基于压缩感知理论的宽带模拟多带信号检测的研究
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摘要
面向第四代移动通信系统的ITU IMT-Advanced系统国际标准化工作已于2008年开始启动,倍受全球关注。未来IMT-Advanced系统将支持多频段宽带无线传输,峰值传输速率高达1Gbps,频段跨越450MHz～3.6GHz范围内多个离散的宽频段(450～470 MHz的20MHz、698～806MHz的108MHz、2.3～2.4 GHz的100MHz、3.4～3.6 GHz的200MHz)。采用现有的基于奈奎斯特采样理论的传统方式实现多频段宽带信号检测时会带来射频单元设计难度、射频复杂度、硬件器件指标要求、成本等大幅度提升,迫切需要新的理论与技术解决多频段宽带信号检测问题,因此如何实现有效地宽带多带信号检测是第四代移动通信系统物理层实现上面临的重要难题。近年提出的压缩感知理论在数据获取时能突破奈奎斯特采样定律的制约,给信号存储、传输和处理带来新途径,它将是解决宽带多带信号检测最具潜力的理论。
本文首先对近年提出的压缩感知理论产生的背景及其要解决的问题进行阐述。接着分别从离散域和模拟域两个方面对当前压缩感知技术进行了总结和归纳。在离散域压缩感知中对信号的稀疏表达、测量矩阵和恢复算法的设计进行了讲解；在模拟域压缩感知中对主流的实现方式MWC和AIC的采样方法,恢复算法原理进行了分析。
基于上述的理论基础,文章创新的提出了基于多天线压缩感知的宽带多带信号的降采样检测结构和联合恢复算法,CRL2算法和CBS算法,解决了宽带多带信号的检测问题,提出的基于多天线的联合恢复算法,相比于单天线结构提高了检测性能。接着基于上述提出的结构和算法,本文给出了压缩感知在宽带多带信号的频谱检测和无线定位中的应用,对其性能进行了仿真,验证了其可行性。另外由于宽带多带信号的检测是以Gbps无线通信系统为代表的未来4G移动通信系统要解决的问题之一,故文章最后介绍了由作者所在研究团队开发的传送速率能够达到1Gbps的TDD-MIMO-OFDM的无线通信关键技术验证演示系统,着重讲解了依据系统指标的参数设计和MIMO检测算法的实现。
International specification of ITU IMT-Advanced,which is the 4th generation wirelss communications system, is started in 2008. The work is much attendtioned on a world wide scale.The future IMT-Advanced system will support wireless transmission of multiband signals on a wide band.The system can reach a peak rate of 1Giga bits per second and cover a spectral range from 450MHz to 3.6GHz in which there are several discrete widebands(20MHz from 450 MHz to 470 MHz,108MHz from 698MHz to 806MHz,100MHz from 2.3GHz to 2.4 GHz,200MHz from 3.4GHz to 3.6 GHz).When wide multiband signals are detected by the traditional method which is based on the present Nyquist sampling theorem,the difficulty of RF unit design, the complexity of RF unit,the requirement of hardware and the costs will all be greatly increased. Therefore, we urgently need a new theory or technology to solve the problem of wide multiband signals detection. So how to effectively realize the detection for multiband signals on a wide band is a important problem which the implementation of physical layer in 4th generation wireless communications system is faced with. The "Compressive Sensing" theory is a new theory proposed in recent years. It can breakthrough restrictions of SyQuest sampling theorem when capturing data and pave a new way for data storage, transmission and processing. So it will be the most potential theory to do wide multiband signals detection.
The thesis firstly explains the background of newly born compressive sensing theory and describes the problem it aims to solve. Then it summarizes the present compressive sensing technology in both discrete and analog domain. Detailed explanation is presented on the signals'sparse representation, measurement matrix design and reconstruction algorithm design in the part of discrete signals' compressive sensing. In the part of analog signals'compressive sensing, it gives discussion on mainstream implementation method of MWC and AIC and analyses their sampling method and recovery principles.
