MIMO-OFDM系统信道估计理论的研究
摘要
对于高速数据业务来说,信息流的符号宽度很小,由于无线信道的多径扩散和移动物体多普勒效应,符号之间的码间干扰比较严重。
无论在技术还是在应用上,为了实现新一代移动通信系统要有质的飞跃这一目标,面临两个主要的技术挑战:多径衰落和带宽效率。
MIMO和OFDM技术正是在这样的大前提下被提出来,成为实现无线信道高速数据传输最具希望的解决方案之一。
正交频分复用(Orthogonal Frequency Division Multiplex,OFDM)是一种特殊的多载波调制技术,也可以把它当作是一种复用技术。它传送数据的基本原理是把数据流分成若干个并行的比特流,并将每个这样的数据流调制在单个子载波或副载波上。高速数据流被分解成许多低速率的字数据流,以并行的方式在多个子信道上传输。这样,在每个子信道上,符号周期将大于原始的符号周期,每个子信道上信号所占用的带宽资源将小于整个无线通信信道的相干带宽,因此各个子通信信道上的衰落特性可以看成是平坦性衰落,由于是平坦性衰落,这样可以消除或者部分消除符号间干扰(ISI)。
MIMO技术大致可以分为两类:分集最大化,即发射/接收分集;数据率最大化,即空间复用。分集技术主要用来对抗信道衰落,在多天线系统中,信息相同的信号由不同的天线发送出去,经过不同的路径,到达接收端,这样,接收端可以获得多个独立衰落复制的数据符号,这样通过增加分集度来克服信道衰落,提高系统的接收可靠性。
对于频率选择性深度衰落,MIMO系统能力有限,OFDM系统可以将频率选择性衰落等效分成若干平坦衰落信道,不仅为MIMO技术在频率选择性信道中的应用创造了条件,而且极大简化了信道均衡。人们希望结合MIMO和OFDM,以期获得更高的频谱利用率。
所以,MIMO-OFDM技术的核心思想是将OFDM与MIMO技术结合,通过多发多收的MIMO系统结构,实现空间分集,通过每个天线上发送OFDM信号,提高传输系统信号质量。
因此将MIMO技术和OFDM技术相结合是下一代无线移动通信的发展的趋势。本文主要围绕MIMO-OFDM信道估计算法开展以下研究:
基于训练序列的MIMO-OFDM系统的信道估计。采用峰值等功率正交导频信号,用来对于特定收发天线对,克服来自其它天线的信号的干扰。首先由LMMSE算法得到训练序列处的信道信息,通过插值得到数据符号处信道响应,再对此迭代计算得到更为精细的信道估计值。
MIMO-OFDM系统信道盲估计算法。提出了利用递归子空间跟踪算法跟踪信号子空间的方法,每次得到一个相对最大的奇异值及其对应的信号空间向量。此方法可有效降低批处理计算复杂度。
克服非高斯噪声的影响,基于粒子滤波的MIMO-OFDM信道估计。把信道模型建模为随时间演化的状态模型和与状态相关的观测模型。此种结构,对于线性、高斯估计问题,卡尔曼滤波给出了最优解;对于非线性、非高斯问题,很难求得其解的解析形式,为此,人们提出各种非线性滤波算法来求解非线性、非高斯问题,粒子滤波器,是目前用来解决非线性问题的有效方法。采用变步长方法对重采样后的粒子分布进行优化调整,由此跟踪MIMO-OFDM信道变化,,增强系统信号检测能力。
Without introducing any additional bandwidth, MIMO technology can multiply communication system capacity, improve link performance, and increase data throughput of the network. MIMO systems include several mutually independent channels between transmitter and the receiver, and to some degree multipath components of transmission can be used to overcome multipath fading, but the frequency selective fading processing capability is still limited.
OFDM system can equivalently divide frequency selective fading channel into several flat fading channels, which can not only create favorable conditions for applications on MIMO in multipath fading channel, but also considerably simplify channel equalization. Based on the assumptions, many domestic and abroad researchers and research organizations have put emphasis on the studies on the combination of MIMO and OFDM technologies in order to achieve higher spectrum utilization efficiency. MIMO-OFDM is a new combination technique of OFDM with MIMO, which uses antenna arrays to realize space diversity and to improve the quality of transmission.
This paper carries out the research of channel estimation based on MIMIO-OFDM , and the main work is as following:
1. It reviews present conditions of development of wireless and mobile communication systems and introduces their technical features and typical technology of the first generation, the second generation and the third generation (3G) mobile communications are introduced in this paper. Then it discusses the history and current situation of the MIMO systems and OFDM, and provides the perspective of the integration of MIMO and OFDM.
2. It discusses the theory of OFDM and channel model of MIMO, and introduces OFDM technology, MIMO wireless communication system model, and MIMO-OFDM channel estimation technology in detail, and which are based on the following chapters.
3. It discusses the channel estimation method based on pilots symbol for MIMO-OFDM systems, and studies that transmitting terminal at regular time intervals plug known pilot into the proper position of OFDM symbol, pilot symbol and data symbol together are received by receiving antenna, and by pilot symbol receiving terminal extract channel response at pilot symbol position, then makes interpolation operation by these position information and obtains channel information for the whole period.
4. It studies subspace blind channel estimation, for the plugged training sequence or pilot inevitably occupy bandwidth and affect transmitting efficiency of communication systems are affected, and reduce the system effective bandwidth utilization. How to use other observed value including all unkown symbols neglected based on pilot channel estimation algorithm besides pilot symbol to compute channel estimation in order to improve the efficiency of communication system.
5. It studies channel estimation based on Bayesian theory, in many problems , required to use observed value with noise to filter and estimate on systems status varying with the time,people often adopt state space method to simulated dynamic system. Bayesian filter theory provide a common frame for dynamic filtering and estimation, and Kalman filter gives the optimal solution for linear and gaussian approximation.But for Non-linear, non-Gaussian problems, it is usually difficult to have analytical form of its solution. So that, all kinds of non-linear filter algorithm are provided. One is extended kalman filter algorithm, which makes a local linearization to a nonlinear system, so Kalman filter can be utilized indirectly to filter and estimate. The other is sequence Monte Carlo algorithm ,that is Particle Filter ,it is the effective methods for non-linear problem s recently appeared .The paper provide a kind of particle filter to realize to channel estimation of MIMO-OFDM system.
Main innovations are as follows:
1. The channel estimation algorithm based on pilot that adopts equal peak power pilot signal has been proposed. And the dsigned pilot transforms the problem of multiple antennas signal channel estimation into signal antenna situation. Singular value decomposition simplifies computation of inverse matrix in MMSE, and LMMSE algorithm will attain rough estimtion which is as an initial value of the estimation. Then by reducing pilot number and carrying on iterative calculation of the aim function, the proposed algorithm can get more accurate channel estimation.
2. In the (semi-)blind channel estimation algorithms in MIMO-OFDM system, the singular value decomposition(SVD) is a useful technique. In this paper, we adopts a adaptive algorithm to estimate the SVD of channel matrix in MIMO-OFDM system, which can fast tracking the signal and noise subspace without eigenvalue decomposition or singular value decomposition. The performance of this method is similar to the source one, but computational complexity is reduction.
3. In the practical wireless communication environment, such as the indoor condition or the city environment, there are a mass of non-Gaussian noise. Aiming at the nonlinear/non-Gaussian, the sequential monte carlo particle filter based on Bayesian theorem is proved to be an effective method. In particle filter, the particle degeneration is a key issue to affect the performance of the algorithm. In this paper, we adopt a variable step-size method to regulate the distribution of the resample particle. The simulation result shows that the proposed method can trace the MIMO-OFDM channel variation which affected by the non-Gaussian noise.
