基于压缩感知的认知无线电频谱感知算法研究
|
摘要
压缩感知(Compressive Sensing, CS)作为现代信号处理的最新进展,在认知无线电等领域中的应用潜力巨大,本文选题来源于国家自然科学基金等项目,具有重要的理论意义及广阔的应用前景。
本文对压缩感知技术的基本原理及其在认知无线电关键技术频谱感知中的应用进行了深入研究,主要完成了以下具有创新性的研究成果：
针对频谱感知在时变信道中检测概率恶化的情况,本文提出了一种自适应静默期管理机制,即根据上一感知周期的感知结果和数据通信获知的ACK反馈等信息,自适应调整本感知周期的感知参数以适应信道参数变化,并通过马尔科夫分析方法,对提出的自适应静默期管理机制的检测概率和平均采样数进行了理论分析。仿真结果表明,在时变信道的环境下,本文机制的平均检测概率要优于传统固定时长的静默期管理机制。
针对多信道频谱感知中,认知用户条件不足以检测整个信道状态的问题,本文提出了一种应用于宽带认知无线网络的协作式宽带频谱感知稀疏模型,和一种基于LDPC码的协作式频谱感知策略。当认知终端需要检测的子信道个数远超过其检测能力时,即可采用该稀疏模型,每个认知终端采用压缩感知的方式对极少的信道进行随机测量,在融合中心将认知终端的随机测量结果合并为一个完整的压缩感知方程从而对信道状态序列进行重构。仿真结果表明,在不同的子信道占用个数情况下,适当调整LDPC测量矩阵的参数,都可以通过较少的子信道测量个数达到较优的系统检测概率。
为降低宽带频谱感知的采样率,提出了一种基于分布式压缩感知算法的频谱感知机制。通过利用授权用户信号的稀疏性,在超帧的每个子帧内进行采用分布式压缩感知的采样；在超帧结束时,采用DCS-SOMP算法对每个静默期的采样数据进行联合重构,再通过对重构数据的合并对信道状态作出合理判决。仿真结果表明,与传统压缩感知算法相比,本文算法可用更少的随机测量数达到预期的系统检测概率。
针对循环平稳特征检测算法复杂度高、耗费时间长的缺点,提出了一种基于压缩感知的循环自相关特征检测算法(CS-Feature Detection)。通过利用授权用户循环自相关谱的稀疏特征对其进行压缩感知；并采用归一化循环自相关统计量作为检验量进行判决,避免了对授权用户先验信息的需求,再用正交匹配追踪(Orthogonal Matching Pursuit, OMP)重构算法,即可以较高概率重构信号循环自相关谱。仿真结果表明,在显著缩短检测时间的情况下,依然可获得较高检测概率。
论文最后对全文进行了总结,并对压缩感知技术与认知无线电系统结合的发展方向及今后的工作进行了展望。
Compressed sensing has a great potential in the cognitive radio (CR) system. This thesis is supported by National Science Funds and attempts to make a contribution to the theory and application of CR system.
In this paper, the basic principle of compressed sensing technology and its application of CR spectrum sensing procedure are investigated. Several creative algorithms are developed in this thesis.
This paper addresses the problem of designing an appropriate quiet period management scheme over time-variant channels. To achieve better performance over time-variant channels, we propose a flexible quiet period management scheme which can adjust the sensing parameters according to the previous sensing result and the information of ACK. The performance of the proposed scheme has been evaluated through Markov analysis. Numerical results show that under time-variant channel conditions, a better probability of detection and higher channel utilization is achieved compared to the traditional fixed quiet period management scheme.
Collaborative spectrum sensing (CSS) can significantly improve the performance of spectrum sensing based on the spatial diversity gain of different cognitive radio. In wideband spectrum sensing scenario, since there might not be enough CRs in the network, or due to hardware limitations, each CR node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Based on the fact that the spectrum usage information the CR nodes collect has a common sparsity pattern, in this paper, we present a compressed collaborative wideband spectrum sensing scheme in cognitive radio networks. Under the hypothesis of joint sparsity, the CRs need to randomly detect a very small number of sub-channels according to a measurement matrix and send the results to a fusion center. To make the compressed sensing more effective, the scheme uses LDPC-like measurement matrix. Then the whole channel status can be recoverd by the fusion center through a low-complexity message passing algorithm. Numerical results show that under a joint sparsity model, using the proposed distributed compressed sensing scheme, the CRs make a small number of measurements and get a high probability of detection.
In order to reduce the sampling rate in the broadband spectrum sensing, a cognitive radio spectrum sensing algorithm based on distributed compressive sensing is proposed. In IEEE802.22standard, Timeline is divided into successive superframes. Each superframe consists of a number of frames, and quiet period is set in each frame. At the end of each superframe, the spectrum sensing results of the quiet periods in this superframe are merged. Because the primaiy user signals are sparse in the frequency domain, compressive sensing method can be adopt. Assume that the primary user varys slowly, then the sampling sighals in the same superframe are joint sparse and meet the joint sparsity model JSM-2. So the second user makes random measurements below the Nyquist sampling rate using the measurement matrix. After that, using DCS-SOMP algorithm, the data of each sample of the quiet periods can be reconstructed. Merge the reconstructed data, then the reasonable judgment can be made.
