MIMO-OFDM无线通信系统信道估计算法研究
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摘要
提高频谱的利用率是下一代无线移动通信的主要目标之一。在带宽和功率受限的无线通信中,多输入多输出(Multi-Input Multi-Output, MIMO)是获得更高的频谱效率最具有潜力的技术。同时正交频分复用(Orthogongal FrequencyDivision Multiplexing, OFDM)技术具有抵抗多径衰落和降低宽带接收机复杂度等优点,MIMO-OFDM被认为是后3G最有潜力的技术之一,能够适应高信道容量、高比特信息速率等宽带多媒体应用的需求。信道估计作为无线通信系统中的一项关键技术,是MIMO-OFDM系统中相干检测和空时解码的必须条件。因此本文以MIMO-OFDM系统的信道估计作为研究的主要内容。
高速无线数据传输导致信道呈现出稀疏特性,利用该特性可提高信道估计性能。针对非时变多径稀疏的MIMO-OFDM信道,提出了基于广义Akaike信息论准则的信道估计算法,该算法能够估计出信道的阶数和每条路径的时延,然后将由最小二乘算法估计出的时域信道冲激响应中非传播路径点置0,由此可降低加性白噪声对信道估计的影响,提高信道估计的精度,同时,信道稀疏性越强,性能提高越大。
接收终端的快速移动使得无线MIMO-OFDM通信信道具有快速时变特性。信道快速时变会破坏子载波间的正交性引起子载波间干扰,从而使系统性能下降。论文中首先对时变无线信道建立了一个离散长椭球体序列扩展模型,近似时变信道。同时提出了一种不依赖信道统计特性信息的迭代的估计方法,利用均衡输出的结果消除未知信息序列对信道估计的干扰。在该模型的基础上,采用导频序列对时变MIMO-OFDM信道进行估计。
信道估计通常使用导频符号辅助方法,这种方法需要增加额外的系统带宽。为提高带宽效率,对叠加训练序列的方法进行了研究,该方法将一低功率周期性的训练序列直接叠加于信息序列,对接收数据求均值,不需要任何额外系统带宽,可估计信道。该方法在接收端由于采用未知信息序列的算术均值代替其统计平均值,使得未知的信息序列对信道估计的性能产生影响。提出了一种迭代叠加训练序列信道估计方法,即把经过频域均衡、译码得到的发送信息序列的估计值和叠加训练序列之和作为新的训练序列,反馈到信道估计器再次估计信道状态信息,通过这种判决反馈的迭代信道估计方法提高信道估计的性能。首先对于非时变SISO-OFDM信道应用该方法进行信道估计,然后基于长椭球序列基扩展模型,将该方法扩展到快速时变的MIMO-OFDM信道估计。
在实际的多用户MIMO-OFDM通信环境中存在有色干扰和信道空间相关,导致信道估计性能的降低。本文在发射总功率恒定的条件下,基于信道估计均方误差最小原则,利用统计“注水”方法对发送端的导频进行预编码,可提高信道估计的精度。该方法只需知道信道的相关特性和干扰的统计特性,不必对其频繁更新,且容易获得。理论分析表明,最佳的发射方向由发射信道相关矩阵和干扰协方差矩阵的特征值分解共同决定。
One main target of next generation wireless mobile communication is to obtainmore efficiency of spectrum. In wireless communication with the limitation ofbandwidth and power, MIMO is a potential technique to achieve high efficiency ofspectrum, and considering OFDM technology has the property of high tolerance tomulti-path fading and low complexity to receiver desigen, MIMO-OFDM techniquehas been widely regarded as one of the most promising techniques for Beyond 3rdGeneration(B3G) wireless communication, which is efficient for widebandmultimedia transmission because of its high ability of anti-fading, high channelcapacity and high bit-rate data. As a key technique for wireless mobilecommunication, channel estimation is required for coherent detection and space-timedecoding in MIMO-OFDM systems. So channel estimation for MIMO-OFDM systemis the main research content in this dissertation.
The wireless transmission channel with high rates has propertyof sparsitywhichcan be used to improve the performance of channel estimation. For time-invariantsparse MIMO-OFDM channel, a channel estimation algorithm based on generalizedAkaike information criterion(GAIC) is proposed, the channel order and the timedelays of each path can be identified by the algorithm, then the tap ofnon-dissemination path in the time-domain impuse reponse which is estimated byleast square (LS) algorithm is set tozero. Bythis method, performance loss caused byAWGN can be reduced and the channel estimation precision can be improved,meanwhile, as the number of channel path decreases, the proposed algorithmperforms better.
Wireless MIMO-OFDM communication channel has property of fasttime-varying due to the high mobility of terminal. The time-selective fading channeldestroys the orthogonality among different subcarriers and cause Inter-CarrierInterference (ICI), then, sytem performance will decrease. A discrete prolatespheroidal sequence based expansion model is established for time-vaying channel,and an iterative channel estimation method that isn’t depended on the statisticalinformation of CSI is presented. To cancel the interference from the unkowninformation, the equalized symbol is reused in channel estimation. Then the fasttime-varying MIMO-OFDM channel is estimated using pilot sequence by the abovemethod.
Pilot-symbol assisted modulation (PSAM) scheme is usually adopted by channelestimation, but this scheme requires extra system bandwidth. To improve efficiencyof bandwidth, a superimposed training (ST) based channel estimation method is researched. It means that a periodic training sequence with low power issuperimposed to the information sequence at transmitter, with the mean of receiceddata, channel parameters can be estimated without consuming any extra systembandwidth. The unknown information sequence can be interference to channelestimation due to replacing the statistical mean with arithmetic mean. Based on thisan iterative superimposed training (IST) method is presented. The method uses sumof equalized information sequence and superimposed training as new trainingsequence, which is feedback to channel estimator to estimate channel stateinformation (CSI) again. The channel estimation performance can be improved bythefeedback iterative method. First time-invariant SISO-OFDM channel is estimated bythis method, then the method is extended to fast time-varying MIMO-OFDM channelestimation based on discrete prolate spheroidal sequences based expansion model.
There exist colored interference and channel spatial correlation in multi-userMIMO-OFDM system, which result in performance degradation of channelestimation. To improve channel estimation precison, a space-frequency statisticalprecoding scheme is proposed in the dissertation. The precoding scheme is designedat transmitter by utilizing water-filling approach based on the minimum criteria ofchannel estimation mean square error (MSE) under the condition of total powerconstraint. It only requires statistical information of channel correlations and coloredinterference, which need not be updating frequently, and can be easily acquired.Theoretical analysis shows that the optimal transmission directions are determined bythe eigen-decompositions of channel covariance matrices and interference covariancematrices.
