压缩感知在60GHz信道估计中的应用研究
摘要
现代无线通信高数据量、大数据流和大带宽的无线通信服务,极大推动了全世界范围内对60GHz毫米波频段通信技术的研究,60GHz毫米波通信发展的主要推动力是该频段高富余的频段资源,能够使得Gbps数量级的数据传输变为现实,同时该频段在世界范围内无需授权,因此吸引了大量的知名的设备制造厂商和有关学者的研究。
本研究课题,从具体现实需求出发叙述了压缩感知的逻辑流程,指出了其对于高达60GHz频段数据传输的适用性。然后从压缩感知的具体步骤入手,详细阐述了其理论核心,最后总结了将该理论应用于实际所带来的巨大价值。接着,我们着重介绍了60GHz毫米波通信在信道估计方面的必要性,深入剖析了60GHz毫米波通信信道的特殊表征：60GHz无线通信占据了极大的通信带宽,但是由于其信道具有高度离散和稀疏分布的实际特点,我们考虑将压缩感知的信号处理方式引入到60GHz毫米波通信系统中来。
此外,在研究分析中,我们总结分析了60GHz无线信道的特殊性,发现其与窄带系统有显著差别,除了整体表现出的稀疏外,在稀疏值的分布上也表现出了独特的一面,将这种特点加以抽象,我们提出了基于团簇的压缩感知算法——基于簇的压缩感知算法(Cluster Sparsity Compressive Sensing CS-CS),并对其在60GHz通信信道估计中的适应性做了仿真,结果也表明了其卓越的性能。
文章最后对CS-CS算法和以往较为典型算法CS-ROMP、CS-OMP和CS-DS算法分别进行了MALTAB平台下的实验仿真。最后对60GHz毫米波通信系统和压缩感知的研究方向做了展望。
本文的剩余部分组织如下：
第一章,绪论,简要介绍了60GHz毫米波通信的发展由来,总结了该项技术的优点和创新性解决方案；由60GHz通信的理论难点,引出了压缩感知,并简要介绍其由来和背景。
第二章,着重介绍了压缩感知的历史发展,详细论述了其关键技术与核心步骤,对其商业化应用做出总结。
第三章,着重介绍了60GHz毫米波通信系统信道的数学模型,介绍了相关标准和技术特征,为下一章压缩感知应用做了铺垫。
第四章,是本文的重点内容,本章完成了将压缩感知应用于60GHz信道估计的任务,对其应用性能做了实验仿真,对于各种传统算法性能也进行了仿真分析。
第五章,是本文的创新点,基于60GHz毫米波通信信道的具体分析,提出了基于簇的压缩感知算法,并对应用性能做了仿真分析。
文章最后对本文研究课题做了总结,对60GHz毫米波通信和压缩感知做了展望。
The growing demands for high-speed data streams and broadband wireless services have significantly driven the worldwide researches on60GHz millimeter-wave frequency band communications, which is mainly designed for the wireless accessing network such as the Pico-cellular mobile systems, the wireless local area networksand wireless personal area networks. In order to achieve the targeted ultra-high throughput in the next generation WLAN systems, the new WLAN/WPAN standards are currently developed by the IEEE802standardization committee. The main motivation for60GHz millimeter-wave communications is the availability of abundant unauthorized spectrum resources, which enables the realization of Gbps transmission as well as the worldwide broader market of60GHz products and therefore attracts a large number of famous manufacturers.