On the basis of theoretical foundation above, a sub-sampling detection structure and jointly recovery algorithms based on multi-antenna compressive sensing for wide multiband signals are proposed in this thesis, which contains CRL2 algorithm and CBS algorithm。The structure can realize wide multiband signal detection and the proposed jointly recovery algorithms based on muti-antenna can improve the detection performance compared with single antenna structure. What's more, based on the proposed structure and algorithms, this thesis gives two application scenarios of compressive sensing which are wide mutiband spectrum sensing and wireless localization. Besides, the simulations are presented to verify their feasibility. Since wide multiband signal detection is a issue in future 4G wireless communication system which is represented by Gbps wireless communication system, the thesis finally introduces the TDD-MIMO-OFDM wireless communication key technology verification demo system developed by author's R&D group, which can reach peak transmisstion rate of 1Gbps. This part focuses on the parameters design according to system targets and implementation of MIMO detection.
本文首先对近年提出的压缩感知理论产生的背景及其要解决的问题进行阐述。接着分别从离散域和模拟域两个方面对当前压缩感知技术进行了总结和归纳。在离散域压缩感知中对信号的稀疏表达、测量矩阵和恢复算法的设计进行了讲解；在模拟域压缩感知中对主流的实现方式MWC和AIC的采样方法,恢复算法原理进行了分析。
基于上述的理论基础,文章创新的提出了基于多天线压缩感知的宽带多带信号的降采样检测结构和联合恢复算法,CRL2算法和CBS算法,解决了宽带多带信号的检测问题,提出的基于多天线的联合恢复算法,相比于单天线结构提高了检测性能。接着基于上述提出的结构和算法,本文给出了压缩感知在宽带多带信号的频谱检测和无线定位中的应用,对其性能进行了仿真,验证了其可行性。另外由于宽带多带信号的检测是以Gbps无线通信系统为代表的未来4G移动通信系统要解决的问题之一,故文章最后介绍了由作者所在研究团队开发的传送速率能够达到1Gbps的TDD-MIMO-OFDM的无线通信关键技术验证演示系统,着重讲解了依据系统指标的参数设计和MIMO检测算法的实现。
International specification of ITU IMT-Advanced,which is the 4th generation wirelss communications system, is started in 2008. The work is much attendtioned on a world wide scale.The future IMT-Advanced system will support wireless transmission of multiband signals on a wide band.The system can reach a peak rate of 1Giga bits per second and cover a spectral range from 450MHz to 3.6GHz in which there are several discrete widebands(20MHz from 450 MHz to 470 MHz,108MHz from 698MHz to 806MHz,100MHz from 2.3GHz to 2.4 GHz,200MHz from 3.4GHz to 3.6 GHz).When wide multiband signals are detected by the traditional method which is based on the present Nyquist sampling theorem,the difficulty of RF unit design, the complexity of RF unit,the requirement of hardware and the costs will all be greatly increased. Therefore, we urgently need a new theory or technology to solve the problem of wide multiband signals detection. So how to effectively realize the detection for multiband signals on a wide band is a important problem which the implementation of physical layer in 4th generation wireless communications system is faced with. The "Compressive Sensing" theory is a new theory proposed in recent years. It can breakthrough restrictions of SyQuest sampling theorem when capturing data and pave a new way for data storage, transmission and processing. So it will be the most potential theory to do wide multiband signals detection.
The thesis firstly explains the background of newly born compressive sensing theory and describes the problem it aims to solve. Then it summarizes the present compressive sensing technology in both discrete and analog domain. Detailed explanation is presented on the signals'sparse representation, measurement matrix design and reconstruction algorithm design in the part of discrete signals' compressive sensing. In the part of analog signals'compressive sensing, it gives discussion on mainstream implementation method of MWC and AIC and analyses their sampling method and recovery principles.
On the basis of theoretical foundation above, a sub-sampling detection structure and jointly recovery algorithms based on multi-antenna compressive sensing for wide multiband signals are proposed in this thesis, which contains CRL2 algorithm and CBS algorithm。The structure can realize wide multiband signal detection and the proposed jointly recovery algorithms based on muti-antenna can improve the detection performance compared with single antenna structure. What's more, based on the proposed structure and algorithms, this thesis gives two application scenarios of compressive sensing which are wide mutiband spectrum sensing and wireless localization. Besides, the simulations are presented to verify their feasibility. Since wide multiband signal detection is a issue in future 4G wireless communication system which is represented by Gbps wireless communication system, the thesis finally introduces the TDD-MIMO-OFDM wireless communication key technology verification demo system developed by author's R&D group, which can reach peak transmisstion rate of 1Gbps. This part focuses on the parameters design according to system targets and implementation of MIMO detection.