A CS-Feature detection spectrum sensing algorithms for cognitive radio is proposed in this paper. The traditional feature detection algorithm based on cyclic spectrum density has a very high accuracy, but it can't be widely used because of the high complexity and very long detection period. The CS-Feature detection in this paper is designed based on the sparsity of the cyclic autocorrelation. Because most of the man-made signals are cyclostationary, the values in cyclic autocorrelation domain are sparse. From the measurements based on the compressed sensing of the cyclic autocorrelation, the actual cyclic autocorrelation of the signal can be recovered. From the simulation, the dissertation can be made that a very simple OMP algorithm can get a high probability of detection.
Finally, the content of the whole dissertation is summarized, and several valuable research directions of compressed sensing and CR spectrum sensing are discussed.
本文对压缩感知技术的基本原理及其在认知无线电关键技术频谱感知中的应用进行了深入研究,主要完成了以下具有创新性的研究成果：
针对频谱感知在时变信道中检测概率恶化的情况,本文提出了一种自适应静默期管理机制,即根据上一感知周期的感知结果和数据通信获知的ACK反馈等信息,自适应调整本感知周期的感知参数以适应信道参数变化,并通过马尔科夫分析方法,对提出的自适应静默期管理机制的检测概率和平均采样数进行了理论分析。仿真结果表明,在时变信道的环境下,本文机制的平均检测概率要优于传统固定时长的静默期管理机制。
针对多信道频谱感知中,认知用户条件不足以检测整个信道状态的问题,本文提出了一种应用于宽带认知无线网络的协作式宽带频谱感知稀疏模型,和一种基于LDPC码的协作式频谱感知策略。当认知终端需要检测的子信道个数远超过其检测能力时,即可采用该稀疏模型,每个认知终端采用压缩感知的方式对极少的信道进行随机测量,在融合中心将认知终端的随机测量结果合并为一个完整的压缩感知方程从而对信道状态序列进行重构。仿真结果表明,在不同的子信道占用个数情况下,适当调整LDPC测量矩阵的参数,都可以通过较少的子信道测量个数达到较优的系统检测概率。
为降低宽带频谱感知的采样率,提出了一种基于分布式压缩感知算法的频谱感知机制。通过利用授权用户信号的稀疏性,在超帧的每个子帧内进行采用分布式压缩感知的采样；在超帧结束时,采用DCS-SOMP算法对每个静默期的采样数据进行联合重构,再通过对重构数据的合并对信道状态作出合理判决。仿真结果表明,与传统压缩感知算法相比,本文算法可用更少的随机测量数达到预期的系统检测概率。
针对循环平稳特征检测算法复杂度高、耗费时间长的缺点,提出了一种基于压缩感知的循环自相关特征检测算法(CS-Feature Detection)。通过利用授权用户循环自相关谱的稀疏特征对其进行压缩感知；并采用归一化循环自相关统计量作为检验量进行判决,避免了对授权用户先验信息的需求,再用正交匹配追踪(Orthogonal Matching Pursuit, OMP)重构算法,即可以较高概率重构信号循环自相关谱。仿真结果表明,在显著缩短检测时间的情况下,依然可获得较高检测概率。
论文最后对全文进行了总结,并对压缩感知技术与认知无线电系统结合的发展方向及今后的工作进行了展望。
Compressed sensing has a great potential in the cognitive radio (CR) system. This thesis is supported by National Science Funds and attempts to make a contribution to the theory and application of CR system.
In this paper, the basic principle of compressed sensing technology and its application of CR spectrum sensing procedure are investigated. Several creative algorithms are developed in this thesis.
This paper addresses the problem of designing an appropriate quiet period management scheme over time-variant channels. To achieve better performance over time-variant channels, we propose a flexible quiet period management scheme which can adjust the sensing parameters according to the previous sensing result and the information of ACK. The performance of the proposed scheme has been evaluated through Markov analysis. Numerical results show that under time-variant channel conditions, a better probability of detection and higher channel utilization is achieved compared to the traditional fixed quiet period management scheme.
Collaborative spectrum sensing (CSS) can significantly improve the performance of spectrum sensing based on the spatial diversity gain of different cognitive radio. In wideband spectrum sensing scenario, since there might not be enough CRs in the network, or due to hardware limitations, each CR node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Based on the fact that the spectrum usage information the CR nodes collect has a common sparsity pattern, in this paper, we present a compressed collaborative wideband spectrum sensing scheme in cognitive radio networks. Under the hypothesis of joint sparsity, the CRs need to randomly detect a very small number of sub-channels according to a measurement matrix and send the results to a fusion center. To make the compressed sensing more effective, the scheme uses LDPC-like measurement matrix. Then the whole channel status can be recoverd by the fusion center through a low-complexity message passing algorithm. Numerical results show that under a joint sparsity model, using the proposed distributed compressed sensing scheme, the CRs make a small number of measurements and get a high probability of detection.
In order to reduce the sampling rate in the broadband spectrum sensing, a cognitive radio spectrum sensing algorithm based on distributed compressive sensing is proposed. In IEEE802.22standard, Timeline is divided into successive superframes. Each superframe consists of a number of frames, and quiet period is set in each frame. At the end of each superframe, the spectrum sensing results of the quiet periods in this superframe are merged. Because the primaiy user signals are sparse in the frequency domain, compressive sensing method can be adopt. Assume that the primary user varys slowly, then the sampling sighals in the same superframe are joint sparse and meet the joint sparsity model JSM-2. So the second user makes random measurements below the Nyquist sampling rate using the measurement matrix. After that, using DCS-SOMP algorithm, the data of each sample of the quiet periods can be reconstructed. Merge the reconstructed data, then the reasonable judgment can be made.
