基于压缩感知的OFDM系统的信号处理
|
摘要
随着无线通信技术的发展和人们对无线通信传输速率、质量等要求的提高,OFDM技术已经成为第四代移动通信的关键技术。准确、实时的信道估计和PAPR抑制算法是OFDM系统的两个主要问题。MIMO-OFDM技术将时间分集、空间分集与频率分集三者有机结合,有效抵频率选择性衰落,使频谱利用率有很大提高。MIMO-OFDM系统需要各种分集的利用,相干解调解码等都需要用到信道估计信息。PAPR过高是OFDM系统的另一个问题,造成系统的性能降低,对PAPR抑制算法可以有效降低PAPR带来的损失。
目前,压缩感知(Compressed Sensing,CS)是信号处理领域一门新兴的技术,能够少量的压缩信号中恢复出原始信号。论文把CS引入到OFDM系统的信号处理中,研究基于CS的信道估计和PAPR抑制算法,重点介绍如何把CS用于MIMO-OFDM信道估计。主要内容安排如下。
第一章介绍了论文的研究OFDM系统,MIMO-OFDM及压缩感知技术的背景,关键技术和文章安排顺序。
第二章介绍CS的关键理论,给出了几种实用的重构算法,Orthogonal Matching Pursuit (OMP)算法,Basis Pursuit(BP)算法,SOMP算法等算法的原理与实现步骤。
第三章重点介绍了BCS算法和稀疏信号的树结构,提出了改进的算法TBCS,较BCS算法取得了更好地算法性能。
第四章介绍无线衰落信道特性,MIMO, MIMO-OFDM目关技术,MIMO-OFDM系统框架,已及常用的OFDM系统中的信道估计方法。
第五章首先介绍了MIMO-OFDM的信道模型,考虑MIMO信道本身具有稀疏性,信道之间具有相关性,构建一组相关的稀疏信道,并根据FFT变换的特点,利用DCS对MIMO子信道进行联合信道估计,仿真验证了基于CS的信道估计方案比原来的LS信道估计方案有更好的估计效果。
第六章研究了目前CS在PAPR抑制算法上的应用。对当前OFDM系统的PAPR抑制算法进行分类研究。在此基础上,研究了应用CS技术的PAPR抑制算法,验证了CS对于降低PAPR有很好的应用前景。
第七章对本文的工作进行总结,给出了今后的研究方向。
As the development of wireless communication technologies and the improvement of need for transfer rate and quality in wireless communication. OFDM has been a key technology in4th wireless communication. Accurate channel estimation and Peak to Average Power Ratio (PAPR) are two major problems in OFDM. MIMO-OFDM can obtain time diversity, spaitial diversity and frequency diversity at the same time, so it can combat frequency selective fading and improve spectrum efficiency greatly. To achieve good performance, channel state information is necessary in MIMO-OFDM. High PAPR is another problem in OFDM, which make the system performance, so we using PAPR alrithmns to reduce the loss of PAPR.
Compressive Sensing (CS) is a new technology in signal process whitch can recover signal from some measurements.this paper introduces CS into OFDM system to process signal, and do rearch on channel eatimation and PAPR. This paper studies how to use CS to MIMO-OFDM channel estimation, and the main contents are described as followings.
Chapter1introduces the background of OFDM system, MIMO-OFDM and CS technology, key technologies and organization of the paper.
Chapter2intruduce CS key theory, put forward some practical reconstruction algorithms, Orthogonal Matching Pursuit (OMP),Basis Pursuit(BP), SOMP and their algorithm phaseso on.
Chapter3introduces BCS algorithm and sparse tree structure of signal. Put forward an improved algorithm TBCS, which achieves achieved a better performance compared to the BCS algorithm.