We may notice from the60GH millimeter-wave channels that, dramatically different from the narrow-band systems, except for the overall sparsity of channel MPCs that attenuated by following an exponent decay function, the local sparse property introduced by the cluster phenomenon may greatly facilitate the practical designs of efficient reconstruction algorithm. Based on the cluster identification results, the local sparsity can be observed, i.e. the few nonzero coefficients occurring in each cluster, which may be are referred to as the block cluster-sparsity. By explicitly taking such a specific block structures into account, both in terms of the recovery algorithm and in terms of the measure that are used to characterize the performance, a novel cluster sparsity compressive sensing (CS-CS) algorithm for60GHz channel estimation is proposed. The reconstruction performance (i.e. reconstruction error and iterative convergence) is compared with the classical ROMP algorithms based on the extensive experimental simulations. It has been shown that the proposed CS-CS algorithm can significantly enhances the accuracy of60GHz channel estimations, and simultaneously exhibits a much faster iteration behavior. The advantage of such a new CS-CS may be essentially attributed to the full exploitation the specific cluster-sparsity, except for the overall sparsity as in conventional sense.
The remainder of this paper is organized as follows. In Section I, we discussed60GHz channeling model and the simulated60GHz millimeter CIRs, for both the line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios in typical short-range indoor applications, have been plotted. In Section Ⅱ, we highlighted that the60GHz multipath channel is relatively special, which may show quite different characteristics and we briefly introduced the classical CS theory. In Section Ⅲ, we analyze the60GHz multipath channel and then draw the conclusion that Attributed to the enormous emission bandwidth (typically surpassing2GHz) and the resulting fair time resolution as well as the involved many objects in operation environments, the60GHz propagation is always intensive multipath channel, which also assumes the received resolvable MPCs arrive in clusters. That is, the rays within a cluster (or a group) may have independent phases as well as independent amplitudes distribution whose variances decay exponentially with cluster and rays delays. In Section IV and V, block sparsity embodied by the multiple clusters is developed, and on this basis, the more competitive cluster-sparsity based compressed sensing algorithm for the channel estimations of60GHz millimeter-wave is developed. Then the comprehensive experimental simulations and comparative performance analysis are provided. Finally, we conclude the whole investigation in Section VI.
本研究课题,从具体现实需求出发叙述了压缩感知的逻辑流程,指出了其对于高达60GHz频段数据传输的适用性。然后从压缩感知的具体步骤入手,详细阐述了其理论核心,最后总结了将该理论应用于实际所带来的巨大价值。接着,我们着重介绍了60GHz毫米波通信在信道估计方面的必要性,深入剖析了60GHz毫米波通信信道的特殊表征：60GHz无线通信占据了极大的通信带宽,但是由于其信道具有高度离散和稀疏分布的实际特点,我们考虑将压缩感知的信号处理方式引入到60GHz毫米波通信系统中来。
此外,在研究分析中,我们总结分析了60GHz无线信道的特殊性,发现其与窄带系统有显著差别,除了整体表现出的稀疏外,在稀疏值的分布上也表现出了独特的一面,将这种特点加以抽象,我们提出了基于团簇的压缩感知算法——基于簇的压缩感知算法(Cluster Sparsity Compressive Sensing CS-CS),并对其在60GHz通信信道估计中的适应性做了仿真,结果也表明了其卓越的性能。
文章最后对CS-CS算法和以往较为典型算法CS-ROMP、CS-OMP和CS-DS算法分别进行了MALTAB平台下的实验仿真。最后对60GHz毫米波通信系统和压缩感知的研究方向做了展望。
本文的剩余部分组织如下：
第一章,绪论,简要介绍了60GHz毫米波通信的发展由来,总结了该项技术的优点和创新性解决方案；由60GHz通信的理论难点,引出了压缩感知,并简要介绍其由来和背景。
第二章,着重介绍了压缩感知的历史发展,详细论述了其关键技术与核心步骤,对其商业化应用做出总结。
第三章,着重介绍了60GHz毫米波通信系统信道的数学模型,介绍了相关标准和技术特征,为下一章压缩感知应用做了铺垫。
第四章,是本文的重点内容,本章完成了将压缩感知应用于60GHz信道估计的任务,对其应用性能做了实验仿真,对于各种传统算法性能也进行了仿真分析。
第五章,是本文的创新点,基于60GHz毫米波通信信道的具体分析,提出了基于簇的压缩感知算法,并对应用性能做了仿真分析。
文章最后对本文研究课题做了总结,对60GHz毫米波通信和压缩感知做了展望。
The growing demands for high-speed data streams and broadband wireless services have significantly driven the worldwide researches on60GHz millimeter-wave frequency band communications, which is mainly designed for the wireless accessing network such as the Pico-cellular mobile systems, the wireless local area networksand wireless personal area networks. In order to achieve the targeted ultra-high throughput in the next generation WLAN systems, the new WLAN/WPAN standards are currently developed by the IEEE802standardization committee. The main motivation for60GHz millimeter-wave communications is the availability of abundant unauthorized spectrum resources, which enables the realization of Gbps transmission as well as the worldwide broader market of60GHz products and therefore attracts a large number of famous manufacturers.
