基于稀疏度检测的宽带压缩频谱感知方法研究
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摘要
近年来,随着人们对无线频谱资源的需求不断增长,认知无线电技术(CR)得到越来越多人的关注与重视,频谱检测作为认知无线电中的关键技术之一,成为人们研究的热点之一。随着无线移动通信不断向着宽带化发展,认知无线电也必须不断探索在宽带范围内进行频谱检测的新方法。
由于奈奎斯特(Nyquist)采样定理的限制,在宽带范围内获取模拟信号成为宽带频谱感知面对的最大难题。压缩传感技术利用信号的稀疏性或者可压缩性,将信号采样和压缩过程合二为一,只采集对信号恢复有用的关键信息,这样就可以以低于Nyquist速率的采样速率对信号进行采样,然后用某种恢复算法对信号进行重构。压缩传感理论的提出,能够在很大程度上降低宽带频谱检测的设备复杂度,为实现宽带频谱检测提供了一种可能。近年来,将压缩感知应用到宽带频谱检测中的研究越来越多,也取得了一定的成果。
本文针对宽带压缩频谱感知中的一些关键技术进行研究,主要包括：宽带信号的获取及恢复、信号稀疏度检测、基于多级判决的协作频谱检测方法等。具体研究内容及成果如下：
首先,研究宽带模拟信号的获取及重构算法,信号获取主要对模拟信息转换器(Analog-to-Information Converter,AIC)进行了研究,并对主要的重构算法进行整理和综合对比仿真,通过对这些算法的运行时间、恢复误差等关键参数进行对比,找出各种算法的优缺点,选择一种适用于本系统的信号重构方法。
其次,针对压缩传感中信号具有稀疏性或者可压缩性的前提条件提出了一种用于信号稀疏度检测的新方法,并对所提方法进行了分析和仿真验证。仿真结果表明该方法能够检测出宽带信号的稀疏度,保证压缩传感技术在宽带频谱检测中的准确运用。
最后,提出了一种基于多级判决的协作频谱检测方法。该方法将软判决和硬判决结合起来,改善了能量检测抗噪性能差和软判决占用公共控制信道带宽较宽的缺点。仿真表明,该算法在满足一定干扰限制的条件下提高了检测概率,而且能够有效避免主用户和次级用户之间的干扰。
In recent years, with the growing of wireless spectrum resources, cognitive radio (CR) technology takes more and more people's attention. As one of the most basic and most critical technology of cognitive radio, spectrum sensing has become a hot research point for researchers. In order to meet the needs of the development of the broadband wireless mobile communications, CR must continue to explore new methods to increase the range of wideband spectrum sensing.
Due to the limitations of the Nyquist sampling rate, how to acquire wideband analog signal is the biggest challenge faced by a wideband spectrum sensing. Using signal sparsity or compressibility, Compressive sensing merges the sampling and compression process into one process. It collects only key information which is useful to recover the original signal, so that it can sample signal under the Nyquist rate, and then use some reconstruction algorithm to reconstruct the original signal. Compressive sensing can not only largely cut down the complexity of the broadband spectrum detection equipment, especially for ADC, but also offers a probability to wideband spectrum detection. In recent years, more and more researchers introduce the compressed sensing into wideband spectrum sensing, and has also made some achievements.
This paper focus on some of the key technologies in wideband compressive spectrum sensing, which includes the acquisition and recovery of wideband signal, signal sparsity detection, cooperative spectrum detection method based on local multi-level judgment and so on.
First, we study how to sample and reconstruct wideband analog signal. On signal acquisition, we mainly interested in the analog information converter (Analog-to-Information Converter, AIC), and on reconstruction algorithm, we represented some reconstruction algorithms, classify them and made some simulation. We also compared some key parameters, such as reconstruction time and reconstruction error, to identify the strengths and weaknesses of various algorithms, and then selected an appropriate method for our system.