Chapter4describes the features of channels and introduces wireless communication fading channels'background. Introduces technologies of MIMO, MIMO-OFDM, MIMO-OFDM systems framework, and common channel estimation methods in OFDM system
Chapter5first introduces MIMO-OFDM channel model, considering MIMO channel itself sparsity correlation, build a set of related sparse channel. Use DCS to estimate MIMO channels.and simulations verify that CS channel estimation scheme has advantage over original LS estimated scheme.
Chapter6studies current PAPR algorithm based on CS. In this chapter, we research on existing work of using CS to reduce PAPR, and bring good performance. Based on research, the application of CS to PAPR suppression algorithm verifies good prospect of application of CS to reduce PAPR.
Chapter7summaries this paper's work, give future research directions.
目前,压缩感知(Compressed Sensing,CS)是信号处理领域一门新兴的技术,能够少量的压缩信号中恢复出原始信号。论文把CS引入到OFDM系统的信号处理中,研究基于CS的信道估计和PAPR抑制算法,重点介绍如何把CS用于MIMO-OFDM信道估计。主要内容安排如下。
第一章介绍了论文的研究OFDM系统,MIMO-OFDM及压缩感知技术的背景,关键技术和文章安排顺序。
第二章介绍CS的关键理论,给出了几种实用的重构算法,Orthogonal Matching Pursuit (OMP)算法,Basis Pursuit(BP)算法,SOMP算法等算法的原理与实现步骤。
第三章重点介绍了BCS算法和稀疏信号的树结构,提出了改进的算法TBCS,较BCS算法取得了更好地算法性能。
第四章介绍无线衰落信道特性,MIMO, MIMO-OFDM目关技术,MIMO-OFDM系统框架,已及常用的OFDM系统中的信道估计方法。
第五章首先介绍了MIMO-OFDM的信道模型,考虑MIMO信道本身具有稀疏性,信道之间具有相关性,构建一组相关的稀疏信道,并根据FFT变换的特点,利用DCS对MIMO子信道进行联合信道估计,仿真验证了基于CS的信道估计方案比原来的LS信道估计方案有更好的估计效果。
第六章研究了目前CS在PAPR抑制算法上的应用。对当前OFDM系统的PAPR抑制算法进行分类研究。在此基础上,研究了应用CS技术的PAPR抑制算法,验证了CS对于降低PAPR有很好的应用前景。
第七章对本文的工作进行总结,给出了今后的研究方向。
As the development of wireless communication technologies and the improvement of need for transfer rate and quality in wireless communication. OFDM has been a key technology in4th wireless communication. Accurate channel estimation and Peak to Average Power Ratio (PAPR) are two major problems in OFDM. MIMO-OFDM can obtain time diversity, spaitial diversity and frequency diversity at the same time, so it can combat frequency selective fading and improve spectrum efficiency greatly. To achieve good performance, channel state information is necessary in MIMO-OFDM. High PAPR is another problem in OFDM, which make the system performance, so we using PAPR alrithmns to reduce the loss of PAPR.
Compressive Sensing (CS) is a new technology in signal process whitch can recover signal from some measurements.this paper introduces CS into OFDM system to process signal, and do rearch on channel eatimation and PAPR. This paper studies how to use CS to MIMO-OFDM channel estimation, and the main contents are described as followings.
Chapter1introduces the background of OFDM system, MIMO-OFDM and CS technology, key technologies and organization of the paper.
Chapter2intruduce CS key theory, put forward some practical reconstruction algorithms, Orthogonal Matching Pursuit (OMP),Basis Pursuit(BP), SOMP and their algorithm phaseso on.
Chapter3introduces BCS algorithm and sparse tree structure of signal. Put forward an improved algorithm TBCS, which achieves achieved a better performance compared to the BCS algorithm.
Chapter4describes the features of channels and introduces wireless communication fading channels'background. Introduces technologies of MIMO, MIMO-OFDM, MIMO-OFDM systems framework, and common channel estimation methods in OFDM system
Chapter5first introduces MIMO-OFDM channel model, considering MIMO channel itself sparsity correlation, build a set of related sparse channel. Use DCS to estimate MIMO channels.and simulations verify that CS channel estimation scheme has advantage over original LS estimated scheme.