We may notice from the60GH millimeter-wave channels that, dramatically different from the narrow-band systems, except for the overall sparsity of channel MPCs that attenuated by following an exponent decay function, the local sparse property introduced by the cluster phenomenon may greatly facilitate the practical designs of efficient reconstruction algorithm. Based on the cluster identification results, the local sparsity can be observed, i.e. the few nonzero coefficients occurring in each cluster, which may be are referred to as the block cluster-sparsity. By explicitly taking such a specific block structures into account, both in terms of the recovery algorithm and in terms of the measure that are used to characterize the performance, a novel cluster sparsity compressive sensing (CS-CS) algorithm for60GHz channel estimation is proposed. The reconstruction performance (i.e. reconstruction error and iterative convergence) is compared with the classical ROMP algorithms based on the extensive experimental simulations. It has been shown that the proposed CS-CS algorithm can significantly enhances the accuracy of60GHz channel estimations, and simultaneously exhibits a much faster iteration behavior. The advantage of such a new CS-CS may be essentially attributed to the full exploitation the specific cluster-sparsity, except for the overall sparsity as in conventional sense.
The remainder of this paper is organized as follows. In Section I, we discussed60GHz channeling model and the simulated60GHz millimeter CIRs, for both the line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios in typical short-range indoor applications, have been plotted. In Section Ⅱ, we highlighted that the60GHz multipath channel is relatively special, which may show quite different characteristics and we briefly introduced the classical CS theory. In Section Ⅲ, we analyze the60GHz multipath channel and then draw the conclusion that Attributed to the enormous emission bandwidth (typically surpassing2GHz) and the resulting fair time resolution as well as the involved many objects in operation environments, the60GHz propagation is always intensive multipath channel, which also assumes the received resolvable MPCs arrive in clusters. That is, the rays within a cluster (or a group) may have independent phases as well as independent amplitudes distribution whose variances decay exponentially with cluster and rays delays. In Section IV and V, block sparsity embodied by the multiple clusters is developed, and on this basis, the more competitive cluster-sparsity based compressed sensing algorithm for the channel estimations of60GHz millimeter-wave is developed. Then the comprehensive experimental simulations and comparative performance analysis are provided. Finally, we conclude the whole investigation in Section VI.
引文
[1]D L Donoho.Compressed sensing[J].IEEE Trans.on Informa-tion Theory.2006,52(4):1289-1306.
[2]E J Cand e s and T Tao. Near optimal signal recovery from random projections:Universal encoding strategies[J].IEEETrans.Info.Theory.2006,52 (12):5406-5425.
[3]Cand e s E, Romberg J, Tao T. Robust uncertainty princi-ples:Exact signal reconstruction from highly incompletefrequency information.IEEE Transactions on InformationTheory.2006,52(2):489-509
[4]李树涛,魏丹.压缩传感综述.自动化学报,2009.01
[5]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,2009,5
[6]D Baron,M B Wakin,M Duarte, etc. Distributed compressedsensing [OL]. http://www. dsp.rice.edu/-drorb/pdf/DCSl 12005.pdf.
