多天线系统信道估计与信号检测技术研究
|
摘要
本论文主要研究无线多天线系统的信道估计与信号检测问题。多天线的应用模式主要包括了空间分集(如空时码)、空间复用(如V-BLAST系统)等。文章对这两类问题都进行了研究,提出了新的改进并与原算法进行了比较。迭代方法可以有效提高算法性能,因此在本文集中研究了迭代算法及联合信道估计和信号检测算法在不同多天线应用模式下的实现及算法改进。文章最后还研究了在没有训练信号情况下的盲估计和均衡问题。研究内容主要包括:
1、研究了一些最基本的V-BLAST系统的连续干扰消除(SIC)检测算法,包括了基于迫零和最小均方误差准则的连续干扰消除技术,基于QR分解的算法等。文中首先给出对系统模型和现有算法的描述和分析,针对连续干扰消除算法中存在的误差传递问题,然后提出一种在QR分解算法基础上,基于混合SIC和并行干扰消除(PIC)的改进算法。通过仿真比较和复杂度分析,对各种算法的性能和实现复杂度做了对比。
2、研究高性能的基于后验概率的MIMO检测算法,其主要包含两个问题,一是如何利用软信息,二是如何实现最大后验所需的有效搜索算法。文中首先给出了软输入软输出接收机系统中如何在解码器和检测器件通过软信息的交换来实现Turbo迭代。另外,基于后验概率的算法主要是要能够实现高效的符号路径搜索来最大化后验概率。因此文中给出了几个主要的搜索算法,包括列表球形译码算法、堆栈算法以及M算法。文中提出了对其中一些算法的改进或新算法。针对堆栈算法中原有算法在搜索过程中需要通过额外的辅助堆栈计算来得到搜索路径长度偏移量的问题,本文提出一种更为有效的在算法中考虑长度偏移量的方法,在性能和算法复杂度上都有所改善。另外对于在搜索类算法中复杂度最小的M算法,其性能有时在MIMO检测中不能令人满意的情况,本文讨论了将结合Soft-Viterbi算法的M算法应用于MIMO检测的情况。在最后通过仿真验证了所提算法的有效性。
3、研究了基于最小均方误差(MMSE)的MIMO检测算法。首先,我们依然先介绍了如何在MMSE MIMO检测中利用来自解码器的软信息,这与后验概率检测算法中的情况有所不同。基于最小均方误差的检测算法包含了线性最小均方误差检测(LMMSE)、非线性最小均方误差检测(NMMSE)以及各种干扰消除等技术,文中都对此进行了分析研究,并在此基础上提出了新的改进。首先,对基于NMMSE检测的GPDA算法进行了改进。引入了分组迭代GPDA算法,即将待检测信号先进行分组,组内用MAP检测,而其它分组信号作为干扰消去后残留干扰的叠加被近似为高斯噪声,并计算相应的匹配均值和匹配方差。这样一方面减少了干扰信号,一方面对组内的少量信号使用MAP算法提高了检测性能并控制了实现复杂度。通过调整分组大小,算法可以取得性能和复杂度的折中。在章节最后的实验部分对比了几种算法,验证了本文算法的较好性能。
4、研究了空时块码正交频分复用(STBC-OFDM)系统的联合迭代信道估计与信号检测问题。STBC的解码性能很大程度上受到信道估计精度的影响,而实际系统中由于训练信号有限或者时变信道环境导致信道估计通常有较大偏差。文章研究通过联合迭代信道估计与信号检测的方法来改善信道估计和STBC解码的性能。在现有文献中包含了基于EM算法和循环最小算法的迭代实现。而这些算法在解码环节通常是通过搜索来实现解码的,而本文中提出了一种解析求解的方法大大降低解码复杂度。在信道估计问题上,本文在时域信道估计中通过有效利用FFT和IFFT在时域和频域信道间转换避免了原有算法中为了降低复杂度而限制信号为恒模的问题。仿真中在等间隔插入导频的OFDM系统和前置训练信号的系统信道跟踪问题中验证了算法有效性。另外在利用信道解码输出的迭代算法中,本文提出利用解码输出的硬判决反馈实现迭代,仿真中性能接近已知信道参数的理想情况性能曲线。
5、研究了多天线系统的盲信道估计与均衡问题。本章给出了一种将子空间方法从单输入多输出扩展到多输入多输出系统的简洁的形式,并研究了有色信号输入下信道的辨识与均衡。多输入多输出系统盲均衡后会存在一个模糊矩阵,即变成一个信号瞬时混合的问题,文中通过联合对角化方法求解了该问题,得到了较为满意的结果。
In this dissertation, we research the problem of channel estimation and signal detection in wireless multiple-input-multiple-output (MIMO) system. The applications of MIMO technique include space diversity (such as space-time coding) or space multiplex (such as V-BLAST). We have studied two systems in this dissertation, and improved the existing algorithms and compared with them. Iterative processing can efficiently improve the performance of algorithm, and in this dissertation, we concern with the iterative algorithm and the joint estimation of channel and signal under different MIMO systems. Further more, we also studied the problem of blind estimation and equalization of MIMO systems without training symbols. The main contents in this dissertation include:
1、Sequential interference cancellation (SIC) algorithm for V-BLAST, including zero-forcing or MMSE based SIC and QR decomposition (QRD) based method, are studied. We have given the system model and description and analysis of these algorithms. To tackle the problem of error propagation, we propose an improved QRD based method which combines SIC and parallel interference cancellation (PIC). Advantages of the new algorithm have been verified by the simulations and complexity analysis.
2、A prior probability (APP) MIMO detection algorithms with error correct coding (ECC) are studied. The two problems of such detection are, how to explore the soft information from the decoder, and how to find the optimal sequence to achieve APP detection by efficient searching. In the dissertation, we show how to implement the Turbo iteration by exchanging soft information between detector and decoder in the soft-in-soft-out (SISO) receiver. To achieve high efficient searching algorithm for APP MIMO detector, some of the prevalent algorithm are given, which include the list sphere decoding (LSD), the list sequential algorithm (LISS) and the M algorithm. In addition, we have proposed an improved LISS algorithm and an method applying soft-Viterbi M algorithm (SOMA) to MIMO detection. The improved LISS algorithm performs stack searching with the length bias, but avoids the time-consuming auxiliary stack operation in the original LISS algorithm. Both of the improved LISS algorithm and the SOMA for MIMO detection are verified via the simulations.
3、The minimum mean square error (MMSE) based MIMO detector is also studied in this dissertation. At first, we still present how to explore soft information from decoder in MMSE based MIMO detector, which is different from the cases of the APP detector. In the relevant chapter, we have given and analyzed some MMSE based MIMO detecting techniques, such as linear MMSE (LMMSE) detector, nonlinear MMSE (NMMSE) detector, PIC/SIC methods and soft IC and hard IC algorithms. A new group-GPDA algorithm based on the NMMSE detection is proposed. Different from the original GPDA algorithm, new algorithm groups the signal to be detected and alternately perform MAP detection in each group, at the meantime, approximate the other groups as Gaussian noise with matched mean and matched variance. Such processing reduces the interference components and improves the performance by applying MAP detector to the group under detecting. By adjust the size of group, tradeoff between performance and complexity can be achieved. The performance improvement has been verified via the simulations.
4、Another important MIMO technique is space-time coding (STC). The performance of STC decoding is greatly affected by channel estimate error, which is hard to avoid in practice due to limit numbers of pilots in OFDM symbol or time varying channel. In the chapter, an iterative joint channel estimate and space-time block coding (STBC) algorithm is proposed, based on the analysis of existing iterative algorithm, such as the EM algorithm and the cyclic minimization algorithm. The new algorithm greatly improves the performance of STBC decoding and channel estimate but avoid the time-consuming searching operations in those existing STBC decoding methods. In addition, the time-domain channel estimate avoids the limitation on constant-modular signals of existing method, which is to reduce the complexity of channel estimate, by efficiently using the FFT and IFFT to convert the channel parameters between time domain and frequency domain. In the simulations, our method has been verified to be approaching the curve with ideal channel state information (CSI), in the case of OFDM system with evenly inserted pilots, and the case of time-varying channel.
5、Blind channel estimate and equalization for MIMO system is a problem to be studied in this dissertation. In the relevant chapter, we proposed a neat derivation for subspace based method for MIMO system. With this method, the problems of channel estimate and equalization with colored signal input have been studied. There exists an ambiguous matrix after the step of MIMO blind equalization. That is a problem of instantaneous mixture to be resolved. In our method, we explore the joint diagonalization to resolve it and the satisfied results have been gained.
1、研究了一些最基本的V-BLAST系统的连续干扰消除(SIC)检测算法,包括了基于迫零和最小均方误差准则的连续干扰消除技术,基于QR分解的算法等。文中首先给出对系统模型和现有算法的描述和分析,针对连续干扰消除算法中存在的误差传递问题,然后提出一种在QR分解算法基础上,基于混合SIC和并行干扰消除(PIC)的改进算法。通过仿真比较和复杂度分析,对各种算法的性能和实现复杂度做了对比。
2、研究高性能的基于后验概率的MIMO检测算法,其主要包含两个问题,一是如何利用软信息,二是如何实现最大后验所需的有效搜索算法。文中首先给出了软输入软输出接收机系统中如何在解码器和检测器件通过软信息的交换来实现Turbo迭代。另外,基于后验概率的算法主要是要能够实现高效的符号路径搜索来最大化后验概率。因此文中给出了几个主要的搜索算法,包括列表球形译码算法、堆栈算法以及M算法。文中提出了对其中一些算法的改进或新算法。针对堆栈算法中原有算法在搜索过程中需要通过额外的辅助堆栈计算来得到搜索路径长度偏移量的问题,本文提出一种更为有效的在算法中考虑长度偏移量的方法,在性能和算法复杂度上都有所改善。另外对于在搜索类算法中复杂度最小的M算法,其性能有时在MIMO检测中不能令人满意的情况,本文讨论了将结合Soft-Viterbi算法的M算法应用于MIMO检测的情况。在最后通过仿真验证了所提算法的有效性。
3、研究了基于最小均方误差(MMSE)的MIMO检测算法。首先,我们依然先介绍了如何在MMSE MIMO检测中利用来自解码器的软信息,这与后验概率检测算法中的情况有所不同。基于最小均方误差的检测算法包含了线性最小均方误差检测(LMMSE)、非线性最小均方误差检测(NMMSE)以及各种干扰消除等技术,文中都对此进行了分析研究,并在此基础上提出了新的改进。首先,对基于NMMSE检测的GPDA算法进行了改进。引入了分组迭代GPDA算法,即将待检测信号先进行分组,组内用MAP检测,而其它分组信号作为干扰消去后残留干扰的叠加被近似为高斯噪声,并计算相应的匹配均值和匹配方差。这样一方面减少了干扰信号,一方面对组内的少量信号使用MAP算法提高了检测性能并控制了实现复杂度。通过调整分组大小,算法可以取得性能和复杂度的折中。在章节最后的实验部分对比了几种算法,验证了本文算法的较好性能。
4、研究了空时块码正交频分复用(STBC-OFDM)系统的联合迭代信道估计与信号检测问题。STBC的解码性能很大程度上受到信道估计精度的影响,而实际系统中由于训练信号有限或者时变信道环境导致信道估计通常有较大偏差。文章研究通过联合迭代信道估计与信号检测的方法来改善信道估计和STBC解码的性能。在现有文献中包含了基于EM算法和循环最小算法的迭代实现。而这些算法在解码环节通常是通过搜索来实现解码的,而本文中提出了一种解析求解的方法大大降低解码复杂度。在信道估计问题上,本文在时域信道估计中通过有效利用FFT和IFFT在时域和频域信道间转换避免了原有算法中为了降低复杂度而限制信号为恒模的问题。仿真中在等间隔插入导频的OFDM系统和前置训练信号的系统信道跟踪问题中验证了算法有效性。另外在利用信道解码输出的迭代算法中,本文提出利用解码输出的硬判决反馈实现迭代,仿真中性能接近已知信道参数的理想情况性能曲线。
5、研究了多天线系统的盲信道估计与均衡问题。本章给出了一种将子空间方法从单输入多输出扩展到多输入多输出系统的简洁的形式,并研究了有色信号输入下信道的辨识与均衡。多输入多输出系统盲均衡后会存在一个模糊矩阵,即变成一个信号瞬时混合的问题,文中通过联合对角化方法求解了该问题,得到了较为满意的结果。
In this dissertation, we research the problem of channel estimation and signal detection in wireless multiple-input-multiple-output (MIMO) system. The applications of MIMO technique include space diversity (such as space-time coding) or space multiplex (such as V-BLAST). We have studied two systems in this dissertation, and improved the existing algorithms and compared with them. Iterative processing can efficiently improve the performance of algorithm, and in this dissertation, we concern with the iterative algorithm and the joint estimation of channel and signal under different MIMO systems. Further more, we also studied the problem of blind estimation and equalization of MIMO systems without training symbols. The main contents in this dissertation include:
1、Sequential interference cancellation (SIC) algorithm for V-BLAST, including zero-forcing or MMSE based SIC and QR decomposition (QRD) based method, are studied. We have given the system model and description and analysis of these algorithms. To tackle the problem of error propagation, we propose an improved QRD based method which combines SIC and parallel interference cancellation (PIC). Advantages of the new algorithm have been verified by the simulations and complexity analysis.
