认知无线电宽带频谱感知技术研究
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摘要
随着无线通信技术的不断进步,无线应用和服务的数量快速增长,频谱资源日益成为一种稀缺资源。可将频谱固定分配给特定用户使用的方式使频谱资源的利用率很低。认知无线电(Cognitive Radio, CR)技术的出现有效的解决了这个问题。频谱感知技术是CR系统的基础,次级用户(Secondary User, SU)通过频谱感知寻找“频谱空穴”,在不影响主用户(?)(Primary User, PU)正常工作的情况下伺机接入并利用授权频段进行通信。
目前大部分频谱感知方式都按照奈奎斯特(Nyquist)采样定理,以两倍于信号最高频率的采样率进行采样,在面对宽带频谱感知的情况时,巨大的数据量难以处理,硬件也难以实现。压缩感知(Compressive Sensing, CS)理论是一种通过较少的采样值来恢复原始信号的方法,所需的采样值远少于传统的采样方法,能够以较低速率对信号采样并精确重构。宽带频谱中存在许多没有被充分利用的频谱资源(这是认知无线电技术应用的前提),具有明显的频谱稀疏性。利用CS技术可以有效的实现对宽频带的检测。
本文主要围绕宽带频谱感知技术展开研究分析。首先分析了频谱感知和压缩感知的基本方法和原理,利用调制宽带转换系统(Modulated Wideband Converter, MWC)对宽带连续信号进行压缩采样并重构。针对无线环境中信号的稀疏度难以确定这一特点,研究分析了稀疏自适应匹配追踪算法(Sparsity Adaptive Matching Pursuit, SAMP)及其改进算法,在信号稀疏度未知的情况下精确的恢复原始信号。然后时传统协作频谱感知模型进行了改进,根据各个SU本地检测的可信度,给其设置不同的权值,使其能量检测的门限值动态变化。在协作频谱感知改进模型中引入压缩感知技术,建立了宽带频谱感知系统模型。各SU利用MWC对宽带信号进行压缩采样并重构,根据能量检测方法判断出宽频带内各子带的占用情况,实现对宽带频谱的检测。仿真结果表明该算法可以精确的重构出原始宽带信号,检测宽带频谱的占用状况,实现了对宽带频谱的检测,提高了系统的检测概率。
With the rapid development of wireless communication technology, the number of wireless applications and services has growth rapidly, spectrum resource has become a scarce resource. However, the method that spectrum is allocated to specific user regularly causes enormous waste of spectrum resource. Cognitive Radio (CR) technology, has effectively solved the problem. Spectrum sensing technology is the foundation of CR systems, Secondary users (SU) uses spectrum sensing to find the "spectrum holes", under the condition of not affecting the Primary User (PU) use in the authorized spectrum.
At present most of the spectrum sensing methods are using the Nyquist sampling theorem, sampling with twice the highest signal frequency, when using broadband spectrum perception, huge amount of data is difficult to handle and hardware is difficult to achieve. Compressed Sensing (CS) theory is a method that reconstruct the original signal using lesser sampling value, the sampling values is lesser than conventional method, which could samples at a relatively low frequency and can reconstruct the signal with high probability. Broadband spectrum exists in a large number of underused spectrum resources, which is the prerequisite, has obvious spectrum sparse, so CS can be used in the wideband spectrum sensing as well.
This article researches on broadband spectrum sensing technology. Fist of all, the basic method and theory of spectrum sensing and compressed sensing is introduced, compressed sampling and reconstruct using modulated wideband converter. Aiming at the sparse characteristic is difficult to determine in wireless environment, the Sparse Adaptive Matching Pursuit algorithm and its improved algorithm are studied, accurately recovering original signal in the case of unknown signal sparse degree. Then it improves conventional cooperation spectrum sensing model, sets different weights according to reliability of SUs, so the threshold of energy detection changes dynamically. This article introduces compressed sensing technology in cooperation spectrum sensing improved model, establishes broadband spectrum sensing system model. Various SUs use MWC to compressed sampling for broadband signal,to compressed sampling and reconstruct the signal, to determine each subband signals take up case according to energy detection method, and the performances are simulated. Simulation results show that this algorithm can use lower rate to sample signals and accurately reconstruct the original signal, detect of spectrum occupancy situation and realize the broadband spectrum perception.