Chapter6studies current PAPR algorithm based on CS. In this chapter, we research on existing work of using CS to reduce PAPR, and bring good performance. Based on research, the application of CS to PAPR suppression algorithm verifies good prospect of application of CS to reduce PAPR.
Chapter7summaries this paper's work, give future research directions.
引文
[1]Erik Dahlman, Stefan Parkvall, and Johan Skold.4G LTE/LTE-Advanced'for Mobile Broadband. Elsevier Ltd.2011.9. pp:1-4.
[2]Farooq Khan. LTE For 4G Mobile Broadband. Cambridge University Press.2009. pp: 1-20.
[3]Andrea Goldsmith. Wireless Communications. Cambridge University Press.2005. pp: 1-8.
[4]Erik Dahlman, Stefan Parkvall, Johan Skold, Per Beming.3G Evolution HSPA and LTE for Mobile Broadband. Academic Press,2009.
[5]Recommendation ITU-RM.1645:Vision frame work and over all objectives of the future development of IMT-2000 and of systems beyond IMT-2000.
[6]N.Natarajan. On systems beyond 3G:requirements and approaches, Proc.IEEE ICCT'03, Apr.2003:1305-1309.
[7]U.Berthold, F.K.Jondral, S.Brandes, et al., OFDM-based overlay systems:A promising approach for enhancing spectral efficiency.IEEE Commun. Mag.,2007, 45(12):52-58.
[8]R.W.Chang, Synthesis of band-limited orthogonal signals for multichannel data transmission, Bell Syst.Tech.J, Dec.1966:1775-1796.
[9]DONOHO D. Compressed sensing[J]. IEEE Trans. Information Theory,2006,52(4): 1289-1306.
[10]CANDES E. Compressive sampling[C]//Proceedings of the International Congress of Mathematicians. Madrid, Spain:[s.n.],2006:1433-1452
[11]MarcoF.Duarte, Shriram Sarvotham, Dror Baron等著. Distributed Compressed Sensing of Jointly Sparse Signals, Department of Electrical and Computer Engineering, Rice University
[12]W. F. Schreiber. Advanced television systems for terrestrial broadcasting:Some problems and some proposed solutions.in Proc. IEEE, vol.83.
[13]I. J. Fevrier, S. B. Gelfand, and M. P. Fitz. Reduced complexity decision feedback equalization for multipath channels with large delay spreads. IEEE Trans. Comm., vol.47, pp.927-937, June 1999.
[14]Takashi DATEKI, Daisuke OGAWA, Hideto FURUKAWA. New OFDM Channel Estimation Method by Adding a Virtual Channel Frequency Response. IEEE,2006.
[15]John GProakis. Digital Communications(Fourth Edition).McGraw-Hill,Inc.2001.(11-55).
[16]C. Berrou and A. Glavieux. "Near Optimum Error Correcting Coding and Decoding: Turbo-codes". IEEE Trans, on Communications, Vol.44, pp.1261-1272,1996.10.
[17]C. Berrou, P. Glavieux and A. Thitimajshima. "Near Shannon Limit Error-correcting Coding and Decoding'. In Proc. IEEE International Conference on Communications, Geneva, Switzerland.1993.
[18]P Hoecher,S Kaiser and P Robertson,Two-dimensional pilot-symbol-aided channel estimation by wiener filtering, Proc(ICASS'97),Vol3:1845-1848
[19]LI Y. Pilot-symbol-aided channel estimation for OFDM in wireless systems[J].IEEE Trasactions on Communications,1988,46(4):1207-1215
[20]Ove E Sandel M,Jaap J,et al.OFDM channel estimation by singular value decomposition[J].IEE Transactions onCommunications,1988,46(7):931-939
[21]Richard G. B. Compressive sensing IEEE Signal Processing Magazine 24(4) July 2007 118-121.