高速无线数据传输导致信道呈现出稀疏特性,利用该特性可提高信道估计性能。针对非时变多径稀疏的MIMO-OFDM信道,提出了基于广义Akaike信息论准则的信道估计算法,该算法能够估计出信道的阶数和每条路径的时延,然后将由最小二乘算法估计出的时域信道冲激响应中非传播路径点置0,由此可降低加性白噪声对信道估计的影响,提高信道估计的精度,同时,信道稀疏性越强,性能提高越大。
接收终端的快速移动使得无线MIMO-OFDM通信信道具有快速时变特性。信道快速时变会破坏子载波间的正交性引起子载波间干扰,从而使系统性能下降。论文中首先对时变无线信道建立了一个离散长椭球体序列扩展模型,近似时变信道。同时提出了一种不依赖信道统计特性信息的迭代的估计方法,利用均衡输出的结果消除未知信息序列对信道估计的干扰。在该模型的基础上,采用导频序列对时变MIMO-OFDM信道进行估计。
信道估计通常使用导频符号辅助方法,这种方法需要增加额外的系统带宽。为提高带宽效率,对叠加训练序列的方法进行了研究,该方法将一低功率周期性的训练序列直接叠加于信息序列,对接收数据求均值,不需要任何额外系统带宽,可估计信道。该方法在接收端由于采用未知信息序列的算术均值代替其统计平均值,使得未知的信息序列对信道估计的性能产生影响。提出了一种迭代叠加训练序列信道估计方法,即把经过频域均衡、译码得到的发送信息序列的估计值和叠加训练序列之和作为新的训练序列,反馈到信道估计器再次估计信道状态信息,通过这种判决反馈的迭代信道估计方法提高信道估计的性能。首先对于非时变SISO-OFDM信道应用该方法进行信道估计,然后基于长椭球序列基扩展模型,将该方法扩展到快速时变的MIMO-OFDM信道估计。
在实际的多用户MIMO-OFDM通信环境中存在有色干扰和信道空间相关,导致信道估计性能的降低。本文在发射总功率恒定的条件下,基于信道估计均方误差最小原则,利用统计“注水”方法对发送端的导频进行预编码,可提高信道估计的精度。该方法只需知道信道的相关特性和干扰的统计特性,不必对其频繁更新,且容易获得。理论分析表明,最佳的发射方向由发射信道相关矩阵和干扰协方差矩阵的特征值分解共同决定。
One main target of next generation wireless mobile communication is to obtainmore efficiency of spectrum. In wireless communication with the limitation ofbandwidth and power, MIMO is a potential technique to achieve high efficiency ofspectrum, and considering OFDM technology has the property of high tolerance tomulti-path fading and low complexity to receiver desigen, MIMO-OFDM techniquehas been widely regarded as one of the most promising techniques for Beyond 3rdGeneration(B3G) wireless communication, which is efficient for widebandmultimedia transmission because of its high ability of anti-fading, high channelcapacity and high bit-rate data. As a key technique for wireless mobilecommunication, channel estimation is required for coherent detection and space-timedecoding in MIMO-OFDM systems. So channel estimation for MIMO-OFDM systemis the main research content in this dissertation.
The wireless transmission channel with high rates has propertyof sparsitywhichcan be used to improve the performance of channel estimation. For time-invariantsparse MIMO-OFDM channel, a channel estimation algorithm based on generalizedAkaike information criterion(GAIC) is proposed, the channel order and the timedelays of each path can be identified by the algorithm, then the tap ofnon-dissemination path in the time-domain impuse reponse which is estimated byleast square (LS) algorithm is set tozero. Bythis method, performance loss caused byAWGN can be reduced and the channel estimation precision can be improved,meanwhile, as the number of channel path decreases, the proposed algorithmperforms better.
Wireless MIMO-OFDM communication channel has property of fasttime-varying due to the high mobility of terminal. The time-selective fading channeldestroys the orthogonality among different subcarriers and cause Inter-CarrierInterference (ICI), then, sytem performance will decrease. A discrete prolatespheroidal sequence based expansion model is established for time-vaying channel,and an iterative channel estimation method that isn’t depended on the statisticalinformation of CSI is presented. To cancel the interference from the unkowninformation, the equalized symbol is reused in channel estimation. Then the fasttime-varying MIMO-OFDM channel is estimated using pilot sequence by the abovemethod.
Pilot-symbol assisted modulation (PSAM) scheme is usually adopted by channelestimation, but this scheme requires extra system bandwidth. To improve efficiencyof bandwidth, a superimposed training (ST) based channel estimation method is researched. It means that a periodic training sequence with low power issuperimposed to the information sequence at transmitter, with the mean of receiceddata, channel parameters can be estimated without consuming any extra systembandwidth. The unknown information sequence can be interference to channelestimation due to replacing the statistical mean with arithmetic mean. Based on thisan iterative superimposed training (IST) method is presented. The method uses sumof equalized information sequence and superimposed training as new trainingsequence, which is feedback to channel estimator to estimate channel stateinformation (CSI) again. The channel estimation performance can be improved bythefeedback iterative method. First time-invariant SISO-OFDM channel is estimated bythis method, then the method is extended to fast time-varying MIMO-OFDM channelestimation based on discrete prolate spheroidal sequences based expansion model.
There exist colored interference and channel spatial correlation in multi-userMIMO-OFDM system, which result in performance degradation of channelestimation. To improve channel estimation precison, a space-frequency statisticalprecoding scheme is proposed in the dissertation. The precoding scheme is designedat transmitter by utilizing water-filling approach based on the minimum criteria ofchannel estimation mean square error (MSE) under the condition of total powerconstraint. It only requires statistical information of channel correlations and coloredinterference, which need not be updating frequently, and can be easily acquired.Theoretical analysis shows that the optimal transmission directions are determined bythe eigen-decompositions of channel covariance matrices and interference covariancematrices.