无论在技术还是在应用上,为了实现新一代移动通信系统要有质的飞跃这一目标,面临两个主要的技术挑战:多径衰落和带宽效率。
MIMO和OFDM技术正是在这样的大前提下被提出来,成为实现无线信道高速数据传输最具希望的解决方案之一。
正交频分复用(Orthogonal Frequency Division Multiplex,OFDM)是一种特殊的多载波调制技术,也可以把它当作是一种复用技术。它传送数据的基本原理是把数据流分成若干个并行的比特流,并将每个这样的数据流调制在单个子载波或副载波上。高速数据流被分解成许多低速率的字数据流,以并行的方式在多个子信道上传输。这样,在每个子信道上,符号周期将大于原始的符号周期,每个子信道上信号所占用的带宽资源将小于整个无线通信信道的相干带宽,因此各个子通信信道上的衰落特性可以看成是平坦性衰落,由于是平坦性衰落,这样可以消除或者部分消除符号间干扰(ISI)。
MIMO技术大致可以分为两类:分集最大化,即发射/接收分集;数据率最大化,即空间复用。分集技术主要用来对抗信道衰落,在多天线系统中,信息相同的信号由不同的天线发送出去,经过不同的路径,到达接收端,这样,接收端可以获得多个独立衰落复制的数据符号,这样通过增加分集度来克服信道衰落,提高系统的接收可靠性。
对于频率选择性深度衰落,MIMO系统能力有限,OFDM系统可以将频率选择性衰落等效分成若干平坦衰落信道,不仅为MIMO技术在频率选择性信道中的应用创造了条件,而且极大简化了信道均衡。人们希望结合MIMO和OFDM,以期获得更高的频谱利用率。
所以,MIMO-OFDM技术的核心思想是将OFDM与MIMO技术结合,通过多发多收的MIMO系统结构,实现空间分集,通过每个天线上发送OFDM信号,提高传输系统信号质量。
因此将MIMO技术和OFDM技术相结合是下一代无线移动通信的发展的趋势。本文主要围绕MIMO-OFDM信道估计算法开展以下研究:
基于训练序列的MIMO-OFDM系统的信道估计。采用峰值等功率正交导频信号,用来对于特定收发天线对,克服来自其它天线的信号的干扰。首先由LMMSE算法得到训练序列处的信道信息,通过插值得到数据符号处信道响应,再对此迭代计算得到更为精细的信道估计值。
MIMO-OFDM系统信道盲估计算法。提出了利用递归子空间跟踪算法跟踪信号子空间的方法,每次得到一个相对最大的奇异值及其对应的信号空间向量。此方法可有效降低批处理计算复杂度。
克服非高斯噪声的影响,基于粒子滤波的MIMO-OFDM信道估计。把信道模型建模为随时间演化的状态模型和与状态相关的观测模型。此种结构,对于线性、高斯估计问题,卡尔曼滤波给出了最优解;对于非线性、非高斯问题,很难求得其解的解析形式,为此,人们提出各种非线性滤波算法来求解非线性、非高斯问题,粒子滤波器,是目前用来解决非线性问题的有效方法。采用变步长方法对重采样后的粒子分布进行优化调整,由此跟踪MIMO-OFDM信道变化,,增强系统信号检测能力。
Without introducing any additional bandwidth, MIMO technology can multiply communication system capacity, improve link performance, and increase data throughput of the network. MIMO systems include several mutually independent channels between transmitter and the receiver, and to some degree multipath components of transmission can be used to overcome multipath fading, but the frequency selective fading processing capability is still limited.
OFDM system can equivalently divide frequency selective fading channel into several flat fading channels, which can not only create favorable conditions for applications on MIMO in multipath fading channel, but also considerably simplify channel equalization. Based on the assumptions, many domestic and abroad researchers and research organizations have put emphasis on the studies on the combination of MIMO and OFDM technologies in order to achieve higher spectrum utilization efficiency. MIMO-OFDM is a new combination technique of OFDM with MIMO, which uses antenna arrays to realize space diversity and to improve the quality of transmission.
This paper carries out the research of channel estimation based on MIMIO-OFDM , and the main work is as following:
1. It reviews present conditions of development of wireless and mobile communication systems and introduces their technical features and typical technology of the first generation, the second generation and the third generation (3G) mobile communications are introduced in this paper. Then it discusses the history and current situation of the MIMO systems and OFDM, and provides the perspective of the integration of MIMO and OFDM.
2. It discusses the theory of OFDM and channel model of MIMO, and introduces OFDM technology, MIMO wireless communication system model, and MIMO-OFDM channel estimation technology in detail, and which are based on the following chapters.
3. It discusses the channel estimation method based on pilots symbol for MIMO-OFDM systems, and studies that transmitting terminal at regular time intervals plug known pilot into the proper position of OFDM symbol, pilot symbol and data symbol together are received by receiving antenna, and by pilot symbol receiving terminal extract channel response at pilot symbol position, then makes interpolation operation by these position information and obtains channel information for the whole period.
4. It studies subspace blind channel estimation, for the plugged training sequence or pilot inevitably occupy bandwidth and affect transmitting efficiency of communication systems are affected, and reduce the system effective bandwidth utilization. How to use other observed value including all unkown symbols neglected based on pilot channel estimation algorithm besides pilot symbol to compute channel estimation in order to improve the efficiency of communication system.
5. It studies channel estimation based on Bayesian theory, in many problems , required to use observed value with noise to filter and estimate on systems status varying with the time,people often adopt state space method to simulated dynamic system. Bayesian filter theory provide a common frame for dynamic filtering and estimation, and Kalman filter gives the optimal solution for linear and gaussian approximation.But for Non-linear, non-Gaussian problems, it is usually difficult to have analytical form of its solution. So that, all kinds of non-linear filter algorithm are provided. One is extended kalman filter algorithm, which makes a local linearization to a nonlinear system, so Kalman filter can be utilized indirectly to filter and estimate. The other is sequence Monte Carlo algorithm ,that is Particle Filter ,it is the effective methods for non-linear problem s recently appeared .The paper provide a kind of particle filter to realize to channel estimation of MIMO-OFDM system.
Main innovations are as follows:
1. The channel estimation algorithm based on pilot that adopts equal peak power pilot signal has been proposed. And the dsigned pilot transforms the problem of multiple antennas signal channel estimation into signal antenna situation. Singular value decomposition simplifies computation of inverse matrix in MMSE, and LMMSE algorithm will attain rough estimtion which is as an initial value of the estimation. Then by reducing pilot number and carrying on iterative calculation of the aim function, the proposed algorithm can get more accurate channel estimation.
2. In the (semi-)blind channel estimation algorithms in MIMO-OFDM system, the singular value decomposition(SVD) is a useful technique. In this paper, we adopts a adaptive algorithm to estimate the SVD of channel matrix in MIMO-OFDM system, which can fast tracking the signal and noise subspace without eigenvalue decomposition or singular value decomposition. The performance of this method is similar to the source one, but computational complexity is reduction.
3. In the practical wireless communication environment, such as the indoor condition or the city environment, there are a mass of non-Gaussian noise. Aiming at the nonlinear/non-Gaussian, the sequential monte carlo particle filter based on Bayesian theorem is proved to be an effective method. In particle filter, the particle degeneration is a key issue to affect the performance of the algorithm. In this paper, we adopt a variable step-size method to regulate the distribution of the resample particle. The simulation result shows that the proposed method can trace the MIMO-OFDM channel variation which affected by the non-Gaussian noise.
引文
[1]中国邮电百科全书.电信卷[M].北京:人民邮电出版社, 1993.
[2]周炯槃.通信原理[M].北京:人民邮电出版社, 2002.
[3]裴昌幸译.移动通信原理[M].北京:电子工业出版社, 2003.
[4]曹志刚,钱亚生.现代通信原理[M].北京:清华大学出版社, 199.
[5]倪维桢.数字电话通信原理[M].北京:人民邮电出版社, 1982.
[6]蔡安妮等.多媒体通信技术[M].北京:电子工业出版社, 2001.
[7]雷振明.现代电信交换基础[M].北京:人民邮电出版社, 1995.
[8]周炯槃.信源编码理论[M].北京:人民邮电出版社, 1996.
[9]樊昌信.通信原理教程[M].北京:电子工业出版社, 2004.
[10]佟学俭,罗涛. OFDM移动通信技术原理与应用[M].北京:人民邮电出版社, 2003.
[11] Traian Abrudan, Marius Sirbu and Visa Koivunen. Blind multi-user receiver for MIMO-OFDM systems [C], 2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications, 2003, 363-367.
[12] Robert J. Piechocki, Andrew R. Nix and Joe P. McGeehan. Joint semi-blind detection and channel estimation in space-frequency trellis coded MIMO-OFDM [C]. Personal Mobile Communications Conference, April 2003, issue. 22, pp. 101-105.
[13] Myung-Sun Baek, Mi-Jeong Kim, Young-Hwan You, and Hyoung-Kyu Song. Semi-blind channel estimation and PAR reduction for MIMO-OFDM system with Multiple Antennas [J]. IEEE Transactions on Broadcasting, Dec. 2004, 50 (4): 414- 424.
[14] Brain P. Crowe, Indra Widjaja et al. IEEE 802.11 wireless LAN [J]. IEEE Comm. Mag, Sept. 1997, 116-126.
[15] ETSI TR 101 683 (V1.1.1): Broadband Radio Access Networks (BRAN); HIPERLAN Type2; System Overview.
[16] R.van Nee, G.Awater, M.Morikura, H.Takanashi, M. Webster, and K.W.Halford. New high-rate wireless LAN standards [J]. IEEE Commun. Mag., December 1999.82-88.
[17] Y. (G.) Li and N. R. Sollenberger. Adaptive antenna array for OFDM with cochannel interference [J]. IEEE Trans. Commun., Feb. 1999, 47(2):217-229.
[18]汪裕民.OFDM关键技术与应用[M] .北京:机械工业出版社, 2007.
[19] A. Peled and A. Ruiz, Frequency domain data transmission using reduced computational complexity algorithms[C], Proc ICASSP, 1980, 964-967.
[20] Bondyopadhyay P K. The first application of array antenna[C]. in Proceeding of IEEE International conference on Phased Array Systems and Technology. Dana Point, USA. 2000, 29-32.
[21] Winters J H. On the capacity of radio communication systems with diversity in a Rayleigh fadingenvironment [J]. IEEE Trans. Commun., 1987, 5 (5).
[22] Winters J H, Salz J and Gitlin R D. The impact of antenna diversity of wireless communication systems [J]. IEEE Trans. Commun., 1994, 5 (42): 1740-1751.
[23] A. Paulraj and T. Kailath, Increasing capacity in wireless broadcast systems using distributed transmission/directional reception [P], US Patent 5345599, 1994.
[24] I. E. Telatar. Capacity of multi-antenna gaussian channels. Technical report AT&T Bell Laboratories Internal Technical Memorandum, June 1995.
