Zhang, S. Wu. Entropy-Based Robust Spectrum Sensing in Cognitive Radio. Communications, IET. Mar. 2010, 4(4): 428-436.
[42] Y. L. Zhang, Q. Y. Zhang, T. Melodia. A frequency-Domain Entropy-Based Detector for Robust Spectrum Sensing in Cognitive Radio Networks. IEEE Communication Letters. Jun. 2010, 14(6): 533-535.
[43] E. J. Candes, J. Romberg. Rubust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information. IEEE Transaction on Information Theory. Feb. 2006, 52(2): 489-509.
[44] I. F. Akyildiz, W. Y. Lee, M.C. Vuran, et al. Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey. Computer Networks. 2006, 50(13): 2127-2159.
[45] J. Zhao, H. Zheng, G. H. Yang. Distributed Coordination in Dynamic Spectrum Allocation Networks. 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Nov. 2005: 259-268.
[46] W. Zhang, R. K. Mallik, K. Ben Letaief. Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks. IEEE International Conference on Communications. May 2008: 4327-4331.
[47] K. B. Myung, J. K. Young. Effective Signal Detection Using Cooperative Spectrum Sensing in Cognitive Radio Systems. Advanced Communication Technology. 2009, 03: 1746-1750.
[48] B. Shen, S. Ullah, K. Kwak. Deflection Coefficient Maximization Criterion based Optimal Cooperative Spectrum Sensing. International Journal of Electronics and Communications. 2010, 64(9): 819-827.
[49]盛骤,谢式千,潘承毅。概率论与数理统计(第3版)。北京:高等教育出版社。2001年12月。
[2] Federal Communication Commission. Facilitating Opportunities for Flexible, Efficient, and Reliable Spectrum Use Employing Cognitive Radio Technologies. NPRM & Order, ET Docket No.03-108, FCC 03-322, 2003.
[3] M. McHenry. Report on Spectrum Occupancy Measurements. Shared Spectrum Company. http://www.sharedspectrum.com/papers/spectrum-reports/.
[4] J. Mitola. Software Radios: Survey, Critical Evaluation and Future Directions. IEEE Aerospace and Electronic Systems Magazine. Apr. 1993, 8(4): 25-36.
[5] J. Mitola. Software Radio Architecture: A Mathematical Perspective. IEEE Journal on Selected Areas in Communications. Apr. 1999, 17(4): 514-538.
[6] Federal Communication Commission. Revision of Part 15 of the Commussion’s Rules Regarding Ultra-Wideband Transmission Systems. Fisrt Report and Order, ET Docket No. 98-153, FCC 02-48, 2002.
[7] J. Mitola, Jr. G. Q. Maguire. Cognitive Radios: Making Software Radios More Personal. IEEE Personal Communications. Aug. 1999: 6(4): 13-18.
[8] S. Haykin. Cognitive Radio: Brain-EmpoWered Wireless Communications. IEEE Journal on Selected Areas in Communications. Feb. 2005, 23(2): 201-220.
[9] J. Mitola. Cognitive radio for flexible mobile multimedia communications. IEEE International Workshop on Mobile Multimedia Communications, Nov. 1999: 3-10.
[10] Federal Communication Commission. Notice of Proposed Rule Making and Order. ET Docket No. 03-322, 2003.
[11] P. Kolodzy. Next Generation Communications: Kickoff Meeting. in Proceedings of the Defense Advanced Research Projects Agency. Haykin, Oct. 2001.
[12] R. W. Brodersen, A. Wolisz, D. Cabric, et al. CORVUS: A Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum. White Paper, Berkeley Wireless Research Center. UC Berkeley, Jul. 2004.
[13] Project Summary. NeTS-ProWIN: High Performance Cognitive Radio Platform with Integrated Physical and Network Layer Capabilities. WINLAB, Rutgers University.
[14] Project Summary. NeTS-ProWIN: Cognitive Radios for Open Access to Spectrum. WINLAB, Rutgers University.
[15] IEEE 802 LAN/MAN Standards Committee 802.22 WG. http://www.ieee802.org /22/.
[16] IEEE 802.16’s License-Exempt (LE) TaskGroup. http://www.ieee802.org/16/le/.
[17] IEEE P1900 Working Group. IEEE P1901.1TM/D01. http://grouper.ieee.org/ groups/emc/emc /1900/index.html.
[18] D. Cabric, S.M. Mishra, R. W. Brodersen. Implementation Issues in Spectrum Sensing for Cognitive Radios. Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers. Nov. 2004, 1: 772-776.
[19] V. D. Chakravarthy, A. K. Shaw. Cognitive Radio-An Adaptive Waveform with Spectral Sharing Capability. IEEE Wireless Communications and Networking Conference. Mar. 2005, 2: 724-729.
[20] R.S. Carl, C. Gerald. Draft Standard for Wireless Regional Area Networks Part 22: Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Polices and Procedures for Operation in The TV Bands. http://www.ieee802.org/22.