引文
1 J. Chuang, N. Sollenberger. Beyond 3G wideband wireless data access basedon OFDM and dynamic packet assignment. IEEE Communications Magazine.2000, 38(7):78~87
2 B. Helmut, A. lcskei, J. Paulraj. Fixed Broadband Wireless Access: State of theArt, Challenges, and Future Directions. IEEE Communications Magazine. 2001,39(1):100~108
3许希斌,赵明,粟欣(译).宽带无线数字通信.电子工业出版社,2002:234~235
4彭林.第三代移动通信技术.电子工业出版社, 2003:402~409
5 H. Cheon, D. Hong. Effect of channel estimation error in OFDM-based WLAN.Communications IEEE Letters. 2002, 6:190~192
6 S. Coleri, M. Ergen,, A. Puri, A. Bahai. Channel estimation techniques basedon pilot arrangement in OFDM systems. IEEE Transactions on Broadcasting.2002, 48(3):223~229
7 J. H. Ryu, Y. H. Lee. Design of implementation-efficient channel estimationfilter for wireless OFDM transmission. IEEE Vehicular TechnologyConference, Jeju, Korea, 2003, IEEE:1590~1594
8修学俭,罗涛. OFDM移动通信技术原理与应用.人民邮电出版社,2003:16~17
9徐发强,益晓新.无线OFDM系统中基于导频的信道估值器的比较分析.通信学报. 2001, 22(5):17~23
10 E. Telataa, Capacity of Multi-Antenna Gaussian Channels, AT&T-Bell Labs,Internal Tech. June. 1995:287~290
11 M. J. Gans, N. Amitay, Y. S. Yeh. Outdoor BLAST Measurement System at
2.44 GHz: Calibration and Initial Results. IEEE Journal on Selected Areas inCommunications. 2003, 20(3):570~582
12 T. Marzetta. B. Hochwald. Capacity of A Mobile Mutiple-AntennaCommunication Link In Rayleigh Flat Fading, IEEE Trans. Inform. Theory.1999, 45:139~157
13 C. Chuah, D. Tse, J. Kahn, R. A. Valenzuela. Capacity scaling in MIMOwireless systems under correlated fading. IEEE Trans. Inform. Theory, 2002,48:637~650
14 X. Mestre, J. R. Fonollosa, A. Pags-Zamora. Capacity of MIMO Channels:Asymptotic Evaluation under Correlated Fading. IEEE Journal on SelectedAreas in Communications, 2003, 21:829~838
15 X. F. Tao, C. Elena. New detection algorithm of V-BLAST space-time code.IEEE 54th Vehicular Technology Conference, Atlantic City, NJ, United states,2001, IEEE: 2421~2423
16 S. Choe. Space-time trellis-codes for high rate wireless CDMA systems.Electronics Letters, 2003, 39(6):541~543
17 W. H. Wong, E. G. Larsson. Orthogonal space-time block coding with antennaselection and power allocation. Electronics Letters, 2003, 39(4):379~381
18 M. Debbah, R. R. Muller. MIMO channel modeling and the principle ofmaximum entropy. IEEE Transactions On Information Theory. 2005,51(5):1667~1690
19 3GPP and 3GPP2,TSG,SCM一134 Text V6.0,Spatial Channel Model TextDescription,Lucent Nokia,Siemens,and Ericsson,April 2003
20 Lucent Nokia,Siemens,and Ericsson. A Standard set of MIMO RadioPropagation Channels. Tech Rep-3GPP TSG Rl-01-1179, 2001
21陈晓敏,朱江.一种新的MIMO无线信道建模方法.通信技术. 2007,12(40):24~28
22 T. Roman, M. Enescu, V. Koivunen. Time-domain method for trackingdispersive channels in MIMO OFDM systems. IEEE International Conferenceon Acoustics, Speech and Signal Processing, Hong Kong, 2003, IEEE:393~396
23 S. L. Loyka. Channel capacity of MIMO architecture using the exponentialcorrelation matrix. IEEE Commun.Lett., 2001, 5(9):369–371.
24孙山林,侯春萍. MIMO-OFDM系统的信道估计算法.信息技术.2006,1:15~17
25 S. Coleri, M. Ergen, A. Puri. A study of channel estimation in OFDM systems.
56th Vehicular Technology Conference, Vancouver, BC, Canada, 2002,IEEE:894~898
26 O. Edfors, M. Sandell. OFDM channel estimation by singular valuedecomposition. I Proceedings of the 1996 IEEE 46th Vehicular TechnologyConference. Part 1, Atlanta, GA, USA, 1996, Piscataway, NJ, United States ,IEEE, 2006: 923~927
27 V. Erceg, K. V. S. Hari, M. S. Smith, et al. Channel Models for Fixed WirelessApplications. IEEE 802.16 Broadband Wireless Access Working Group ,2003,http://ieee802.org/16
28韩冰,高西奇,尤肖虎. OFDM系统的一种新的信道参数估计方法.应用科学学报. 2004,22(1):41~45
29 B. Yang, K. B. Letaief, R. S. Cheng. Channel estimation for OFDMtransmission in multi-path fading channels based on parametric channelmodeling. IEEE Trans. Commun., 2001, 49(3):467~479
30 J. Du, Y. Li, MIMO-OFDM channel estimation based on subspace tracking.IEEE Vehicular TechnologyConference, Jeju, Korea, 2003, IEEE: 1084~1088
31董亮,曹秀英,毕光国.基于空时分组训练序列的低复杂度MIMO-OFDM信道估计.通信学报. 2006, 27(6):57~63
32 O. Simeone, Y. Bar-Ness. Pilot-based channel estimation for OFDM systemsby tracking the delay-subspace. IEEE Trans on Wireless Commun.,2004,3:315~325
33 C. J. Wu, D. W. Lin. Sparse channel estimation for OFDM transmission basedon representative subspace fitting. IEEE VTC 2005, Stockholm, Sweden 2005,IEEE:495~499
34 W. Zheng, C. Zhou, Y. Zhao, M. Zhao. Sparse channel estimation for OFDMsystems based on pilot signals and MUSIC algorithm. ICWMMN 2006Hangzhou, China, 2006, IEEE:709~712
35居敏,许宗泽. OFDM系统的自适应低秩信道估计.北京航空航天大学学报. 2006, 32(3):328~332
36 B. Friedlander. Random Projections for Sparse Channel Estimation andEqualization. IEEE Conf. on Signals, Systems and Computers: Pacific Grove,2006, Piscataway, NJ, United States, IEEE Computer Society, 2006:453~457
37李立萍,文忠,陈天麒.多径信号到达角和时延高分辨联合估计算法.电子学报. 2006, 34(4):746~750
38 Gong Y, Letaief K B. Low complexitychannel estimation for space-time codedwide band OFDM systems. IEEE Trans on Wireless Commun, 2003, 2:876~882