2、A prior probability (APP) MIMO detection algorithms with error correct coding (ECC) are studied. The two problems of such detection are, how to explore the soft information from the decoder, and how to find the optimal sequence to achieve APP detection by efficient searching. In the dissertation, we show how to implement the Turbo iteration by exchanging soft information between detector and decoder in the soft-in-soft-out (SISO) receiver. To achieve high efficient searching algorithm for APP MIMO detector, some of the prevalent algorithm are given, which include the list sphere decoding (LSD), the list sequential algorithm (LISS) and the M algorithm. In addition, we have proposed an improved LISS algorithm and an method applying soft-Viterbi M algorithm (SOMA) to MIMO detection. The improved LISS algorithm performs stack searching with the length bias, but avoids the time-consuming auxiliary stack operation in the original LISS algorithm. Both of the improved LISS algorithm and the SOMA for MIMO detection are verified via the simulations.
3、The minimum mean square error (MMSE) based MIMO detector is also studied in this dissertation. At first, we still present how to explore soft information from decoder in MMSE based MIMO detector, which is different from the cases of the APP detector. In the relevant chapter, we have given and analyzed some MMSE based MIMO detecting techniques, such as linear MMSE (LMMSE) detector, nonlinear MMSE (NMMSE) detector, PIC/SIC methods and soft IC and hard IC algorithms. A new group-GPDA algorithm based on the NMMSE detection is proposed. Different from the original GPDA algorithm, new algorithm groups the signal to be detected and alternately perform MAP detection in each group, at the meantime, approximate the other groups as Gaussian noise with matched mean and matched variance. Such processing reduces the interference components and improves the performance by applying MAP detector to the group under detecting. By adjust the size of group, tradeoff between performance and complexity can be achieved. The performance improvement has been verified via the simulations.
4、Another important MIMO technique is space-time coding (STC). The performance of STC decoding is greatly affected by channel estimate error, which is hard to avoid in practice due to limit numbers of pilots in OFDM symbol or time varying channel. In the chapter, an iterative joint channel estimate and space-time block coding (STBC) algorithm is proposed, based on the analysis of existing iterative algorithm, such as the EM algorithm and the cyclic minimization algorithm. The new algorithm greatly improves the performance of STBC decoding and channel estimate but avoid the time-consuming searching operations in those existing STBC decoding methods. In addition, the time-domain channel estimate avoids the limitation on constant-modular signals of existing method, which is to reduce the complexity of channel estimate, by efficiently using the FFT and IFFT to convert the channel parameters between time domain and frequency domain. In the simulations, our method has been verified to be approaching the curve with ideal channel state information (CSI), in the case of OFDM system with evenly inserted pilots, and the case of time-varying channel.
5、Blind channel estimate and equalization for MIMO system is a problem to be studied in this dissertation. In the relevant chapter, we proposed a neat derivation for subspace based method for MIMO system. With this method, the problems of channel estimate and equalization with colored signal input have been studied. There exists an ambiguous matrix after the step of MIMO blind equalization. That is a problem of instantaneous mixture to be resolved. In our method, we explore the joint diagonalization to resolve it and the satisfied results have been gained.
引文
[1] Rappapport T. S. Wireless Communications: Principles and Practice,Beijing: Publishing House of Electronics Industry, 1998.
[2] Bi Q., Zysman G. L., and Menkes H. Wireless mobile communication at the start of the 21st century, IEEE Commun. Mag., 2001, 39(1), pp.110 - 116.
[3] Berezdivin R., Breinig R., and Topp R.,Next-generation wireless communications concepts and technologies, IEEE Commun.Mag., 2002, 40(3), pp. 108-116.
[4] Lu W. W. Compact multidimensional broadband wireless: the convergence of wireless mobile and access, IEEE Commun.Mag., 2000, 38(11), pp119-123.
[5] Tachikawa K. A perspective on the evolution of mobile communications, IEEE Commun. Mag., 2003, 41(10), pp66-73.
[6] Chuang J., SolIenberger N., Beyond 3G:wideband wireless data access based on OFDM and dynamic packet assignment, IEEE Cormoun. Mag., 2 000, 38(7), pp78-87.
[7] Kim Y., Jeong B. J., Chung J., et al, Beyond 3G: vision, requirements, and enabling technologies, IEEE Commiun. Mag., 2 003, 41(3), pp.120-124.
[8] Zhang P., Tao X., Zhang J., et al., Avision from the future: beyond 3G TDD, IEEE Commun.Mag., 2005, 43(1) ,pp.38-44.
[9] Paulraj A. J. and Papadias C. B., Space-time processing for wireless communications, IEEE Signal Processing Mag., 1997, 14(11), pp.4 9-83.
[10] NaguibA .F ., SeshadriN ., andC alderbankA .R., Increasing data rate over wireless channels,, IEEE Signal Processing Mag., 2000, 17(5), pp76- 92.
[11] Gesbert D., Shafi M., Shiu D., Smith P. J., and Naguib A., From theory to prac tice: an overview of MIMO space-time coded wireless systems, IEEE J .Select Areas Commun., 2003, 21(4), pp. 281-302.
[12] Paulraj A. J., Gore D. A., Nabar R. U., and Balcskei H., An overview of MIMO Communications- a key to gigabit wireless, Proc.of the IEEE, 2004, 92(2), pp198-218.
[13] Foschini G. J. and Gans M. J.,On limits of wireless communications in a fading environment when using multiple antennas,Wireless Pers.Commun., 1998, 6(3), pp311-335.
[14] Telatar I. E., Capacity of multi-antenna Gaussian channels, European Trans. Telecomm., 1999, 10(11), pp585-595.
[15] Tarokh V., Seshadri N. and Calderbank A. R., Space-time codes for high data rate wireless communication: performance criterion and code consruction, IEEE Trans. Inform.Theory, 1998, 44(3), pp.744-765 .
[16] Alamouti S. M.,A simple transmit diversity technique for wireless communications, IEEE J. Select. Areas Commun., 1998, 16(10), pp.1451- 1458,.
[17] Tarokh V., Jafarkhani H., and Calderbank A. R., Space-time block codes from orthogonal designs, IEEE Trans.Inform.Theory, 1999, 45(7), pp1456-1467.
[18] Tarokh V., Jafarkhani H., and Calderbank A. R., Space-time block coding for wirelessc ommunications: performance results, IEEE J .Select. Areas Cormnun.,1999, 17(3), pp.451-460.
[19] Foschini G. J., Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas,, Bell Labs Tech. J., 1996, 1(8), pp.41-59.
[20] Einstein S.W and bert P .E, Datat ransmissionb yf requency-division multiplexing using the discrete Fourier transform, IEEE Trans.Comm., 1971, 19(10), pp. 628-634.
[21] Jr. Cimini L.,Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing, IEEE Trans.Comm., 1985, 33(7), pp.665-675.
[22] Bingham J.A.C.,Multicarrier modulation for data transmission: an idea whose time has come, IEEE Comm.Mag., May 1990, 28(5), pp.5-14.
[23] Zou W. Y. and Wu Y.,COFDM: an overview, IEEE Trans. Broadcasting, 1995,41(3), pp. 1-8.
[24] Wang Z. and Giannakis G. B., Wireless multicarrier communications, IEEE Signal Processing Mag., 2000, 17(5), pp. 29-48,
[25] Nee R. and Prasad R.,OFDM for wireless Multimedia Communications, Boston : Artech House, 2000.
[26] Sampath H.,Talwar S.,Tellado J.,Erceg V.,and Paulraj A., A fourth- generation MIMO-OFDM broadband wireless system: design, performance, and field trialresults, IEEE Commun.Mag., 2002, 40(9), pp. 143-149.
[27] Dubuc C., Starks D., Creasy T., and Hou Y., A MIMO-OFDM prototype for next-generation wireless WANs, IEEE Commun. Mag., 2004, 42(12), pp.82-87.
[28] Van Zelst A. and Schenk T. C. W.,Implementation of a MIMO OFDM-based wireless LAN system, IEEE Trans. Signal Processing, 2004, 52(2), pp.483- 494.
[29] StuberG .L., Barry J .R ., McLaughlinS .W.,LiY ., et .al, Broadband MIMO–OFDM wireless communications, Proc.of the IEEE, 2004, 92(2),pp.271- 294.
[30] Yang H ., A road to future broadband wireless access: MIMO-OFDM-based air interface, IEEE Commun. Mag., 2005, 43(1), pp.53-60.
[31] Al-Naffouri T. Y., Awoniyi 0., Oteri 0., and Paulraj A., Receiver design for MIMO-OFDM transmission over time variant channels,in Proc. IEEE GLOBECOM' 04, 2004, 4(12), pp.2487-2492.
[32] Fozunbal., McLauhlin S. and Schafer R. W., On performance limits of MIMO- OFDM systems over block-fading channels, in Proc. IEEE WCNC’04, 2004, 2(3), pp.976-980.
[33] Astély D., Dahlman E., Furusk?r A. et.al., LTE: The Evolution of Mobile Broadband, IEEE Communications Magazine ? April 2009, pp44-51
[34] E. Dahlman et al., 3G Evolution: HSPA and LTE for Mobile Broadband, 2nd ed., Academic Press, 2008.
[35] A. Larmo et al.,“The LTE Link Layer Design,”IEEE Commun. Mag., 2009, (4), pp52-59
[36] H. Ekstrom et al., Technical Solutions for the 3G Long-Term Evolution, IEEE Commun. Mag., vol. 44, no. 3, Mar. 2006, pp. 38–45.
[37] Ahmadi S.,An Overview of Next-Generation Mobile WiMAX Technology, IEEE Communications Magazine ? June 2009, pp84-98
[38] Wang F., Ghosh A., Sankaran C., et.al., Mobile WiMAX Systems: Performance and Evolution, IEEE Communications Mag. Oct. 2008, pp41-49
[39] F. Wang et al.,“WiMAX System Performance with Multiple Transmit and MultipleReceive Antennas,”IEEE VTC, Apr. 2007.