目前大部分频谱感知方式都按照奈奎斯特(Nyquist)采样定理,以两倍于信号最高频率的采样率进行采样,在面对宽带频谱感知的情况时,巨大的数据量难以处理,硬件也难以实现。压缩感知(Compressive Sensing, CS)理论是一种通过较少的采样值来恢复原始信号的方法,所需的采样值远少于传统的采样方法,能够以较低速率对信号采样并精确重构。宽带频谱中存在许多没有被充分利用的频谱资源(这是认知无线电技术应用的前提),具有明显的频谱稀疏性。利用CS技术可以有效的实现对宽频带的检测。
本文主要围绕宽带频谱感知技术展开研究分析。首先分析了频谱感知和压缩感知的基本方法和原理,利用调制宽带转换系统(Modulated Wideband Converter, MWC)对宽带连续信号进行压缩采样并重构。针对无线环境中信号的稀疏度难以确定这一特点,研究分析了稀疏自适应匹配追踪算法(Sparsity Adaptive Matching Pursuit, SAMP)及其改进算法,在信号稀疏度未知的情况下精确的恢复原始信号。然后时传统协作频谱感知模型进行了改进,根据各个SU本地检测的可信度,给其设置不同的权值,使其能量检测的门限值动态变化。在协作频谱感知改进模型中引入压缩感知技术,建立了宽带频谱感知系统模型。各SU利用MWC对宽带信号进行压缩采样并重构,根据能量检测方法判断出宽频带内各子带的占用情况,实现对宽带频谱的检测。仿真结果表明该算法可以精确的重构出原始宽带信号,检测宽带频谱的占用状况,实现了对宽带频谱的检测,提高了系统的检测概率。
With the rapid development of wireless communication technology, the number of wireless applications and services has growth rapidly, spectrum resource has become a scarce resource. However, the method that spectrum is allocated to specific user regularly causes enormous waste of spectrum resource. Cognitive Radio (CR) technology, has effectively solved the problem. Spectrum sensing technology is the foundation of CR systems, Secondary users (SU) uses spectrum sensing to find the "spectrum holes", under the condition of not affecting the Primary User (PU) use in the authorized spectrum.
At present most of the spectrum sensing methods are using the Nyquist sampling theorem, sampling with twice the highest signal frequency, when using broadband spectrum perception, huge amount of data is difficult to handle and hardware is difficult to achieve. Compressed Sensing (CS) theory is a method that reconstruct the original signal using lesser sampling value, the sampling values is lesser than conventional method, which could samples at a relatively low frequency and can reconstruct the signal with high probability. Broadband spectrum exists in a large number of underused spectrum resources, which is the prerequisite, has obvious spectrum sparse, so CS can be used in the wideband spectrum sensing as well.
This article researches on broadband spectrum sensing technology. Fist of all, the basic method and theory of spectrum sensing and compressed sensing is introduced, compressed sampling and reconstruct using modulated wideband converter. Aiming at the sparse characteristic is difficult to determine in wireless environment, the Sparse Adaptive Matching Pursuit algorithm and its improved algorithm are studied, accurately recovering original signal in the case of unknown signal sparse degree. Then it improves conventional cooperation spectrum sensing model, sets different weights according to reliability of SUs, so the threshold of energy detection changes dynamically. This article introduces compressed sensing technology in cooperation spectrum sensing improved model, establishes broadband spectrum sensing system model. Various SUs use MWC to compressed sampling for broadband signal,to compressed sampling and reconstruct the signal, to determine each subband signals take up case according to energy detection method, and the performances are simulated. Simulation results show that this algorithm can use lower rate to sample signals and accurately reconstruct the original signal, detect of spectrum occupancy situation and realize the broadband spectrum perception.
引文
11] H.N.Kang, On the feasibility of cognitive radio[D],Master Dissertation,University of California, Berkeley, Spring,2005.
[2]赵知劲,郑仕链,尚俊娜,认知无线电技术[D],科学出版社,2008.