引文
[1]Donoho D. L., "Compressed sensing", IEEE Trans. Information Theory,2006, pp.1289-1306.
[2]Candes E.J., Tao T., "Near-Optimal Signal Recovery From Random Projections:Universal Encoding Strategies", IEEE Trans. Information Theory,2006, pp.5406-5425.
[3]Candes E.J., Wakin M.B., "An Introduction To Compressive Sampling", IEEE Signal Processing Magazine, Volume 25, Issue 2,2008, pp.21-30.
[4]M. Mishali, Y.C. Eldar, "From Theory to Practice:Sub-Nyquist sampling of Sparse Wideband Analog Signals", IEEE Journal Selected Topics in Signal Processing,2010, pp. 375-391.
[5]Sami Kirolos, Jason Laska, "Analog-to-Information Conversion via Random Demodulation" IEEE DCAS,2006, pp.71-74.
[6]Tropp J.A., Wakin M.B., "Random Filters for Compressive Sampling and Reconstruction", IEEE International Conference on Acoustics, Speech and Signal Processing,2006.
[7]Fudge G.L., Bland R.E., "A Nyquist folding analog-to-information receiver",42nd Asilomar Conference on Signals, Systems and Computers,2008.
[8]Duarte M.F., Sarvotham S., "Distributed Compressed Sensing of Jointly Sparse Signals", Thirty-Ninth Asilomar Conference on Signals, Systems and Computers,2005.
[9]Shihao Ji, Ya Xue, Carin L., "Bayesian Compressive Sensing", IEEE Transactions on Signal Processing, Volume 56, Issue 6,2008, pp.2346-2356.
[10]Baraniuk R.G., Cevher V., "Model-Based Compressive Sensing", IEEE Transactions on Information Theory, Volume 56, Issue 4,2010.
[11]Chinh La and Minh Do, "Signal reconstruction using sparse tree representations", SPIE Wavelets XI,2005
[12]Yonina C. Eldar and Moshe Mishali, "Robust Recovery of Signals From a Structured Union of Subspaces", IEEE Trans. Inform. Theory, vol.55, no.11,2009, pp.5302-5316.
[13]石光明,刘丹华, “压缩感知理论及其研究进展”,电子学报,2009年5月。
[14]Emmanuel Candes, "Compressive sampling", Int. Congress of Mathematics,2006, pp. 1433-1452.
[15]Yang Rui, Tao Xiaofeng, "Analysis and FPGA Implementation of Fast Ordering Detection Algorithm for V-BLAST System", IEEE IC-BNMT,2010,pp.278-283.
[16]Baraniuk Richard G, "Compressive sensing", IEEE Signal Processing Magazine,2007, pp: 120-124.
[17]Tropp J.A, Gilbert A.C, "Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit", IEEE Trans. Information Theory,2007, pp.4655-4666.
[18]David L. Donoho, Yaakov Tsaig, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit", Preprint,2007.
[19]Needell D, Vershynin R, "Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit", IEEE Journal of Selected Topics in Signal Processing, Volume 4, Issue 2,2010, pp.310-316.
[20]Chinh La, Do M.N., "Tree-Based Orthogonal Matching Pursuit Algorithm for Signal Reconstruction", IEEE International Conference on Image Processing,2006, pp:1277-1280.
[21]Tsaig Yaakov, Donoho David L, "Extensions of compressed sensing", Signal Processing, v 86, n3,2006, pp.549-571.
[22]Wainwright M.J., "Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using L1-Constrained Quadratic Programming (Lasso)", IEEE Transactions on Information Theory, Volume 55, Issue 5,2009, pp.2183-2202.
[23]Figueiredo M.A.T., Nowak R.D., Wright S.J., "Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems", IEEE Journal of Selected Topics in Signal Processing, Volume 1, Issue 4,2007, pp.586-597.