[40] C. Sankaran, A. Ghosh, and F. Wang,“Performance Study of a Fractional Frequency Reuse (FFR) Scheme for an 802.16e Based WiMAX System,”2008, preprint.
[41] IEEE 802.16-2004. IEEE standard for local and metropolitan area networks, Part 16: Air interface for fixed broadband wireless access systems [S]. 2004.
[42] CCSA WG6.新一代无线通信空中接口技术纲要[S]. V4.0. 2006.
[43] Host S, Johannesson R, Zyablov V, et al. A first encounter with binary Woven convolutional codes,Proceedings of International Symposium Communication Theory and Applications, Jul 1997, Lake District, UK. 1997,pp13-18
[44] Sheikh K.,Gesbert D.,Gore D.,and Paulraj A.,Smart antennas for broad band wireless access networks, IEEE Comm. Mag., vo l. 37, Nov. 1999.
[45] Bossert M., Huebner A., Schuehlein F., et.al., On cyclic delay diversity in OFDM based transmission schemes, in OFDM Workshop, 2002.
[46] J. Tan and G. Stuber, Multicarrier delay diversity modulation, in Proc. IEEE Globecom 2003.
[47] Dammann A., Kaiser S., Standard conformable antenna diversity techniques for OFDM and its application to DVB-T system, in Proc. IEEE GLOBECOM 2001, vol. 5, Nov. 2001, pp. 3100-3105.
[48] S. Alamouti, A simple transmit diversity technique for wireless comminications, IEEE J. Select. Areas Commun., vol. 16, pp.1451–1458, Oct. 1998.
[49] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, Space–time block codes from orthogonal designs, IEEE Trans. Inform. Theory, vol.45, pp. 1456–1467, July 1999.
[50] Guey J. C., Fitz M. P., Bell M. R., et.al.,Signal design for tr ansmitter diversity wireless communications systems over Rayleigh fadingc hannels,IEEE T rans. on Comm.,vol. 47,Apr. 999, pp. 527-537
[51] Baro S.,Bauch G.,and Hansman A., Improved codes for space-time trellis codes modulation,IEEE Commu. Lett., vol. 4, January 2000, pp.20-22.
[52] Ionescu D. M., On space-time code design,IEEE Transactions on Wireless Commu., vol.2 , Jan. 2003, pp.2 0-28.
[53] Hassibi B. and Hochwald B. M., High-Rate Codes That Are Linear in Space andTime, IEEE Trans. on infomation theory, vol.48, July 2002, pp1804-1824
[54] Heath R. W. and Paulraj A. J., Linear dispersion codes for MIMO systems based on frame theory,IEEE Trans. Signal Processing, vol.50 , Oct. 2002, pp.2429-2441.
[55] Foschini G. J., et al., Analysis and performance of some basic space-timearchitectures, IEEE Journal on Selected Areas in Comm., vol.21,Apr. 2003, pp303-320.
[56] Golden G. D., Foschini G. J., Valenzuela R. A., et.al. Detection algorithm and initial laboratory results using V–BLAST space-time communication architecture,Electronics Letters, vol.35,1999, pp.14-15
[57] Li Q., Lin X. T., Zhang J. Z. and Roh W., Advancement of MIMO Technology in WiMAX: From IEEE 802.16d/e/j to 802.16m, IEEE Communications Magazine, 2009, 6, pp100-107
[58] Berrou C. and Glavieux A., Near optimum error- correcting coding and decoding: Turbo codes, IEEE Trans. Commun., vol. 44, pp. 1261–1271, Oct. 1996.
[59] T¨uchler M., Koetter R., and Singer A. C., Turbo equalization: Principles and New Results, IEEE Trans. on Comm., vol. 50, May 2002, pp. 754–767.
[60] Koetter R., Singer A. C., and Michael Tüchler, Turbo equalization, IEEE signal processing Mag. Jan. 2004, pp67-80
[61] T¨uchler M., Singer A. C., and Koetter R., Minimun mean squared error equalization using a prior information, IEEE Trans. Signal Processing, vol.50, Mar.2002, pp673-683
[62] Caire G., Taricco G., and Biglieri E., Bit-Interleaved Coded Modulation, IEEE Trans. Info. Theory, vol.44, May 1998, pp927-946
[63] Wang Xiaodong, , and Vincent Poor H., Iterative (Turbo) Soft Interference Cancellation and Decoding for Coded CDMA, IEEE Trans. Comm., vol. 47, July 1999, pp1046-1061
[64] Gamal H. El and Geraniotis E., Iterative multiuser detection for coded CDMA signals in AWGN and fading channel, IEEE J. Select. Areas Comm., vol. 18, pp. 30–41, Jan. 2000.
[65] Lu B., ,Wang Xiaodong, and Li Ye (Geoffrey), Iterative Receivers for Space–Time Block-Coded OFDM Systems in Dispersive Fading Channels, IEEE Trans. Wireless Comm., vol. 1, Apr. 2002, pp213-225
[66] Stefanov A., and Duman T. M., Turbo-Coded Modulation for Systems with Transmitand Receive Antenna Diversity over Block Fading Channels: System Model, Decoding Approaches, and Practical Considerations, IEEE J. Select. Areas Comm., vol.19, May 2001, pp958-968
[67] Sellathurai M., and Haykin S., TURBO-BLAST for Wireless Communications: Theory and Experiments, IEEE Trans. Signal Processing, vol. 50, Oct. 2002, pp2538-2546
[68] Sellathurai M. and Haykin S., A nonlinear iterative beamforming technique for wireless communications, in 33rd Asilomar Conf. Signals, Syst., Comput., vol. 2, Pacific Grove, CA, Oct. 1999, pp. 957–961.
[69] Sellathurai M. and Haykin S.,“Asimplified diagonal BLAST architecture with iterative parallelinterferencecancellation,”in Proc. IEEE Int. Conf. Commun., vol. 10, June 2001,pp. 3067–3071.
[70] Sellathurai M. and Haykin S.,“Turbo-BLAST: A novel technique for multi-transmit multi-receivewireless communications,”in Multiaccess, Mobility, andTeletraffic for Wireless Communications. Boston, MA: Kluwer, 2000,vol. 5, pp. 13–24.
[71] Gamal H. El and Hammons A. R., New approach for space-time transmitter receiver design, in Proc. 37th Allerton Conf. Commun., Contr., Comput., Oct. 1999, pp. 186–194.
[72] Ariyavisitakul S. L., Turbo space-time processing to improve wireless channel capacity, IEEE Trans. Commun., vol. 48, Aug. 2000, pp. 1347–1359.
[73] Li X., Huang H., Foschini G. J., et.al., Effects of iterative detection and decoding on the performance of BLAST,”Proc. IEEE Globecom, vol. 1, pp. 1062–1066, Nov. 2000.
[74] Choi W., Cheong K., and Cioffi J. M., Iterative soft interference cancellation for multiple antenna system, in Proc.Wireless Commun. Network Conf., vol. 1, 2000, pp. 304–309.
[75] Gamal H. El and Hammons A. R., New approach to layered space-time coding and signal processing,”IEEE Trans. Inform. Theory, vol. 47, Sept. 2001, pp2321–2334.
[76] Mexia T. and Cheng S. K., Low complexity post-ordered iterative decoding for generalized layered space-time coding systems, in Proc. IEEE Int. Conf.Commun., vol. 4, June 2001, pp. 1137–1141.
[77] Baro S., Bauch G., Pavlic A., Improving BLAST performance using space-time block codes and turbo decoding, Proc. IEEE Globecom, vol. 2, Nov. 2000 pp1061–1066.
[78] Hochwald B, Brink S. Achieving Near-Capacity on a Multiple-Antenna Channel. IEEE Transactions on Comm., 2003 51(3), pp389-399
[79] Liu Jianhua, Li Jian. A soft detector with Good performance/complexity trrade-off for a MIMO system, EURA SIP Journal on Applied Signal Processing, 2004 (9), pp1266-1274
[80] Damen M O, Chkeif A, Belfiore J C., Lattice code decoder for space-time codes ,IEEE Communication Letters, 2000,4(5), pp161-163
[81] Agrell E, Eriksson T, Vardy A, Zeger K. Closest point search in lattices, IEEE Transaction on Information Theory, 2002,48(8), pp2201-2214
[82] Wang R ,Giannakis G. B., Approaching MIMO capacity with reduced-complexity softs phere-decoding. in Proc. WCNC, Atlanta, 2004, pp1620-1625
[83] Zhao W, Giannakis G B. Sphere decoding algorithms with improved radius search, IEEE Transactions on Communication, 2005, 53(7), pp1104-1109
[84] Damen M O, Gamal H El, Caire G. On maximum-likelihood detection and the search for the closest lattice point, IEEE Transaction on Information Theory, 2003,49 (10), pp2389-240
[85] Lee H ,Hu Xinde. Robust Iterative Tree-Pruning Detection and LDPC Decoding, IEEE Journalon Selected Areas in Communications, 2005, 23(5), pp1013-1025.
[86] de Jong Y. L. C., Willink T J, Iterative tree search detection for MIMO wireless system, IEEE Transaction on Communication, 2005, 53(6), pp930-935
[87] AtRustamani A, Stefanov A, Vojcic B.R., Turbo-greedy coding for multiple antenna systems, in proc. ICC, Helsinki, Finlan 2001, (6), pp1684-1689.
[88] Barn S, Hagenauer J, Witzke M. Iterative detection of MIMO transmission using a list-sequential (LISS) detector, in Proc. ICC, Anchorage,AK,2003,4 (5), pp2653-2657.