A CS-Feature detection spectrum sensing algorithms for cognitive radio is proposed in this paper. The traditional feature detection algorithm based on cyclic spectrum density has a very high accuracy, but it can't be widely used because of the high complexity and very long detection period. The CS-Feature detection in this paper is designed based on the sparsity of the cyclic autocorrelation. Because most of the man-made signals are cyclostationary, the values in cyclic autocorrelation domain are sparse. From the measurements based on the compressed sensing of the cyclic autocorrelation, the actual cyclic autocorrelation of the signal can be recovered. From the simulation, the dissertation can be made that a very simple OMP algorithm can get a high probability of detection.
Finally, the content of the whole dissertation is summarized, and several valuable research directions of compressed sensing and CR spectrum sensing are discussed.
引文
[1]Federal Communication Commission, Spectrum Policy Task Force Report, ET Docket No.02-155,2002.
[2]R. W. Broderson, A. Wolisz, D. Cabric, S. M. Mishra, and D. Willkomm. White paper, CORVUS:A cognitive radio approach for usage of virtual unlicensed spectrum,2004
[3]http://bwrc.eecs.berkeley.edu/Research/MCMA/CR White paper finall.pdf
[4]D. Cabric, Implementation issues in spectrum sensing for cognitive radios, Conference Record of the Thirty-Eighth Asilomar Conference on Signals Systems and Computers, vol.1,2004, pp:772-776.
[5]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.
[6]J Mitola, G J Maquire, Cognitive radios:making software radios more personal, IEEE Personal Communications,6(4),1999, pp:13-18.
[7]J. Mitola, Cognitive radio:an integrated agent architecture for software defined radio, PhD Dissertation, Royal Institute of Technology, Sweden,2000.
[8]张洪涛,认知无线电功率控制算法研究,硕士论文,西安电子科技大学西安,中国,2008年1月.
[9]S Haykin, Cognitive radio:brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications,23(2),2005, pp:201-220.
[10]ITU-P WP8A Contributions, http://www.itu.int/md/R03-WP8A-C/en
[11]赵成仕,认知无线网络中无线资源管理的研究,博士论文,北京邮电大学北京,中国,2010年1月.
[12]Akyildiz I. F., Lee W. Y., Vuran M. C. Next generation/dynamic spectrum access/cognitive radio wireless network:a survey, Computer Networks,50, 2006, pp:2127-2153.
[13]IEEE 802.22 Working Group on Wireless Regional Area Networks, http://www. ieee802. org/22
[14]A. Ghasemi and E. S. Sousa, Collaborative spectrum sensing for opportunistic access in fading environments, DySPAN 2005,2005, pp:131-136.
[15]谭学治,姜靖,孙洪剑,认知无线电的频谱感知技术研究,通信技术,3,2007,pp：61-63.
[16]D. Cabric, Implementation issues in spectrum sensing for cognitive radios, Conference Record of the Thirty-Eighth Asilomar Conference on Signals Systems and Computers,1,2004, pp:772-776.
[17]P. Qihang, Z. Kun, W. Jun, and L. Shaoqian, A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio context, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications,2006, pp:1-5.
[18]Liang Qian, Feng Ye, Lin Gao, Spectrum Trading in Cognitive Radio Networks: An Agent-Based Model under Demand Uncertainty. IEEE Transaction on Communications,59(11),2011, pp:3192-3203.
[19]T. A. Weiss, F. K. Jondral, Spectrum pooling:an innovative strategy for the enhancement of spectrum efficiency, IEEE Communication Magazine,42(3) 2004, pp:8-14.
[20]贾杰,王闯,张朝阳,陈剑,认知无线电网络中基于图着色的动态频谱分配,东北大学学报(自然科学版),3,2012.
[21]李鹏,基于博弈论的认知无线电频谱分配算法研究,博士论文,北京邮电大学,北京,中国,2010年10月.
[22]Z. Ji and K. J. R. Liu, Cognitive radios for dynamic spectrum access-dynamic spectrum sharing:a game theoretical overview, IEEE Communications Magazine,45,2007, pp:88-94.
[23]王文焕,王衍文,认知无线电在宽带无线通信系统中的应用,中兴通讯技术,3,2007,pp：31-33.
[24]Cabric D, Bordersen R W, Physical layer design issues unique to cognitive radio systems, Proc of 16th Int Symposium on Personal Indoor and Mobile Radio Communications,2005, pp:759-763.
[25]Tang H. Some physical layer issues of wide-band cognitive radio systems. Proc of IEEE Int Symposium on New Frontiers in Dynamic Spectrum Access Networks,2005, pp:151-159.
[26]漆春梅,认知无线电中的频谱感知技术研究,硕士论文,电子科技大学,成都,中国,2009.
[27]Digham F, Alouini M, Simon M, On the energy detection of unknown signals over fading channels, Proc of Int Conf on Communications, Anchorage, Alaska, USA,2003, pp:3575-3579.
[28]Kim K, Akbar A, Bae K K, et al, Cyclostationary approaches to signal detection and classification in cognitive radio, Proc of the 2nd IEEE Int Symposium on New Frontiers in Dynamic Spectrum Access Networks, Dublin, 2007, pp:212-215.
[29]Zuo. R, Information Theory, Information View, and Software Testing, Proceedings of ITNG2010,2010, pp:998-1003.
[30]J.G. Proakis, Digital Communications.4th ed. McGraw-Hill,2001.
[31]D. L. Donoho. Compressed sensing. IEEE Transaction on Information Theory, 52(4),2006, pp:1289-1306.
[32]E Candes. Compressive sampling, Proceedings of the International Congress of Mathematicians, Madrid,2006, pp:1433-1452.