[22]S. Mallat, A Wavelet Tour of Signal Processing. New York:Academic,1999.
[23]R.G. Baraniuk, M. Davenport, R. DeVore, and M.B. Wakin, "A simple proof of the restricted isometry principle for random matrices (aka the Johnson-Lindenstrauss lemma meets compressed sensing)," Constructive Approximation,2007
[24]J. Tropp and A.C. Gilbert, "Signal recovery from partial information via orthogonal matching pursuit," Apr.2005
[25]Candes, E. J., Tao, T., The Dantzig selector:statistical estimation when p is much larger than n. Ann. Statist., to appear.
[26]M.E. Tipping and A.C. Faul,.Fast marginal likelihood maximisation for sparse Bayesian models,. Ninth Int. Workshop Artificial Intelligence and Statistics, Jan.2003.
[27]Michael E. Tipping, Sparse Bayesian Learning and the Relevance Vector Machine, Journal of Machine Learning Research, vol.1, Jun.2001, pages 211-244.
[28]D. Baron, M.B. Wakin, M. Duarte, S. Sarvotham, and R.G. Baraniuk, "Distributed compressed sensing," 2005.
[29]M. F. Duarte, S. Sarvotham, D. Baron, M. B. Wakin and R. G. Baraniuk, "Distributed Compressed Sensing of Jointly Sparse Signals," in Asilomar Conference on Signals, Systems and Computers, pp.1537-1541,2005.
[30]D. L. Donoho, Y. Tsaig, I. Drori, and J.-C. Starck, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit," in Proc Wavelets XI at SPIE Optics and Photonics, San Diego, Aug.2005.
[31]Shihao Ji, Ya Xue, Carin. L, "Bayesian Compressive Sensing", Signal Processing, IEEE Transactions on Volume:56, Issue:6, Publication Year:2008, Page(s):2346-2356
[32]Chinh La, Minh N. Do, "Signal Reconstruction using Sparse Tree Representations" 2005-Citeseer
[33]A. Papoulis and S. U. Pillai, Probability, "Random Variables and Stochastic Processes",4th ed. New York: McGraw-Hill,2002.
[34]M. E. Tipping. "Sparse Bayesian learning and the relevance vector machine. " Journal of Machine Learning Research,1:211{244,2001.
[35]Michael E. Tipping and Anita C. Faul, "Fast marginal likelihood maximisation for sparse Bayesian models." Proceedings of the Ninth International Workshop on ArtificialIntelligence and Statistics, Key West, FL, Jan 3-6
[36]A. C. Faul and M. E. Tipping, "Analysis of sparse Bayesian learning". Advances in Neural Information Processing Systems 14, pages 383{389.MIT Press,2002.
[37]Lihan He; Carin. L,"Exploiting Structure in Wavelet-Based Bayesian Compressive Se nsing",signal Processing, IEEE Transactions, on Volume:57, Issue:9,2009, Page(s): 3488-3497
[38]R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, "Model-based compressive sensing," IEEE Transactions on Information Theory,2008, submitted.
[39]D. Donoho, "Compressed sensing," IEEE Trans. Inform. Theory, vol.52, no.4, pp. 1289-1306, Apr.2006.
[40]S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM J. Sci. Comput., vol.20, no.1, pp.33-61,1999.
[41]J. A. Tropp and A. C. Gilbert, "Signal recovery from partial information
[42]via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol.53, no.
[43]Donghao Wang, Kai Niu, "A Novel OFDM Channel Estimation Method Based On Kalman Filtering and Distributed Compressed Sensing", Wireless Information Technology and Systems (ICWITS),2010 IEEE International Conference on Aug. 2010.
[44]Tan Yun*, Xu Wenbo, "MIMO-OFDM Channel Estimation Based On Distributed Compressed Sensing and Kalman Filter", Signal Processing, Communications and Computing (ICSPCC),2011. IEEE International Conference on Sept.2011.