[89] Wei L.,Rasmussen L. K. ,Wyrwas R., Near optimum tree-search detection schemes for bit-synchronous multiuser CDMA systems over Gaussian and two-path Raleigh-fading channels, IEEE Transaction on Communication, 1997,45(6), pp691-700
[90] Reid A. B., Grant A. J., Alexander P. D., List detection for multi-access channels , in Proc. GLOBECOM, Taipei,Taiwan, 2002, 2( 11), pp1083-1087
[91] Wang Xiaodong, and Poor H., Iterative (Turbo) Soft Interference Cancellationa nd Decoding for Coded CDMA, IEEE Transactions on Communications, 1999, 47(7), pp1046-1061
[92] Li Jungiang, Letaief K. B., Cao Zhigang, Space-time Turbo multiuser detection for coded MC-CDMA, IEEE Transaction on Wireless Communication, 2005, 4(3), pp538-549
[93] Gigheri A. E. ,Nordio,Taricco G., MIMO doubly-iterativere ceivers: Pre- vs. Po st- cancellation filtering, IEEE Communication letters, 2005, 9(2), pp106-108
[94] Omori H., Asai T., Matsumoto T. A matched filter approximation for SC-MMSE iterativee qualizations, IEEE Communications Letters, 2001, 5 (7), pp310-312
[95] Zhang Yumin, Blum R S. Iterative multiuser detection for Turbo-Coded synchronous CDMA in Gaussian and Non-Gaussian impulsive noise, IEEE Transaction on Communication, 2001, 49(3), pp397-400
[96] Li Q, Wang X. and Geoghiades C., Iterative multiuser detection for turbo coded CDMA in multipath fading channels, IEEE Transaction on Vehicular Technology, 2002, 51(5), pp1096-1108
[97] Reynolds D, and Wang X., Turbo multiuser detection with unknown interferers, IEEE Transaction on Communication, 2002, 50(4), pp616-622
[98] Luo J ,Patipati K.,Willet P. ,Hasegawa F., Near-optimal Multiuser Detection in Synchronous CDMA Using Probabilistic Data Association, IEEE Communication Letters, 2001, 5(9), pp361-363
[99] Luo J, Pattipati K, Willet P.A., Sliding Window PDA for Asynchronous CDMA, and a Proposal for Deliberate Asyncbronicity, IEEE Transaction on Communication,2003,51(12), pp1970-1974
[100] Pham D, Luo J, Pattipati K, Willett P. A PDA-Kalman approach to multiuser detection in asynchronous CDMA, IEEE Communication Letters, 2002, 6(11), pp475-477
[101] Liu Shoumin, Tian Zhi. Near-Optimum Soft Decision Equalization for Frequency Selective MIMO Channels, IEEE Transaction on Signal Processing, 2004, 52(3), pp721-733
[102] Liu Shoumin, Tian Zhi. A Kalman-PDA Approach to Soft-Decision Equalization for Frequency-Selective MIMO Channels, IEEE Transaction on Signal Processing,2005, 53(10), pp3819-3830
[103] Yin Yufang, Huang Yufei, Zhang Jiangqiu, Turbo Equalization using Probabilistic Data Association, IEEE Globecom, 2004, pp2535-2539
[104] Lonear M., Miiller R., Wehinger J., Iterative joint detection, decoding and channel estimation for dual antenna arrays in frequency selective fading, in IEEE Wireless Personal Multimedia Conununication, 2002, 1(10), pp27-30
[105] Park S. Y., Kang C. G., Complexity-Reduced Iterative MAP Receiver for Interference Suppression in OFDM-Based Spatial Multiplexing Systems, IEEE Transaction on Vehicular Technology, 2004, 53(9), pp1316-1326
[106] Park S. Y., Kim Y. G., Kang C. G., Iterative receiver with joint detection and channel estimation for OFDM systems with multiple receiver antennas in mobile radio channels, IEEE GlobeCom, 2001, 5, pp3085-3089
[107] Dong Bing, Wang Xiaodong, Doucet A., A new class of soft M IMO demodulation algorithms, IEEE Transaction on Signal Processing, 2003, 51(11), pp2752-2763
[108] Lu Ben, Wang Xiaodong, Namyanan K. LDPC-Based Space-Time Coded OFDM Systems Over Correlated Fading Channels: Performance Analysis and Receiver Design, IEEE Transaction on Communication, 2002, 50 (1), pp74-88
[109] Syed M. J., Cances J. P., Meghdadi V LDPC-based space-time coded OFDM systems with channel estimation, IEEE Proceedings Communication, 2004, 151(12), pp567-573
[110] Z heng Jun, Rao B. D. LDPC-Coded MIMO systems with unknown block fading channels soft MIMO detector design, channel estimation, and code optimization, IEEE Transaction on Signal Processing, 2006,54(4):1504-1518
[111] Gonzulez-Lopez, Miguez J., Castedo L. Turbo aided maximum likelihood channel estimation for space-timec oded systems, In 13 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Lisbon, Portugal, 2002, (9), pp364-368
[112] Lonbar M. ,Muller R., Wehinger J., Mecklenbrayjer C. F.,Abe T., Iterative channel estimation and data detection in frequency-selective fading MIMO channels, European Transaction on Telecommunication, 2004, pp459-470
[1] Rappapport T. S.,无线通信原理与应用,电子工业出版社, 1998.
[2] Bi Q., Zysman G. L., and Menkes H., Wireless mobile communication at the start of the 21st century, IEEE Comm. Mag. , 2001, 39(1), pp.110-116.
[3] Berezdivin R., Breinig R., and Topp R., Next-generation wireless communication concepts and technologies, IEEE Comm.Mag., 2002, 40(3), pp.108-116.
[4] Lu W. W., Compact multidimensional broadband wireless: the convergence of wireless mobile and access, IEEE Comm. Mag., 2000, 38(11), pp.119-123.
[5] Nakagami M., The m-distribution: a general fomula of intensity distribution of rapid fading,in Statistical Methods in Radio Wave Propagation, Pergamon, Oxford,England, 1960.
[6] Jakes W.,Microwave Mobile Communications, IEEE Press, 1993.
[7] Foschini G. J. and Gans M. J., On limits of wireless communications in a fading environment when using multiple antennas, Wireless Personal Communications, 1998,6(3), pp.311-335.
[8] Shin H. and Lee J. H., Capacity of multiple-antenna fading channels: spatial fading correlation, doubles cattering, and keyhole, IEEE Transactions on Information Theory 2003, 49(10), pp.2636- 2647.
[9] Erceg V. et al., Channel models for fixed wireless applications, IEEE 802.16 work group technical report, 2001.
[10] Lucent,N okia, Siemens, and Ericsson, As tandardized set of M IMO radio propagationc hannels",3GPPT SG-RANW G1, Jeju, Korea, 2001, 23(11).
[11] Kermoal J., Schumacher L., Pedersen K., Mogensen P. et.al., A stochastic MIMO radio channel model with experimental validation",IEEE Journal on Selected Areas in Communications, 2002, 20(8) , pp1211-1226
[12] Yu K., Bengtsson., Ottersten B., Mcnamara D.,et.al., A wide band statistical model for NLOS indoor MIMO channels, in Proceeding s of IEEE VTC, Spring, 2002, (5) pp357-366.
[13] Muller R. and Hofstetter H., Confirmation of random matrix model for t he antenna array channel by indoor measurements, in Int. Symp.Antennas and Propagation, 2001, 1, pp472-475.
[14] Kermoal J .P., Schumacher L ., et.al., Experimental investigation of correlation properties of MIMO radio channels for indoor picocell scenarios, in Proceedings of IEEEVTC, Fall, 2002, pp14-21.
[15] PedersenK .I, Andersen J. B., et.al. A stochastic multiple-input multiple-output radio channel model for evaluation of space-time coding algorithm, in Proceedings of IEEE VTC, Fall, 2000, pp.893-897.
[16] SchumscherL., Kermoal J. P., et.al. MIMO Channel Characterisation, IST Project IST-1999-1172 9 METRA Deliverable D2, 2001,2.
[17] Saleh A. A. M., and Valenzuela R. A., A statistical model for indoor multipath propagation, IEEE Journal on Selected Areas in Comm., 1987,5, pp128-132.
[18] Spencer Q. H., Jeffs B. D., Jensen M. A., et.al. Modeling the statistical macrochannel model for mobile environments, IEEE Journal on Selected Areas in Comm., 2000,18(3) , pp.347-360.
[19] Gesbert D., Bolcskei H., Gore D., and Paulraj A. J., Outdoor MIMO wirelesschannels: models and performance prediction, IEEE Transactions on Commu.,December2002, 50(12).
[20] Gesbert D., Bolcskei H., Gore D., and Paulraj A. J., MIMO wireless channels: capacity and performance, In Proceedings Global Telecommunications Conference, 2002, 2(11), pp1083-1088.
[21] Shiu D. S., Foschini G. J., Gans M. J., and Kahn J. M., Fading correlation and its effect on the capacity of multielement antenna systems, IEEE Transactions onComm., 2000, 48(3), pp 502-513,
[22] Salz J. and Winters J. H., Effect of fading correlation on adaptive arrays in digital wireless communications, in Proceedings of IEEE VTC, 1993, 3 ,pp1758-1774.
[23] Vucetic B., Yuan J., Space-Time Coding, John Wiley&Sons Ltd., 2003.
[24] Lee W. C. Y., Effects on correlation between two mobile radio basestation antennas, IEEE Transactions on Comm., 1973, 21(11), pp1214-1224.
[25] Driessen P. and Foschini G. J., On the capacity formula for multiple input multiple output wireless channels: a geometric interpretation, IEEE Transactions on Commu., 1999, 47(2), pp173-176. .
[1] Golden G.D, Foschini C.J., R.A.,et al. Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture,Electronics Letters, 1999, 35(1)
[2] Foschini G. J., Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas, Bell Labs Tech. J., 1996, 1(8), pp.41-59.
[3] Foschini G. J., Golden G. D., Valenzela A., and Wolniansky P. W., Simplied processing for high spectral eciency wireless communications emplying multi-element arrays, IEEE J.Selected Areas in Comm., November 1999, 17(11), pp. 1841-1852.
[4] W¨ubben D., B¨ohnke R., Rinas J., et.al., Efficient Algorithm for Decoding Layered Space-Time Codes, IEE Electronics Letters, 2001, 37(10), pp.1348–1350.
[5] Dirk W¨ubben, Ronald B¨ohnke and Volker K¨uhn, et.al., MMSE Extension of V-BLAST based on Sorted QR Decomposition,
[6] Hassibi B., An Efficient Square-Root Algorithm for BLAST, in Proc. IEEE Intl. Conf. Acoustic, Speech, Signal Processing, Istanbul, Turkey, 2000, 6, pp. 5–9.
[7] Chin W. H., Constantinides G. and Ward D.B., Parallel multistage detection for multiple antenna wireless systems, Electron. Lett., 2002, 6 (38), pp.14-16
[8] Proakis, J. G. Digital Communication, 4th ed,电子工业出版社, 2001.
[9] Benesty J., Huang Yiteng (Arden), Chen Jingdong, A Fast Recursive Algorithm for Optimum Sequential Signal Detection in a BLAST System, IEEE Trans. Signal Processing, 2003, 51(7), pp1722-1730.
[10] Liu T. H., and Liu Y. L., Modified Fast Recursive Algorithm for Efficient MMSE-SIC Detection of the V-BLAST System, IEEE Trans. Wireless comm., 2008, 7(10)
[11] Golub G. H., Loan V. C. F., Matrix Computation (3rd Ed.), The John Hopkins Univ. Press, 1996
[1] Berrou C., Glavieux A., and Titmajshima P., Near Shannon Limit Error-Correction Coding and Decoding: Turbo Codes, ICC, Geneva, Switzerland, 1993, (5), pp.1064–90.
[2] Sellathurai M. and Haykin S., Turbo-BLAST for Wireless Communications: Theory and Experiments, IEEE Trans. Sig. Proc., 2002, 50,(10),pp. 2538–46.
[3] Stefanov A. and Duman T. M., Turbo-coded modulation for systems with transmit and receive antenna diversity over block fading channels: System model, decoding approaches, and practical considerations, IEEE J. Sel. Areas Comm., 2001, 19(5), pp. 958–968.
[4] Hochwald B. M. and Brink S. Achieving near-capacity on a multiple-antenna channel, IEEE Trans. Commun., 2003, 51(3) , pp. 389–399.
[5] Bahl L., Cocke J., Jelinek F., and Raviv J., Optimal decoding of linear codes for minimizing symbol error rate, IEEE Trans. Inf. Theory, 1974, 20(3) , pp. 284–287.
[6] Hagenauer, J.; Hoeher, P, A Viterbi algorithm with soft-decision outputs and its applications, GLOBECOM '89., IEEE, 1989, 3(11) pp.1680-1686
[7] Babak. Hassibi, Haris Vikalo, On the sphere-decoding algorithm I. expected complexity, IEEE Trans. Signal processing, 2005, 53(8) pp.2806-2818
[8] Shu.L. Daniel. J.C.差错控制码(第二版),机械工业出版社
[9] Joachim. H., Christian. K., The list-sequential (LISS) algorithm and its application, IEEE. Trans. Comm., 2007, 55(5), pp918-928.
[10] Stephan B., Joachim H., Melanie W. ,Iterative Detection of MIMO Transmission Using a List-Sequential (LISS) Detector, in Proc. ICC, Anchorage, AK, 2003,4 (5), pp.2653-2657.