[3]R. I. C. Chiang, G. B. Rowe, K. W. Sowerby. A Quantitative Analysis of Spectral Occupancy Measurements for Cognitive Radio[C],VTC Spring, Dublin, Ireland,2007.IEEE, April,2007:3016-3020.
[4]Mitola J, Maguire G Q, Cognitive radio:making soft ware radios more personal[J].IEEE Personal Communications,1999,6(4):13-18.
[5]Federal Communication Commission, Notice of proposed rule making and order[OL]hraunfoss.fcc.gov/edocs_public/attachmatch/FCC_03_322A1.pdf,2003: 12-17.
[6]Haykin S, Cognitive Radio:Brain-Empower Wireless Communications[J], IEEE JSAC,2005,23(02):201-220.
17] Akyldiz I F, Next generation/dynamic specturm access/cognitive radio wireless networks:A survey[J],Computcr Networks Journal,2006,50:2127-2159.
[8]Unni Krishnan J,Veeravalliv V,Cooperative sensing for primary detection in cognitive radio[J],IEEE Journal of Selected Topics in Signal Processing,2008,2(1):18-27.
[9]F E Visser,G J Janssen, P Pawelczak,Multinode spectrum sensing based on energy detection for dynamic spectrum access[C], IEEE Veh Technol Conf-Spring(VTC 2008-Spring), Marina Bay, Singapore, May 2008:1394-1398.
[10]The XG Project,White Paper[DB/OL],available online:www.xgteehnology.com/.
[11]Zeng Y H, Liang Y C, Covariance Based Signal Detections for Cognitive Radio[C],IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks,2007:202-207.
[12]Lehtomaki J.J., Vartiainen J., Juntti M, Saamisaari H, Spectrum Sensing with Forward Methods[C], IEEE Military Communications Conference,2006:1-7.
[13]Donoho D L,Compresscd sensing [J], IEEE Transactions on Information Theory, 2006,52(4):1289-1306.
[14]E.Candes,Compressivc Sampling[C],Proceedings of the International Congress of Mathcmaticians.Madrid,Spain,2006,3:1433-1452.
[15]Baraniuk R, Compressive scnsing[J], IEEE Signal Processing Magazine, 2007,24(4):118-121.
[16]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[J],电子学报,2009,37(7)：1070-1081.
[17]Moshc Mishali, Yonina C. Eldar,Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals[J].IEEE Transactions on Signal Processing,2009,57(3):993-1009.
[18]赵知劲,张鹏,王海泉,尚俊娜,基于OMP算法的宽带频谱感知[J],信号处理,2012,28(5)：723-728.
[19]王悦,冯春燕,曾志民,郭彩丽,认知无线电网络中基于合作的频谱检测研究[J],电信科学.2008,24(7)：54-57.
[20]张平,冯志勇,认知无线例络[M].北京：科学出版社,2010.
[21]Urkowitz H,Energy detection of unknown deterministic signals[J].Proceedings of the IEEE.1967,55(4):523-531.
[22]郭彩丽,冯春燕,曾志民.认知无线电网络技术与应用[M].电子工业出版社,2010.
[23]周贤伟,王建萍，王春江.认知无线电[M].国防工业出版社,2008.
[24]FCC,ET Docket No 03-222 Notice of proposed rule making and order[S], Dec 2003.
[25]T. C. Clancy,W. A. Arbaugh, Measuring interference temperature[OL], http://www.cs.unxl.edu/clancy/docs/itma-vt06.pdf, Virginia symposium on wireless personal communication,2006.
[26]Wild B,Ramchandran K,Detecting primary receivers for cognitive radio applications[C],IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks,2005,pp.124-130.
[27]Shen J, Xie G, Liu S, Zeng L, Gao J and Liu Y,Soft versus hard cooperative energy detection under low SNR[J]. IEICE Transactions on Communications, 2008,91(11):3732-3735.
[28]Kattepur A. K, Hoang A.T.Data and decision fusion for distributed spectrum sensing in cognitive radio networks[C]. Information Communications & Signal Processing International Conference,2007:1-5.
[29]Polo Y L, Wang Y, Pandharipande A, Compressive wide-band spectrum sensing [C].IEEE International Conference on Acoustics, Speech, and Signal Processing,2009:178-183.