[45]Daniel Eiwen, Georg Taubock, "Multichannel Compressive Estimation of Doubly Selective Channels in MIMO-OFDM Systems:Exploiting and Enhancing Joint Sparsity", IEEE ICASSP 2010 Dallas, TX, Mar.2010, pp.3082-3085.
[46]Georg Taubock, Franz Hlawatsch, "A Compressed Sensing Technique for OFDM Channel Estimation in Mobile Environments:Exploiting Channel Sparsity for Reducing Pilots", Proc. IEEE ICASSP-08 LasVegas, NV, March-April 2008.
[47]D. Wang, K. Niu, Z. Bie, etc., "A new channel estimation method based on distributed compressed sensing," 2010 WCNC, pp.1-4.
[48]W. Bajwa, J. Haupt, A. Sayeed, and R. Nowak. "Compressed channel sensing:A new approach to estimating sparse multipath channels". Proc. IEEE,98(6):1058{1076, 2010}
[49]OFDM系统中压缩感知信道估计技术的研究王东昊-《北京邮电大学博士论文》
[50]Marco F. Duarte, Shriram Sarvotham, "Distributed Compressed Sensing of Jointly Sparse Signals", Asilomar Conf. Signals, Sys., Comput., Nov.2005.
[51]L. Safatly, G. Andrieux, and J. Diouris, "LTE multi-antenna techniquesbased on alamouti SFBC with correlated channels," in Proc. Software, Telecommunications and Computer Networks,2010, pp.214-218.
[52]3GPP TS 36.211 V10.4.0, "Evolved universal terrestrial radio access (EUTRA); physical channels and modulation," www.3gpp.org, Dec.2011.
[53]Kala Praveen Bagadi, Susmita Das, "MIMO-OFDM Channel Estimation using Pilot Carries", International Journao of Computer Applications, VOL.2-No.3, May 2010
[54]Won-Gyu Song, Jong-Tae Lim, "Channel Estimation and Signal Detection for MIMO-OFDM with Time Varying Channels'", IEEE Communication Letters, VOL.10, NO.7, July 2006
[55]Jium Siew, Robert Piechocki, "A Channel Estimation Method for MIMO-OFDM Systems", Proc. London Commun. Symp., pp.1-4, London, England, Sept.2002
[56]Waheed U. Bajwa, Jarvis Haupt, "Compressed Channel Sensing", Proc.42nd Annu. Conf. Information Sciences and Systems(CISS'08), Princeton, NJ, Mar.19-21,2008
[57]Zhaohui Wang, Jianzhong Huang, "Application for Compressive Sensing to Sparse Channel Estimation", IEEE Communications Magazine, Nov.2010.
[58]Georg Taubock, Franz Hlawatsch, "A Compressed Sensing Technique for OFDM Channel Estimation in Mobile Environments:Exploiting Channel Sparsity for Reducing Pilots", Proc. IEEE ICASSP-08 Las Vegas, NV, March-April 2008.
[59]Marco F. Duarte, Shriram Sarvotham, "Distributed Compressed Sensing of Jointly Sparse Signals", Asilomar Conf. Signals, Sys., Comput., Nov.2005.
[60]Kee-Hoon Kim,Hosung Park,Jong-Seon No and Habong Chung.Clipping noise Cancellation for OFDM and OFDMA Systems Using Compressed Sensing [J].IEEE TRANSACTIONS ON SIGNAL PROCESSING.October 20.2011.arXiv:1110.4174vl.
[61]H. Chen and A. Haimovich, "Iterative estimation and cancellation of clipping noise for OFDM signals," IEEE Commun.Lett., vol.7, pp.305-307, Jul.2003.
[62]E. B. Al-Safadi and T. Y. Al-Naffouri, "On reducing the complexity of tone reservation based PAPR reduction schemes by compressive sensing," in Proc. IEEE Globecom 2009, Honolulu HI, Nov.2009.