39 Y. Jing, D. Wang, M. Chen. Reduced complexity MIMO-OFDM channelestimation based on parametric channel model. IEEE Vehicular TechnologyConference, Los Angeles, 2004, IEEE:1372~1376
40李国松,周正欧.无线OFDM系统中信道冲激响应有效阶数估计.通信学报. 2006, 27(1):112~118
41胡蝶,杨绿溪. OFDM系统中基于导频的时变信道估计.电子与信息学报.2004, 26(9):1376~1382
42姜永权,徐湘明.双选择性衰落信道中一种新的OFDM均衡算法.汕头大学学报. 2006, 21(3):59~64
43 G. Leus, I. Barhumi, M. Moonen. MMSE Time-varying FIR Equalization ofDoubly-Selective Channels. IEEE International Conference on Acoustics,Speech and Signal Processing, Hong Kong, 2003, IEEE:3246–3250
44 G. Leus. On the estimation of rapidly time-varying channels. Eusipco2004,Vienna,2004, http://www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/papers/cr1362.pdf
45于晓燕,王加庆,杨绿溪.基于多项式内插的MIMO时间一频率双选择性信道的信道估计.电子与信息学报. 2006, 28(5):871~874
46陈恩庆,陶然,张卫强.一种基于分数阶傅立叶变换的时变信道参数估计方法.电子学报. 2005, 33(12):2101~2104
47 Gorokhov A, Linnartz J P.Robust. OFDM receivers for dispersive time-varyingchannels: equalization and channel acquisition. IEEE Trans, Commun. 2004,52(4):572~583
48任晓敏,倪卫明.一种快时变信道下多载波传输系统的信道估计方法.复旦大学学报. 2007, 46(1):100~105
49于晓燕,王加庆,杨绿溪,何振亚. MIMO-OFDM系统中时间-频率双选择性衰落信道的信道估计.中国科学E辑信息科学. 2005, 35(8): 875~886
50 T. Cui, C. Tellambura, Y. Wu. Low-Complexity Pilot-Aided ChannelEstimation for OFDM Systems Over Doubly-Selective Channels. ICC 2005,Seoul, Korea 2005,IEEE:1980~1984
51 H. Pozidis, A. P. Petropulu. Cross-spectrum based blind channel estimation.IEEE Transactions on Signal Processing. 1997, 45(12):2977~2992
52 J. Tugnait. Identification and deconvolution of multichannel linearnon-Gaussian process using higher order statistics and inverse criteria. IEEETransactions on Signal Processing. 1997, 3(45):658~672
53 L. Tong, G. Xu, T. Kailath. Blind identification and equalization based onsecond-order statistics:a time domain approach. IEEE Transactions onInformation Theory. 1994, 40(2):340~349
54 R. F. Zhang. Blind OFDM channel estimation through linear precoding: asubspace approach. The Thirty-Sixth Asilomar Conference on Signals Systemsand Computers, Pacific Groove, CA, United states, 2002, IEEE ComputerSociety: 631~633
55 S. Roy, C. Y. Li. A subspace blind channel estimation method for OFDMsystems without cyclic prefix. IEEE Transactions on Wireless Communications.2002.1(4):572~579
56 X. Zhou, X. Wang. Channel estimation for OFDM systems using adaptiveradial basis function networks. IEEE Transactions on Vehicular Technology,2003, 52(1):48~59
57 A. Gorokhov, J. P. Linnartz. Robust OFDM receivers for dispersivetime-varying channels: equalization and channel acquisition. IEEE Trans,Commun. 2004, 52(4):572~583
58黄学军,毕厚杰,余松煜. OFDM系统信道盲估计的子空间算法.上海交通大学学报. 2004,38(增刊):6~9
59 B. Muquet, M. Courville, P. Duhamel. Subspace-based blind and semi-blindchannel estimation for OFDM systems, IEEE Transactions on SignalProcessing, 2002, 50(7):1699~1712
60 Y. H. Zeng, T. S. Ng. Subspace-based semi-blind channel estimation forSTC-OFDM, WiCOM 2007, Shanghai, China 2004, Inst. of Elec. and Elec.Eng. Computer Society: 436~439
61 Q. Zhao, L. Tong. Semi-blind equalization by least square smoothing. IEEEConf. Rec. 32nd Asilomar Conf. Signals Syst., Comput, Pacific Grove, ,1998,Los Alamitos, CA, United States, IEEE: 645~649
62 J. K. Tugnait, W. Luo. On Channel Estimation Using Superimposed Trainingand First-Order Statistics. IEEE Communications Letters, 2003, 7 (9):413~415
63徐斌,张建秋,胡波.正交频分复用信道估计的隐藏导频法.信息与电子工程. 2006,4(1):1~5
64刘琦,胡波,袁清升.改进的基于叠加导频序列的OFDM系统的信道估计方法.复旦学报. 2005, 44(6):956~959
65 L. D. Wang, D. M. Lim. A Pilot Embedded Scheme for Doubly-SelectiveMIMO-OFDM Channel Estimation. IEEE Conf. on Asia-Pacific Conference onCommunications, Busan, Korea ,2006, Piscataway, NJ, UnitedStates ,IEEE:1~5
66 L. X. Yang, J. Tao. MIMO Channel Estimation in Time-VaryingFrequency-Selective Fading Case: A First Order Statistical Method. sciencepaper online,2005, http://www.paper.edu.cn/en/paper.php?serial_number=200505-110
67 X. H. Meag, J. K. Tugnait. Doubly-selective MIMO channel estimation usinigsuperimposed training. Sensor Array and Multichannel Signal ProcessingWorkshop Proceedings, Barcelona, Spain, 2004 IEEE:407~ 411
68 J. P. Kermoal, L. Schumacher, P. E. Mogensen. Experimental investigation ofcorrelation properties of MIMO radio channels for indoor picocell scenario. inProc. IEEE Veh. Technol. conf., Boston ,2000, Piscataway, NJ, United States,IEEE:14~21
69 P. Kyritsi and D. C. Cox. Correlation properties of MIMO radio channels forindoor scenarios. in Proc. PIMRC Conf., Pacific Grove, 2002, IEEE:587~592
70 E. Telatar. Capacity of Multi-Antenna Gaussian Channels. European Trans onTelecommunications, 1999, 10(6):585~595
71 S. A. Jafar, A. Goldsmith. On Optimality of Beamforming for MultipleAntenna Systems with Imperfect Feedback. IEEE International Symposium onInformation Theory, Washington, DC , 2001, IEEE:321.
72 E. Jorswiech, H. Boche. On Transmit Diversity with Imperfect Channel StateInformation. IEEE ICASSP, Orlando, Florida USA, 2002, IEEE:2181~2184
73 S. A. Jafar, S. Vishwanath, A. Goldsmith. Channel Capacity and Beamformingfor Multiple Transmit and Receive Antennas with Covariance Feedback.ICC2001, Helsinki, Finland , 2001, IEEE :2266~2271.