[7]Won Namgoong. A Channelized Digital Ultrawideband Receiver.IEEETransactions onWirelessCommunications, vol.2, No.3, May 2003, pp.502-510
[8]张先玉,刘郁林,王开,60GHz通信压缩感知信通估计与信号检测方法,西安交通大学学报,2010,2
[9]高睿.基于压缩感的匹配追踪重建算法研究[硕士学位论文],北京：北京交通大学,2009.
[10]Thomas Zasowski, Armin Wittneben, Performance of 60GHZ Receivers with Partial CSIUsing a Simple Body Area Network Channel Model, IEEE Journalonselectedareasincommunications, VOL.27, NO.1, January 2009
[11]Theodore S. Rappaport.蔡涛,李旭,杜振民等译.无线通信原理与应用.北京：电子工业出版社,1999：50-140
[12]周美霞.压缩感知在脉冲60GHZ中的应用研究[硕士学位论文].北京：北京邮电大学,2009.
[13]Cand,S E J. Ridgelets:theory and applications [D]. Stanford:Stanlbrd University.1998.
[14]E Cand e s. D L Donoho.Curvelets [R].USA:Department of Statistics.Stanford University.1999.
[15]E Candes.Compressive sampling[A].Proceedings of the International Congress of Mathematicians[C].Madrid.Spain.2006.3:1433-1452.
[16]E L Pennec, S Mallat. Image compression with geometricalwavelets [A]. Proc.of IEEE International Conference on Image Processing, ICIP'2000[C].Vaneouver.BC':IEEE Computer Socicty,2000.1:661-664.
[17]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.
[18]R Baraniuk.A lecture on compressive sensing [J].IEEE Signal Processing Magazine.2007,24 (4):118-121.
[19]E Candes.The restricted isometry property and its implicationsfor compressed sensing[J]. Academie des sciences,2006,346(Ⅰ):598-592.
[20]Y.L.. Chao.Optimal integration time for 60GHZ transmitted reference correlation receivers. The 38th Asilomar Conference on Signals, Systems and Computers,2004. Volume 1, Nov.2004:647-651
[21]E.j.Candes and T.Tao,The Dantzig selector:Statistical estimation when p is much larger than n,Ann.Stat.,vol.35,no.6,pp.2313-2351,Dec.2007
[22]R.Tibshirani,Regression shrinkage and selection via the lasso, J. R. Stat, vol.58, no.1, pp.267-288,1996
[23]E J Candes,J Romberg. Practical signal recovery from randomprojections [OL]. http://www. acm.caltech. edu/-emmanuel/papers/PracticalRecovery. pdf.
[24]Marco F.Duarte,Shriram Sarvotham,Dror Baron,Michael B.Wakin and Richard G.Baraniuk,Distributed Compressed Sensing of Jointly Sparse Signals,2005,Signals,Systems and Computers,2005
[25]A C Gilbert,M J Strauss,J A Tropp, etc. Algorithmic lineardimension reduction in the norm for sparse vectors [OL].http://www. math.ucdavis.edu/-vershynin/ papers/algori thmic2dim2reduction.pdf
[26]G Cormode and S Muthukrishnan. Towards an algorithmictheory of compressed sensing [R]. DIMACS Tech. Report2005-25,September 2005.
[27]宁刚.基于压缩感知的信号重建算法研究[硕士学位论文],吉林：吉林大学,2010.
[28]Waheed U.BajwaJarvis Haupt,Akbar M.Sayeed,Robert Nowak,Compressed Channel Sensing:A New Approach to Estimating Sparse Multipath Channels,2010, Proceedings of the IEEE,VOL.98,NO.6,June
[29]Jose L,Gonzalo R.Arce,Zhongmin Wang,Ultra-Wideband Compressed Sensing:Channel Estimation,2007, IEEE Jounral of selected topics in signal processing,Vol.1,NO.3
[30]Mark A.Davenport,Michael B.Wakin,Analysis of Orthogonal Matching Pursuit Using the Restricted Isometry Property, IEEE transactions on information theory, VOL.56, NO.9, September 2010
[31]Deanna Needell.Roman Vershynin,Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit,2010,IEEE Jounral of selected topics in signal processing,Vol.4,NO.2,April 2010
[32]周灿梅.基于压缩感知的匹配追踪重建算法研究[硕士学位论文],北京：北京交通大学,2009.