[11] Anderson J.B., Mohan S., Sequential coding algorithm: a survey and cost analysis, IEEE Trans. Comm., 1984, 32(2), pp169-176
[12] Ruyet D. L., Bertozzi T., and Ozebek B., Breadth first algorithms for APP detectors over mimo channels, in Proc. ICC 2004, Paris, France, 2004, pp. 926–930.
[13] Kitty K.Y. Wong, Peter J. M., Reduced-Complexity Equalization Techniques for ISI and MIMO Wireless Channels in Iterative Decoding, IEEE J. Selected Areas in Comm., 2008, 26(2), pp256-268
[14] Yvo L.C., de J., Tricia J.W., Iterative Tree Search Detection for MIMO Wireless Systems, IEEE Trans. on Comm., 2005, 53(6), pp930-935
[15] Simmons S. J., Breadth-first trellis decoding with adaptive effort, IEEE Trans. Inform. Theory, 1990, 38(1), pp.3-12.
[16] Steingrimsson B., Luo Z.-Q., and Wong K. M., Soft quasi-maximum likelihood detection for multiple-antenna wireless channels,”IEEE Trans. Signal Process., 2003, 51(11) , pp.2710-2719.
[1] Sellathurai M. and Haykin S., Turbo-BLAST for wireless communications: Theory and experiments, IEEE Trans. Signal Processing, 2002,50(10), pp.2538–2546.
[2] Liu Daniel N., Michael P. Fitz, Low Complexity Affine MMSE Detector for Iterative Detection-Decoding MIMO OFDM Systems,, IEEE Trans. comm., 2008, 56(1), pp.150-158
[3] Wang X. and Poor H. V., Iterative(turbo) soft interference cancellation and decoding for coded CDMA, IEEE Trans. Comm., 1999, 47(7), pp.1046–1061.
[4] H. Lee, B. Lee, I. Lee, Iterative Detection and Decoding With an Improved V-BLAST for MIMO-OFDM Systems, IEEE Journal on selected area in comm, 2006, 24(3), pp504-519
[5] Song K.-B. and Mujtaba S. A., A low complexity space-frequency BICM MIMO-OFDM system for next-generation WLANs, in Proc. IEEE Global Telecommunications Conf.,2003, pp. 1059–1063.
[6] Steven M. Key,统计信号处理基础—估计与检测理论,电子工业出版社, 2003
[7] Luo J., K. Pattipati R., Willett P. K., et.al., Near-Optimal Multiuser Detection in Synchronous CDMA Using Probabilistic Data Association, IEEE Comm. letters, 2001, 5(9), pp361-363
[8] Liu S., Tian.Z., Near-Optimum Soft Decision Equalization for Frequency Selective MIMO Channels, IEEE Trans. Signal Processin, 2004, 52(3), pp721-733
[9] Pham D., Pattipati K. R., Willett P. K.,et.al., A Generalized Probabilistic Data Association Detector for Multiple Antenna Systems, IEEE Comm. letters, 2004, 8(4), pp205-207
[10] Poor H. V. and Verdu S., Probability of error in MMSE multiuser detection,IEEE Trans. Inf. Theory, 1997, 43(5), pp. 858–871.
[11] Lee H. and Lee I., Newapproach for coded layered space-time architecture for MIMO-OFDM systems, in Proc. ICC, 2005, 5, pp. 608–612.
[12] Cioffi J. M., EE379A Class Note: Signal Processing and Detection. Stanford, CA: Stanford Univ.
[13] Borjesson P. O. and Sundberg C.-E., Simple approximation of the error function Q(x), IEEE Trans. Commun., 1979,27(3), pp. 639–643.
[14] Shu.L. Daniel. J.C.差错控制码(第二版),机械工业出版社
[1] Alamouti S. M.. A simple transmit diversity technique for wireless communications. IEEE J. Select. Areas Commun., 1998 16(10),pp.1451-1458.
[2] Tarokh V., Jafarkhani H., and Calderbank A. R.. Space-time blockcodes from orthogonal designs. IEEE Trans.Inform. Theory, 1999, 45(6), pp.1456-1467.
[3] Lee K. and Williams D.. A space-time coded transmitter diversity technique for frequency selective fading channels, Proc. IEEE Sensor Array and Multichannel Signal Processing Workshop, New York: IEEE, 2000, pp.149-152.
[4] Suber G. L., Barry J. R. and Mclaughlin S. W. et al. Broadband MIMO-OFDM Wireless Communications, Proceedings of the IEEE, 2004 92(2), pp.271-294.
[5] Larsson E. G., Stoica P., and Li J.. Orthogonal Space–Time Block Codes: Maximum Likelihood Detection for Unknown Channels and Unstructured Interferences, IEEE Trans. Signal Processing, 2003 51(2), pp.362-372.
[6] Lu B., Wang X. D. and Li Y.. Iterative Receivers for Space-Time Block-Coded OFDM Systems in Dispersive Fading Channels, IEEE Trans. Wireless Comm., 2002 1(4), pp. 213-225.
[7] McLachlan G. J. and Krishnan T., The EM Algorithm and Extensions. New York: Wiley, 1997.
[8] Tarokh V., Jafarkhani H., and Calderbank A . R., Space–time block coding for wireless communications: Performance results, IEEE J. Select. Areas Commun., 1999, 17(3) , pp.451–460.
[9] McLachlan G. J. and Krishnan T., The EM Algorithm and Extensions. New York: Wiley, 1997.
[10] Li Y., Cimini L. J., and Sollenberger N. R., Robust channel estimation for OFDM systems with rapid dispersive fading channels, IEEE Trans. Commun., 1998,. 46(7), pp. 902–915
[11] Stoica P., and Selen Y.. Cyclic Minimizers, Majorization Techniques, and the Expectation-Maximization Algorithm: A Refresher, IEEE Signal Processing Mag., 2004 1(1), pp.112-114.
[12] Steven M. Key,“统计信号处理基础—估计与检测理论,”电子工业出版社, 2003
[13] Li Y., Simplified channel estimation for OFDM systems with multiple transmit antennas, IEEE Trans. Wireless Commun., 1999. 1(11), pp. 67–75.
[1] Pozidis H., A P Petropulu, Cross-spectrum based blind channel estimation. IEEE Trans SP,1997, 45(12):.2977-2993
[2] Ding z., Liang J., A cumulant Matrix Subspace Algorithm for Blind Single FIR Channel Identification. IEEE Trans SP, 2001,49(3):.325-333
[3] Inouye Y. and Hirano K., Cumulat-based blind identification of linear multi-input-multi-output systems driven by colored inputs. IEEE Trans SP, 1997,45(6): 1543-1552
[4] Liang J., Ding z., Blind MIMO System Identification Based on Cumulant Subspace Decomposition. IEEE Trans SP, 2003,51(6): 1457-1468
[5] Giannakis G. B., Inouye Y., Mendel J. M,. Cumulant Based Identification of Multichannel Moving-Average Models, IEEE Trans SP, 1989,34(7): 783-787
[6] Moulines E, Duhamel P, Cardoso J, et al. Subspace methods for the blind identification of multichannel FIR filters. IEEE Trans SP, 1995, 43(2): 516-525
[7] Xu Guanghan, liu H., Tong L., et al. A Least-Squares Approach to Blind Channel Identification. IEEE Trans SP 1995,43(12) 2982-2993
[8] Slock D. M. T., Blind Fractionally-Spaced Equalization Perfect-Reconstuction Filter-Banks and MULtichannel Linear Prediction. Proc.IEEE ICASSP, 1994, 585-588
[9] Abed-meraim K., Loubaton P., Molines E.. A Subspace Algorithm for Certain Blind Identifecation Problems. IEEE Trans IT, 1997, 43(2): 499-51
[10] Adel Belouchrani, Karim Abed-Meraim, Cardoso J.F., et al. A blind source separation technique using second-order statistics. IEEE Trans SP, 1997, 45(2): 434-444
[11] Tong L., Inouye Y., Liu w., A Finite-Step Global Convergence Algorithm for the Parameter Estimation of Multichannel MA Processes. IEEE Trans SP, 1992, 40(3): 2547-2558
[12] Haykin S.著.自适应滤波器原理(第四版).北京:电子工业出版社,2002, 698-701
[13] Gorokhov A., Loubaton P., Blind identiTcation of MIMO-FIR systems: A generalized linear prediction approach, Signal Processing, 1999, 73: pp105-124
[2] Bi Q., Zysman G. L., and Menkes H. Wireless mobile communication at the start of the 21st century, IEEE Commun. Mag., 2001, 39(1), pp.110 - 116.
[3] Berezdivin R., Breinig R., and Topp R.,Next-generation wireless communications concepts and technologies, IEEE Commun.Mag., 2002, 40(3), pp. 108-116.
[4] Lu W. W. Compact multidimensional broadband wireless: the convergence of wireless mobile and access, IEEE Commun.Mag., 2000, 38(11), pp119-123.
[5] Tachikawa K. A perspective on the evolution of mobile communications, IEEE Commun. Mag., 2003, 41(10), pp66-73.
[6] Chuang J., SolIenberger N., Beyond 3G:wideband wireless data access based on OFDM and dynamic packet assignment, IEEE Cormoun. Mag., 2 000, 38(7), pp78-87.
[7] Kim Y., Jeong B. J., Chung J., et al, Beyond 3G: vision, requirements, and enabling technologies, IEEE Commiun. Mag., 2 003, 41(3), pp.120-124.
[8] Zhang P., Tao X., Zhang J., et al., Avision from the future: beyond 3G TDD, IEEE Commun.Mag., 2005, 43(1) ,pp.38-44.
[9] Paulraj A. J. and Papadias C. B., Space-time processing for wireless communications, IEEE Signal Processing Mag., 1997, 14(11), pp.4 9-83.
[10] NaguibA .F ., SeshadriN ., andC alderbankA .R., Increasing data rate over wireless channels,, IEEE Signal Processing Mag., 2000, 17(5), pp76- 92.
[11] Gesbert D., Shafi M., Shiu D., Smith P. J., and Naguib A., From theory to prac tice: an overview of MIMO space-time coded wireless systems, IEEE J .Select Areas Commun., 2003, 21(4), pp. 281-302.
[12] Paulraj A. J., Gore D. A., Nabar R. U., and Balcskei H., An overview of MIMO Communications- a key to gigabit wireless, Proc.of the IEEE, 2004, 92(2), pp198-218.
[13] Foschini G. J. and Gans M. J.,On limits of wireless communications in a fading environment when using multiple antennas,Wireless Pers.Commun., 1998, 6(3), pp311-335.
[14] Telatar I. E., Capacity of multi-antenna Gaussian channels, European Trans. Telecomm., 1999, 10(11), pp585-595.
[15] Tarokh V., Seshadri N. and Calderbank A. R., Space-time codes for high data rate wireless communication: performance criterion and code consruction, IEEE Trans. Inform.Theory, 1998, 44(3), pp.744-765 .
[16] Alamouti S. M.,A simple transmit diversity technique for wireless communications, IEEE J. Select. Areas Commun., 1998, 16(10), pp.1451- 1458,.
[17] Tarokh V., Jafarkhani H., and Calderbank A. R., Space-time block codes from orthogonal designs, IEEE Trans.Inform.Theory, 1999, 45(7), pp1456-1467.
[18] Tarokh V., Jafarkhani H., and Calderbank A. R., Space-time block coding for wirelessc ommunications: performance results, IEEE J .Select. Areas Cormnun.,1999, 17(3), pp.451-460.
[19] Foschini G. J., Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas,, Bell Labs Tech. J., 1996, 1(8), pp.41-59.
[20] Einstein S.W and bert P .E, Datat ransmissionb yf requency-division multiplexing using the discrete Fourier transform, IEEE Trans.Comm., 1971, 19(10), pp. 628-634.