[25] G. J. Foschini. Layered space-time architecture for wireless communication in fading environment when using multiple antennas [J]. Bell Labs Technical Journal, Aug. 1996, pp.41-59.
[26] V. Tarokh, H. Jafarkhani and A. R. Calderbank. Space-time block code from orthogonal designs [J]. IEEE Trans. on Communications, 1999, 4 (5):1456-1467.
[27] S. M. Alamouti. A simple transmit diversity technique for wireless communications [J]. IEEE Journal on Communications, Oct.1999, vol.16, 1451-1458.
[28] V. Tarokh, N. Seshadri and A. R. Calderbank. Space-time codes for high data rate wireless communication: performance criterion and code construction [J]. IEEE Trans. on Commun, 1998, vol.44:744-765.
[29] V. Tarokh, H. Jafarkhani and A. R. Calderbank. Space-time block coding for wireless communications: performance result[J]. IEEE Journal on selected areas in communications, 1999, vol.17: 451-460.
[30] P. W. Wolniansky, G. J. Foschini, G. D. Golden and R. A. Valenzuela. V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel [C]. Proc. IEEE ISSSE, Pisa, Italy, Sept. 1998.
[31] Sellathurai, M. and Haykin, S. Turbo-blast for wireless communication: first experimental result [J]. IEEE Trans. On vehicular Technology,.May 2003,vol.53(3) : 530-545
[32] Lau K N, Chen T A Liu Y. On the design of MIMO block fading channels with feedback-link capacity constraint [J]. IEEE Trans. Commun, vol.52(1):.62-702004
[33] R. W. Chang. Synthesis of band-limited orthogonal signals for multichannel data transmission [J]. Bell System Tech. Journal, Dec. 1966, vol.45: 1775-1796.
[34] S. B. Weinsten and P. M. Ebert. Data transmission in frequency division multiplexing using the discrete Fourier transform [J]. IEEE Trans. Commun., Oct. 1971, vol.3 (5).
[35] Y. Xie and C. N. Georghiades. Two EM-type channel estimation algorithms for OFDM with transmitter diversity [J]. IEEE Trans. Commun, Jan. 2003, vol.51 (1): 106-115.
[36] B. Lu, X. Wang and Y. (G.) Li. Iterative receivers for space-time block coded OFDM systems in dispersive fading channels [J]. IEEE Trans. Wireless Commun., Apr. 2002, vol.1 (2): 213-225.
[37] Chunyan Gao, Ming Zhao, Shidong Zhou and Yan Yao. Blind channel estimation algorithm for MIMO-OFDM systems. Electronics Letters, 2003, vol. 39 (19): 1420-1422.
[38] Wei Bai, Chen He, Ling-ge Jiang, and Hong-wen Zhu. Blind channel estimation in MIMO-OFDM systems [C]. In Proc.2002 IEEE Vehicle Conf., 2003, 317-321.
[39] Jiang Du, Qicong Peng, Yubei Li. Adaptive blind equalization for MIMO-OFDM wireless communication systems [C]. Proceedings of ICCT2003, Beijing, China, April 2003, 1455-1478.
[40] Myung-Sun Baek, Mi-Jeong Kim. Young-Hwan You, and Hyoung-Kyu Song, Semi-blind channel estimation and PAR reduction for MIMO-OFDM system with multiple antennas [J]. IEEE Transactions on Broadcasting, Dec. 2004, vol. 50(4): 414- 424.
[41] Jiang Yue, Kyeong Jin Kim, Tony Reid, Jerry D. Gibson. Joint semi-blind channel estimation and data detection for MIMO-OFDM systems [C]. IEEE 6th CAS Symp. on Emerging Technologies: Mobile and Wireless Comm., 2004, 709-712.
[42] Kyeong Jin Kim, Tony Reid and A Ronald. Data detection and soft-kalman filter based semi-blind channel estimation algorithms for MIMO-OFDM systems [C]. Proc. IEEE International Conference on Communications (ICC’05), May 2005, vol. 4: 2488–2492.
[43] Hongyi Fu, Patrick H.W. Fung and Sumei Sun. Semi-blind channel estimation for MIMO-OFDM[C]. 15th IEEE International Symposium on Personal Indoor and Mobile Radio Communications, Sept. 2004, Vol. 3(5), 1850-1854.
[44] Z. Liu, G. Giannakis, S. Barbarosa, and A. Scaglione. Transmit-antennae space-time block coding for generalized OFDM in the presence of unknown multipath [J]. IEEE Journal on selected areas in Communications, Jul. 2001, vol. 19(7), 1352–1364.
[45] S. Zhou, B. Muquet, and G. Giannakis. Subspace based blind channel estimation for block precoded space time OFDM [J]. IEEE Trans. Signal Process, May 2002, vol. 50(5):1215–1228.
[46] Georgios B. Giannakis, Yingbo Hua, Petre Stoica and Lang Tong. Signal Processing Advances in Wireless and Mobile Communications [M]. Prentice Hall PTR, 2001.
[47] Jones F, Wilkinson T A. Blocking coding scheme for reduction of peak to mean envelope power ratio of multicarrier transmission scheme. IEE Electronics Letters, 1994,(30): 2098-2099
[48] Richard Van Nee. OFDM codes for peak-to-average pover reduction and error correction [C]. Global Telecommunications Conference 2003: 740-744.
[49] Van Eetvelt P, Wade G and Tomlinson M. Peak to Average Power Reduction for OFDM Schemes by Selective Scrambling. In Proc,1997 AUVSI,1997.
[50] Muller S H, Bauml R W, Fisher R F H and Huber J B. OFDM with reduced Peak-to average power ratio by multiple signal representation [J]. Annals of Telecommunications, 1997, vol.52 (1):58-67.
[51] R. Negi and J. Cioffi. Pilot tone selection for channel estimation in a mobile OFDM system [J]. IEEE Trans. on Communication, Electron, Aug. 1998, vol.44:1122-1128.
[52] Frieder Sanzi and Joachim Speidel. An adaptive two-dimensional channel estimation for wireless OFDM with application to mobile DVB-T [J]. IEEE Trans. on Broadcasting, June 2000, vol.46 (2):128-133.
[53] Che-Shen Yeh, Yinyi Lin. Channel estimation using pilot tones in OFDM systems [J]. IEEE Transactions on Broadcasting, 1999, vol. 45 (4): 400-409.
[54] B.Muquet and M. de Courville. Blind and semi-blind channel identification methods using second order statistics for OFDM systems. in Proc. IEEE Int. Conf. Acoust. Speech, Signal Processing.Mar. 1999, vol.3:2745-2748.
[55] X. Cai and A. N. Akansu. A subspace method for blind channel identification in OFDM systems [C]. in Proc. ICC, New Orleans, LA, Jul. 2000,929-933.
[56] B. Muquet, M. de Courville, P. Duhamel. Subspace-based blind and semi-blind channel estimation for OFDM systems [J]. IEEE Trans. on Signal Processing, Jul. 2002, vol. 50 (7):1699-1712.
[57] R. W. Heath and G. B. Giannakis. Exploiting input cyclostationarity for blind channel identification in OFDM systems [J]. IEEE Trans. On Signal Processing, Mar. 1999, vol.47 (3)3:.848-856.
[58] S. Zhou, B. Muquet and G. B. Ciannakis. Subspace-based semi-blind channel estimation for block precoded space-time OFDM [J]. IEEE Trans. On Signal Processing, May 2002, vol.50 (5): 1215-1228.
[59] C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch. Multicarrier OFDM with adaptive subcarrier, bit, and power allocation [J]. IEEE Journal on Selected Areas in Communications, Oct 1999, vol.17(10):1563-1582.
[60] W. Rhee and J. M. Cioffi. Increase in capacity of multiuser. OFDM system using dynamic subchannel allocation[C]. in Proc IEEE International Vehicular Technology Conference, May 2000 , vol.2:1085-1089.
[61] C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch. Multicarrier OFDM with adaptive subcarrier, bit, and power allocation [J]. IEEE Journal on Selected Areas in Communications Oct 1999, vol.17 (10) 1563-1582.
[62] T. M. Cover and J. A. Thoma. Elements of information Theory[M], Wiley, New York, NY 1991
[63] J. Jang and J. M. Cioffi. Transmit power adaptation for multiuser OFDM system [J]. IEEE Journal on Selected Areas in Communications, Feb 1999, vol.21 (2):171-178.
[64] W. Rhee and J. M. Cioffi. Increase in capacity of multiuser. OFDM system using dynamic subchannel allocation[C]. in Proc IEEE International Vehicular Technology Conference, May 2000, vol.2: 1085-1089.
[65] Zukang Shen, Jeffery G. Andrews and Brian L. Evans. Optimal power allocation in multiuser OFDM systems[C]. IEEE GLOBECOM. 2003.
[66] Zukang Shen, Jeffery G. Andrews and Brian L. Evans. Adaptive resource allocation in multiuser OFDM systems with proportional fairness [J]. IEEE Transaction on Wireless Communications. October 2004.
[67] Y. Qiao, S. Yu, P. Su, and L. Zhang. Research on an iIterative algorithm of LS channel estimation in MIMO OFDM systems [J]. IEEE Trans. on Broadcasting, March 2005, vol. 51(1): 149-153.