[33JShihao Ji.Ya Xue, Lawrence Carin,Bayesian Compressive Sensing,2008,IEEE Transactions on Signal Processing,VOL.56,NO.6,June
[34]Shihao Ji,David Dunson,Lawrence Carin,Multitask Compressive Scnsing.2009. IEEE Transactions on Signal Processing,VOL.57,NO.l,January
[35]解武,曹家年,刘世航,吕学龙,IEEE802.15.4a对60GHZ功率谱影响分析,2010,深圳大学学报理工版,2010.1
[36]丘琳.脉冲60GHz通信的频域接收技术研究[硕士学位论文].成都：电子科技大学,2007.
[37]王东梅,侯晓赞,压缩感知理论及其应用前景,中国新通信,2010,11
[38]朱刚编著60GHz (60GHZ)原理与干扰.北京：清华大学出版社/北京交通大学出版社,2009.4
[39]阮松杰,60GHz技术及其军事应用,企业技术开发,2010,6
[40][意]Maria-Gabriel la, Di Benedetto, Gerino著；葛利嘉,朱林,袁晓芳,陈帮富等译.60GHz无线电基础.北京：电子工业出版社,2005,5
[41]付翠云60GHZ TransferJet物理层协议的研究与开发[硕士学位论文].北京：北京邮电大学,2009
[42]AndreasF.Molisch.Ultrawideband Propagation Channels-Theory,Measurement,and Modeling. IEEE Transactions on vehicular technology,Vol.54.NO.5,September 2005
[43]Andreas F.Molisch,Kannan Balakrishnan,Dajana Cassioli,et al.IEEE 802.15.4a channel model-final report[R].IEEE 802.15 WPAN Low Rate Alternative PHY Task Group 4a(TG4a),Tech.Rep.,2004,11
[44]曾勇波.60GHz信道估计技术的研究[博士学位论文],北京：北京邮电大学,2007.
[45]Daniele Domenicali, Luca De Nardis, Maria-Gabriella Di Benedetto,60GHZ Body Area Network Coexistence bylnterference Mitigation,2009, IC60GHZ 2009
[46]Heinrich Luecken, Thomas Zasowski,Christoph Steiner,Florian Troesch, Armin Wittneben,Location-aware Adaptation and Precoding for Low Complexity IR-60GHZ Receivers,Proceedings of the 2008 IEEE International Conference on Ultra-Wideband (IC60GHZ2008), Vol.3
[47]Kenichi Takizawa,Takahiro Aoyagi,Ryuji Kohno,Channel Modeling and Performance Evaluation of 60GHZ-based Wireless Body Area Networks.IEEEJCC 2009 proceedings
[48]Thomas Zasowski,Florian Troesch, Armin Wittneben,Partial Channel State Information and Intcrsymbol Interference in Low Complexity 60GHZ PPM Detection,Ultra-Wideband,2006. IE-EE International Conference
[49]魏浩,郑宝玉,岳文静,基于压缩感知的稀疏多径信道估计,2010午通信理论与信号处理学术年会论文集,2010
[50]Benjamin Friedlander,Random Projections for Sparse Channel Estimation and Equalization. Signals Systems and Computers,2006.ACSSC"06. Fortieth Asilomar Conference
[51]P. Zhang. Z. Hu, R. C. Qiu. and B.M. Sadler. A compressed sensing based ultra-wideband communication system,IEEE International Conference on Communications.2OO9.(ICC2OO9).14-18 June 2009
[52]Emmanuel J.Candes and Michael B.Wakin.An introduction to compressive sampling.IEEE signal processing magazine,March,2008
[53]The IEEE 802.15.4a standard. available for purchase at:http://standards.ieee.org/getieee802/.