74 E.Jorswiech, H. Boche. Channel Capacityand Capacity-range of Beamformingin MIMO Wireless Systems under Correlated Fading with CovarianceFeedback. IEEE Trans on Wireless Commun, 2004, 3(5): 1543~1553
75 S. Zhou, G. B. Giannakis. Optimal transmitter eigen-Beamforming andSpace-Time Block Coding Based on Channel Correlations. IEEE Trans onInform Theory, 2003, 49(7): 1673~1690
76 J. Y. Pang, J. D. Li, L. J. Zhao. Optimal Training Sequences forFrequency-Selective MIMO Correlated Fading Channels. Proc IEEEAINA2007. Niagara Falls, ON, Canada, 2007, Piscataway, NJ, United States,IEEE :820~824
77李宝金,张磊,常永宇.频率选择性空间相关信道下的空频均衡.北京邮电大学学报. 2007, 30(4):107~110
78 Y. Liu, T. F. Wong, W. W. Hager. Training signal design for estimation ofcorrelated MIMO channels with colored interference. IEEE Transactions signalprocessing, 2007, 55(4):1486~1497
79 X. D. Cai, G. B. Giannakis, M. D. Zoltowski. Space–time spreading and blockcoding for correlated fading channels in the presence of interference. IEEETransactions on communications, 2005, 53(3): 515~525
80 T. F. Wong, B. Park. Training sequence optimization in MIMO systems withcolored interference. IEEE Transactions on communications, 2004, 52(11):1939~1947
81 E. Visotsky, U. Madhow. Space-time Transmit Precoding with ImperfectFeedback. IEEE Trans on Inform Theory, 2001, 47(6): 2632~2639
82赵振山,徐国治,范璟.空间相关的MIMO系统发射端最优设计.西安电子科技大学学报. 2006, 33(5): 814~818
83蔡涛,李旭,杜振民译.无线通信原理与应用.电子工业出版社.1999:120~121
84 H. Li, D. Liu, J. Li, P. Stoica. Channel order and RMS delay spread estimationwith application to AC power line communications. Digital Signal Processing.2003, 13(2):284–300
85 I. Barhumi, G. Leus, M. Moonen. Optimal training sequences for channelestimation in MIMO OFDM systems in mobile wireless channels. InInternational Zurich Seminar on Broadband Communications, Access,Transmission, Networking. 2002:441~446
86胡蝶. MIMO OFDM系统中信道估计及最优导频序列设计的研究.东南大学博士学位论文. 2006:76-84
87王宏禹.信号处理相关理论综合与统一法.国防工业出版社, 2005: 6~27
88 T. Zemen, C. F. Mecklenbr?uker, and R. R. Müller. Time variant channelequalization for MC-CDMA via fourier basis functions. Multi-CarrierSpread-Spectrum Workshop, Oberpaffenhofen, Germany, 2003, IEEE:451~454
89 I. Barhumi, G. Leus, M. Moonen. Time-varying FIR equalization ofdoubly-selective channels. International Conference on Communications,Anchorage, AK, United states, 2003, IEEE: 3246~3250
90 D. Slepian. Prolate spheroidal wave functions, Fourier analysis, anduncertainty—V: the discrete case. The Bell System Technical Journal, 1978,57(5):1371~1430
91 D. J. Thomson. Spectrum estimation and harmonic analysis. Mathemticaltechniques. 1982, 70(9):1055~1096.
92 T. Zemen, C. Mecklenbrauker, F. Kaltenberger, and B. H. Fleury.Minimum-energy and band-limited predictor with dynamic subspace selectionfor time-variant fat-fading channels. IEEE Transactions on Signal Processing.2007, 55(9):4534~4548
93 X. Cai and G. B. Giannakis. Bounding performance and suppressingintercarrier interference in wireless mobile OFDM. IEEE Transactions onCommunications. 2003, 51(12):2047~2056
94 W. G. Jeon, K. H. Chang, and Y. S. Cho. An equalization technique fororthogonal divisionmultiplexing systems in time-variant multipath channels.IEEE Transactions on Communications. 1999, 47:27~32
95 Z. J. Tang, R. C. Cannizzaro, G. Leus. Pilot-Assisted Time-Varying ChannelEstimation for OFDM Systems. IEEE Transactions on Signal Processing. 2007,55(5):2226~2238
96李小军,尹长川,乐光新.双衰信道下MIMO-OFDM系统的最优频域导频设计方案.北京邮电大学学报. 2006, 29(3), 53~56
97张贤达.矩阵分析与应用.北京:清华大学出版社, 2004: 244~245
98 L. Rugini, P. Banelli. Banded Equalizers FOR MIMO-OFDM in FastTime-Vaying Channels, Eusipco2006 , 2006, http://www.eurasip.org/Proceedings/Eusipco/Eusipco2006/papers/1568982916.pdf
99 ASTM E2213, Standard Specification for Telecommunications and InformationExchange Between Roadside and Vehicle Systems–5GHz Band DedicatedShort Range Communications (DSRC) Medium Access Control (MAC) andPhysical Layer (PHY) Specifications, http://www.astm.org, February2007.
100B. B. Wang, I. Sen, D. W. Matolak. Performance Evaluation of 802.16e inVehicle to Vehicle Channels. VTC 2007-Fall, Baltimore, MD, United states,2007, Piscataway, NJ, United States, IEEE:1406~1410
101Y. R. Zheng and C. Xiao. Simulation models with correct statistical propertiesfor rayleigh fading channels. IEEE Transactions on Communications. 2003,51(6):920~928
102D. F. Xu, L. X. Yang. Channel Estimation for OFDM Using SuperimposedTraining. First IEEE and IFIP International Conference in Central Asia onInternet, Bishkek, Kyrgyzstan, 2005, Inst. of Elec. and Elec. Eng. ComputerSociety:1~5
103N. Chen, G. T. Zhou. What is the price paid for superimposed training inOFDM. IEEE International Conference on Acoustics, Speech, and SignalProcessing, Montreal, Que, Canada ,2004, IEEE:421~424
104A. Jayalath, C. Tellambura. Reducing the peak-to-average power ratio of anOFDM signal through bit or symbol interleaving. Electronics Letters, 2000,36(13):1161~1163
105M. Breiling. SLM peak-power reduction without explicit side information.IEEE Comm. Letters, 2000, 5(6): 239~241
106G. T. Zhou and N. Chen. Superimposed training for doubly selective channels.IEEE Workshop on Statistical Signal Processing, 2003, IEEE: 1652~1655
107A. G. Orozco-Lugo, M. M. Lara, and D. C. McLernon. Channel estimationusing implicit training. IEEE Transactions on Signal Processing. 2004,52(1):240–254
108N. Chen, G. T. Zhou. Superimposed Training for OFDM: A Peak-to-AveragePower Ratio Analysis. IEEE Transactions on Signal Processing. 2006,
54(6):2277~2287
109L. D. Wang, D. M. Lim. A Pilot Embedded Scheme for Doubly-SelectiveMIMO-OFDM Channel Estimation. Asia-Pacific Conference onCommunications, Busan, Korea, 2006, Piscataway, NJ, United States, IEEE:1~5