[68] Honglei Miao and Markku J. Juntti. Space-Time channel estimation and performance Analysis for wireless MIMO-OFDM systems with spatial correlation [J]. IEEE Trans. on Vehicular Technology, November 2005, vol. 54 (6): 2003-2016.
[69] Die Hu, Luxi Yang, Yuhui Shi and Lianghua He. Optimal pilot sequence design for channel estimation in MIMO OFDM systems. IEEE Communications Letters, January 2006, vol. 10 (1):1-3.
[70] A. K. Jagannatham and B. D. Rao. Whitening-rotation-based semi-blind MIMO channel estimation [J]. IEEE Trans. on Signal Processing, March 2006 vol. 54 (3): 861-869.
[71] S. Yatawatta and A. P. Petropulu. Blind channel estimation in MIMO OFDM systems with multiuser interference [J]. IEEE Trans. on Signal Processing Mar. 2006, vol. 4 (3): 1054-1068.
[72] B.Hassibi and B.M.Hochwald. How much training is needed in multiple-antenna wireless links[J]. IEEE Trans.inf.Theory, Apr. 2003, vol 49 (4): 951-963.
[73] L.Zheng and D.N.C.Tse. Communication on the grassmann manifold: A geometric approach to the noncoherent multiple-antenna channe l[J]. IEEE Trans.inf.Theory, Feb.2002, vol 48 (2):359-383.
[74] S.A.Feechtel and H.Meyr. Optimal parametric feedforward estimation of frequency-selective fading radio channels [J]. IEEE Trans. on Communications, Feb.1994, vol.42(2):1639-1650.
[75] X.Ma, G.B.Giannakis,and S.Ohno. Optimal training for block transmission over doubly-selective wireless fading channels [J]. IEEE Trans. on Signal Processing, May, 2003, vol.51 (5):1350-1366.
[76] S.Ohno and G.B. Giannakis. Optimal training and redundant precoding for block transmissions with application to wireless OFDM [J]. IEEE Trans.Commun, Dec. 2002, vol. 50 (12):2113-2123.
[77] H.Vikalo, B.Hassibi, B.Hochwald and T.Kailath. Optimal training for frequency-selective fading channels [C]. in Proc.Int.Conf.ASSP,Salt Lake City, UT, May 7-11, 2001, vol.4:2105-2108.
[78] H.Bolcskei, R.W.Heath Jr.and A.J.Paulraj. Blind channel identification and equalization OFDM- based multi-antenna systems [J]. IEEE Trans.on Signal Processing, Jan.2002, vol.50 (1): 96-109.
[79] Z.Liu, G.B. Giannakis,Barbarossa, and A.Scaglione. Transmit-antennae space-time block coding for generalized OFDM in the presence of unknown multipath [J]. IEEE J.Selected Areas on Communications, July.2001, vol.19 (7):1352-1364.
[80] S.Zhou, B.Muquet, and G.B. Giannakis. Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM [J]. IEEE Trans.on Signal Processing, May.2002, vol.50: 1215-1228.
[81] Myung-Sun Baek, Mi-Jeong Kim, Young-Hwan You, and Hyoung-Kyu Song, Semi-Blind Channel Estimation and PAR Reduction for MIMO-OFDM System with Multiple Antennas [J], IEEE Transactions on Broadcasting, Dec. 2004, vol. 50(4) : 414- 424.
[82] Jiang Yue, Kyeong Jin Kim, Tony Reid, Jerry D. Gibson. Joint semi-blind channel estimation and data detection for MIMO-OFDM systems [C]. IEEE 6th CAS Symp. on Emerging Technologies: Mobile and Wireless Comm, 2004, 709-712.
[83] Kyeong Jin Kim, Tony Reid and Ronald A. Iltis. Data detection and soft-kalman filter based semi-Blind channel estimation algorithms for MIMO-OFDM systems[C]. Proc. IEEE International Conference on Communications (ICC’05), May 2005, vol. 4:2488–2492.
[84] Yonghong Zeng, W. H. Lam and Tung Sang Ng. Semiblind channel estimation and equalization for MIMO space-time coded OFDM [J]. IEEE Transactions on Circuits and SystemsFeb. 2006, vol. 53(3): 463-474.
[85] Yi Hu and Philipos C. loizou. A generalized subspace approach for enhancing speech corrupted by colored Noise [J]. IEEE Trans. On Speech and Audio Processing, April, 2003, vol. 11:334-340.
[86] S. Zhou, B. Muquet, and G. Giannakis. Subspace based blind channel estimation for blockprecoded space time OFDM [J]. IEEE Trans. on Signal Processing, May 2002, vol. 50(5):1215–1228.
[87] Z.Shengli,M.Bertrand,B.G.Georgios.Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM[J].IEEE Trans. on Signal Processing,2002:1215-1227.
[88] Rousseaus O, Leus G, Stoica P. Gaussian maximum- like lihood channel estimation with short training sequences [J]. IEEE Transactions on Wireless Communications, 2005, vol. 4 (6): 2945- 2955.
[89] Scharf L L. Statistical signal processing: detection, estimation and time series anslysis [M].New York Addision-Wesley,1991.
[90] Ye Li, Seshadri N, Ariyavisitakul S. Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels [J]. IEEE Journal on Selected areas in Communications, March 1999, Vol. 17 (3): 461-471.
[91] Mody A N,Stuber G L. Parameter Estimation for OFDM With Transmit Receive Diversity [C]. IEEE Vehicular Technology Conference, Rhodes, Greece, 2001.
[92] Siew J, et al. A channel estimation method for MIMO-OFDM systems [C]. London Communication Symposium. London,2002.
[93] Hassibi B, Hochwald B. High-rate codes that are linear in space and time [J].IEEE Trans. on Information Theory, 2002, vol.48 (7):1804-1824.
[94]罗仁泽.新一代无线移动通信系统关键技术[M] .北京:北京邮电出版社,2007.
[95]康桂华. MIMO无线通信原理及应用[M] .北京:电子工业出版社,2009.
[96] Hassibi B, Hochwald B M.How much training is needed in multiple antenna wireless links [J].IEEE Trans on Information Theory, 2003, vol.49 (4):951-963.
[97] S.Haykin. Adaptive Filter Theory [M].Englewood Cliffs, NJ Prentice-Hall, 1986.
[98] C.L.Liu and K.Feher. Polot symbol aided coherent M-ary PSK in frequency-selective fast rayleigh fading channel [J] IEEE Trans. on Communications, 1994, vol.42 (1):54-62.
[99] W.C.Lo, K. B. Letaief. Adaptive equalization and TCM for wireless communication systems [J]. IEEE JSAC, Dec. 1998, vol.16:1679-1690.
[100] Michele Moreli and Umberto Mengali. A comparison of pilot-aided channel estimation methods for OFDM systems [J]. IEEE Trans on Signal Processing Dec 2001, vol.49 (2):3065-3073.
[101] J rine and M Renfors. Pilot spacing in OFDM systems in practical channel [J]. IEEE Trans.on Consumer Electronics, Nov. 1996, vol.12: 959-962.
[102] C W Farrow. A contiously variable digital delay element [C]. IEICE International Symposium Cicuites and systems, June 1988, 2641-2645.
[103] Coleri S, Ergen M,Puri A ,Bahai A. Channel estimation techniques based on pilot arrangement in OFDM systems [J]. IEEE Trans. on Communications, Sept 2002, vol.48 (3):223-229.
[104] Imad Barhumi, Geert leus, Macrc Mooden. Optimal training design for MIMO-OFDM systems in mobile wireless channels [J]. IEEE Trans on Signal Processing 2003, vol.51 (6): 1651-1624.
[105] Van Duc Nguye, Matthias Patzolc. Least square channel estimation using special training sequences for MIMO-OFDM systems. in the presence of intersymbol interference [J]. IEEE Trans. o on Communications, Sept. 2004, vol.48 (3):233-239.
[106] Wen-Rong Wu; Chao-Yuan Hsu. A Low complexity LMMSE channel estimation for OFDM systems [C]. Circuits and Systems, 2004. Proceedings. The 2004 IEEE Asia-Pacific Conference, Dec. 2004, vol. 2: 689–692.
[107] K. Y. Ho, S. H. Leung. A Generalized semi-blind channel estimation for pilot-aided OFDM systems [C] IEEE, Circuits and Systems, ISCAS 2005. 2005, vol.6 (5): 6086-6089.
[108]周洁.基于LMMSE的OFDM系统半盲信道估计算法[D].吉林:吉林大学,2007.
[109] D.N.Godard. Self-recovering equalization and carrier tracking in two-dimensional data communication systems [J], IEEE Trans.Communications, Nov. 1980, vol.28 (11): 1867-1875.
[110] Moguez,J. and Castedo, L. Semiblind space-time decoding in wireless communications: A maximum likelihood approach [J]. Signal Processing, 2002.8, vol.2 (1):1-18.
[111] Swindlehurst, A.L., and Leus, G. Blind and semi-blind equalization for generalized space-time block codes [J]. IEEE Trans on Signal Processing, 2002, vol.50 (10): 2489-2498.
[112] S. Zhou, B. Muquet, and G. B. Giannakis. Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM [J]. IEEE Trans on Signal Processing, 2002, vol.50 (5): 1215–1228.