[24]Shane F.Cotter, Bhaskar D.Rao, Kjersti Engan, "Sparse solutions to linear inverse problems with multiple measurement vectors", IEEE Trans. Signal Processing,2005, pp.2477-2488.
[25]Mallat S.G., Zhifeng Zhang, "Matching pursuits with time-frequency dictionaries", IEEE Transactions on Signal Processing, Volume 41, Issue 12,1993, pp.3397-3415.
[26]Chen S.S., Donoho D.L., "Atomic decomposition by basis pursuit", SIAM Review,2001, pp. 129-159.
[27]Gorodnitsky I.F., Rao B.D., "Sparse signal reconstruction from limited data using FOCUSS:a re-weighted minimum norm algorithm", IEEE Transactions on Signal Processing, Volume:45, Issue:3,1997, pp.600-616.
[28]Mishali M., Eldar Y.C., Tropp J.A., Efficient sampling of sparse wideband analog signals IEEE 25th Convention of Electrical and Electronics Engineers in Israel,2008. IEEEI 2008, pp.290-294.
[29]Tropp J.A., Laska, J.N., Duarte, M.F., Romberg, J.K., Baraniuk, R.G., "Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals", IEEE Transactions on Information Theory, 2010, pp.520-544.
[30]Ian F. Akyildiz, Won-Yeol Lee, "Next generation/dynamic spectrum access/cognitive radio wireless networks:a survey", The International Journal of Computer and Telecommunications Networking, Sep.2006.
[31]Simon Haykin, "Cognitive radio:brain-empowered wireless communications", IEEE Journal on Selected Areas in Communications, Volume 2, Issue 2,2005, pp.201-220.
[32]Zhi Quan, Shuguang Cui, "Collaborative wideband sensing for cognitive radios", IEEE Signal Processing Magazine,2008, pp.60-73.
[33]N. Patwari, J. N. Ash, and S. Kyperountas, "Locating the nodes:cooperative localization in wireless sensor networks", IEEE Signal Processing Magazine, pp.54-69, July 2005.
[34]U. Grossmann, M. Schauch, and S. Hakobyan, "RSSI based wlan indoor positoning with personal digital assistants," IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems:Technology and Applications, September 2007.
[35]Feng Chen, Valaee Shahrokh, Tan Zhenhui, "Multiple target localization using compressive sensing", IEEE Global Telecommunications Conference,2009, pp.1-6.
[2]Candes E.J., Tao T., "Near-Optimal Signal Recovery From Random Projections:Universal Encoding Strategies", IEEE Trans. Information Theory,2006, pp.5406-5425.
[3]Candes E.J., Wakin M.B., "An Introduction To Compressive Sampling", IEEE Signal Processing Magazine, Volume 25, Issue 2,2008, pp.21-30.
[4]M. Mishali, Y.C. Eldar, "From Theory to Practice:Sub-Nyquist sampling of Sparse Wideband Analog Signals", IEEE Journal Selected Topics in Signal Processing,2010, pp. 375-391.
[5]Sami Kirolos, Jason Laska, "Analog-to-Information Conversion via Random Demodulation" IEEE DCAS,2006, pp.71-74.
[6]Tropp J.A., Wakin M.B., "Random Filters for Compressive Sampling and Reconstruction", IEEE International Conference on Acoustics, Speech and Signal Processing,2006.
[7]Fudge G.L., Bland R.E., "A Nyquist folding analog-to-information receiver",42nd Asilomar Conference on Signals, Systems and Computers,2008.
[8]Duarte M.F., Sarvotham S., "Distributed Compressed Sensing of Jointly Sparse Signals", Thirty-Ninth Asilomar Conference on Signals, Systems and Computers,2005.
[9]Shihao Ji, Ya Xue, Carin L., "Bayesian Compressive Sensing", IEEE Transactions on Signal Processing, Volume 56, Issue 6,2008, pp.2346-2356.
[10]Baraniuk R.G., Cevher V., "Model-Based Compressive Sensing", IEEE Transactions on Information Theory, Volume 56, Issue 4,2010.
[11]Chinh La and Minh Do, "Signal reconstruction using sparse tree representations", SPIE Wavelets XI,2005
[12]Yonina C. Eldar and Moshe Mishali, "Robust Recovery of Signals From a Structured Union of Subspaces", IEEE Trans. Inform. Theory, vol.55, no.11,2009, pp.5302-5316.