[33]D. Baron, M. B. Wakin and M. F. Duarte, Distributed compressed sensing. http://www.dsp.rice.edu/-drorb/pdf/DCS112005.pdf
[34]S Ji, D Dunson and L Carin, Multitask compressive sensing. IEEE Transactions on Signal Processing,57(1),2009, pp:92-106.
[35]S Ji, Y Xue and L Carin, Bayesian compressive sensing. IEEE Transaction on Signal Processing,56(6),2008, pp:2346-2356.
[36]石光明,刘丹华,高大化,刘哲,林杰,王良君,压缩感知理论及其研究进展,电子学报,5(2),2009,pp：1070-1081.
[37]石磊,压缩感知在超宽带信道估计中的应用研究,博士论文,北京邮电大 学,北京,中国,2011年5月.
[38]张春梅,尹忠科,肖明霞,基于冗余字典的信号超完备表示与稀疏分解,科学通报,51(6),2006,pp：628-633,
[39]E. Candes and M. Wakin, An introduction to compressive sampling, IEEE Signal Process,25,2008, pp:21-30.
[40]Z. Tian and G. B. Giannakis, Compressed sensing for wideband cognitive radios, IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu,2007, pp:1357-1360.
[41]Y. L. Polo, Y.Wang, A. Pandharipande, and G. Leus, Compressive wide-band spectrum sensing, IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei,2009, pp:2337-2340.
[42]Havary-Nassab V, Hassan S, Valaee S, Compressive detection for wide-band spectrum sensing, IEEE International Conference on Acoustics, Speech and Signal Processing, USA,2010, pp:3094-3097.
[43]E J Candes, T Tao, Near optimal signal recovery from random projections: Universal encoding strategies, IEEE Transaction on Information Theory, 52(12),2006, pp:5406-5425.
[44]R Baraniuk, A lecture on compressive sensing, IEEE Signal Processing Magazine,24(4),2007, pp:118-121.
[1]S. Haykin, Cognitive radio:brain-empowered wireless communications, IEEE J. Select. Areas Commun,23,2005, pp:201-220.
[2]Yiyang Pei, Ying-Chang Liang, Kah Chan Teh and Kwok Hung Li, How much time is needed for wideband spectrum sensing?, IEEE transactions on wireless communications,8(4),2009, pp:5466-5471.
[3]Lionel Gueguen and Berna Sayrac, Sensing in cognitive radio channels:a theoretical perspective, IEEE transactions on wireless communications,8(3), 2009, pp:1194-1198.
[4]Youping Zhao, Shiwen Mao, James O. Neel and Jeffrey H. Reed, Performance evaluation of cognitive radios:metrics, utility functions, and methodology, Proceedings of the IEEE,97(4),2009, pp:642-659.
[5]Bo Yang and Yanyan Shen, Channel-aware access for cognitive radio networks, IEEE transactions on vehicular technology,58(7),2009, pp: 3726-3737.
[6]IEEE. IEEE P802.221.22TM/D0.1 Draft Standard for Wireless Regional Area Networks Part 22:Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) specifications:Policies and procedures for operation in the TV Bands, IEEE.2006.
[7]D. Cabric, S. M. Mishra, and R. W. Brodersen, Implementation issues in spectrum sensing for cognitive radios, Proc. Asilomar Conf. Signals, Syst, Computers,1,2004, pp:772-776.
[8]S. Shellhammer, Performance of the power detector, IEEE 802.22-06/0075r0, May 2006.
[9]Wha Sook Jeon and Dong Geun Jeong, An advanced quiet period management scheme for cognitive radio systems, unpublished.
[10]Zhaoxia Song, Zheng Zhou, et al, Nonquiet primary user detection scheme,6th International ICST Conference on Communications and Networking in China, 2011, Harbin, pp:473-477.
[11]吴伟陵,牛凯,移动通信原理,北京：电子工业出版社,2005.
[12]A. F. Molisch, L. J. Greenstein and M. Shafi, Propagation issues for cognitive radio, Proceedings of the IEEE,97(5), May 2009, pp:787-804.
[13]Anh Tuan Hoang, Ying-chang Liang, David Tung Chong Wong, Opportunistic spectrum access for energy-constrained cognitive radios, IEEE transactions on wireless communications,8(3), March 2009, pp:1206-1211.
[14]Ying-Chang Liang, Yonghong Zeng, Edward C.Y. Peh and Anh Tuan Hoang, Sensing-throughput tradeoff for cognitive radio networks, IEEE transactions on wireless communications,7(4), April 2008, pp:1326-1337.
[1]Qiu Jing, Zhou Zheng, Dynamic spectrum sharing strategy in cognitive radio systems, Journal of Beijing University of Posts and Telecommunications, 32(1),2009, pp:69-72.
[2]S. Haykin, Cognitive radio:brain-empowered wireless communications, IEEE J. Select Areas Commun,23,2005, pp:201-220.
[3]B.Wang, K. Ray Liu and T. Clancy, Evolutionary cooperative spectrum sensing game:how to collaborate, IEEE Transactions on communications,58(3), Mar. 2010, pp:890-900.
[4]R. G. Gallager, Low-density parity-check codes, IEEE Transactions on information theory,8, Jan.1962, pp:21-28.
[5]David Madigan, Jeremy York, Denis Allard, Bayesian Graphical Models for Discrete Data, International Statistical Review/Revue Internationale de Statistique,63(2),1995, pp:215-232.
[6]Tanner. R, A recursive approach to low complexity codes, IEEE Transactions on Information Theory,27(5),1981, pp:533-547.