[113] S. Yatawatta and A. P. Petropulu. Blind channel estimation in MIMO-OFDM systems [C]. In: Proc IEEE Workshop Statistical Signal Process. St. Louis, MO. 2003. 363–366.
[114] C. Shin, R. W. Heath, Jr., and E. J. Powers. Blind channel estimation for MIMO-OFDM systems [J]. IEEE Trans Vehicular Technology, 2007, 5, vol.6 (2): 670–685.
[115] B. Champagne. Adaptive eigendecomposition of data covariance matrices based on first-order perturbations [J]. IEEE Trans Signal Processing, 1994, vol.42: 2758-2770.
[116] K. B. Yu. Recursive updating the eigenvalue decomposition of a covariance matrix [J]. IEEE Trans Signal Processing, 1991, vol.39: 1136-1145.
[117]王刚. MIMO-OFDM宽带无线通信系统信道估计技术研究[D].吉林:中国科学院长春光学精密机械与物理研究所. 2010.
[118] Yang B .Projection approximation subspace tracking [J]. IEEE Trans on Signal Processing, 1995, vol.43 (1): 95-107.
[119] Abed Meraim K, Chheif A, Hua Y. Fast orthonormal PAST algorithm. IEEE Signal Processing letters, 2000, vol.7 (3) 60-62.
[120]董玥昕.基于子空间的改进STBC-MIMO-OFDM系统半盲信道估计算法[D].吉林:吉林大学. 2007.
[121] Z.Liu and G.B.Giannakis, Space-time block coded multiple access through frequency-selective fading channels [J]. IEEE Trans.Communications, June 2001, vol.49: 1033-1044.
[122] Z.Liu, G.B.Giannakis, B.Muquet, and S.Zhou, Space-time coding for broadband wirelesscommunications, Wireless Communications and Mobile Computing [J]. New York: Wiley, Jan-Mar.2001, vol.1: 33-53.
[123] S.Zhou, B.Muquet and G.B.Giannakis, Subspace-based (semi-)blind channel estimation for block precoded space-time OFDM [J]. IEEE Trans. on Signal Processing, May 2002 ,vol.50 (5): 1215-1228.
[124] Guodong Zhang. Subcarrier and bit allocation for real-time services in multiuser OFDM systems communications [C]. IEEE GOLBECOM, Jun 2004, vol.5 52985-2989.
[125] Bin Yang. Projection approximation subspace tracking [J], IEEE Trans on Signal Processing, Jan. 1995, vol.43 (1):95-107.
[126]杜正聪,唐斌,刘立新.粒子滤波的MIMO-FODM空频分层检测算法电子科技大学学报,2008, vol.37 (2):194-197.
[127] Wang X, Poor V. Robust multiuser detection in non-gaussian channel [J]. IEEE Trans Signal Processing, 1999, vol.4 (1): 289-305.
[128] Feifei Gao and A. Nallanathan. Subspace-based blind channel estimation for SISO, MISO and MIMO OFDM systems [C]. Int.Conf. Communication (ICC), Istanbul, Turkey, Jun, 2006.
[129] G.L.Stuber. Principles of Mobile Communication [M].Kluwer Academic Publishers.1996.
[130] Lu B and Wang X. Space-time code design in OFDM systems [C]. IEEE Telecommunications Conference, San Francisco,2000,vol.1: 1000-1004
[131] Verdu S. Multiuser Detection [M].Cambridge, MA: Cambridge University Press,1998.
[132] Stoica P and Ganesan G. Maximum SNR space-time designs for MIMO channels [C].IEEE Interational Conference on Acoustics, speech, and signal processing, 2001, vol. 4:2425-2428.
[133] Yan Q and Blum R S. Optimum space-time convolutional codes [C]. IEEE wireless communications and networking conference, 2000, vol.3 1351-1355.
[134] Jankiraman M. Space-time codes and MIMO systems [M]. Boston, London: Artech house,Inc.,2004.
[135] P. M. Djuric, J. H. Kotecha, Jiangqui Zhang, Yufei Huang, T. Ghirmai, M. F. Bugallo, J. Miguez. Particle filtering [M]. IEEE Signal Processing Magazie, Sept. 2003, Vol. 20(5):19-38.
[136] T K Y Lo. Maximum ratio transmission [J]. IEEE Trans. on Communication, Oct 1999,vol.47 (10):1458-1461
[137] D.Schafhuber, M.Rupp, G.Matz, F.Hlawatsch. Adaptive identification and tracking of doubly selective fading channels for wireless MIMO-OFDM systems[C], 4th IEEE Workshop on SPAWC, June 2003:417-421.
[138] Zhiqiang Liu, Xiaoli Ma, G.B.Giannakis. Space-time coding and kalman filtering for time-varying channels in wireless MIMO-OFDM systems [C]. 37th Asilomar Coference on Signals, Systems and Computer, Nov 2003, vol.2:1261-1265.
[139] K.Myers, B.Tapley. Adaptive sequential estimation with unknown noise statistics [J]. IEEE Trans on Automatic Control, Aug, 1976. vol.21 (4): 520-523.
[140] D.Schafhuber, G. Matz, F. Hlawatsch. Kalman tracking of time-varying channels in wirelessMIMO-OFDM systems [J]. IEEE Signal Processing Magazie, Sept. 2003, vol. 20 (9):19-38.
[141] M.S.Arulampalam,S . Maskell, N.Gordon and T.Clapp. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J], IEEE Trans. on Signal Processing, Feb, 2002, vol. 50 (2): 174–188.
[142] Y.Boers and J.N.Driessen, Particle filter based detection for tracking [C], in Proc. Amer. Contr, Conf, 1999, vol.6:1-6.
[143] P.Boyle, M.Broadie, and P.Glasserman Monte Carlo methods for security pricing [J]. Economic Dynamics and control, 1998, vol.3: 1267-1321.
[144] D.Brigo, B.Hanzon, and F.LeGland. A differential geometric approach to nonlinear filtering: the projection filter [J]. IEEE Trans Automate, Contr., 1998, vol.43 (2):247-252.
[145] W.L. Bruntine and A.S. Weigend, Bayesian back-propagation [J]. Complex Syst., 1991, vol.5: 603 -643.
[146] R.S.Bucy. Linear and nonlinear filtering[j]. Systems and Computer, Nov 2003, vol.2:1261-1265.
[147] R.S.Bucy and H. Youssef Nonlinear filter representation via spline functions [C]. in Proc.5th Symp.Nonlinear Estimation,1974, 51-60.
[148] J.carpenter, P.Clifford and P.Fearnhead. Improved Particle filter for nonlinear problems [C].IEE Proc.-f radar, sonar Naving, 1999, vol.146 (1):2-7.
[149] N.Chopin A sequential particle filter method for static models [J], BiometrikaAug.1991, vol.89 (3): 539-552.
[150] D.Crisan and A. Doucet. A survey of convergence results on particle filtering methods for practioners [J]. IEEE Trans. on Signal Processing, 2002, vol 50 (3):736-746.
[151] Yonghong Zeng, W. H. Lam and Tung Sang Ng. Semiblind channel estimation and equalization for MIMO space-Time coded OFDM [J]. IEEE Transactions on Circuits and Systems, Feb. 2006, vol. 53 (2): 463-474.
[152] D. Schafhuber, M.Rupp, G. Matz, F. Hlawatsch. Adaptive identification and tracking of doubly selective fading channels for wireless MIMO-OFDM systems [C], 4th IEEE Workshop on SPAWC, June 2003:417-421.
[153]景源,殷福亮,曾硕,基于粒子滤波的MIMO-OFDM时变信道半盲估计.通信学报. 2007, 28, (8): 67-75.
[2]周炯槃.通信原理[M].北京:人民邮电出版社, 2002.
[3]裴昌幸译.移动通信原理[M].北京:电子工业出版社, 2003.
[4]曹志刚,钱亚生.现代通信原理[M].北京:清华大学出版社, 199.
[5]倪维桢.数字电话通信原理[M].北京:人民邮电出版社, 1982.
[6]蔡安妮等.多媒体通信技术[M].北京:电子工业出版社, 2001.
[7]雷振明.现代电信交换基础[M].北京:人民邮电出版社, 1995.
[8]周炯槃.信源编码理论[M].北京:人民邮电出版社, 1996.
[9]樊昌信.通信原理教程[M].北京:电子工业出版社, 2004.
[10]佟学俭,罗涛. OFDM移动通信技术原理与应用[M].北京:人民邮电出版社, 2003.
[11] Traian Abrudan, Marius Sirbu and Visa Koivunen. Blind multi-user receiver for MIMO-OFDM systems [C], 2003 4th IEEE Workshop on Signal Processing Advances in Wireless Communications, 2003, 363-367.
[12] Robert J. Piechocki, Andrew R. Nix and Joe P. McGeehan. Joint semi-blind detection and channel estimation in space-frequency trellis coded MIMO-OFDM [C]. Personal Mobile Communications Conference, April 2003, issue. 22, pp. 101-105.
[13] Myung-Sun Baek, Mi-Jeong Kim, Young-Hwan You, and Hyoung-Kyu Song. Semi-blind channel estimation and PAR reduction for MIMO-OFDM system with Multiple Antennas [J]. IEEE Transactions on Broadcasting, Dec. 2004, 50 (4): 414- 424.
[14] Brain P. Crowe, Indra Widjaja et al. IEEE 802.11 wireless LAN [J]. IEEE Comm. Mag, Sept. 1997, 116-126.