[30]Donoho D L, Tsaig Y and Jean-Luc Starck. Sparse solution of underdetermined linear equations by stage-wise orthogonal matching pursuit[C].Technical Report, Mar.2006.
[31]Eldar Y. C.,Mishali M,Block sparsity and sampling over a union of subspaces[C], In Proceedings of International Conference on Digital Signal Processing,2009: 1-8.
[32]孙玉宝、肖亮,韦志辉,邵文泽,基于Gabor (?)感知多成份字典的图像稀疏表示算法研究[J],自动化学报,2008,34(11)：1379-1385.
[33]I. Daubechies,Ten lectures on wavelets[M]. SIAM,1992.
[34]V Temlyakov,Nonlinear Methods of Approximation[J],Foundations of Computational Mathematics January 2003,3(1):33-107.
[35]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[J].科学通报,2006,51(6)：628-633.
[36]S Mallat, Z Zhang,Matching pursuits with time-frequency dictionaries[J], IEEE Trans Signal Process,1993,41(12):3397-3415.
[37]Rick Chartrand,Valentina Staneva. Restricted isometry properties and nonconvex compressive sensing[J], Inverse Problems,2008,24:1-14.
[38]Donoho D L, Tsaig Y.,Extensions of compressed sensing [J]. Signal Processing. 2006,86(3):533-548.
[39]S. Mallat,A Wavelet Tour of Signal Processing:The Sparse Way[M] Academic Press,2008.
[40]杨海蓉,张成,丁大为,韦穗,压缩传感理论与重构算法[J].电子学报,2011,29(1)：142-148.
[41]Tropp J, Gilbert A,Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2007,53(12): 4655-4666.
[42]Thong T Do, Lu Gan, Nam Nguyen and Trac D. Tran,Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, 2008:581-587.
[43]Mishali M and Eldar Y C,From theory to practice:Sub-nyquist sampling of sparse wideband analog signals[J], IEEE Journal of Selected Topics in Signal Processing,2010,4(2):375-391.
[2]赵知劲,郑仕链,尚俊娜,认知无线电技术[D],科学出版社,2008.
[3]R. I. C. Chiang, G. B. Rowe, K. W. Sowerby. A Quantitative Analysis of Spectral Occupancy Measurements for Cognitive Radio[C],VTC Spring, Dublin, Ireland,2007.IEEE, April,2007:3016-3020.
[4]Mitola J, Maguire G Q, Cognitive radio:making soft ware radios more personal[J].IEEE Personal Communications,1999,6(4):13-18.
[5]Federal Communication Commission, Notice of proposed rule making and order[OL]hraunfoss.fcc.gov/edocs_public/attachmatch/FCC_03_322A1.pdf,2003: 12-17.
[6]Haykin S, Cognitive Radio:Brain-Empower Wireless Communications[J], IEEE JSAC,2005,23(02):201-220.
17] Akyldiz I F, Next generation/dynamic specturm access/cognitive radio wireless networks:A survey[J],Computcr Networks Journal,2006,50:2127-2159.
[8]Unni Krishnan J,Veeravalliv V,Cooperative sensing for primary detection in cognitive radio[J],IEEE Journal of Selected Topics in Signal Processing,2008,2(1):18-27.
[9]F E Visser,G J Janssen, P Pawelczak,Multinode spectrum sensing based on energy detection for dynamic spectrum access[C], IEEE Veh Technol Conf-Spring(VTC 2008-Spring), Marina Bay, Singapore, May 2008:1394-1398.
[10]The XG Project,White Paper[DB/OL],available online:www.xgteehnology.com/.
[11]Zeng Y H, Liang Y C, Covariance Based Signal Detections for Cognitive Radio[C],IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks,2007:202-207.
[12]Lehtomaki J.J., Vartiainen J., Juntti M, Saamisaari H, Spectrum Sensing with Forward Methods[C], IEEE Military Communications Conference,2006:1-7.
[13]Donoho D L,Compresscd sensing [J], IEEE Transactions on Information Theory, 2006,52(4):1289-1306.
[14]E.Candes,Compressivc Sampling[C],Proceedings of the International Congress of Mathcmaticians.Madrid,Spain,2006,3:1433-1452.