[21] J. Benko, S. Y. Chang, Y. Cheong, et al. Draft PHY/MAC Specification for IEEE 802.22. IEEE 802.22-06/0069r2, May 2006.
[22] J. Benko, Y. Cheong, C. Cordeiro, et al. A PHY/MAC Proposal for IEEE 802.22 WRAN Systems. IEEE 802.22-06/0003r3. Mar. 2006.
[23] K. CHallapali. Spectrum Agile Radios: Real-Time Measurements. Cognitive Radio Conference. Washington DC, Oct. 2004.
[24] E. Larsson, M. Skoglund. Cognitive Radio in a Frequency-Planned Environment: Some Basic Limits. IEEE Transactions on Wireless Communications. Dec. 2008, 7(12): 4800-4806.
[25]赵知劲,郑仕链,孔宪正。认知无线电中频谱感知技术。现代雷达。2008, 30(5): 65-69。
[26] A. Ghasemi, E. S. Sousa. Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Nov. 2005: 131-136.
[27]赵树杰,赵建勋。信号检测与估计理论。北京:清华大学出版社,2005。
[28] H. Urkowitz. Energy Detection of Unknown Deterministic Signals. Proceedings of the IEEE. Apr. 1967, 55(4): 523–231.
[29] H. Y. Tang. Some Physical Layer Issues of Wide-band Cognitive Radio Systems. IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Baltimore, MD, Nov. 2005: 151-159.
[30] R. Tandra, A. Sahai. Fundamental Limit on Detection in Low SNR under Noise Uncertainty. International Conference on Wireless Networks, Communications and Moble Computing. Jun. 2005, 1: 464-469.
[31] A. Sonnenschein, P. M. Fishman. Radiometric Detection of Spread-Spectrum Signals in Noise of Uncertain Power. IEEE Transactions on Aerospace and Electronic Systems. Jul.1992, 28(3): 654-660.
[32] R. Tandra, A. Sahai. SNR Walls for Signal Detection. IEEE Journal of Selected Topics in Signal Processing. Feb. 2008, 2(1): 4-17.
[33] W. A. Gardner. Spectral Correlation of Modulated Signals: Part I - Analog Modulation. IEEE Transactions on Communications. June 1987, 35(6): 584-594.
[34] V. Amod, B. Dandawate, B. Georgios. Statistical Tests for Presence of Cyclostationarity. IEEE Transactions on Signal Processing. Sep. 1994, 42(9):652355-2369.
[35] V. Turunen, M. Kosunen, A. Huttunen, et al. Implementation of Cyclostationary Feature Detector for Cognitive Radios. 4th International Conference on Cognitive Radio Oriented Wireless Networks and Communications. Jun. 2009: 1-4.
[36] P. J. Kolodzy. Interference Temperature: A Metric for Dynamic Spectrum Utilization. International Journal of Network Management. 2006, 16: 103-113.
[37]周小飞,张宏纲。认知无线电原理及应用。北京:北京邮电大学出版社,2007年3月。
[38] B. Wild, K. Ramchandrm. Detecting Primary Receivers for Cognitive Radio Applications. First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Nov. 2005: 124-130.
[39]科弗(T. M. Cover),托马斯(J. A. Thomas)著;阮吉寿,张华译。信息论基础。北京:机械工业出版社。2007年11月。
[40] J.-F. Bercher, C. Vignat. Estimating the Entropy of a Signal with Applications. IEEE Transactions on Signal Processing. 2000, 48(6): 1687-1694.
[41] Y. Zhang, Q. Zhang, S. Wu. Entropy-Based Robust Spectrum Sensing in Cognitive Radio. Communications, IET. Mar. 2010, 4(4): 428-436.
[42] Y. L. Zhang, Q. Y. Zhang, T. Melodia. A frequency-Domain Entropy-Based Detector for Robust Spectrum Sensing in Cognitive Radio Networks. IEEE Communication Letters. Jun. 2010, 14(6): 533-535.
[43] E. J. Candes, J. Romberg. Rubust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information. IEEE Transaction on Information Theory. Feb. 2006, 52(2): 489-509.
[44] I. F. Akyildiz, W. Y. Lee, M.C. Vuran, et al. Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey. Computer Networks. 2006, 50(13): 2127-2159.
[45] J. Zhao, H. Zheng, G. H. Yang. Distributed Coordination in Dynamic Spectrum Allocation Networks. 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks. Nov. 2005: 259-268.
[46] W. Zhang, R. K. Mallik, K. Ben Letaief. Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks. IEEE International Conference on Communications. May 2008: 4327-4331.
[47] K. B. Myung, J. K. Young. Effective Signal Detection Using Cooperative Spectrum Sensing in Cognitive Radio Systems. Advanced Communication Technology. 2009, 03: 1746-1750.
[48] B. Shen, S. Ullah, K. Kwak. Deflection Coefficient Maximization Criterion based Optimal Cooperative Spectrum Sensing. International Journal of Electronics and Communications. 2010, 64(9): 819-827.
[49]盛骤,谢式千,潘承毅。概率论与数理统计(第3版)。北京:高等教育出版社。2001年12月。
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