[21] Jr. Cimini L.,Analysis and simulation of a digital mobile channel using orthogonal frequency division multiplexing, IEEE Trans.Comm., 1985, 33(7), pp.665-675.
[22] Bingham J.A.C.,Multicarrier modulation for data transmission: an idea whose time has come, IEEE Comm.Mag., May 1990, 28(5), pp.5-14.
[23] Zou W. Y. and Wu Y.,COFDM: an overview, IEEE Trans. Broadcasting, 1995,41(3), pp. 1-8.
[24] Wang Z. and Giannakis G. B., Wireless multicarrier communications, IEEE Signal Processing Mag., 2000, 17(5), pp. 29-48,
[25] Nee R. and Prasad R.,OFDM for wireless Multimedia Communications, Boston : Artech House, 2000.
[26] Sampath H.,Talwar S.,Tellado J.,Erceg V.,and Paulraj A., A fourth- generation MIMO-OFDM broadband wireless system: design, performance, and field trialresults, IEEE Commun.Mag., 2002, 40(9), pp. 143-149.
[27] Dubuc C., Starks D., Creasy T., and Hou Y., A MIMO-OFDM prototype for next-generation wireless WANs, IEEE Commun. Mag., 2004, 42(12), pp.82-87.
[28] Van Zelst A. and Schenk T. C. W.,Implementation of a MIMO OFDM-based wireless LAN system, IEEE Trans. Signal Processing, 2004, 52(2), pp.483- 494.
[29] StuberG .L., Barry J .R ., McLaughlinS .W.,LiY ., et .al, Broadband MIMO–OFDM wireless communications, Proc.of the IEEE, 2004, 92(2),pp.271- 294.
[30] Yang H ., A road to future broadband wireless access: MIMO-OFDM-based air interface, IEEE Commun. Mag., 2005, 43(1), pp.53-60.
[31] Al-Naffouri T. Y., Awoniyi 0., Oteri 0., and Paulraj A., Receiver design for MIMO-OFDM transmission over time variant channels,in Proc. IEEE GLOBECOM' 04, 2004, 4(12), pp.2487-2492.
[32] Fozunbal., McLauhlin S. and Schafer R. W., On performance limits of MIMO- OFDM systems over block-fading channels, in Proc. IEEE WCNC’04, 2004, 2(3), pp.976-980.
[33] Astély D., Dahlman E., Furusk?r A. et.al., LTE: The Evolution of Mobile Broadband, IEEE Communications Magazine ? April 2009, pp44-51
[34] E. Dahlman et al., 3G Evolution: HSPA and LTE for Mobile Broadband, 2nd ed., Academic Press, 2008.
[35] A. Larmo et al.,“The LTE Link Layer Design,”IEEE Commun. Mag., 2009, (4), pp52-59
[36] H. Ekstrom et al., Technical Solutions for the 3G Long-Term Evolution, IEEE Commun. Mag., vol. 44, no. 3, Mar. 2006, pp. 38–45.
[37] Ahmadi S.,An Overview of Next-Generation Mobile WiMAX Technology, IEEE Communications Magazine ? June 2009, pp84-98
[38] Wang F., Ghosh A., Sankaran C., et.al., Mobile WiMAX Systems: Performance and Evolution, IEEE Communications Mag. Oct. 2008, pp41-49
[39] F. Wang et al.,“WiMAX System Performance with Multiple Transmit and MultipleReceive Antennas,”IEEE VTC, Apr. 2007.
[40] C. Sankaran, A. Ghosh, and F. Wang,“Performance Study of a Fractional Frequency Reuse (FFR) Scheme for an 802.16e Based WiMAX System,”2008, preprint.
[41] IEEE 802.16-2004. IEEE standard for local and metropolitan area networks, Part 16: Air interface for fixed broadband wireless access systems [S]. 2004.
[42] CCSA WG6.新一代无线通信空中接口技术纲要[S]. V4.0. 2006.
[43] Host S, Johannesson R, Zyablov V, et al. A first encounter with binary Woven convolutional codes,Proceedings of International Symposium Communication Theory and Applications, Jul 1997, Lake District, UK. 1997,pp13-18
[44] Sheikh K.,Gesbert D.,Gore D.,and Paulraj A.,Smart antennas for broad band wireless access networks, IEEE Comm. Mag., vo l. 37, Nov. 1999.
[45] Bossert M., Huebner A., Schuehlein F., et.al., On cyclic delay diversity in OFDM based transmission schemes, in OFDM Workshop, 2002.
[46] J. Tan and G. Stuber, Multicarrier delay diversity modulation, in Proc. IEEE Globecom 2003.
[47] Dammann A., Kaiser S., Standard conformable antenna diversity techniques for OFDM and its application to DVB-T system, in Proc. IEEE GLOBECOM 2001, vol. 5, Nov. 2001, pp. 3100-3105.
[48] S. Alamouti, A simple transmit diversity technique for wireless comminications, IEEE J. Select. Areas Commun., vol. 16, pp.1451–1458, Oct. 1998.
[49] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, Space–time block codes from orthogonal designs, IEEE Trans. Inform. Theory, vol.45, pp. 1456–1467, July 1999.
[50] Guey J. C., Fitz M. P., Bell M. R., et.al.,Signal design for tr ansmitter diversity wireless communications systems over Rayleigh fadingc hannels,IEEE T rans. on Comm.,vol. 47,Apr. 999, pp. 527-537
[51] Baro S.,Bauch G.,and Hansman A., Improved codes for space-time trellis codes modulation,IEEE Commu. Lett., vol. 4, January 2000, pp.20-22.
[52] Ionescu D. M., On space-time code design,IEEE Transactions on Wireless Commu., vol.2 , Jan. 2003, pp.2 0-28.
[53] Hassibi B. and Hochwald B. M., High-Rate Codes That Are Linear in Space andTime, IEEE Trans. on infomation theory, vol.48, July 2002, pp1804-1824
[54] Heath R. W. and Paulraj A. J., Linear dispersion codes for MIMO systems based on frame theory,IEEE Trans. Signal Processing, vol.50 , Oct. 2002, pp.2429-2441.
[55] Foschini G. J., et al., Analysis and performance of some basic space-timearchitectures, IEEE Journal on Selected Areas in Comm., vol.21,Apr. 2003, pp303-320.
[56] Golden G. D., Foschini G. J., Valenzuela R. A., et.al. Detection algorithm and initial laboratory results using V–BLAST space-time communication architecture,Electronics Letters, vol.35,1999, pp.14-15
[57] Li Q., Lin X. T., Zhang J. Z. and Roh W., Advancement of MIMO Technology in WiMAX: From IEEE 802.16d/e/j to 802.16m, IEEE Communications Magazine, 2009, 6, pp100-107
[58] Berrou C. and Glavieux A., Near optimum error- correcting coding and decoding: Turbo codes, IEEE Trans. Commun., vol. 44, pp. 1261–1271, Oct. 1996.
[59] T¨uchler M., Koetter R., and Singer A. C., Turbo equalization: Principles and New Results, IEEE Trans. on Comm., vol. 50, May 2002, pp. 754–767.
[60] Koetter R., Singer A. C., and Michael Tüchler, Turbo equalization, IEEE signal processing Mag. Jan. 2004, pp67-80
[61] T¨uchler M., Singer A. C., and Koetter R., Minimun mean squared error equalization using a prior information, IEEE Trans. Signal Processing, vol.50, Mar.2002, pp673-683
[62] Caire G., Taricco G., and Biglieri E., Bit-Interleaved Coded Modulation, IEEE Trans. Info. Theory, vol.44, May 1998, pp927-946
[63] Wang Xiaodong, , and Vincent Poor H., Iterative (Turbo) Soft Interference Cancellation and Decoding for Coded CDMA, IEEE Trans. Comm., vol. 47, July 1999, pp1046-1061
[64] Gamal H. El and Geraniotis E., Iterative multiuser detection for coded CDMA signals in AWGN and fading channel, IEEE J. Select. Areas Comm., vol. 18, pp. 30–41, Jan. 2000.
[65] Lu B., ,Wang Xiaodong, and Li Ye (Geoffrey), Iterative Receivers for Space–Time Block-Coded OFDM Systems in Dispersive Fading Channels, IEEE Trans. Wireless Comm., vol. 1, Apr. 2002, pp213-225
[66] Stefanov A., and Duman T. M., Turbo-Coded Modulation for Systems with Transmitand Receive Antenna Diversity over Block Fading Channels: System Model, Decoding Approaches, and Practical Considerations, IEEE J. Select. Areas Comm., vol.19, May 2001, pp958-968
[67] Sellathurai M., and Haykin S., TURBO-BLAST for Wireless Communications: Theory and Experiments, IEEE Trans. Signal Processing, vol. 50, Oct. 2002, pp2538-2546
[68] Sellathurai M. and Haykin S., A nonlinear iterative beamforming technique for wireless communications, in 33rd Asilomar Conf. Signals, Syst., Comput., vol. 2, Pacific Grove, CA, Oct. 1999, pp. 957–961.
[69] Sellathurai M. and Haykin S.,“Asimplified diagonal BLAST architecture with iterative parallelinterferencecancellation,”in Proc. IEEE Int. Conf. Commun., vol. 10, June 2001,pp. 3067–3071.
[70] Sellathurai M. and Haykin S.,“Turbo-BLAST: A novel technique for multi-transmit multi-receivewireless communications,”in Multiaccess, Mobility, andTeletraffic for Wireless Communications. Boston, MA: Kluwer, 2000,vol. 5, pp. 13–24.
[71] Gamal H. El and Hammons A. R., New approach for space-time transmitter receiver design, in Proc. 37th Allerton Conf. Commun., Contr., Comput., Oct. 1999, pp. 186–194.
[72] Ariyavisitakul S. L., Turbo space-time processing to improve wireless channel capacity, IEEE Trans. Commun., vol. 48, Aug. 2000, pp. 1347–1359.
[73] Li X., Huang H., Foschini G. J., et.al., Effects of iterative detection and decoding on the performance of BLAST,”Proc. IEEE Globecom, vol. 1, pp. 1062–1066, Nov. 2000.
[74] Choi W., Cheong K., and Cioffi J. M., Iterative soft interference cancellation for multiple antenna system, in Proc.Wireless Commun. Network Conf., vol. 1, 2000, pp. 304–309.
[75] Gamal H. El and Hammons A. R., New approach to layered space-time coding and signal processing,”IEEE Trans. Inform. Theory, vol. 47, Sept. 2001, pp2321–2334.
[76] Mexia T. and Cheng S. K., Low complexity post-ordered iterative decoding for generalized layered space-time coding systems, in Proc. IEEE Int. Conf.Commun., vol. 4, June 2001, pp. 1137–1141.
[77] Baro S., Bauch G., Pavlic A., Improving BLAST performance using space-time block codes and turbo decoding, Proc. IEEE Globecom, vol. 2, Nov. 2000 pp1061–1066.