[63]M. Mohammadnia-Avval, A. Ghassemi, and L. Lampe, "Compressive sensing recovery of nonlinearity distorted OFDM signals," in Proc. IEEE Int. Conf. Commun., Jun.2011.
[2]Farooq Khan. LTE For 4G Mobile Broadband. Cambridge University Press.2009. pp: 1-20.
[3]Andrea Goldsmith. Wireless Communications. Cambridge University Press.2005. pp: 1-8.
[4]Erik Dahlman, Stefan Parkvall, Johan Skold, Per Beming.3G Evolution HSPA and LTE for Mobile Broadband. Academic Press,2009.
[5]Recommendation ITU-RM.1645:Vision frame work and over all objectives of the future development of IMT-2000 and of systems beyond IMT-2000.
[6]N.Natarajan. On systems beyond 3G:requirements and approaches, Proc.IEEE ICCT'03, Apr.2003:1305-1309.
[7]U.Berthold, F.K.Jondral, S.Brandes, et al., OFDM-based overlay systems:A promising approach for enhancing spectral efficiency.IEEE Commun. Mag.,2007, 45(12):52-58.
[8]R.W.Chang, Synthesis of band-limited orthogonal signals for multichannel data transmission, Bell Syst.Tech.J, Dec.1966:1775-1796.
[9]DONOHO D. Compressed sensing[J]. IEEE Trans. Information Theory,2006,52(4): 1289-1306.
[10]CANDES E. Compressive sampling[C]//Proceedings of the International Congress of Mathematicians. Madrid, Spain:[s.n.],2006:1433-1452
[11]MarcoF.Duarte, Shriram Sarvotham, Dror Baron等著. Distributed Compressed Sensing of Jointly Sparse Signals, Department of Electrical and Computer Engineering, Rice University
[12]W. F. Schreiber. Advanced television systems for terrestrial broadcasting:Some problems and some proposed solutions.in Proc. IEEE, vol.83.
[13]I. J. Fevrier, S. B. Gelfand, and M. P. Fitz. Reduced complexity decision feedback equalization for multipath channels with large delay spreads. IEEE Trans. Comm., vol.47, pp.927-937, June 1999.
[14]Takashi DATEKI, Daisuke OGAWA, Hideto FURUKAWA. New OFDM Channel Estimation Method by Adding a Virtual Channel Frequency Response. IEEE,2006.
[15]John GProakis. Digital Communications(Fourth Edition).McGraw-Hill,Inc.2001.(11-55).
[16]C. Berrou and A. Glavieux. "Near Optimum Error Correcting Coding and Decoding: Turbo-codes". IEEE Trans, on Communications, Vol.44, pp.1261-1272,1996.10.
[17]C. Berrou, P. Glavieux and A. Thitimajshima. "Near Shannon Limit Error-correcting Coding and Decoding'. In Proc. IEEE International Conference on Communications, Geneva, Switzerland.1993.
[18]P Hoecher,S Kaiser and P Robertson,Two-dimensional pilot-symbol-aided channel estimation by wiener filtering, Proc(ICASS'97),Vol3:1845-1848
[19]LI Y. Pilot-symbol-aided channel estimation for OFDM in wireless systems[J].IEEE Trasactions on Communications,1988,46(4):1207-1215
[20]Ove E Sandel M,Jaap J,et al.OFDM channel estimation by singular value decomposition[J].IEE Transactions onCommunications,1988,46(7):931-939
[21]Richard G. B. Compressive sensing IEEE Signal Processing Magazine 24(4) July 2007 118-121.
[22]S. Mallat, A Wavelet Tour of Signal Processing. New York:Academic,1999.