[15] ETSI TR 101 683 (V1.1.1): Broadband Radio Access Networks (BRAN); HIPERLAN Type2; System Overview.
[16] R.van Nee, G.Awater, M.Morikura, H.Takanashi, M. Webster, and K.W.Halford. New high-rate wireless LAN standards [J]. IEEE Commun. Mag., December 1999.82-88.
[17] Y. (G.) Li and N. R. Sollenberger. Adaptive antenna array for OFDM with cochannel interference [J]. IEEE Trans. Commun., Feb. 1999, 47(2):217-229.
[18]汪裕民.OFDM关键技术与应用[M] .北京:机械工业出版社, 2007.
[19] A. Peled and A. Ruiz, Frequency domain data transmission using reduced computational complexity algorithms[C], Proc ICASSP, 1980, 964-967.
[20] Bondyopadhyay P K. The first application of array antenna[C]. in Proceeding of IEEE International conference on Phased Array Systems and Technology. Dana Point, USA. 2000, 29-32.
[21] Winters J H. On the capacity of radio communication systems with diversity in a Rayleigh fadingenvironment [J]. IEEE Trans. Commun., 1987, 5 (5).
[22] Winters J H, Salz J and Gitlin R D. The impact of antenna diversity of wireless communication systems [J]. IEEE Trans. Commun., 1994, 5 (42): 1740-1751.
[23] A. Paulraj and T. Kailath, Increasing capacity in wireless broadcast systems using distributed transmission/directional reception [P], US Patent 5345599, 1994.
[24] I. E. Telatar. Capacity of multi-antenna gaussian channels. Technical report AT&T Bell Laboratories Internal Technical Memorandum, June 1995.
[25] G. J. Foschini. Layered space-time architecture for wireless communication in fading environment when using multiple antennas [J]. Bell Labs Technical Journal, Aug. 1996, pp.41-59.
[26] V. Tarokh, H. Jafarkhani and A. R. Calderbank. Space-time block code from orthogonal designs [J]. IEEE Trans. on Communications, 1999, 4 (5):1456-1467.
[27] S. M. Alamouti. A simple transmit diversity technique for wireless communications [J]. IEEE Journal on Communications, Oct.1999, vol.16, 1451-1458.
[28] V. Tarokh, N. Seshadri and A. R. Calderbank. Space-time codes for high data rate wireless communication: performance criterion and code construction [J]. IEEE Trans. on Commun, 1998, vol.44:744-765.
[29] V. Tarokh, H. Jafarkhani and A. R. Calderbank. Space-time block coding for wireless communications: performance result[J]. IEEE Journal on selected areas in communications, 1999, vol.17: 451-460.
[30] P. W. Wolniansky, G. J. Foschini, G. D. Golden and R. A. Valenzuela. V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel [C]. Proc. IEEE ISSSE, Pisa, Italy, Sept. 1998.
[31] Sellathurai, M. and Haykin, S. Turbo-blast for wireless communication: first experimental result [J]. IEEE Trans. On vehicular Technology,.May 2003,vol.53(3) : 530-545
[32] Lau K N, Chen T A Liu Y. On the design of MIMO block fading channels with feedback-link capacity constraint [J]. IEEE Trans. Commun, vol.52(1):.62-702004
[33] R. W. Chang. Synthesis of band-limited orthogonal signals for multichannel data transmission [J]. Bell System Tech. Journal, Dec. 1966, vol.45: 1775-1796.
[34] S. B. Weinsten and P. M. Ebert. Data transmission in frequency division multiplexing using the discrete Fourier transform [J]. IEEE Trans. Commun., Oct. 1971, vol.3 (5).
[35] Y. Xie and C. N. Georghiades. Two EM-type channel estimation algorithms for OFDM with transmitter diversity [J]. IEEE Trans. Commun, Jan. 2003, vol.51 (1): 106-115.
[36] B. Lu, X. Wang and Y. (G.) Li. Iterative receivers for space-time block coded OFDM systems in dispersive fading channels [J]. IEEE Trans. Wireless Commun., Apr. 2002, vol.1 (2): 213-225.
[37] Chunyan Gao, Ming Zhao, Shidong Zhou and Yan Yao. Blind channel estimation algorithm for MIMO-OFDM systems. Electronics Letters, 2003, vol. 39 (19): 1420-1422.
[38] Wei Bai, Chen He, Ling-ge Jiang, and Hong-wen Zhu. Blind channel estimation in MIMO-OFDM systems [C]. In Proc.2002 IEEE Vehicle Conf., 2003, 317-321.
[39] Jiang Du, Qicong Peng, Yubei Li. Adaptive blind equalization for MIMO-OFDM wireless communication systems [C]. Proceedings of ICCT2003, Beijing, China, April 2003, 1455-1478.
[40] Myung-Sun Baek, Mi-Jeong Kim. Young-Hwan You, and Hyoung-Kyu Song, Semi-blind channel estimation and PAR reduction for MIMO-OFDM system with multiple antennas [J]. IEEE Transactions on Broadcasting, Dec. 2004, vol. 50(4): 414- 424.
[41] Jiang Yue, Kyeong Jin Kim, Tony Reid, Jerry D. Gibson. Joint semi-blind channel estimation and data detection for MIMO-OFDM systems [C]. IEEE 6th CAS Symp. on Emerging Technologies: Mobile and Wireless Comm., 2004, 709-712.
[42] Kyeong Jin Kim, Tony Reid and A Ronald. Data detection and soft-kalman filter based semi-blind channel estimation algorithms for MIMO-OFDM systems [C]. Proc. IEEE International Conference on Communications (ICC’05), May 2005, vol. 4: 2488–2492.
[43] Hongyi Fu, Patrick H.W. Fung and Sumei Sun. Semi-blind channel estimation for MIMO-OFDM[C]. 15th IEEE International Symposium on Personal Indoor and Mobile Radio Communications, Sept. 2004, Vol. 3(5), 1850-1854.
[44] Z. Liu, G. Giannakis, S. Barbarosa, and A. Scaglione. Transmit-antennae space-time block coding for generalized OFDM in the presence of unknown multipath [J]. IEEE Journal on selected areas in Communications, Jul. 2001, vol. 19(7), 1352–1364.
[45] S. Zhou, B. Muquet, and G. Giannakis. Subspace based blind channel estimation for block precoded space time OFDM [J]. IEEE Trans. Signal Process, May 2002, vol. 50(5):1215–1228.
[46] Georgios B. Giannakis, Yingbo Hua, Petre Stoica and Lang Tong. Signal Processing Advances in Wireless and Mobile Communications [M]. Prentice Hall PTR, 2001.
[47] Jones F, Wilkinson T A. Blocking coding scheme for reduction of peak to mean envelope power ratio of multicarrier transmission scheme. IEE Electronics Letters, 1994,(30): 2098-2099
[48] Richard Van Nee. OFDM codes for peak-to-average pover reduction and error correction [C]. Global Telecommunications Conference 2003: 740-744.
[49] Van Eetvelt P, Wade G and Tomlinson M. Peak to Average Power Reduction for OFDM Schemes by Selective Scrambling. In Proc,1997 AUVSI,1997.
[50] Muller S H, Bauml R W, Fisher R F H and Huber J B. OFDM with reduced Peak-to average power ratio by multiple signal representation [J]. Annals of Telecommunications, 1997, vol.52 (1):58-67.
[51] R. Negi and J. Cioffi. Pilot tone selection for channel estimation in a mobile OFDM system [J]. IEEE Trans. on Communication, Electron, Aug. 1998, vol.44:1122-1128.
[52] Frieder Sanzi and Joachim Speidel. An adaptive two-dimensional channel estimation for wireless OFDM with application to mobile DVB-T [J]. IEEE Trans. on Broadcasting, June 2000, vol.46 (2):128-133.
[53] Che-Shen Yeh, Yinyi Lin. Channel estimation using pilot tones in OFDM systems [J]. IEEE Transactions on Broadcasting, 1999, vol. 45 (4): 400-409.
[54] B.Muquet and M. de Courville. Blind and semi-blind channel identification methods using second order statistics for OFDM systems. in Proc. IEEE Int. Conf. Acoust. Speech, Signal Processing.Mar. 1999, vol.3:2745-2748.
[55] X. Cai and A. N. Akansu. A subspace method for blind channel identification in OFDM systems [C]. in Proc. ICC, New Orleans, LA, Jul. 2000,929-933.
[56] B. Muquet, M. de Courville, P. Duhamel. Subspace-based blind and semi-blind channel estimation for OFDM systems [J]. IEEE Trans. on Signal Processing, Jul. 2002, vol. 50 (7):1699-1712.
[57] R. W. Heath and G. B. Giannakis. Exploiting input cyclostationarity for blind channel identification in OFDM systems [J]. IEEE Trans. On Signal Processing, Mar. 1999, vol.47 (3)3:.848-856.
[58] S. Zhou, B. Muquet and G. B. Ciannakis. Subspace-based semi-blind channel estimation for block precoded space-time OFDM [J]. IEEE Trans. On Signal Processing, May 2002, vol.50 (5): 1215-1228.
[59] C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch. Multicarrier OFDM with adaptive subcarrier, bit, and power allocation [J]. IEEE Journal on Selected Areas in Communications, Oct 1999, vol.17(10):1563-1582.