[78] Hochwald B, Brink S. Achieving Near-Capacity on a Multiple-Antenna Channel. IEEE Transactions on Comm., 2003 51(3), pp389-399
[79] Liu Jianhua, Li Jian. A soft detector with Good performance/complexity trrade-off for a MIMO system, EURA SIP Journal on Applied Signal Processing, 2004 (9), pp1266-1274
[80] Damen M O, Chkeif A, Belfiore J C., Lattice code decoder for space-time codes ,IEEE Communication Letters, 2000,4(5), pp161-163
[81] Agrell E, Eriksson T, Vardy A, Zeger K. Closest point search in lattices, IEEE Transaction on Information Theory, 2002,48(8), pp2201-2214
[82] Wang R ,Giannakis G. B., Approaching MIMO capacity with reduced-complexity softs phere-decoding. in Proc. WCNC, Atlanta, 2004, pp1620-1625
[83] Zhao W, Giannakis G B. Sphere decoding algorithms with improved radius search, IEEE Transactions on Communication, 2005, 53(7), pp1104-1109
[84] Damen M O, Gamal H El, Caire G. On maximum-likelihood detection and the search for the closest lattice point, IEEE Transaction on Information Theory, 2003,49 (10), pp2389-240
[85] Lee H ,Hu Xinde. Robust Iterative Tree-Pruning Detection and LDPC Decoding, IEEE Journalon Selected Areas in Communications, 2005, 23(5), pp1013-1025.
[86] de Jong Y. L. C., Willink T J, Iterative tree search detection for MIMO wireless system, IEEE Transaction on Communication, 2005, 53(6), pp930-935
[87] AtRustamani A, Stefanov A, Vojcic B.R., Turbo-greedy coding for multiple antenna systems, in proc. ICC, Helsinki, Finlan 2001, (6), pp1684-1689.
[88] Barn S, Hagenauer J, Witzke M. Iterative detection of MIMO transmission using a list-sequential (LISS) detector, in Proc. ICC, Anchorage,AK,2003,4 (5), pp2653-2657.
[89] Wei L.,Rasmussen L. K. ,Wyrwas R., Near optimum tree-search detection schemes for bit-synchronous multiuser CDMA systems over Gaussian and two-path Raleigh-fading channels, IEEE Transaction on Communication, 1997,45(6), pp691-700
[90] Reid A. B., Grant A. J., Alexander P. D., List detection for multi-access channels , in Proc. GLOBECOM, Taipei,Taiwan, 2002, 2( 11), pp1083-1087
[91] Wang Xiaodong, and Poor H., Iterative (Turbo) Soft Interference Cancellationa nd Decoding for Coded CDMA, IEEE Transactions on Communications, 1999, 47(7), pp1046-1061
[92] Li Jungiang, Letaief K. B., Cao Zhigang, Space-time Turbo multiuser detection for coded MC-CDMA, IEEE Transaction on Wireless Communication, 2005, 4(3), pp538-549
[93] Gigheri A. E. ,Nordio,Taricco G., MIMO doubly-iterativere ceivers: Pre- vs. Po st- cancellation filtering, IEEE Communication letters, 2005, 9(2), pp106-108
[94] Omori H., Asai T., Matsumoto T. A matched filter approximation for SC-MMSE iterativee qualizations, IEEE Communications Letters, 2001, 5 (7), pp310-312
[95] Zhang Yumin, Blum R S. Iterative multiuser detection for Turbo-Coded synchronous CDMA in Gaussian and Non-Gaussian impulsive noise, IEEE Transaction on Communication, 2001, 49(3), pp397-400
[96] Li Q, Wang X. and Geoghiades C., Iterative multiuser detection for turbo coded CDMA in multipath fading channels, IEEE Transaction on Vehicular Technology, 2002, 51(5), pp1096-1108
[97] Reynolds D, and Wang X., Turbo multiuser detection with unknown interferers, IEEE Transaction on Communication, 2002, 50(4), pp616-622
[98] Luo J ,Patipati K.,Willet P. ,Hasegawa F., Near-optimal Multiuser Detection in Synchronous CDMA Using Probabilistic Data Association, IEEE Communication Letters, 2001, 5(9), pp361-363
[99] Luo J, Pattipati K, Willet P.A., Sliding Window PDA for Asynchronous CDMA, and a Proposal for Deliberate Asyncbronicity, IEEE Transaction on Communication,2003,51(12), pp1970-1974
[100] Pham D, Luo J, Pattipati K, Willett P. A PDA-Kalman approach to multiuser detection in asynchronous CDMA, IEEE Communication Letters, 2002, 6(11), pp475-477
[101] Liu Shoumin, Tian Zhi. Near-Optimum Soft Decision Equalization for Frequency Selective MIMO Channels, IEEE Transaction on Signal Processing, 2004, 52(3), pp721-733
[102] Liu Shoumin, Tian Zhi. A Kalman-PDA Approach to Soft-Decision Equalization for Frequency-Selective MIMO Channels, IEEE Transaction on Signal Processing,2005, 53(10), pp3819-3830
[103] Yin Yufang, Huang Yufei, Zhang Jiangqiu, Turbo Equalization using Probabilistic Data Association, IEEE Globecom, 2004, pp2535-2539
[104] Lonear M., Miiller R., Wehinger J., Iterative joint detection, decoding and channel estimation for dual antenna arrays in frequency selective fading, in IEEE Wireless Personal Multimedia Conununication, 2002, 1(10), pp27-30
[105] Park S. Y., Kang C. G., Complexity-Reduced Iterative MAP Receiver for Interference Suppression in OFDM-Based Spatial Multiplexing Systems, IEEE Transaction on Vehicular Technology, 2004, 53(9), pp1316-1326
[106] Park S. Y., Kim Y. G., Kang C. G., Iterative receiver with joint detection and channel estimation for OFDM systems with multiple receiver antennas in mobile radio channels, IEEE GlobeCom, 2001, 5, pp3085-3089
[107] Dong Bing, Wang Xiaodong, Doucet A., A new class of soft M IMO demodulation algorithms, IEEE Transaction on Signal Processing, 2003, 51(11), pp2752-2763
[108] Lu Ben, Wang Xiaodong, Namyanan K. LDPC-Based Space-Time Coded OFDM Systems Over Correlated Fading Channels: Performance Analysis and Receiver Design, IEEE Transaction on Communication, 2002, 50 (1), pp74-88
[109] Syed M. J., Cances J. P., Meghdadi V LDPC-based space-time coded OFDM systems with channel estimation, IEEE Proceedings Communication, 2004, 151(12), pp567-573
[110] Z heng Jun, Rao B. D. LDPC-Coded MIMO systems with unknown block fading channels soft MIMO detector design, channel estimation, and code optimization, IEEE Transaction on Signal Processing, 2006,54(4):1504-1518
[111] Gonzulez-Lopez, Miguez J., Castedo L. Turbo aided maximum likelihood channel estimation for space-timec oded systems, In 13 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Lisbon, Portugal, 2002, (9), pp364-368
[112] Lonbar M. ,Muller R., Wehinger J., Mecklenbrayjer C. F.,Abe T., Iterative channel estimation and data detection in frequency-selective fading MIMO channels, European Transaction on Telecommunication, 2004, pp459-470
[1] Rappapport T. S.,无线通信原理与应用,电子工业出版社, 1998.
[2] Bi Q., Zysman G. L., and Menkes H., Wireless mobile communication at the start of the 21st century, IEEE Comm. Mag. , 2001, 39(1), pp.110-116.
[3] Berezdivin R., Breinig R., and Topp R., Next-generation wireless communication concepts and technologies, IEEE Comm.Mag., 2002, 40(3), pp.108-116.
[4] Lu W. W., Compact multidimensional broadband wireless: the convergence of wireless mobile and access, IEEE Comm. Mag., 2000, 38(11), pp.119-123.
[5] Nakagami M., The m-distribution: a general fomula of intensity distribution of rapid fading,in Statistical Methods in Radio Wave Propagation, Pergamon, Oxford,England, 1960.
[6] Jakes W.,Microwave Mobile Communications, IEEE Press, 1993.
[7] Foschini G. J. and Gans M. J., On limits of wireless communications in a fading environment when using multiple antennas, Wireless Personal Communications, 1998,6(3), pp.311-335.
[8] Shin H. and Lee J. H., Capacity of multiple-antenna fading channels: spatial fading correlation, doubles cattering, and keyhole, IEEE Transactions on Information Theory 2003, 49(10), pp.2636- 2647.
[9] Erceg V. et al., Channel models for fixed wireless applications, IEEE 802.16 work group technical report, 2001.
[10] Lucent,N okia, Siemens, and Ericsson, As tandardized set of M IMO radio propagationc hannels",3GPPT SG-RANW G1, Jeju, Korea, 2001, 23(11).
[11] Kermoal J., Schumacher L., Pedersen K., Mogensen P. et.al., A stochastic MIMO radio channel model with experimental validation",IEEE Journal on Selected Areas in Communications, 2002, 20(8) , pp1211-1226
[12] Yu K., Bengtsson., Ottersten B., Mcnamara D.,et.al., A wide band statistical model for NLOS indoor MIMO channels, in Proceeding s of IEEE VTC, Spring, 2002, (5) pp357-366.
[13] Muller R. and Hofstetter H., Confirmation of random matrix model for t he antenna array channel by indoor measurements, in Int. Symp.Antennas and Propagation, 2001, 1, pp472-475.
[14] Kermoal J .P., Schumacher L ., et.al., Experimental investigation of correlation properties of MIMO radio channels for indoor picocell scenarios, in Proceedings of IEEEVTC, Fall, 2002, pp14-21.
[15] PedersenK .I, Andersen J. B., et.al. A stochastic multiple-input multiple-output radio channel model for evaluation of space-time coding algorithm, in Proceedings of IEEE VTC, Fall, 2000, pp.893-897.
[16] SchumscherL., Kermoal J. P., et.al. MIMO Channel Characterisation, IST Project IST-1999-1172 9 METRA Deliverable D2, 2001,2.
[17] Saleh A. A. M., and Valenzuela R. A., A statistical model for indoor multipath propagation, IEEE Journal on Selected Areas in Comm., 1987,5, pp128-132.
[18] Spencer Q. H., Jeffs B. D., Jensen M. A., et.al. Modeling the statistical macrochannel model for mobile environments, IEEE Journal on Selected Areas in Comm., 2000,18(3) , pp.347-360.
[19] Gesbert D., Bolcskei H., Gore D., and Paulraj A. J., Outdoor MIMO wirelesschannels: models and performance prediction, IEEE Transactions on Commu.,December2002, 50(12).
[20] Gesbert D., Bolcskei H., Gore D., and Paulraj A. J., MIMO wireless channels: capacity and performance, In Proceedings Global Telecommunications Conference, 2002, 2(11), pp1083-1088.
[21] Shiu D. S., Foschini G. J., Gans M. J., and Kahn J. M., Fading correlation and its effect on the capacity of multielement antenna systems, IEEE Transactions onComm., 2000, 48(3), pp 502-513,
[22] Salz J. and Winters J. H., Effect of fading correlation on adaptive arrays in digital wireless communications, in Proceedings of IEEE VTC, 1993, 3 ,pp1758-1774.
[23] Vucetic B., Yuan J., Space-Time Coding, John Wiley&Sons Ltd., 2003.
[24] Lee W. C. Y., Effects on correlation between two mobile radio basestation antennas, IEEE Transactions on Comm., 1973, 21(11), pp1214-1224.
[25] Driessen P. and Foschini G. J., On the capacity formula for multiple input multiple output wireless channels: a geometric interpretation, IEEE Transactions on Commu., 1999, 47(2), pp173-176. .
[1] Golden G.D, Foschini C.J., R.A.,et al. Detection algorithm and initial laboratory results using V-BLAST space-time communication architecture,Electronics Letters, 1999, 35(1)
[2] Foschini G. J., Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas, Bell Labs Tech. J., 1996, 1(8), pp.41-59.
[3] Foschini G. J., Golden G. D., Valenzela A., and Wolniansky P. W., Simplied processing for high spectral eciency wireless communications emplying multi-element arrays, IEEE J.Selected Areas in Comm., November 1999, 17(11), pp. 1841-1852.