Secondly, in order to ensure that the compressed sensing technology can be accurately applied to wideband spectrum sensing, we proposed a new method for signal sparsity detection on account of the suppose that signal over wideband is always sparse or compressible. Then we conduct a feasibility analysis and simulation to the mentioned method. Simulation results show that the method can detect the sparsity of wideband signal, so that it can ensure that compressive sensing can accurately be applied to wideband spectrum sensing system.
Finally, on account of the lower detection accuracy of hard decision in low SNR condition and the wide bandwidth of the common control channel needed for SUs to upload data of soft decision, we proposes a cooperative spectrum sensing method based on multi-level judgment which combines the hard decision with soft decision to improve the performance of wideband spectrum sensing system. Simulation results show that this algorithm presented better performance in the detection probability, and channel collision probability of PUs and SUs can be effectively avoided.
由于奈奎斯特(Nyquist)采样定理的限制,在宽带范围内获取模拟信号成为宽带频谱感知面对的最大难题。压缩传感技术利用信号的稀疏性或者可压缩性,将信号采样和压缩过程合二为一,只采集对信号恢复有用的关键信息,这样就可以以低于Nyquist速率的采样速率对信号进行采样,然后用某种恢复算法对信号进行重构。压缩传感理论的提出,能够在很大程度上降低宽带频谱检测的设备复杂度,为实现宽带频谱检测提供了一种可能。近年来,将压缩感知应用到宽带频谱检测中的研究越来越多,也取得了一定的成果。
本文针对宽带压缩频谱感知中的一些关键技术进行研究,主要包括：宽带信号的获取及恢复、信号稀疏度检测、基于多级判决的协作频谱检测方法等。具体研究内容及成果如下：
首先,研究宽带模拟信号的获取及重构算法,信号获取主要对模拟信息转换器(Analog-to-Information Converter,AIC)进行了研究,并对主要的重构算法进行整理和综合对比仿真,通过对这些算法的运行时间、恢复误差等关键参数进行对比,找出各种算法的优缺点,选择一种适用于本系统的信号重构方法。
其次,针对压缩传感中信号具有稀疏性或者可压缩性的前提条件提出了一种用于信号稀疏度检测的新方法,并对所提方法进行了分析和仿真验证。仿真结果表明该方法能够检测出宽带信号的稀疏度,保证压缩传感技术在宽带频谱检测中的准确运用。
最后,提出了一种基于多级判决的协作频谱检测方法。该方法将软判决和硬判决结合起来,改善了能量检测抗噪性能差和软判决占用公共控制信道带宽较宽的缺点。仿真表明,该算法在满足一定干扰限制的条件下提高了检测概率,而且能够有效避免主用户和次级用户之间的干扰。
In recent years, with the growing of wireless spectrum resources, cognitive radio (CR) technology takes more and more people's attention. As one of the most basic and most critical technology of cognitive radio, spectrum sensing has become a hot research point for researchers. In order to meet the needs of the development of the broadband wireless mobile communications, CR must continue to explore new methods to increase the range of wideband spectrum sensing.
Due to the limitations of the Nyquist sampling rate, how to acquire wideband analog signal is the biggest challenge faced by a wideband spectrum sensing. Using signal sparsity or compressibility, Compressive sensing merges the sampling and compression process into one process. It collects only key information which is useful to recover the original signal, so that it can sample signal under the Nyquist rate, and then use some reconstruction algorithm to reconstruct the original signal. Compressive sensing can not only largely cut down the complexity of the broadband spectrum detection equipment, especially for ADC, but also offers a probability to wideband spectrum detection. In recent years, more and more researchers introduce the compressed sensing into wideband spectrum sensing, and has also made some achievements.
This paper focus on some of the key technologies in wideband compressive spectrum sensing, which includes the acquisition and recovery of wideband signal, signal sparsity detection, cooperative spectrum detection method based on local multi-level judgment and so on.