110X Meng, J. K. Tugnait. Doubly-selective MIMO channel estimation usingsuperimposed training. Sensor Array and Multichannel Signal ProcessingWorkshop, Barcelona, Spain , 2004, IEEE Computer Society: 407~411
111W. M. Younis, A. H. Sayed. Interference suppression of asynchronousmulti-user space-time block coded transmissions. IEEE InternationalConference on Acoustics, Speech and Signal Processing, Montreal, Que,Canada, 2004, IEEE: 789~792
112C. Windpassinger, R. F. H. Fischer, J. B. Huber. Precoding in multiantenna andmultiuser communications. IEEE Transactions on Wireless Communications.2004, (4): 1305~1316
113P. Viswanath and D. Tse. Sum capacity of the vector Gaussian broadcastchannel and downlink-uplink duality. IEEE Transactions on InformationTheory. 2003, 49(8): 1912~1921
114Q. H. Spencer, L. Swindlehurst, M. Haardt. Zero-forcing methods for downlinkspatial multiplexingin multiuser MIMO channels. IEEE Transactions on SignalProcessing. 2004, 2(2): 461~471
115E. Telatar. Capacity of multi-antenna gaussian channels. AT&T-Bell Labs,Internal Tech. Memo, 1995:87~290
116G. J. Foschini. Layered space-time architecture for wireless communication ina fading environ-ment when using multiple antennas. Bell Labs Tech. J. 1996,1(2):41~59
117J. P. Kermoal, L. Schumacher, P. E. Mogensen. Experimental investigation ofcorrelation properties of MIMO radio channels for indoor picocell scenario.IEEE Vehicular TechnologyConfere, Boston, MA, USA, 2000, Piscataway, NJ,United States, IEEE:14–21
118P. Kyritsi, D. C. Cox. Correlation properties of MIMO radio channels forindoor scenarios. Conference Record of the Asilomar Conference on Signals,Systems and Computers, Pacific Grove, CA, United states, 2001, IEEEComputer Society: 994~998
119A. J. Paulraj, R. U. Nabar, and D. Gore. Introduction to space-Time wirelesscommunications. Introd. Space-Time Wireless Commun, 2003, CambridgeUniversityPress : 1~270
120C. N. Chuah, J. M. Kahn, D. N. C. Tse. Capacity Scaling in MIMO WirelessSystems under Correlated Fading. IEEE Trans. Inform. Theory, 2002, 48(3):637–650
121D. Shiu, G. J. Foschini, M. Gans, J. Kahn. Fading Correlation and Its Effect onthe Capacity of Multielement Antenna Systems. IEEE Transactions onCommunications. 2000, 48(3):502–513
122A. M. Sayeed. Deconstructing multi-antenna fading channels. IEEETransactions on Signal Processing. 2002, 50(10):2563~2579
123K. Liu, V. Raghavan, A. M. Sayeed. Capacity Scaling and Spectral Efficiencyin Wideband Correlated MIMO Channels. IEEE Trans. Inform. Theory, 2003,49(10):2504~2526
124V. Veeravalli, Y. Liang, A. M. Sayeed. Correlated MIMO Rayleigh FadingChannels: Capacity, Optimal Signaling and Asymptotics. IEEE Trans. Inform.Theory, 2005. 51 (6):2058~2072
125史荣昌.矩阵分析.北京理工大学出版社, 1996:142~166
126S. Serbetli, Y. Yener. MMSE transmitter design for correlated MIMO systemswith imperfect channel estimates: ower allocation trade-offs. IEEETransactions on wireless communications. 2006, 5(8): 2295~2304.
2 B. Helmut, A. lcskei, J. Paulraj. Fixed Broadband Wireless Access: State of theArt, Challenges, and Future Directions. IEEE Communications Magazine. 2001,39(1):100~108
3许希斌,赵明,粟欣(译).宽带无线数字通信.电子工业出版社,2002:234~235
4彭林.第三代移动通信技术.电子工业出版社, 2003:402~409
5 H. Cheon, D. Hong. Effect of channel estimation error in OFDM-based WLAN.Communications IEEE Letters. 2002, 6:190~192
6 S. Coleri, M. Ergen,, A. Puri, A. Bahai. Channel estimation techniques basedon pilot arrangement in OFDM systems. IEEE Transactions on Broadcasting.2002, 48(3):223~229
7 J. H. Ryu, Y. H. Lee. Design of implementation-efficient channel estimationfilter for wireless OFDM transmission. IEEE Vehicular TechnologyConference, Jeju, Korea, 2003, IEEE:1590~1594
8修学俭,罗涛. OFDM移动通信技术原理与应用.人民邮电出版社,2003:16~17
9徐发强,益晓新.无线OFDM系统中基于导频的信道估值器的比较分析.通信学报. 2001, 22(5):17~23
10 E. Telataa, Capacity of Multi-Antenna Gaussian Channels, AT&T-Bell Labs,Internal Tech. June. 1995:287~290
11 M. J. Gans, N. Amitay, Y. S. Yeh. Outdoor BLAST Measurement System at
2.44 GHz: Calibration and Initial Results. IEEE Journal on Selected Areas inCommunications. 2003, 20(3):570~582
12 T. Marzetta. B. Hochwald. Capacity of A Mobile Mutiple-AntennaCommunication Link In Rayleigh Flat Fading, IEEE Trans. Inform. Theory.1999, 45:139~157
13 C. Chuah, D. Tse, J. Kahn, R. A. Valenzuela. Capacity scaling in MIMOwireless systems under correlated fading. IEEE Trans. Inform. Theory, 2002,48:637~650
14 X. Mestre, J. R. Fonollosa, A. Pags-Zamora. Capacity of MIMO Channels:Asymptotic Evaluation under Correlated Fading. IEEE Journal on SelectedAreas in Communications, 2003, 21:829~838
15 X. F. Tao, C. Elena. New detection algorithm of V-BLAST space-time code.IEEE 54th Vehicular Technology Conference, Atlantic City, NJ, United states,2001, IEEE: 2421~2423
16 S. Choe. Space-time trellis-codes for high rate wireless CDMA systems.Electronics Letters, 2003, 39(6):541~543
17 W. H. Wong, E. G. Larsson. Orthogonal space-time block coding with antennaselection and power allocation. Electronics Letters, 2003, 39(4):379~381
18 M. Debbah, R. R. Muller. MIMO channel modeling and the principle ofmaximum entropy. IEEE Transactions On Information Theory. 2005,51(5):1667~1690
19 3GPP and 3GPP2,TSG,SCM一134 Text V6.0,Spatial Channel Model TextDescription,Lucent Nokia,Siemens,and Ericsson,April 2003
20 Lucent Nokia,Siemens,and Ericsson. A Standard set of MIMO RadioPropagation Channels. Tech Rep-3GPP TSG Rl-01-1179, 2001
21陈晓敏,朱江.一种新的MIMO无线信道建模方法.通信技术. 2007,12(40):24~28
22 T. Roman, M. Enescu, V. Koivunen. Time-domain method for trackingdispersive channels in MIMO OFDM systems. IEEE International Conferenceon Acoustics, Speech and Signal Processing, Hong Kong, 2003, IEEE:393~396
23 S. L. Loyka. Channel capacity of MIMO architecture using the exponentialcorrelation matrix. IEEE Commun.Lett., 2001, 5(9):369–371.