[54]Ted C.-K.Liu,Xiaodai Dong and Wu-sheng Lu.Compressed Sensing Maximum Likelihood Channel Estimation for Ultra-Wideband Impulse Radio,2009,IEEE ICC 2009 proceedings
[55]Waheed U.Bajwa,Jarvis haupt,Gil Raz and Robert Nowak,Compressed Channel sensing,2008,Information Sciences and Systems,2008. CISS 2008.42nd Annual Conference
[56]Justin Romberg,The Dantzig Selector and Generalized Thresholding,2008,Information Sciences and Systems,2008. CISS 2008.42nd Annual Conference
[57]Deanna Needell,Joel Tropp,Roman Vershynin,Greedy Signal Recovery Review, Signals, Systems and Computers,200842nd Asilomar Conference
[58]Deanna Needell,Roman Vershynin,Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit, Found. Comput. Math.,2007, DOI:10.1007/s 10208-008-9031-3
[59]张贤达.现代信号处理(第2版).北京：清华大学出版社2002年
[60]黄贻培,游晓容,基于稀疏特性的TH-PPM 60GHZ信道估计算法.重庆邮电大学学报(自然科学版),2010,2
[61]崔琪楣,陶晓峰,邓昕才,张平,基于60GHz脉冲信号的时/频域信道估计,电路与系统学报,2008,2
[2]E J Cand e s and T Tao. Near optimal signal recovery from random projections:Universal encoding strategies[J].IEEETrans.Info.Theory.2006,52 (12):5406-5425.
[3]Cand e s E, Romberg J, Tao T. Robust uncertainty princi-ples:Exact signal reconstruction from highly incompletefrequency information.IEEE Transactions on InformationTheory.2006,52(2):489-509
[4]李树涛,魏丹.压缩传感综述.自动化学报,2009.01
[5]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,2009,5
[6]D Baron,M B Wakin,M Duarte, etc. Distributed compressedsensing [OL]. http://www. dsp.rice.edu/-drorb/pdf/DCSl 12005.pdf.
[7]Won Namgoong. A Channelized Digital Ultrawideband Receiver.IEEETransactions onWirelessCommunications, vol.2, No.3, May 2003, pp.502-510
[8]张先玉,刘郁林,王开,60GHz通信压缩感知信通估计与信号检测方法,西安交通大学学报,2010,2
[9]高睿.基于压缩感的匹配追踪重建算法研究[硕士学位论文],北京：北京交通大学,2009.
[10]Thomas Zasowski, Armin Wittneben, Performance of 60GHZ Receivers with Partial CSIUsing a Simple Body Area Network Channel Model, IEEE Journalonselectedareasincommunications, VOL.27, NO.1, January 2009
[11]Theodore S. Rappaport.蔡涛,李旭,杜振民等译.无线通信原理与应用.北京：电子工业出版社,1999：50-140
[12]周美霞.压缩感知在脉冲60GHZ中的应用研究[硕士学位论文].北京：北京邮电大学,2009.
[13]Cand,S E J. Ridgelets:theory and applications [D]. Stanford:Stanlbrd University.1998.
[14]E Cand e s. D L Donoho.Curvelets [R].USA:Department of Statistics.Stanford University.1999.
[15]E Candes.Compressive sampling[A].Proceedings of the International Congress of Mathematicians[C].Madrid.Spain.2006.3:1433-1452.
[16]E L Pennec, S Mallat. Image compression with geometricalwavelets [A]. Proc.of IEEE International Conference on Image Processing, ICIP'2000[C].Vaneouver.BC':IEEE Computer Socicty,2000.1:661-664.
[17]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.