First, we study how to sample and reconstruct wideband analog signal. On signal acquisition, we mainly interested in the analog information converter (Analog-to-Information Converter, AIC), and on reconstruction algorithm, we represented some reconstruction algorithms, classify them and made some simulation. We also compared some key parameters, such as reconstruction time and reconstruction error, to identify the strengths and weaknesses of various algorithms, and then selected an appropriate method for our system.
Secondly, in order to ensure that the compressed sensing technology can be accurately applied to wideband spectrum sensing, we proposed a new method for signal sparsity detection on account of the suppose that signal over wideband is always sparse or compressible. Then we conduct a feasibility analysis and simulation to the mentioned method. Simulation results show that the method can detect the sparsity of wideband signal, so that it can ensure that compressive sensing can accurately be applied to wideband spectrum sensing system.
Finally, on account of the lower detection accuracy of hard decision in low SNR condition and the wide bandwidth of the common control channel needed for SUs to upload data of soft decision, we proposes a cooperative spectrum sensing method based on multi-level judgment which combines the hard decision with soft decision to improve the performance of wideband spectrum sensing system. Simulation results show that this algorithm presented better performance in the detection probability, and channel collision probability of PUs and SUs can be effectively avoided.
引文
[1]《中华人民共和国无线电频率划分规定》,中华人民共和国信息产业部,2006年10月。
[2]FCC. Spectrum policy task force report[R]. ET Docket no.02-135, November 2002.
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[33]Sebert F, Zou Y M, Ying L. Toeplitz block matrices in compressed sensing and their applications in imaging. In:Proceedings of International Conference on Technology and Applications in Biomedicine. Washington D. C, USA:IEEE,2008:47-50.
[34]Do T T, Trany T D, Gan L. Fast compressive sampling with structurally random matrices. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington D. C, USA:IEEE,2008:3369-3372.
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[2]FCC. Spectrum policy task force report[R]. ET Docket no.02-135, November 2002.
[3]Mitola J. Ⅲ and Maguire G.Q. Jr.Cognitive Radio:making software radio more personal[J]. IEEE Personal communications,1999,6 (4):13-18.
[4]Haykin S. Cognitive Radio:Brain-empowered Wireless Communications [J]. IEEE JSAC,2003, 23(2):201-220.
[5]Ian F. Akyildiz, et al. Next Generation Dynamic Spectrum Access Cognitive Radio Wireless Networks:A Survey [J]. Computer Networks Journal,2006,50(3):2127-2159.
[6]程世伦,杨震.基于信干比的认知无线电自适应功率控制算法[J].电子与信息学报,2008年第1期.
[7]毛小矛,余官定,仇佩亮.认知无线电中的用户合作通信[J].浙江大学学报,2009年第1期.
[8]Khaled Ben L etaief, and Wei Z hang, Cooperative Communications for Cognitive Radio Networks[J]. Proceedings of the IEEE,2009,97 (5):878-893.
[9]H. Urkowitz, "Energy Detection of Unknown Deterministic Signals", in Proc. IEEE,1967, 55:523-531.
[10]Tevfik Yucek, Huseyin Arslan, "A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications", IEEE Communications surveys & Tutorials,2009,11(1):未知.
[11]W. A. Gardner. Exploitation of spectral redundancy in cyclostationary signals[J]. IEEE Signal Processing Magazine, April 1991,8(2):14-36.
[12]Donoho D L. Compressed sensing. IEEE Transactions on Information Theory[M] 2006 52(4):1289-1306.
[13]Richard G Baraniuk. Compressive Sensing[J]. IEEE Signal processing Magazine 2007:118-120.
[14]李树涛,魏丹.压缩传感综述[J].自动化学报,2009.11(35)：1369-1377.
[15]金坚,谷源涛,梅顺良.压缩采样技术及其应用[J].电子与信息学报,2010.2(32)：470-475.