[7]F. R. Kschischang, B. J. Frey, H. A. Loeliger, Factor graphs and the sum-product algorithm, IEEE Transactions on Information Theory,47(2), 2001, pp:498-519.
[8]Mao, Y. and Kschischang F.R., On factor graphs and the Fourier transform, IEEE Transactions on information theory,51(5),2005, pp:1635-1649.
[9]Mohammad M. Mansour, A Turbo-Decoding Message-Passing Algorithm for Sparse Parity-Check Matrix Codes, IEEE Trans on Signal Processing,54(11), 2006, pp:4376-4392.
[10]Seung-Jun Kim, Emiliano Dall'Anese and Georgios B. Giannakis, Cooperative Spectrum Sensing for Cognitive Radios Using Kriged Kalman Filtering, IEEE Journal of Selected Topics in Signal Processing,5(1),2011, pp:24-36.
[11]Dror Baron, Shriram Sarvotham, Richard G. Baraniuk, Bayesian Compressive Sensing Via Belief Propagation, IEEE Trans on Signal Processing,58(1),2010, pp:269-280.
[12]D. Koller and N. Friedman, Probabilistic Graphical Models:Principles and Techniques, Cambridge, MA:MIT Press,2009.
[13]Venkat Chandar, Devavrat Shah and Gregory W. Wornell, A Simple Message-Passing Algorithm for Compressed Sensing, IEEE International Symposium on Information Theory Proceedings (ISIT), Austin, Texas, U.S.A. 2010, pp:1968-1972.
[14]Volkan Cevher, Piotr Indyk, Lawrence Carin and Richard G. Baraniuk, Sparse Signal Recovery and Acquisition with Graphical Models, IEEE Signal Processing Magazine,27(6),2010, pp:92-103.
[1]D. Baron, M. B. Wakin, M. F. Duarte, S. Sarvotham and R. G. Baraniuk, Distributed Compressed Sensing, http://www.dsp.rice.edu/-drorb/ pdf/DCS 112005.pdf
[2]J. Tropp, A. C. Gilbert and M. J. Strauss, Simulataneous sparse approximation via greedy pursuit, IEEE 2005 Int. Conf. Acoustics, Mar.2005, pp:721-724.
[3]田宝玉,工程信息论,北京邮电大学出版社,2004年8月.
[4]D. Slepian. J. K. Wolf, Noiseless coding of correlated information sources, IEEE Transaction on Information Theory,19,1973, pp:471-480.
[5]A. Wyner, J. Ziv, The Rate-distortion Function for Source Coding with Side Information at the Decoder, IEEE Transaction on Information Theory,22(1), 1976, pp:1-10.
[6]S.S Pradhan, K. Ramchandran, Distributed Source Coding using Syndromes: Design and Construction, IEEE Transactions on Information Theory,49(3), Mar.2003, pp:6626-643.
[7]Wipf D.P. and Rao B.D., An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem, IEEE Trans. Signal Processing,55,2007, pp:3704-3716.
[8]S. F. Cotter, B. D. Rao, K. Engan and K. Kreutz-Delgado, Sparse solutions to linear inverse problems with multiple measurement vectors, IEEE Trans. Signal Processing,51, July 2005, pp:2477-2488.
[9]S. Ji, D. Dunson and L. Carin, Multitask Compressive Sensing, IEEE Trans, on Signal Processing,57(1),2009, pp:92-106.
[10]IEEE. IEEE P802.221.22TM/D0.1 Draft Standard for Wireless Regional Area Networks Part 22:Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) specifications:Policies and procedures for operation in the TV Bands, IEEE.2006.
[1]S Haykin, Cognitive radio:brain-empowered wireless communications, IEEE J. Select. Areas Commun,23,2005, pp:201-220.
[2]王红军,毕光国,一种改进的认知无线电循环功率谱特征检测算法,信号处理,26(7),2010,pp：1089-1093.
[3]石光明,刘丹华,高大化,压缩感知理论及其研究进展,电子学报,37(5),2009，pp：1070-1081.
[4]曲强,多项式相位信号与循环平稳信号处理算法研究,博士论文,大连,中国,2010年1月.
[5]E. Candes and M. Wakin, An introduction to compressive sampling. IEEE Signal Process,25, March 2008, pp:21-30.
[6]陈星,贺志强,吴伟陵,基于循环平稳的多天线感知无线电频谱检测,北京邮电大学学报,31(2),2008,pp：85-89.
[7]Grace K Yeung and William A Gardner, Search-Efficient Methods of Detection of Cyclostationary Signals, IEEE Transactions On Signal Processing, 44(5),1996, pp:1214-1223.
[8]张瑜,认知无线电中基于循环谱的信号检测识别,硕士论文,哈尔滨工程大学,2010.
[9]赵丹,循环平稳检测在认知无线电中的应用,大众科技,1,2008,pp：16-17.
[10]D L Donoho, Compressed sensing, IEEE Trans, on Information Theory,52(4), 2006, pp:1289-1306.
[11]E Candes, Compressive sampling, Proceedings of the International Congress of Mathematicians, Madrid, Spain,3,2006, pp:1433-1452.
[12]E Candes, An Introduction To Compressive Sampling, IEEE Signal Processing Magazine,25(2),2008:21-30.
[13]石磊,压缩感知在超宽带信道估计中的应用研究,博士论文,北京邮电大学,北京,中国,2011年5月.
[14]Z. Tian and G. B. Giannakis, Compressed sensing for wideband cognitive radios, IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, HI, Apr.2007, pp:1357-1360.