24孙山林,侯春萍. MIMO-OFDM系统的信道估计算法.信息技术.2006,1:15~17
25 S. Coleri, M. Ergen, A. Puri. A study of channel estimation in OFDM systems.
56th Vehicular Technology Conference, Vancouver, BC, Canada, 2002,IEEE:894~898
26 O. Edfors, M. Sandell. OFDM channel estimation by singular valuedecomposition. I Proceedings of the 1996 IEEE 46th Vehicular TechnologyConference. Part 1, Atlanta, GA, USA, 1996, Piscataway, NJ, United States ,IEEE, 2006: 923~927
27 V. Erceg, K. V. S. Hari, M. S. Smith, et al. Channel Models for Fixed WirelessApplications. IEEE 802.16 Broadband Wireless Access Working Group ,2003,http://ieee802.org/16
28韩冰,高西奇,尤肖虎. OFDM系统的一种新的信道参数估计方法.应用科学学报. 2004,22(1):41~45
29 B. Yang, K. B. Letaief, R. S. Cheng. Channel estimation for OFDMtransmission in multi-path fading channels based on parametric channelmodeling. IEEE Trans. Commun., 2001, 49(3):467~479
30 J. Du, Y. Li, MIMO-OFDM channel estimation based on subspace tracking.IEEE Vehicular TechnologyConference, Jeju, Korea, 2003, IEEE: 1084~1088
31董亮,曹秀英,毕光国.基于空时分组训练序列的低复杂度MIMO-OFDM信道估计.通信学报. 2006, 27(6):57~63
32 O. Simeone, Y. Bar-Ness. Pilot-based channel estimation for OFDM systemsby tracking the delay-subspace. IEEE Trans on Wireless Commun.,2004,3:315~325
33 C. J. Wu, D. W. Lin. Sparse channel estimation for OFDM transmission basedon representative subspace fitting. IEEE VTC 2005, Stockholm, Sweden 2005,IEEE:495~499
34 W. Zheng, C. Zhou, Y. Zhao, M. Zhao. Sparse channel estimation for OFDMsystems based on pilot signals and MUSIC algorithm. ICWMMN 2006Hangzhou, China, 2006, IEEE:709~712
35居敏,许宗泽. OFDM系统的自适应低秩信道估计.北京航空航天大学学报. 2006, 32(3):328~332
36 B. Friedlander. Random Projections for Sparse Channel Estimation andEqualization. IEEE Conf. on Signals, Systems and Computers: Pacific Grove,2006, Piscataway, NJ, United States, IEEE Computer Society, 2006:453~457
37李立萍,文忠,陈天麒.多径信号到达角和时延高分辨联合估计算法.电子学报. 2006, 34(4):746~750
38 Gong Y, Letaief K B. Low complexitychannel estimation for space-time codedwide band OFDM systems. IEEE Trans on Wireless Commun, 2003, 2:876~882
39 Y. Jing, D. Wang, M. Chen. Reduced complexity MIMO-OFDM channelestimation based on parametric channel model. IEEE Vehicular TechnologyConference, Los Angeles, 2004, IEEE:1372~1376
40李国松,周正欧.无线OFDM系统中信道冲激响应有效阶数估计.通信学报. 2006, 27(1):112~118
41胡蝶,杨绿溪. OFDM系统中基于导频的时变信道估计.电子与信息学报.2004, 26(9):1376~1382
42姜永权,徐湘明.双选择性衰落信道中一种新的OFDM均衡算法.汕头大学学报. 2006, 21(3):59~64
43 G. Leus, I. Barhumi, M. Moonen. MMSE Time-varying FIR Equalization ofDoubly-Selective Channels. IEEE International Conference on Acoustics,Speech and Signal Processing, Hong Kong, 2003, IEEE:3246–3250
44 G. Leus. On the estimation of rapidly time-varying channels. Eusipco2004,Vienna,2004, http://www.eurasip.org/Proceedings/Eusipco/Eusipco2004/defevent/papers/cr1362.pdf
45于晓燕,王加庆,杨绿溪.基于多项式内插的MIMO时间一频率双选择性信道的信道估计.电子与信息学报. 2006, 28(5):871~874
46陈恩庆,陶然,张卫强.一种基于分数阶傅立叶变换的时变信道参数估计方法.电子学报. 2005, 33(12):2101~2104
47 Gorokhov A, Linnartz J P.Robust. OFDM receivers for dispersive time-varyingchannels: equalization and channel acquisition. IEEE Trans, Commun. 2004,52(4):572~583
48任晓敏,倪卫明.一种快时变信道下多载波传输系统的信道估计方法.复旦大学学报. 2007, 46(1):100~105
49于晓燕,王加庆,杨绿溪,何振亚. MIMO-OFDM系统中时间-频率双选择性衰落信道的信道估计.中国科学E辑信息科学. 2005, 35(8): 875~886
50 T. Cui, C. Tellambura, Y. Wu. Low-Complexity Pilot-Aided ChannelEstimation for OFDM Systems Over Doubly-Selective Channels. ICC 2005,Seoul, Korea 2005,IEEE:1980~1984
51 H. Pozidis, A. P. Petropulu. Cross-spectrum based blind channel estimation.IEEE Transactions on Signal Processing. 1997, 45(12):2977~2992
52 J. Tugnait. Identification and deconvolution of multichannel linearnon-Gaussian process using higher order statistics and inverse criteria. IEEETransactions on Signal Processing. 1997, 3(45):658~672
53 L. Tong, G. Xu, T. Kailath. Blind identification and equalization based onsecond-order statistics:a time domain approach. IEEE Transactions onInformation Theory. 1994, 40(2):340~349
54 R. F. Zhang. Blind OFDM channel estimation through linear precoding: asubspace approach. The Thirty-Sixth Asilomar Conference on Signals Systemsand Computers, Pacific Groove, CA, United states, 2002, IEEE ComputerSociety: 631~633
55 S. Roy, C. Y. Li. A subspace blind channel estimation method for OFDMsystems without cyclic prefix. IEEE Transactions on Wireless Communications.2002.1(4):572~579
56 X. Zhou, X. Wang. Channel estimation for OFDM systems using adaptiveradial basis function networks. IEEE Transactions on Vehicular Technology,2003, 52(1):48~59
57 A. Gorokhov, J. P. Linnartz. Robust OFDM receivers for dispersivetime-varying channels: equalization and channel acquisition. IEEE Trans,Commun. 2004, 52(4):572~583
58黄学军,毕厚杰,余松煜. OFDM系统信道盲估计的子空间算法.上海交通大学学报. 2004,38(增刊):6~9
59 B. Muquet, M. Courville, P. Duhamel. Subspace-based blind and semi-blindchannel estimation for OFDM systems, IEEE Transactions on SignalProcessing, 2002, 50(7):1699~1712
60 Y. H. Zeng, T. S. Ng. Subspace-based semi-blind channel estimation forSTC-OFDM, WiCOM 2007, Shanghai, China 2004, Inst. of Elec. and Elec.Eng. Computer Society: 436~439
61 Q. Zhao, L. Tong. Semi-blind equalization by least square smoothing. IEEEConf. Rec. 32nd Asilomar Conf. Signals Syst., Comput, Pacific Grove, ,1998,Los Alamitos, CA, United States, IEEE: 645~649
62 J. K. Tugnait, W. Luo. On Channel Estimation Using Superimposed Trainingand First-Order Statistics. IEEE Communications Letters, 2003, 7 (9):413~415
63徐斌,张建秋,胡波.正交频分复用信道估计的隐藏导频法.信息与电子工程. 2006,4(1):1~5
64刘琦,胡波,袁清升.改进的基于叠加导频序列的OFDM系统的信道估计方法.复旦学报. 2005, 44(6):956~959
65 L. D. Wang, D. M. Lim. A Pilot Embedded Scheme for Doubly-SelectiveMIMO-OFDM Channel Estimation. IEEE Conf. on Asia-Pacific Conference onCommunications, Busan, Korea ,2006, Piscataway, NJ, UnitedStates ,IEEE:1~5
66 L. X. Yang, J. Tao. MIMO Channel Estimation in Time-VaryingFrequency-Selective Fading Case: A First Order Statistical Method. sciencepaper online,2005, http://www.paper.edu.cn/en/paper.php?serial_number=200505-110
67 X. H. Meag, J. K. Tugnait. Doubly-selective MIMO channel estimation usinigsuperimposed training. Sensor Array and Multichannel Signal ProcessingWorkshop Proceedings, Barcelona, Spain, 2004 IEEE:407~ 411
68 J. P. Kermoal, L. Schumacher, P. E. Mogensen. Experimental investigation ofcorrelation properties of MIMO radio channels for indoor picocell scenario. inProc. IEEE Veh. Technol. conf., Boston ,2000, Piscataway, NJ, United States,IEEE:14~21
69 P. Kyritsi and D. C. Cox. Correlation properties of MIMO radio channels forindoor scenarios. in Proc. PIMRC Conf., Pacific Grove, 2002, IEEE:587~592
70 E. Telatar. Capacity of Multi-Antenna Gaussian Channels. European Trans onTelecommunications, 1999, 10(6):585~595
71 S. A. Jafar, A. Goldsmith. On Optimality of Beamforming for MultipleAntenna Systems with Imperfect Feedback. IEEE International Symposium onInformation Theory, Washington, DC , 2001, IEEE:321.