[15]Baraniuk R, Compressive scnsing[J], IEEE Signal Processing Magazine, 2007,24(4):118-121.
[16]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[J],电子学报,2009,37(7)：1070-1081.
[17]Moshc Mishali, Yonina C. Eldar,Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals[J].IEEE Transactions on Signal Processing,2009,57(3):993-1009.
[18]赵知劲,张鹏,王海泉,尚俊娜,基于OMP算法的宽带频谱感知[J],信号处理,2012,28(5)：723-728.
[19]王悦,冯春燕,曾志民,郭彩丽,认知无线电网络中基于合作的频谱检测研究[J],电信科学.2008,24(7)：54-57.
[20]张平,冯志勇,认知无线例络[M].北京：科学出版社,2010.
[21]Urkowitz H,Energy detection of unknown deterministic signals[J].Proceedings of the IEEE.1967,55(4):523-531.
[22]郭彩丽,冯春燕,曾志民.认知无线电网络技术与应用[M].电子工业出版社,2010.
[23]周贤伟,王建萍，王春江.认知无线电[M].国防工业出版社,2008.
[24]FCC,ET Docket No 03-222 Notice of proposed rule making and order[S], Dec 2003.
[25]T. C. Clancy,W. A. Arbaugh, Measuring interference temperature[OL], http://www.cs.unxl.edu/clancy/docs/itma-vt06.pdf, Virginia symposium on wireless personal communication,2006.
[26]Wild B,Ramchandran K,Detecting primary receivers for cognitive radio applications[C],IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks,2005,pp.124-130.
[27]Shen J, Xie G, Liu S, Zeng L, Gao J and Liu Y,Soft versus hard cooperative energy detection under low SNR[J]. IEICE Transactions on Communications, 2008,91(11):3732-3735.
[28]Kattepur A. K, Hoang A.T.Data and decision fusion for distributed spectrum sensing in cognitive radio networks[C]. Information Communications & Signal Processing International Conference,2007:1-5.
[29]Polo Y L, Wang Y, Pandharipande A, Compressive wide-band spectrum sensing [C].IEEE International Conference on Acoustics, Speech, and Signal Processing,2009:178-183.
[30]Donoho D L, Tsaig Y and Jean-Luc Starck. Sparse solution of underdetermined linear equations by stage-wise orthogonal matching pursuit[C].Technical Report, Mar.2006.
[31]Eldar Y. C.,Mishali M,Block sparsity and sampling over a union of subspaces[C], In Proceedings of International Conference on Digital Signal Processing,2009: 1-8.
[32]孙玉宝、肖亮,韦志辉,邵文泽,基于Gabor (?)感知多成份字典的图像稀疏表示算法研究[J],自动化学报,2008,34(11)：1379-1385.
[33]I. Daubechies,Ten lectures on wavelets[M]. SIAM,1992.
[34]V Temlyakov,Nonlinear Methods of Approximation[J],Foundations of Computational Mathematics January 2003,3(1):33-107.
[35]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[J].科学通报,2006,51(6)：628-633.
[36]S Mallat, Z Zhang,Matching pursuits with time-frequency dictionaries[J], IEEE Trans Signal Process,1993,41(12):3397-3415.
[37]Rick Chartrand,Valentina Staneva. Restricted isometry properties and nonconvex compressive sensing[J], Inverse Problems,2008,24:1-14.
[38]Donoho D L, Tsaig Y.,Extensions of compressed sensing [J]. Signal Processing. 2006,86(3):533-548.
[39]S. Mallat,A Wavelet Tour of Signal Processing:The Sparse Way[M] Academic Press,2008.
[40]杨海蓉,张成,丁大为,韦穗,压缩传感理论与重构算法[J].电子学报,2011,29(1)：142-148.
[41]Tropp J, Gilbert A,Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Transactions on Information Theory,2007,53(12): 4655-4666.
[42]Thong T Do, Lu Gan, Nam Nguyen and Trac D. Tran,Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, 2008:581-587.
[43]Mishali M and Eldar Y C,From theory to practice:Sub-nyquist sampling of sparse wideband analog signals[J], IEEE Journal of Selected Topics in Signal Processing,2010,4(2):375-391.