[13]石光明,刘丹华, “压缩感知理论及其研究进展”,电子学报,2009年5月。
[14]Emmanuel Candes, "Compressive sampling", Int. Congress of Mathematics,2006, pp. 1433-1452.
[15]Yang Rui, Tao Xiaofeng, "Analysis and FPGA Implementation of Fast Ordering Detection Algorithm for V-BLAST System", IEEE IC-BNMT,2010,pp.278-283.
[16]Baraniuk Richard G, "Compressive sensing", IEEE Signal Processing Magazine,2007, pp: 120-124.
[17]Tropp J.A, Gilbert A.C, "Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit", IEEE Trans. Information Theory,2007, pp.4655-4666.
[18]David L. Donoho, Yaakov Tsaig, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit", Preprint,2007.
[19]Needell D, Vershynin R, "Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit", IEEE Journal of Selected Topics in Signal Processing, Volume 4, Issue 2,2010, pp.310-316.
[20]Chinh La, Do M.N., "Tree-Based Orthogonal Matching Pursuit Algorithm for Signal Reconstruction", IEEE International Conference on Image Processing,2006, pp:1277-1280.
[21]Tsaig Yaakov, Donoho David L, "Extensions of compressed sensing", Signal Processing, v 86, n3,2006, pp.549-571.
[22]Wainwright M.J., "Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using L1-Constrained Quadratic Programming (Lasso)", IEEE Transactions on Information Theory, Volume 55, Issue 5,2009, pp.2183-2202.
[23]Figueiredo M.A.T., Nowak R.D., Wright S.J., "Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems", IEEE Journal of Selected Topics in Signal Processing, Volume 1, Issue 4,2007, pp.586-597.
[24]Shane F.Cotter, Bhaskar D.Rao, Kjersti Engan, "Sparse solutions to linear inverse problems with multiple measurement vectors", IEEE Trans. Signal Processing,2005, pp.2477-2488.
[25]Mallat S.G., Zhifeng Zhang, "Matching pursuits with time-frequency dictionaries", IEEE Transactions on Signal Processing, Volume 41, Issue 12,1993, pp.3397-3415.
[26]Chen S.S., Donoho D.L., "Atomic decomposition by basis pursuit", SIAM Review,2001, pp. 129-159.
[27]Gorodnitsky I.F., Rao B.D., "Sparse signal reconstruction from limited data using FOCUSS:a re-weighted minimum norm algorithm", IEEE Transactions on Signal Processing, Volume:45, Issue:3,1997, pp.600-616.
[28]Mishali M., Eldar Y.C., Tropp J.A., Efficient sampling of sparse wideband analog signals IEEE 25th Convention of Electrical and Electronics Engineers in Israel,2008. IEEEI 2008, pp.290-294.
[29]Tropp J.A., Laska, J.N., Duarte, M.F., Romberg, J.K., Baraniuk, R.G., "Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals", IEEE Transactions on Information Theory, 2010, pp.520-544.
[30]Ian F. Akyildiz, Won-Yeol Lee, "Next generation/dynamic spectrum access/cognitive radio wireless networks:a survey", The International Journal of Computer and Telecommunications Networking, Sep.2006.
[31]Simon Haykin, "Cognitive radio:brain-empowered wireless communications", IEEE Journal on Selected Areas in Communications, Volume 2, Issue 2,2005, pp.201-220.
[32]Zhi Quan, Shuguang Cui, "Collaborative wideband sensing for cognitive radios", IEEE Signal Processing Magazine,2008, pp.60-73.
[33]N. Patwari, J. N. Ash, and S. Kyperountas, "Locating the nodes:cooperative localization in wireless sensor networks", IEEE Signal Processing Magazine, pp.54-69, July 2005.
[34]U. Grossmann, M. Schauch, and S. Hakobyan, "RSSI based wlan indoor positoning with personal digital assistants," IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems:Technology and Applications, September 2007.
[35]Feng Chen, Valaee Shahrokh, Tan Zhenhui, "Multiple target localization using compressive sensing", IEEE Global Telecommunications Conference,2009, pp.1-6.