[4] W¨ubben D., B¨ohnke R., Rinas J., et.al., Efficient Algorithm for Decoding Layered Space-Time Codes, IEE Electronics Letters, 2001, 37(10), pp.1348–1350.
[5] Dirk W¨ubben, Ronald B¨ohnke and Volker K¨uhn, et.al., MMSE Extension of V-BLAST based on Sorted QR Decomposition,
[6] Hassibi B., An Efficient Square-Root Algorithm for BLAST, in Proc. IEEE Intl. Conf. Acoustic, Speech, Signal Processing, Istanbul, Turkey, 2000, 6, pp. 5–9.
[7] Chin W. H., Constantinides G. and Ward D.B., Parallel multistage detection for multiple antenna wireless systems, Electron. Lett., 2002, 6 (38), pp.14-16
[8] Proakis, J. G. Digital Communication, 4th ed,电子工业出版社, 2001.
[9] Benesty J., Huang Yiteng (Arden), Chen Jingdong, A Fast Recursive Algorithm for Optimum Sequential Signal Detection in a BLAST System, IEEE Trans. Signal Processing, 2003, 51(7), pp1722-1730.
[10] Liu T. H., and Liu Y. L., Modified Fast Recursive Algorithm for Efficient MMSE-SIC Detection of the V-BLAST System, IEEE Trans. Wireless comm., 2008, 7(10)
[11] Golub G. H., Loan V. C. F., Matrix Computation (3rd Ed.), The John Hopkins Univ. Press, 1996
[1] Berrou C., Glavieux A., and Titmajshima P., Near Shannon Limit Error-Correction Coding and Decoding: Turbo Codes, ICC, Geneva, Switzerland, 1993, (5), pp.1064–90.
[2] Sellathurai M. and Haykin S., Turbo-BLAST for Wireless Communications: Theory and Experiments, IEEE Trans. Sig. Proc., 2002, 50,(10),pp. 2538–46.
[3] Stefanov A. and Duman T. M., Turbo-coded modulation for systems with transmit and receive antenna diversity over block fading channels: System model, decoding approaches, and practical considerations, IEEE J. Sel. Areas Comm., 2001, 19(5), pp. 958–968.
[4] Hochwald B. M. and Brink S. Achieving near-capacity on a multiple-antenna channel, IEEE Trans. Commun., 2003, 51(3) , pp. 389–399.
[5] Bahl L., Cocke J., Jelinek F., and Raviv J., Optimal decoding of linear codes for minimizing symbol error rate, IEEE Trans. Inf. Theory, 1974, 20(3) , pp. 284–287.
[6] Hagenauer, J.; Hoeher, P, A Viterbi algorithm with soft-decision outputs and its applications, GLOBECOM '89., IEEE, 1989, 3(11) pp.1680-1686
[7] Babak. Hassibi, Haris Vikalo, On the sphere-decoding algorithm I. expected complexity, IEEE Trans. Signal processing, 2005, 53(8) pp.2806-2818
[8] Shu.L. Daniel. J.C.差错控制码(第二版),机械工业出版社
[9] Joachim. H., Christian. K., The list-sequential (LISS) algorithm and its application, IEEE. Trans. Comm., 2007, 55(5), pp918-928.
[10] Stephan B., Joachim H., Melanie W. ,Iterative Detection of MIMO Transmission Using a List-Sequential (LISS) Detector, in Proc. ICC, Anchorage, AK, 2003,4 (5), pp.2653-2657.
[11] Anderson J.B., Mohan S., Sequential coding algorithm: a survey and cost analysis, IEEE Trans. Comm., 1984, 32(2), pp169-176
[12] Ruyet D. L., Bertozzi T., and Ozebek B., Breadth first algorithms for APP detectors over mimo channels, in Proc. ICC 2004, Paris, France, 2004, pp. 926–930.
[13] Kitty K.Y. Wong, Peter J. M., Reduced-Complexity Equalization Techniques for ISI and MIMO Wireless Channels in Iterative Decoding, IEEE J. Selected Areas in Comm., 2008, 26(2), pp256-268
[14] Yvo L.C., de J., Tricia J.W., Iterative Tree Search Detection for MIMO Wireless Systems, IEEE Trans. on Comm., 2005, 53(6), pp930-935
[15] Simmons S. J., Breadth-first trellis decoding with adaptive effort, IEEE Trans. Inform. Theory, 1990, 38(1), pp.3-12.
[16] Steingrimsson B., Luo Z.-Q., and Wong K. M., Soft quasi-maximum likelihood detection for multiple-antenna wireless channels,”IEEE Trans. Signal Process., 2003, 51(11) , pp.2710-2719.
[1] Sellathurai M. and Haykin S., Turbo-BLAST for wireless communications: Theory and experiments, IEEE Trans. Signal Processing, 2002,50(10), pp.2538–2546.
[2] Liu Daniel N., Michael P. Fitz, Low Complexity Affine MMSE Detector for Iterative Detection-Decoding MIMO OFDM Systems,, IEEE Trans. comm., 2008, 56(1), pp.150-158
[3] Wang X. and Poor H. V., Iterative(turbo) soft interference cancellation and decoding for coded CDMA, IEEE Trans. Comm., 1999, 47(7), pp.1046–1061.
[4] H. Lee, B. Lee, I. Lee, Iterative Detection and Decoding With an Improved V-BLAST for MIMO-OFDM Systems, IEEE Journal on selected area in comm, 2006, 24(3), pp504-519
[5] Song K.-B. and Mujtaba S. A., A low complexity space-frequency BICM MIMO-OFDM system for next-generation WLANs, in Proc. IEEE Global Telecommunications Conf.,2003, pp. 1059–1063.
[6] Steven M. Key,统计信号处理基础—估计与检测理论,电子工业出版社, 2003
[7] Luo J., K. Pattipati R., Willett P. K., et.al., Near-Optimal Multiuser Detection in Synchronous CDMA Using Probabilistic Data Association, IEEE Comm. letters, 2001, 5(9), pp361-363
[8] Liu S., Tian.Z., Near-Optimum Soft Decision Equalization for Frequency Selective MIMO Channels, IEEE Trans. Signal Processin, 2004, 52(3), pp721-733
[9] Pham D., Pattipati K. R., Willett P. K.,et.al., A Generalized Probabilistic Data Association Detector for Multiple Antenna Systems, IEEE Comm. letters, 2004, 8(4), pp205-207
[10] Poor H. V. and Verdu S., Probability of error in MMSE multiuser detection,IEEE Trans. Inf. Theory, 1997, 43(5), pp. 858–871.
[11] Lee H. and Lee I., Newapproach for coded layered space-time architecture for MIMO-OFDM systems, in Proc. ICC, 2005, 5, pp. 608–612.
[12] Cioffi J. M., EE379A Class Note: Signal Processing and Detection. Stanford, CA: Stanford Univ.
[13] Borjesson P. O. and Sundberg C.-E., Simple approximation of the error function Q(x), IEEE Trans. Commun., 1979,27(3), pp. 639–643.
[14] Shu.L. Daniel. J.C.差错控制码(第二版),机械工业出版社
[1] Alamouti S. M.. A simple transmit diversity technique for wireless communications. IEEE J. Select. Areas Commun., 1998 16(10),pp.1451-1458.
[2] Tarokh V., Jafarkhani H., and Calderbank A. R.. Space-time blockcodes from orthogonal designs. IEEE Trans.Inform. Theory, 1999, 45(6), pp.1456-1467.
[3] Lee K. and Williams D.. A space-time coded transmitter diversity technique for frequency selective fading channels, Proc. IEEE Sensor Array and Multichannel Signal Processing Workshop, New York: IEEE, 2000, pp.149-152.
[4] Suber G. L., Barry J. R. and Mclaughlin S. W. et al. Broadband MIMO-OFDM Wireless Communications, Proceedings of the IEEE, 2004 92(2), pp.271-294.
[5] Larsson E. G., Stoica P., and Li J.. Orthogonal Space–Time Block Codes: Maximum Likelihood Detection for Unknown Channels and Unstructured Interferences, IEEE Trans. Signal Processing, 2003 51(2), pp.362-372.
[6] Lu B., Wang X. D. and Li Y.. Iterative Receivers for Space-Time Block-Coded OFDM Systems in Dispersive Fading Channels, IEEE Trans. Wireless Comm., 2002 1(4), pp. 213-225.
[7] McLachlan G. J. and Krishnan T., The EM Algorithm and Extensions. New York: Wiley, 1997.
[8] Tarokh V., Jafarkhani H., and Calderbank A . R., Space–time block coding for wireless communications: Performance results, IEEE J. Select. Areas Commun., 1999, 17(3) , pp.451–460.
[9] McLachlan G. J. and Krishnan T., The EM Algorithm and Extensions. New York: Wiley, 1997.
[10] Li Y., Cimini L. J., and Sollenberger N. R., Robust channel estimation for OFDM systems with rapid dispersive fading channels, IEEE Trans. Commun., 1998,. 46(7), pp. 902–915
[11] Stoica P., and Selen Y.. Cyclic Minimizers, Majorization Techniques, and the Expectation-Maximization Algorithm: A Refresher, IEEE Signal Processing Mag., 2004 1(1), pp.112-114.
[12] Steven M. Key,“统计信号处理基础—估计与检测理论,”电子工业出版社, 2003
[13] Li Y., Simplified channel estimation for OFDM systems with multiple transmit antennas, IEEE Trans. Wireless Commun., 1999. 1(11), pp. 67–75.
[1] Pozidis H., A P Petropulu, Cross-spectrum based blind channel estimation. IEEE Trans SP,1997, 45(12):.2977-2993
[2] Ding z., Liang J., A cumulant Matrix Subspace Algorithm for Blind Single FIR Channel Identification. IEEE Trans SP, 2001,49(3):.325-333
[3] Inouye Y. and Hirano K., Cumulat-based blind identification of linear multi-input-multi-output systems driven by colored inputs. IEEE Trans SP, 1997,45(6): 1543-1552
[4] Liang J., Ding z., Blind MIMO System Identification Based on Cumulant Subspace Decomposition. IEEE Trans SP, 2003,51(6): 1457-1468
[5] Giannakis G. B., Inouye Y., Mendel J. M,. Cumulant Based Identification of Multichannel Moving-Average Models, IEEE Trans SP, 1989,34(7): 783-787
[6] Moulines E, Duhamel P, Cardoso J, et al. Subspace methods for the blind identification of multichannel FIR filters. IEEE Trans SP, 1995, 43(2): 516-525
[7] Xu Guanghan, liu H., Tong L., et al. A Least-Squares Approach to Blind Channel Identification. IEEE Trans SP 1995,43(12) 2982-2993
[8] Slock D. M. T., Blind Fractionally-Spaced Equalization Perfect-Reconstuction Filter-Banks and MULtichannel Linear Prediction. Proc.IEEE ICASSP, 1994, 585-588
[9] Abed-meraim K., Loubaton P., Molines E.. A Subspace Algorithm for Certain Blind Identifecation Problems. IEEE Trans IT, 1997, 43(2): 499-51
[10] Adel Belouchrani, Karim Abed-Meraim, Cardoso J.F., et al. A blind source separation technique using second-order statistics. IEEE Trans SP, 1997, 45(2): 434-444
[11] Tong L., Inouye Y., Liu w., A Finite-Step Global Convergence Algorithm for the Parameter Estimation of Multichannel MA Processes. IEEE Trans SP, 1992, 40(3): 2547-2558
[12] Haykin S.著.自适应滤波器原理(第四版).北京:电子工业出版社,2002, 698-701
[13] Gorokhov A., Loubaton P., Blind identiTcation of MIMO-FIR systems: A generalized linear prediction approach, Signal Processing, 1999, 73: pp105-124