[16]贾艳云,赵航芳.压缩传感-稀疏信号的采样与重构[J].声学与电子工程,2010.100(4)：13-17.
[17]Candes E, Romberg J, Tao T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory,2006,52(2): 489-509.
[18]Zhi Tian and Georgios B Giannakis. Compressed Sensing for Wideband Cognitive Radios[J]. IEEE Signal processing Magazine,2007:118-120.
[19]Zhi Tian. Compressed Wideband Sensing in Cooperative Cognitive Radio Networks[J]. IEEE Global Telecommunications Conference,2008:1-5.
[20]Zhi Quan, Shuguang Cui, Ali H. Sayed and H. Vincent Poor. Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks[J]. IEEE Transactions on Signal Processing, Vol.57, No.3,2009(3):1128-1140.
[21]Wotao Yin, Zaiwen Wen, Shuyi Li, Jia Meng and Zhu Han. Dynamic Compressive Spectrum Sensing for Cognitive Radio Networks[J]. IEEE CISS,2011:1-6.
[22]http://dsp.rice.edu/cs[OL].
[23]G. Ganesan, Y. G. Li. Cooperative spectrum sensing in cognitive radio networks[C]. Baltimore, USA:IEEE DySPAN 2005,12:137-143.
[24]S M Mishra, A Sahai, and R Brodersen, Cooperative sensing among cognitive radios[C]. IEEE International Conference on,2006:1658-1663.
[25]Visotsky E, Kuffner S, Peterson R. On collaborative detection of TV transmission in Support of dynamic spectrum sharing[J]. IEEE Int Symposium on New Frontiers in Dynamic Spectrum Access Network.2005:338-345.
[26]Unnikrishnan J, Veeravalli V V. Cooperative sensing for Primary detection in cognitive radio[J]. IEEE Journal on Selected Topics in Signal Processing,2008,2(1):18-27.
[27]Weiss T, Hillenbrand J, Jondral F. A diversity approach for the detection of idle spectral resources in spectrum pooling systems. In:Proc of the 48th Int Scientific Colloquium, Ilmenau, Germany,2003:37-38.
[28]Ganesan G, Li Y. Cooperative spectrum sensing in cognitive radio, Part II:two user networks. IEEE Trans on Wireless Communications,2007,6(6):2214-2222.
[29]Gandetto M, Regazzoni C. Spectrum sensing:a distributed approach for cognitive terminals. IEEE Journal on Selected Areas in Communications,2007,25(3):546-557.
[30]Tropp J.A. and Gilbert A.C. Signal Recovery from Random Measurements via Orthogonal Matching Pursuit[J]. IEEE Trans. on Information Theory,2007,53(12):4655-4666.
[31]Tibshirani R. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B.,1996,58(1):267-288.
[32]Candes E and Romberg J.11-magic:A collection of MATLAB routines for solving the convex optimization programs central to compressive sampling.2006, www.acm.caltech. edu/11 magic/.
[33]Sebert F, Zou Y M, Ying L. Toeplitz block matrices in compressed sensing and their applications in imaging. In:Proceedings of International Conference on Technology and Applications in Biomedicine. Washington D. C, USA:IEEE,2008:47-50.
[34]Do T T, Trany T D, Gan L. Fast compressive sampling with structurally random matrices. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington D. C, USA:IEEE,2008:3369-3372.
[35]Donoho D, Tsaig Y. Extensions of compressed sensing[J]. Signal Processing,2006,86(3): 533-548.
[36]Donoho D L. For most large underdetermined systems of linear equations, the minimal (?)-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics, 2006,59(6):797-829.
[37]R Baraniuk. A lecture on compressive sensing[J]. IEEE Signal Processing Magazine, July 2007, 24(4):118-121.
[38]S. S. Chen, D. L. Donoho, M. A. Saunders. Atomic decomposition by basis pursuit[J]. SIAM Review,2001,43(1):129-159.
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