[23]R.G. Baraniuk, M. Davenport, R. DeVore, and M.B. Wakin, "A simple proof of the restricted isometry principle for random matrices (aka the Johnson-Lindenstrauss lemma meets compressed sensing)," Constructive Approximation,2007
[24]J. Tropp and A.C. Gilbert, "Signal recovery from partial information via orthogonal matching pursuit," Apr.2005
[25]Candes, E. J., Tao, T., The Dantzig selector:statistical estimation when p is much larger than n. Ann. Statist., to appear.
[26]M.E. Tipping and A.C. Faul,.Fast marginal likelihood maximisation for sparse Bayesian models,. Ninth Int. Workshop Artificial Intelligence and Statistics, Jan.2003.
[27]Michael E. Tipping, Sparse Bayesian Learning and the Relevance Vector Machine, Journal of Machine Learning Research, vol.1, Jun.2001, pages 211-244.
[28]D. Baron, M.B. Wakin, M. Duarte, S. Sarvotham, and R.G. Baraniuk, "Distributed compressed sensing," 2005.
[29]M. F. Duarte, S. Sarvotham, D. Baron, M. B. Wakin and R. G. Baraniuk, "Distributed Compressed Sensing of Jointly Sparse Signals," in Asilomar Conference on Signals, Systems and Computers, pp.1537-1541,2005.
[30]D. L. Donoho, Y. Tsaig, I. Drori, and J.-C. Starck, "Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit," in Proc Wavelets XI at SPIE Optics and Photonics, San Diego, Aug.2005.
[31]Shihao Ji, Ya Xue, Carin. L, "Bayesian Compressive Sensing", Signal Processing, IEEE Transactions on Volume:56, Issue:6, Publication Year:2008, Page(s):2346-2356
[32]Chinh La, Minh N. Do, "Signal Reconstruction using Sparse Tree Representations" 2005-Citeseer
[33]A. Papoulis and S. U. Pillai, Probability, "Random Variables and Stochastic Processes",4th ed. New York: McGraw-Hill,2002.
[34]M. E. Tipping. "Sparse Bayesian learning and the relevance vector machine. " Journal of Machine Learning Research,1:211{244,2001.
[35]Michael E. Tipping and Anita C. Faul, "Fast marginal likelihood maximisation for sparse Bayesian models." Proceedings of the Ninth International Workshop on ArtificialIntelligence and Statistics, Key West, FL, Jan 3-6
[36]A. C. Faul and M. E. Tipping, "Analysis of sparse Bayesian learning". Advances in Neural Information Processing Systems 14, pages 383{389.MIT Press,2002.
[37]Lihan He; Carin. L,"Exploiting Structure in Wavelet-Based Bayesian Compressive Se nsing",signal Processing, IEEE Transactions, on Volume:57, Issue:9,2009, Page(s): 3488-3497
[38]R. G. Baraniuk, V. Cevher, M. F. Duarte, and C. Hegde, "Model-based compressive sensing," IEEE Transactions on Information Theory,2008, submitted.
[39]D. Donoho, "Compressed sensing," IEEE Trans. Inform. Theory, vol.52, no.4, pp. 1289-1306, Apr.2006.
[40]S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM J. Sci. Comput., vol.20, no.1, pp.33-61,1999.
[41]J. A. Tropp and A. C. Gilbert, "Signal recovery from partial information
[42]via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol.53, no.
[43]Donghao Wang, Kai Niu, "A Novel OFDM Channel Estimation Method Based On Kalman Filtering and Distributed Compressed Sensing", Wireless Information Technology and Systems (ICWITS),2010 IEEE International Conference on Aug. 2010.
[44]Tan Yun*, Xu Wenbo, "MIMO-OFDM Channel Estimation Based On Distributed Compressed Sensing and Kalman Filter", Signal Processing, Communications and Computing (ICSPCC),2011. IEEE International Conference on Sept.2011.
[45]Daniel Eiwen, Georg Taubock, "Multichannel Compressive Estimation of Doubly Selective Channels in MIMO-OFDM Systems:Exploiting and Enhancing Joint Sparsity", IEEE ICASSP 2010 Dallas, TX, Mar.2010, pp.3082-3085.