[15]S S Chen, D L Donoho and M A Saunders, Atomic decomposition by basis pursuit, SIAM Review,43(1),2001, pp:129-159.
[16]Pati Y C, Rezaiifar R, Krishnaprasad P S, Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition, ACSSC,1,1993, pp:40-44.
[2]R. W. Broderson, A. Wolisz, D. Cabric, S. M. Mishra, and D. Willkomm. White paper, CORVUS:A cognitive radio approach for usage of virtual unlicensed spectrum,2004
[3]http://bwrc.eecs.berkeley.edu/Research/MCMA/CR White paper finall.pdf
[4]D. Cabric, Implementation issues in spectrum sensing for cognitive radios, Conference Record of the Thirty-Eighth Asilomar Conference on Signals Systems and Computers, vol.1,2004, pp:772-776.
[5]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.
[6]J Mitola, G J Maquire, Cognitive radios:making software radios more personal, IEEE Personal Communications,6(4),1999, pp:13-18.
[7]J. Mitola, Cognitive radio:an integrated agent architecture for software defined radio, PhD Dissertation, Royal Institute of Technology, Sweden,2000.
[8]张洪涛,认知无线电功率控制算法研究,硕士论文,西安电子科技大学西安,中国,2008年1月.
[9]S Haykin, Cognitive radio:brain-empowered wireless communications, IEEE Journal on Selected Areas in Communications,23(2),2005, pp:201-220.
[10]ITU-P WP8A Contributions, http://www.itu.int/md/R03-WP8A-C/en
[11]赵成仕,认知无线网络中无线资源管理的研究,博士论文,北京邮电大学北京,中国,2010年1月.
[12]Akyildiz I. F., Lee W. Y., Vuran M. C. Next generation/dynamic spectrum access/cognitive radio wireless network:a survey, Computer Networks,50, 2006, pp:2127-2153.
[13]IEEE 802.22 Working Group on Wireless Regional Area Networks, http://www. ieee802. org/22
[14]A. Ghasemi and E. S. Sousa, Collaborative spectrum sensing for opportunistic access in fading environments, DySPAN 2005,2005, pp:131-136.
[15]谭学治,姜靖,孙洪剑,认知无线电的频谱感知技术研究,通信技术,3,2007,pp：61-63.
[16]D. Cabric, Implementation issues in spectrum sensing for cognitive radios, Conference Record of the Thirty-Eighth Asilomar Conference on Signals Systems and Computers,1,2004, pp:772-776.
[17]P. Qihang, Z. Kun, W. Jun, and L. Shaoqian, A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio context, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications,2006, pp:1-5.
[18]Liang Qian, Feng Ye, Lin Gao, Spectrum Trading in Cognitive Radio Networks: An Agent-Based Model under Demand Uncertainty. IEEE Transaction on Communications,59(11),2011, pp:3192-3203.
[19]T. A. Weiss, F. K. Jondral, Spectrum pooling:an innovative strategy for the enhancement of spectrum efficiency, IEEE Communication Magazine,42(3) 2004, pp:8-14.
[20]贾杰,王闯,张朝阳,陈剑,认知无线电网络中基于图着色的动态频谱分配,东北大学学报(自然科学版),3,2012.
[21]李鹏,基于博弈论的认知无线电频谱分配算法研究,博士论文,北京邮电大学,北京,中国,2010年10月.
[22]Z. Ji and K. J. R. Liu, Cognitive radios for dynamic spectrum access-dynamic spectrum sharing:a game theoretical overview, IEEE Communications Magazine,45,2007, pp:88-94.
[23]王文焕,王衍文,认知无线电在宽带无线通信系统中的应用,中兴通讯技术,3,2007,pp：31-33.
[24]Cabric D, Bordersen R W, Physical layer design issues unique to cognitive radio systems, Proc of 16th Int Symposium on Personal Indoor and Mobile Radio Communications,2005, pp:759-763.
[25]Tang H. Some physical layer issues of wide-band cognitive radio systems. Proc of IEEE Int Symposium on New Frontiers in Dynamic Spectrum Access Networks,2005, pp:151-159.
[26]漆春梅,认知无线电中的频谱感知技术研究,硕士论文,电子科技大学,成都,中国,2009.
[27]Digham F, Alouini M, Simon M, On the energy detection of unknown signals over fading channels, Proc of Int Conf on Communications, Anchorage, Alaska, USA,2003, pp:3575-3579.
[28]Kim K, Akbar A, Bae K K, et al, Cyclostationary approaches to signal detection and classification in cognitive radio, Proc of the 2nd IEEE Int Symposium on New Frontiers in Dynamic Spectrum Access Networks, Dublin, 2007, pp:212-215.
[29]Zuo. R, Information Theory, Information View, and Software Testing, Proceedings of ITNG2010,2010, pp:998-1003.
[30]J.G. Proakis, Digital Communications.4th ed. McGraw-Hill,2001.
[31]D. L. Donoho. Compressed sensing. IEEE Transaction on Information Theory, 52(4),2006, pp:1289-1306.
[32]E Candes. Compressive sampling, Proceedings of the International Congress of Mathematicians, Madrid,2006, pp:1433-1452.
[33]D. Baron, M. B. Wakin and M. F. Duarte, Distributed compressed sensing. http://www.dsp.rice.edu/-drorb/pdf/DCS112005.pdf
[34]S Ji, D Dunson and L Carin, Multitask compressive sensing. IEEE Transactions on Signal Processing,57(1),2009, pp:92-106.