[18]R Baraniuk.A lecture on compressive sensing [J].IEEE Signal Processing Magazine.2007,24 (4):118-121.
[19]E Candes.The restricted isometry property and its implicationsfor compressed sensing[J]. Academie des sciences,2006,346(Ⅰ):598-592.
[20]Y.L.. Chao.Optimal integration time for 60GHZ transmitted reference correlation receivers. The 38th Asilomar Conference on Signals, Systems and Computers,2004. Volume 1, Nov.2004:647-651
[21]E.j.Candes and T.Tao,The Dantzig selector:Statistical estimation when p is much larger than n,Ann.Stat.,vol.35,no.6,pp.2313-2351,Dec.2007
[22]R.Tibshirani,Regression shrinkage and selection via the lasso, J. R. Stat, vol.58, no.1, pp.267-288,1996
[23]E J Candes,J Romberg. Practical signal recovery from randomprojections [OL]. http://www. acm.caltech. edu/-emmanuel/papers/PracticalRecovery. pdf.
[24]Marco F.Duarte,Shriram Sarvotham,Dror Baron,Michael B.Wakin and Richard G.Baraniuk,Distributed Compressed Sensing of Jointly Sparse Signals,2005,Signals,Systems and Computers,2005
[25]A C Gilbert,M J Strauss,J A Tropp, etc. Algorithmic lineardimension reduction in the norm for sparse vectors [OL].http://www. math.ucdavis.edu/-vershynin/ papers/algori thmic2dim2reduction.pdf
[26]G Cormode and S Muthukrishnan. Towards an algorithmictheory of compressed sensing [R]. DIMACS Tech. Report2005-25,September 2005.
[27]宁刚.基于压缩感知的信号重建算法研究[硕士学位论文],吉林：吉林大学,2010.
[28]Waheed U.BajwaJarvis Haupt,Akbar M.Sayeed,Robert Nowak,Compressed Channel Sensing:A New Approach to Estimating Sparse Multipath Channels,2010, Proceedings of the IEEE,VOL.98,NO.6,June
[29]Jose L,Gonzalo R.Arce,Zhongmin Wang,Ultra-Wideband Compressed Sensing:Channel Estimation,2007, IEEE Jounral of selected topics in signal processing,Vol.1,NO.3
[30]Mark A.Davenport,Michael B.Wakin,Analysis of Orthogonal Matching Pursuit Using the Restricted Isometry Property, IEEE transactions on information theory, VOL.56, NO.9, September 2010
[31]Deanna Needell.Roman Vershynin,Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit,2010,IEEE Jounral of selected topics in signal processing,Vol.4,NO.2,April 2010
[32]周灿梅.基于压缩感知的匹配追踪重建算法研究[硕士学位论文],北京：北京交通大学,2009.
[33JShihao Ji.Ya Xue, Lawrence Carin,Bayesian Compressive Sensing,2008,IEEE Transactions on Signal Processing,VOL.56,NO.6,June
[34]Shihao Ji,David Dunson,Lawrence Carin,Multitask Compressive Scnsing.2009. IEEE Transactions on Signal Processing,VOL.57,NO.l,January
[35]解武,曹家年,刘世航,吕学龙,IEEE802.15.4a对60GHZ功率谱影响分析,2010,深圳大学学报理工版,2010.1
[36]丘琳.脉冲60GHz通信的频域接收技术研究[硕士学位论文].成都：电子科技大学,2007.