[46]Georg Taubock, Franz Hlawatsch, "A Compressed Sensing Technique for OFDM Channel Estimation in Mobile Environments:Exploiting Channel Sparsity for Reducing Pilots", Proc. IEEE ICASSP-08 LasVegas, NV, March-April 2008.
[47]D. Wang, K. Niu, Z. Bie, etc., "A new channel estimation method based on distributed compressed sensing," 2010 WCNC, pp.1-4.
[48]W. Bajwa, J. Haupt, A. Sayeed, and R. Nowak. "Compressed channel sensing:A new approach to estimating sparse multipath channels". Proc. IEEE,98(6):1058{1076, 2010}
[49]OFDM系统中压缩感知信道估计技术的研究王东昊-《北京邮电大学博士论文》
[50]Marco F. Duarte, Shriram Sarvotham, "Distributed Compressed Sensing of Jointly Sparse Signals", Asilomar Conf. Signals, Sys., Comput., Nov.2005.
[51]L. Safatly, G. Andrieux, and J. Diouris, "LTE multi-antenna techniquesbased on alamouti SFBC with correlated channels," in Proc. Software, Telecommunications and Computer Networks,2010, pp.214-218.
[52]3GPP TS 36.211 V10.4.0, "Evolved universal terrestrial radio access (EUTRA); physical channels and modulation," www.3gpp.org, Dec.2011.
[53]Kala Praveen Bagadi, Susmita Das, "MIMO-OFDM Channel Estimation using Pilot Carries", International Journao of Computer Applications, VOL.2-No.3, May 2010
[54]Won-Gyu Song, Jong-Tae Lim, "Channel Estimation and Signal Detection for MIMO-OFDM with Time Varying Channels'", IEEE Communication Letters, VOL.10, NO.7, July 2006
[55]Jium Siew, Robert Piechocki, "A Channel Estimation Method for MIMO-OFDM Systems", Proc. London Commun. Symp., pp.1-4, London, England, Sept.2002
[56]Waheed U. Bajwa, Jarvis Haupt, "Compressed Channel Sensing", Proc.42nd Annu. Conf. Information Sciences and Systems(CISS'08), Princeton, NJ, Mar.19-21,2008
[57]Zhaohui Wang, Jianzhong Huang, "Application for Compressive Sensing to Sparse Channel Estimation", IEEE Communications Magazine, Nov.2010.
[58]Georg Taubock, Franz Hlawatsch, "A Compressed Sensing Technique for OFDM Channel Estimation in Mobile Environments:Exploiting Channel Sparsity for Reducing Pilots", Proc. IEEE ICASSP-08 Las Vegas, NV, March-April 2008.
[59]Marco F. Duarte, Shriram Sarvotham, "Distributed Compressed Sensing of Jointly Sparse Signals", Asilomar Conf. Signals, Sys., Comput., Nov.2005.
[60]Kee-Hoon Kim,Hosung Park,Jong-Seon No and Habong Chung.Clipping noise Cancellation for OFDM and OFDMA Systems Using Compressed Sensing [J].IEEE TRANSACTIONS ON SIGNAL PROCESSING.October 20.2011.arXiv:1110.4174vl.
[61]H. Chen and A. Haimovich, "Iterative estimation and cancellation of clipping noise for OFDM signals," IEEE Commun.Lett., vol.7, pp.305-307, Jul.2003.
[62]E. B. Al-Safadi and T. Y. Al-Naffouri, "On reducing the complexity of tone reservation based PAPR reduction schemes by compressive sensing," in Proc. IEEE Globecom 2009, Honolulu HI, Nov.2009.
[63]M. Mohammadnia-Avval, A. Ghassemi, and L. Lampe, "Compressive sensing recovery of nonlinearity distorted OFDM signals," in Proc. IEEE Int. Conf. Commun., Jun.2011.