[35]S Ji, Y Xue and L Carin, Bayesian compressive sensing. IEEE Transaction on Signal Processing,56(6),2008, pp:2346-2356.
[36]石光明,刘丹华,高大化,刘哲,林杰,王良君,压缩感知理论及其研究进展,电子学报,5(2),2009,pp：1070-1081.
[37]石磊,压缩感知在超宽带信道估计中的应用研究,博士论文,北京邮电大 学,北京,中国,2011年5月.
[38]张春梅,尹忠科,肖明霞,基于冗余字典的信号超完备表示与稀疏分解,科学通报,51(6),2006,pp：628-633,
[39]E. Candes and M. Wakin, An introduction to compressive sampling, IEEE Signal Process,25,2008, pp:21-30.
[40]Z. Tian and G. B. Giannakis, Compressed sensing for wideband cognitive radios, IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu,2007, pp:1357-1360.
[41]Y. L. Polo, Y.Wang, A. Pandharipande, and G. Leus, Compressive wide-band spectrum sensing, IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei,2009, pp:2337-2340.
[42]Havary-Nassab V, Hassan S, Valaee S, Compressive detection for wide-band spectrum sensing, IEEE International Conference on Acoustics, Speech and Signal Processing, USA,2010, pp:3094-3097.
[43]E J Candes, T Tao, Near optimal signal recovery from random projections: Universal encoding strategies, IEEE Transaction on Information Theory, 52(12),2006, pp:5406-5425.
[44]R Baraniuk, A lecture on compressive sensing, IEEE Signal Processing Magazine,24(4),2007, pp:118-121.
[1]S. Haykin, Cognitive radio:brain-empowered wireless communications, IEEE J. Select. Areas Commun,23,2005, pp:201-220.
[2]Yiyang Pei, Ying-Chang Liang, Kah Chan Teh and Kwok Hung Li, How much time is needed for wideband spectrum sensing?, IEEE transactions on wireless communications,8(4),2009, pp:5466-5471.
[3]Lionel Gueguen and Berna Sayrac, Sensing in cognitive radio channels:a theoretical perspective, IEEE transactions on wireless communications,8(3), 2009, pp:1194-1198.
[4]Youping Zhao, Shiwen Mao, James O. Neel and Jeffrey H. Reed, Performance evaluation of cognitive radios:metrics, utility functions, and methodology, Proceedings of the IEEE,97(4),2009, pp:642-659.
[5]Bo Yang and Yanyan Shen, Channel-aware access for cognitive radio networks, IEEE transactions on vehicular technology,58(7),2009, pp: 3726-3737.
[6]IEEE. IEEE P802.221.22TM/D0.1 Draft Standard for Wireless Regional Area Networks Part 22:Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) specifications:Policies and procedures for operation in the TV Bands, IEEE.2006.
[7]D. Cabric, S. M. Mishra, and R. W. Brodersen, Implementation issues in spectrum sensing for cognitive radios, Proc. Asilomar Conf. Signals, Syst, Computers,1,2004, pp:772-776.
[8]S. Shellhammer, Performance of the power detector, IEEE 802.22-06/0075r0, May 2006.
[9]Wha Sook Jeon and Dong Geun Jeong, An advanced quiet period management scheme for cognitive radio systems, unpublished.
[10]Zhaoxia Song, Zheng Zhou, et al, Nonquiet primary user detection scheme,6th International ICST Conference on Communications and Networking in China, 2011, Harbin, pp:473-477.
[11]吴伟陵,牛凯,移动通信原理,北京：电子工业出版社,2005.
[12]A. F. Molisch, L. J. Greenstein and M. Shafi, Propagation issues for cognitive radio, Proceedings of the IEEE,97(5), May 2009, pp:787-804.
[13]Anh Tuan Hoang, Ying-chang Liang, David Tung Chong Wong, Opportunistic spectrum access for energy-constrained cognitive radios, IEEE transactions on wireless communications,8(3), March 2009, pp:1206-1211.
[14]Ying-Chang Liang, Yonghong Zeng, Edward C.Y. Peh and Anh Tuan Hoang, Sensing-throughput tradeoff for cognitive radio networks, IEEE transactions on wireless communications,7(4), April 2008, pp:1326-1337.
[1]Qiu Jing, Zhou Zheng, Dynamic spectrum sharing strategy in cognitive radio systems, Journal of Beijing University of Posts and Telecommunications, 32(1),2009, pp:69-72.
[2]S. Haykin, Cognitive radio:brain-empowered wireless communications, IEEE J. Select Areas Commun,23,2005, pp:201-220.
[3]B.Wang, K. Ray Liu and T. Clancy, Evolutionary cooperative spectrum sensing game:how to collaborate, IEEE Transactions on communications,58(3), Mar. 2010, pp:890-900.
[4]R. G. Gallager, Low-density parity-check codes, IEEE Transactions on information theory,8, Jan.1962, pp:21-28.
[5]David Madigan, Jeremy York, Denis Allard, Bayesian Graphical Models for Discrete Data, International Statistical Review/Revue Internationale de Statistique,63(2),1995, pp:215-232.
[6]Tanner. R, A recursive approach to low complexity codes, IEEE Transactions on Information Theory,27(5),1981, pp:533-547.
[7]F. R. Kschischang, B. J. Frey, H. A. Loeliger, Factor graphs and the sum-product algorithm, IEEE Transactions on Information Theory,47(2), 2001, pp:498-519.
[8]Mao, Y. and Kschischang F.R., On factor graphs and the Fourier transform, IEEE Transactions on information theory,51(5),2005, pp:1635-1649.