[60] W. Rhee and J. M. Cioffi. Increase in capacity of multiuser. OFDM system using dynamic subchannel allocation[C]. in Proc IEEE International Vehicular Technology Conference, May 2000 , vol.2:1085-1089.
[61] C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch. Multicarrier OFDM with adaptive subcarrier, bit, and power allocation [J]. IEEE Journal on Selected Areas in Communications Oct 1999, vol.17 (10) 1563-1582.
[62] T. M. Cover and J. A. Thoma. Elements of information Theory[M], Wiley, New York, NY 1991
[63] J. Jang and J. M. Cioffi. Transmit power adaptation for multiuser OFDM system [J]. IEEE Journal on Selected Areas in Communications, Feb 1999, vol.21 (2):171-178.
[64] W. Rhee and J. M. Cioffi. Increase in capacity of multiuser. OFDM system using dynamic subchannel allocation[C]. in Proc IEEE International Vehicular Technology Conference, May 2000, vol.2: 1085-1089.
[65] Zukang Shen, Jeffery G. Andrews and Brian L. Evans. Optimal power allocation in multiuser OFDM systems[C]. IEEE GLOBECOM. 2003.
[66] Zukang Shen, Jeffery G. Andrews and Brian L. Evans. Adaptive resource allocation in multiuser OFDM systems with proportional fairness [J]. IEEE Transaction on Wireless Communications. October 2004.
[67] Y. Qiao, S. Yu, P. Su, and L. Zhang. Research on an iIterative algorithm of LS channel estimation in MIMO OFDM systems [J]. IEEE Trans. on Broadcasting, March 2005, vol. 51(1): 149-153.
[68] Honglei Miao and Markku J. Juntti. Space-Time channel estimation and performance Analysis for wireless MIMO-OFDM systems with spatial correlation [J]. IEEE Trans. on Vehicular Technology, November 2005, vol. 54 (6): 2003-2016.
[69] Die Hu, Luxi Yang, Yuhui Shi and Lianghua He. Optimal pilot sequence design for channel estimation in MIMO OFDM systems. IEEE Communications Letters, January 2006, vol. 10 (1):1-3.
[70] A. K. Jagannatham and B. D. Rao. Whitening-rotation-based semi-blind MIMO channel estimation [J]. IEEE Trans. on Signal Processing, March 2006 vol. 54 (3): 861-869.
[71] S. Yatawatta and A. P. Petropulu. Blind channel estimation in MIMO OFDM systems with multiuser interference [J]. IEEE Trans. on Signal Processing Mar. 2006, vol. 4 (3): 1054-1068.
[72] B.Hassibi and B.M.Hochwald. How much training is needed in multiple-antenna wireless links[J]. IEEE Trans.inf.Theory, Apr. 2003, vol 49 (4): 951-963.
[73] L.Zheng and D.N.C.Tse. Communication on the grassmann manifold: A geometric approach to the noncoherent multiple-antenna channe l[J]. IEEE Trans.inf.Theory, Feb.2002, vol 48 (2):359-383.
[74] S.A.Feechtel and H.Meyr. Optimal parametric feedforward estimation of frequency-selective fading radio channels [J]. IEEE Trans. on Communications, Feb.1994, vol.42(2):1639-1650.
[75] X.Ma, G.B.Giannakis,and S.Ohno. Optimal training for block transmission over doubly-selective wireless fading channels [J]. IEEE Trans. on Signal Processing, May, 2003, vol.51 (5):1350-1366.
[76] S.Ohno and G.B. Giannakis. Optimal training and redundant precoding for block transmissions with application to wireless OFDM [J]. IEEE Trans.Commun, Dec. 2002, vol. 50 (12):2113-2123.
[77] H.Vikalo, B.Hassibi, B.Hochwald and T.Kailath. Optimal training for frequency-selective fading channels [C]. in Proc.Int.Conf.ASSP,Salt Lake City, UT, May 7-11, 2001, vol.4:2105-2108.
[78] H.Bolcskei, R.W.Heath Jr.and A.J.Paulraj. Blind channel identification and equalization OFDM- based multi-antenna systems [J]. IEEE Trans.on Signal Processing, Jan.2002, vol.50 (1): 96-109.
[79] Z.Liu, G.B. Giannakis,Barbarossa, and A.Scaglione. Transmit-antennae space-time block coding for generalized OFDM in the presence of unknown multipath [J]. IEEE J.Selected Areas on Communications, July.2001, vol.19 (7):1352-1364.
[80] S.Zhou, B.Muquet, and G.B. Giannakis. Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM [J]. IEEE Trans.on Signal Processing, May.2002, vol.50: 1215-1228.
[81] Myung-Sun Baek, Mi-Jeong Kim, Young-Hwan You, and Hyoung-Kyu Song, Semi-Blind Channel Estimation and PAR Reduction for MIMO-OFDM System with Multiple Antennas [J], IEEE Transactions on Broadcasting, Dec. 2004, vol. 50(4) : 414- 424.
[82] Jiang Yue, Kyeong Jin Kim, Tony Reid, Jerry D. Gibson. Joint semi-blind channel estimation and data detection for MIMO-OFDM systems [C]. IEEE 6th CAS Symp. on Emerging Technologies: Mobile and Wireless Comm, 2004, 709-712.
[83] Kyeong Jin Kim, Tony Reid and Ronald A. Iltis. Data detection and soft-kalman filter based semi-Blind channel estimation algorithms for MIMO-OFDM systems[C]. Proc. IEEE International Conference on Communications (ICC’05), May 2005, vol. 4:2488–2492.
[84] Yonghong Zeng, W. H. Lam and Tung Sang Ng. Semiblind channel estimation and equalization for MIMO space-time coded OFDM [J]. IEEE Transactions on Circuits and SystemsFeb. 2006, vol. 53(3): 463-474.
[85] Yi Hu and Philipos C. loizou. A generalized subspace approach for enhancing speech corrupted by colored Noise [J]. IEEE Trans. On Speech and Audio Processing, April, 2003, vol. 11:334-340.
[86] S. Zhou, B. Muquet, and G. Giannakis. Subspace based blind channel estimation for blockprecoded space time OFDM [J]. IEEE Trans. on Signal Processing, May 2002, vol. 50(5):1215–1228.
[87] Z.Shengli,M.Bertrand,B.G.Georgios.Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM[J].IEEE Trans. on Signal Processing,2002:1215-1227.
[88] Rousseaus O, Leus G, Stoica P. Gaussian maximum- like lihood channel estimation with short training sequences [J]. IEEE Transactions on Wireless Communications, 2005, vol. 4 (6): 2945- 2955.
[89] Scharf L L. Statistical signal processing: detection, estimation and time series anslysis [M].New York Addision-Wesley,1991.
[90] Ye Li, Seshadri N, Ariyavisitakul S. Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels [J]. IEEE Journal on Selected areas in Communications, March 1999, Vol. 17 (3): 461-471.
[91] Mody A N,Stuber G L. Parameter Estimation for OFDM With Transmit Receive Diversity [C]. IEEE Vehicular Technology Conference, Rhodes, Greece, 2001.
[92] Siew J, et al. A channel estimation method for MIMO-OFDM systems [C]. London Communication Symposium. London,2002.
[93] Hassibi B, Hochwald B. High-rate codes that are linear in space and time [J].IEEE Trans. on Information Theory, 2002, vol.48 (7):1804-1824.
[94]罗仁泽.新一代无线移动通信系统关键技术[M] .北京:北京邮电出版社,2007.
[95]康桂华. MIMO无线通信原理及应用[M] .北京:电子工业出版社,2009.
[96] Hassibi B, Hochwald B M.How much training is needed in multiple antenna wireless links [J].IEEE Trans on Information Theory, 2003, vol.49 (4):951-963.
[97] S.Haykin. Adaptive Filter Theory [M].Englewood Cliffs, NJ Prentice-Hall, 1986.
[98] C.L.Liu and K.Feher. Polot symbol aided coherent M-ary PSK in frequency-selective fast rayleigh fading channel [J] IEEE Trans. on Communications, 1994, vol.42 (1):54-62.
[99] W.C.Lo, K. B. Letaief. Adaptive equalization and TCM for wireless communication systems [J]. IEEE JSAC, Dec. 1998, vol.16:1679-1690.
[100] Michele Moreli and Umberto Mengali. A comparison of pilot-aided channel estimation methods for OFDM systems [J]. IEEE Trans on Signal Processing Dec 2001, vol.49 (2):3065-3073.
[101] J rine and M Renfors. Pilot spacing in OFDM systems in practical channel [J]. IEEE Trans.on Consumer Electronics, Nov. 1996, vol.12: 959-962.
[102] C W Farrow. A contiously variable digital delay element [C]. IEICE International Symposium Cicuites and systems, June 1988, 2641-2645.
[103] Coleri S, Ergen M,Puri A ,Bahai A. Channel estimation techniques based on pilot arrangement in OFDM systems [J]. IEEE Trans. on Communications, Sept 2002, vol.48 (3):223-229.
[104] Imad Barhumi, Geert leus, Macrc Mooden. Optimal training design for MIMO-OFDM systems in mobile wireless channels [J]. IEEE Trans on Signal Processing 2003, vol.51 (6): 1651-1624.