[37]王东梅,侯晓赞,压缩感知理论及其应用前景,中国新通信,2010,11
[38]朱刚编著60GHz (60GHZ)原理与干扰.北京：清华大学出版社/北京交通大学出版社,2009.4
[39]阮松杰,60GHz技术及其军事应用,企业技术开发,2010,6
[40][意]Maria-Gabriel la, Di Benedetto, Gerino著；葛利嘉,朱林,袁晓芳,陈帮富等译.60GHz无线电基础.北京：电子工业出版社,2005,5
[41]付翠云60GHZ TransferJet物理层协议的研究与开发[硕士学位论文].北京：北京邮电大学,2009
[42]AndreasF.Molisch.Ultrawideband Propagation Channels-Theory,Measurement,and Modeling. IEEE Transactions on vehicular technology,Vol.54.NO.5,September 2005
[43]Andreas F.Molisch,Kannan Balakrishnan,Dajana Cassioli,et al.IEEE 802.15.4a channel model-final report[R].IEEE 802.15 WPAN Low Rate Alternative PHY Task Group 4a(TG4a),Tech.Rep.,2004,11
[44]曾勇波.60GHz信道估计技术的研究[博士学位论文],北京：北京邮电大学,2007.
[45]Daniele Domenicali, Luca De Nardis, Maria-Gabriella Di Benedetto,60GHZ Body Area Network Coexistence bylnterference Mitigation,2009, IC60GHZ 2009
[46]Heinrich Luecken, Thomas Zasowski,Christoph Steiner,Florian Troesch, Armin Wittneben,Location-aware Adaptation and Precoding for Low Complexity IR-60GHZ Receivers,Proceedings of the 2008 IEEE International Conference on Ultra-Wideband (IC60GHZ2008), Vol.3
[47]Kenichi Takizawa,Takahiro Aoyagi,Ryuji Kohno,Channel Modeling and Performance Evaluation of 60GHZ-based Wireless Body Area Networks.IEEEJCC 2009 proceedings
[48]Thomas Zasowski,Florian Troesch, Armin Wittneben,Partial Channel State Information and Intcrsymbol Interference in Low Complexity 60GHZ PPM Detection,Ultra-Wideband,2006. IE-EE International Conference
[49]魏浩,郑宝玉,岳文静,基于压缩感知的稀疏多径信道估计,2010午通信理论与信号处理学术年会论文集,2010
[50]Benjamin Friedlander,Random Projections for Sparse Channel Estimation and Equalization. Signals Systems and Computers,2006.ACSSC"06. Fortieth Asilomar Conference
[51]P. Zhang. Z. Hu, R. C. Qiu. and B.M. Sadler. A compressed sensing based ultra-wideband communication system,IEEE International Conference on Communications.2OO9.(ICC2OO9).14-18 June 2009
[52]Emmanuel J.Candes and Michael B.Wakin.An introduction to compressive sampling.IEEE signal processing magazine,March,2008
[53]The IEEE 802.15.4a standard. available for purchase at:http://standards.ieee.org/getieee802/.
[54]Ted C.-K.Liu,Xiaodai Dong and Wu-sheng Lu.Compressed Sensing Maximum Likelihood Channel Estimation for Ultra-Wideband Impulse Radio,2009,IEEE ICC 2009 proceedings
[55]Waheed U.Bajwa,Jarvis haupt,Gil Raz and Robert Nowak,Compressed Channel sensing,2008,Information Sciences and Systems,2008. CISS 2008.42nd Annual Conference
[56]Justin Romberg,The Dantzig Selector and Generalized Thresholding,2008,Information Sciences and Systems,2008. CISS 2008.42nd Annual Conference
[57]Deanna Needell,Joel Tropp,Roman Vershynin,Greedy Signal Recovery Review, Signals, Systems and Computers,200842nd Asilomar Conference
[58]Deanna Needell,Roman Vershynin,Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit, Found. Comput. Math.,2007, DOI:10.1007/s 10208-008-9031-3
[59]张贤达.现代信号处理(第2版).北京：清华大学出版社2002年
[60]黄贻培,游晓容,基于稀疏特性的TH-PPM 60GHZ信道估计算法.重庆邮电大学学报(自然科学版),2010,2
[61]崔琪楣,陶晓峰,邓昕才,张平,基于60GHz脉冲信号的时/频域信道估计,电路与系统学报,2008,2
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