[9]Mohammad M. Mansour, A Turbo-Decoding Message-Passing Algorithm for Sparse Parity-Check Matrix Codes, IEEE Trans on Signal Processing,54(11), 2006, pp:4376-4392.
[10]Seung-Jun Kim, Emiliano Dall'Anese and Georgios B. Giannakis, Cooperative Spectrum Sensing for Cognitive Radios Using Kriged Kalman Filtering, IEEE Journal of Selected Topics in Signal Processing,5(1),2011, pp:24-36.
[11]Dror Baron, Shriram Sarvotham, Richard G. Baraniuk, Bayesian Compressive Sensing Via Belief Propagation, IEEE Trans on Signal Processing,58(1),2010, pp:269-280.
[12]D. Koller and N. Friedman, Probabilistic Graphical Models:Principles and Techniques, Cambridge, MA:MIT Press,2009.
[13]Venkat Chandar, Devavrat Shah and Gregory W. Wornell, A Simple Message-Passing Algorithm for Compressed Sensing, IEEE International Symposium on Information Theory Proceedings (ISIT), Austin, Texas, U.S.A. 2010, pp:1968-1972.
[14]Volkan Cevher, Piotr Indyk, Lawrence Carin and Richard G. Baraniuk, Sparse Signal Recovery and Acquisition with Graphical Models, IEEE Signal Processing Magazine,27(6),2010, pp:92-103.
[1]D. Baron, M. B. Wakin, M. F. Duarte, S. Sarvotham and R. G. Baraniuk, Distributed Compressed Sensing, http://www.dsp.rice.edu/-drorb/ pdf/DCS 112005.pdf
[2]J. Tropp, A. C. Gilbert and M. J. Strauss, Simulataneous sparse approximation via greedy pursuit, IEEE 2005 Int. Conf. Acoustics, Mar.2005, pp:721-724.
[3]田宝玉,工程信息论,北京邮电大学出版社,2004年8月.
[4]D. Slepian. J. K. Wolf, Noiseless coding of correlated information sources, IEEE Transaction on Information Theory,19,1973, pp:471-480.
[5]A. Wyner, J. Ziv, The Rate-distortion Function for Source Coding with Side Information at the Decoder, IEEE Transaction on Information Theory,22(1), 1976, pp:1-10.
[6]S.S Pradhan, K. Ramchandran, Distributed Source Coding using Syndromes: Design and Construction, IEEE Transactions on Information Theory,49(3), Mar.2003, pp:6626-643.
[7]Wipf D.P. and Rao B.D., An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem, IEEE Trans. Signal Processing,55,2007, pp:3704-3716.
[8]S. F. Cotter, B. D. Rao, K. Engan and K. Kreutz-Delgado, Sparse solutions to linear inverse problems with multiple measurement vectors, IEEE Trans. Signal Processing,51, July 2005, pp:2477-2488.
[9]S. Ji, D. Dunson and L. Carin, Multitask Compressive Sensing, IEEE Trans, on Signal Processing,57(1),2009, pp:92-106.
[10]IEEE. IEEE P802.221.22TM/D0.1 Draft Standard for Wireless Regional Area Networks Part 22:Cognitive Wireless RAN Medium Access Control (MAC) and Physical Layer (PHY) specifications:Policies and procedures for operation in the TV Bands, IEEE.2006.
[1]S Haykin, Cognitive radio:brain-empowered wireless communications, IEEE J. Select. Areas Commun,23,2005, pp:201-220.
[2]王红军,毕光国,一种改进的认知无线电循环功率谱特征检测算法,信号处理,26(7),2010,pp：1089-1093.
[3]石光明,刘丹华,高大化,压缩感知理论及其研究进展,电子学报,37(5),2009，pp：1070-1081.
[4]曲强,多项式相位信号与循环平稳信号处理算法研究,博士论文,大连,中国,2010年1月.
[5]E. Candes and M. Wakin, An introduction to compressive sampling. IEEE Signal Process,25, March 2008, pp:21-30.
[6]陈星,贺志强,吴伟陵,基于循环平稳的多天线感知无线电频谱检测,北京邮电大学学报,31(2),2008,pp：85-89.
[7]Grace K Yeung and William A Gardner, Search-Efficient Methods of Detection of Cyclostationary Signals, IEEE Transactions On Signal Processing, 44(5),1996, pp:1214-1223.
[8]张瑜,认知无线电中基于循环谱的信号检测识别,硕士论文,哈尔滨工程大学,2010.
[9]赵丹,循环平稳检测在认知无线电中的应用,大众科技,1,2008,pp：16-17.
[10]D L Donoho, Compressed sensing, IEEE Trans, on Information Theory,52(4), 2006, pp:1289-1306.
[11]E Candes, Compressive sampling, Proceedings of the International Congress of Mathematicians, Madrid, Spain,3,2006, pp:1433-1452.
[12]E Candes, An Introduction To Compressive Sampling, IEEE Signal Processing Magazine,25(2),2008:21-30.
[13]石磊,压缩感知在超宽带信道估计中的应用研究,博士论文,北京邮电大学,北京,中国,2011年5月.
[14]Z. Tian and G. B. Giannakis, Compressed sensing for wideband cognitive radios, IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, HI, Apr.2007, pp:1357-1360.
[15]S S Chen, D L Donoho and M A Saunders, Atomic decomposition by basis pursuit, SIAM Review,43(1),2001, pp:129-159.
[16]Pati Y C, Rezaiifar R, Krishnaprasad P S, Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition, ACSSC,1,1993, pp:40-44.