[105] Van Duc Nguye, Matthias Patzolc. Least square channel estimation using special training sequences for MIMO-OFDM systems. in the presence of intersymbol interference [J]. IEEE Trans. o on Communications, Sept. 2004, vol.48 (3):233-239.
[106] Wen-Rong Wu; Chao-Yuan Hsu. A Low complexity LMMSE channel estimation for OFDM systems [C]. Circuits and Systems, 2004. Proceedings. The 2004 IEEE Asia-Pacific Conference, Dec. 2004, vol. 2: 689–692.
[107] K. Y. Ho, S. H. Leung. A Generalized semi-blind channel estimation for pilot-aided OFDM systems [C] IEEE, Circuits and Systems, ISCAS 2005. 2005, vol.6 (5): 6086-6089.
[108]周洁.基于LMMSE的OFDM系统半盲信道估计算法[D].吉林:吉林大学,2007.
[109] D.N.Godard. Self-recovering equalization and carrier tracking in two-dimensional data communication systems [J], IEEE Trans.Communications, Nov. 1980, vol.28 (11): 1867-1875.
[110] Moguez,J. and Castedo, L. Semiblind space-time decoding in wireless communications: A maximum likelihood approach [J]. Signal Processing, 2002.8, vol.2 (1):1-18.
[111] Swindlehurst, A.L., and Leus, G. Blind and semi-blind equalization for generalized space-time block codes [J]. IEEE Trans on Signal Processing, 2002, vol.50 (10): 2489-2498.
[112] S. Zhou, B. Muquet, and G. B. Giannakis. Subspace-based (semi-) blind channel estimation for block precoded space-time OFDM [J]. IEEE Trans on Signal Processing, 2002, vol.50 (5): 1215–1228.
[113] S. Yatawatta and A. P. Petropulu. Blind channel estimation in MIMO-OFDM systems [C]. In: Proc IEEE Workshop Statistical Signal Process. St. Louis, MO. 2003. 363–366.
[114] C. Shin, R. W. Heath, Jr., and E. J. Powers. Blind channel estimation for MIMO-OFDM systems [J]. IEEE Trans Vehicular Technology, 2007, 5, vol.6 (2): 670–685.
[115] B. Champagne. Adaptive eigendecomposition of data covariance matrices based on first-order perturbations [J]. IEEE Trans Signal Processing, 1994, vol.42: 2758-2770.
[116] K. B. Yu. Recursive updating the eigenvalue decomposition of a covariance matrix [J]. IEEE Trans Signal Processing, 1991, vol.39: 1136-1145.
[117]王刚. MIMO-OFDM宽带无线通信系统信道估计技术研究[D].吉林:中国科学院长春光学精密机械与物理研究所. 2010.
[118] Yang B .Projection approximation subspace tracking [J]. IEEE Trans on Signal Processing, 1995, vol.43 (1): 95-107.
[119] Abed Meraim K, Chheif A, Hua Y. Fast orthonormal PAST algorithm. IEEE Signal Processing letters, 2000, vol.7 (3) 60-62.
[120]董玥昕.基于子空间的改进STBC-MIMO-OFDM系统半盲信道估计算法[D].吉林:吉林大学. 2007.
[121] Z.Liu and G.B.Giannakis, Space-time block coded multiple access through frequency-selective fading channels [J]. IEEE Trans.Communications, June 2001, vol.49: 1033-1044.
[122] Z.Liu, G.B.Giannakis, B.Muquet, and S.Zhou, Space-time coding for broadband wirelesscommunications, Wireless Communications and Mobile Computing [J]. New York: Wiley, Jan-Mar.2001, vol.1: 33-53.
[123] S.Zhou, B.Muquet and G.B.Giannakis, Subspace-based (semi-)blind channel estimation for block precoded space-time OFDM [J]. IEEE Trans. on Signal Processing, May 2002 ,vol.50 (5): 1215-1228.
[124] Guodong Zhang. Subcarrier and bit allocation for real-time services in multiuser OFDM systems communications [C]. IEEE GOLBECOM, Jun 2004, vol.5 52985-2989.
[125] Bin Yang. Projection approximation subspace tracking [J], IEEE Trans on Signal Processing, Jan. 1995, vol.43 (1):95-107.
[126]杜正聪,唐斌,刘立新.粒子滤波的MIMO-FODM空频分层检测算法电子科技大学学报,2008, vol.37 (2):194-197.
[127] Wang X, Poor V. Robust multiuser detection in non-gaussian channel [J]. IEEE Trans Signal Processing, 1999, vol.4 (1): 289-305.
[128] Feifei Gao and A. Nallanathan. Subspace-based blind channel estimation for SISO, MISO and MIMO OFDM systems [C]. Int.Conf. Communication (ICC), Istanbul, Turkey, Jun, 2006.
[129] G.L.Stuber. Principles of Mobile Communication [M].Kluwer Academic Publishers.1996.
[130] Lu B and Wang X. Space-time code design in OFDM systems [C]. IEEE Telecommunications Conference, San Francisco,2000,vol.1: 1000-1004
[131] Verdu S. Multiuser Detection [M].Cambridge, MA: Cambridge University Press,1998.
[132] Stoica P and Ganesan G. Maximum SNR space-time designs for MIMO channels [C].IEEE Interational Conference on Acoustics, speech, and signal processing, 2001, vol. 4:2425-2428.
[133] Yan Q and Blum R S. Optimum space-time convolutional codes [C]. IEEE wireless communications and networking conference, 2000, vol.3 1351-1355.
[134] Jankiraman M. Space-time codes and MIMO systems [M]. Boston, London: Artech house,Inc.,2004.
[135] P. M. Djuric, J. H. Kotecha, Jiangqui Zhang, Yufei Huang, T. Ghirmai, M. F. Bugallo, J. Miguez. Particle filtering [M]. IEEE Signal Processing Magazie, Sept. 2003, Vol. 20(5):19-38.
[136] T K Y Lo. Maximum ratio transmission [J]. IEEE Trans. on Communication, Oct 1999,vol.47 (10):1458-1461
[137] D.Schafhuber, M.Rupp, G.Matz, F.Hlawatsch. Adaptive identification and tracking of doubly selective fading channels for wireless MIMO-OFDM systems[C], 4th IEEE Workshop on SPAWC, June 2003:417-421.
[138] Zhiqiang Liu, Xiaoli Ma, G.B.Giannakis. Space-time coding and kalman filtering for time-varying channels in wireless MIMO-OFDM systems [C]. 37th Asilomar Coference on Signals, Systems and Computer, Nov 2003, vol.2:1261-1265.
[139] K.Myers, B.Tapley. Adaptive sequential estimation with unknown noise statistics [J]. IEEE Trans on Automatic Control, Aug, 1976. vol.21 (4): 520-523.
[140] D.Schafhuber, G. Matz, F. Hlawatsch. Kalman tracking of time-varying channels in wirelessMIMO-OFDM systems [J]. IEEE Signal Processing Magazie, Sept. 2003, vol. 20 (9):19-38.
[141] M.S.Arulampalam,S . Maskell, N.Gordon and T.Clapp. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J], IEEE Trans. on Signal Processing, Feb, 2002, vol. 50 (2): 174–188.
[142] Y.Boers and J.N.Driessen, Particle filter based detection for tracking [C], in Proc. Amer. Contr, Conf, 1999, vol.6:1-6.
[143] P.Boyle, M.Broadie, and P.Glasserman Monte Carlo methods for security pricing [J]. Economic Dynamics and control, 1998, vol.3: 1267-1321.
[144] D.Brigo, B.Hanzon, and F.LeGland. A differential geometric approach to nonlinear filtering: the projection filter [J]. IEEE Trans Automate, Contr., 1998, vol.43 (2):247-252.
[145] W.L. Bruntine and A.S. Weigend, Bayesian back-propagation [J]. Complex Syst., 1991, vol.5: 603 -643.
[146] R.S.Bucy. Linear and nonlinear filtering[j]. Systems and Computer, Nov 2003, vol.2:1261-1265.
[147] R.S.Bucy and H. Youssef Nonlinear filter representation via spline functions [C]. in Proc.5th Symp.Nonlinear Estimation,1974, 51-60.
[148] J.carpenter, P.Clifford and P.Fearnhead. Improved Particle filter for nonlinear problems [C].IEE Proc.-f radar, sonar Naving, 1999, vol.146 (1):2-7.
[149] N.Chopin A sequential particle filter method for static models [J], BiometrikaAug.1991, vol.89 (3): 539-552.
[150] D.Crisan and A. Doucet. A survey of convergence results on particle filtering methods for practioners [J]. IEEE Trans. on Signal Processing, 2002, vol 50 (3):736-746.
[151] Yonghong Zeng, W. H. Lam and Tung Sang Ng. Semiblind channel estimation and equalization for MIMO space-Time coded OFDM [J]. IEEE Transactions on Circuits and Systems, Feb. 2006, vol. 53 (2): 463-474.
[152] D. Schafhuber, M.Rupp, G. Matz, F. Hlawatsch. Adaptive identification and tracking of doubly selective fading channels for wireless MIMO-OFDM systems [C], 4th IEEE Workshop on SPAWC, June 2003:417-421.
[153]景源,殷福亮,曾硕,基于粒子滤波的MIMO-OFDM时变信道半盲估计.通信学报. 2007, 28, (8): 67-75.