72 E. Jorswiech, H. Boche. On Transmit Diversity with Imperfect Channel StateInformation. IEEE ICASSP, Orlando, Florida USA, 2002, IEEE:2181~2184
73 S. A. Jafar, S. Vishwanath, A. Goldsmith. Channel Capacity and Beamformingfor Multiple Transmit and Receive Antennas with Covariance Feedback.ICC2001, Helsinki, Finland , 2001, IEEE :2266~2271.
74 E.Jorswiech, H. Boche. Channel Capacityand Capacity-range of Beamformingin MIMO Wireless Systems under Correlated Fading with CovarianceFeedback. IEEE Trans on Wireless Commun, 2004, 3(5): 1543~1553
75 S. Zhou, G. B. Giannakis. Optimal transmitter eigen-Beamforming andSpace-Time Block Coding Based on Channel Correlations. IEEE Trans onInform Theory, 2003, 49(7): 1673~1690
76 J. Y. Pang, J. D. Li, L. J. Zhao. Optimal Training Sequences forFrequency-Selective MIMO Correlated Fading Channels. Proc IEEEAINA2007. Niagara Falls, ON, Canada, 2007, Piscataway, NJ, United States,IEEE :820~824
77李宝金,张磊,常永宇.频率选择性空间相关信道下的空频均衡.北京邮电大学学报. 2007, 30(4):107~110
78 Y. Liu, T. F. Wong, W. W. Hager. Training signal design for estimation ofcorrelated MIMO channels with colored interference. IEEE Transactions signalprocessing, 2007, 55(4):1486~1497
79 X. D. Cai, G. B. Giannakis, M. D. Zoltowski. Space–time spreading and blockcoding for correlated fading channels in the presence of interference. IEEETransactions on communications, 2005, 53(3): 515~525
80 T. F. Wong, B. Park. Training sequence optimization in MIMO systems withcolored interference. IEEE Transactions on communications, 2004, 52(11):1939~1947
81 E. Visotsky, U. Madhow. Space-time Transmit Precoding with ImperfectFeedback. IEEE Trans on Inform Theory, 2001, 47(6): 2632~2639
82赵振山,徐国治,范璟.空间相关的MIMO系统发射端最优设计.西安电子科技大学学报. 2006, 33(5): 814~818
83蔡涛,李旭,杜振民译.无线通信原理与应用.电子工业出版社.1999:120~121
84 H. Li, D. Liu, J. Li, P. Stoica. Channel order and RMS delay spread estimationwith application to AC power line communications. Digital Signal Processing.2003, 13(2):284–300
85 I. Barhumi, G. Leus, M. Moonen. Optimal training sequences for channelestimation in MIMO OFDM systems in mobile wireless channels. InInternational Zurich Seminar on Broadband Communications, Access,Transmission, Networking. 2002:441~446
86胡蝶. MIMO OFDM系统中信道估计及最优导频序列设计的研究.东南大学博士学位论文. 2006:76-84
87王宏禹.信号处理相关理论综合与统一法.国防工业出版社, 2005: 6~27
88 T. Zemen, C. F. Mecklenbr?uker, and R. R. Müller. Time variant channelequalization for MC-CDMA via fourier basis functions. Multi-CarrierSpread-Spectrum Workshop, Oberpaffenhofen, Germany, 2003, IEEE:451~454
89 I. Barhumi, G. Leus, M. Moonen. Time-varying FIR equalization ofdoubly-selective channels. International Conference on Communications,Anchorage, AK, United states, 2003, IEEE: 3246~3250
90 D. Slepian. Prolate spheroidal wave functions, Fourier analysis, anduncertainty—V: the discrete case. The Bell System Technical Journal, 1978,57(5):1371~1430
91 D. J. Thomson. Spectrum estimation and harmonic analysis. Mathemticaltechniques. 1982, 70(9):1055~1096.
92 T. Zemen, C. Mecklenbrauker, F. Kaltenberger, and B. H. Fleury.Minimum-energy and band-limited predictor with dynamic subspace selectionfor time-variant fat-fading channels. IEEE Transactions on Signal Processing.2007, 55(9):4534~4548
93 X. Cai and G. B. Giannakis. Bounding performance and suppressingintercarrier interference in wireless mobile OFDM. IEEE Transactions onCommunications. 2003, 51(12):2047~2056
94 W. G. Jeon, K. H. Chang, and Y. S. Cho. An equalization technique fororthogonal divisionmultiplexing systems in time-variant multipath channels.IEEE Transactions on Communications. 1999, 47:27~32
95 Z. J. Tang, R. C. Cannizzaro, G. Leus. Pilot-Assisted Time-Varying ChannelEstimation for OFDM Systems. IEEE Transactions on Signal Processing. 2007,55(5):2226~2238
96李小军,尹长川,乐光新.双衰信道下MIMO-OFDM系统的最优频域导频设计方案.北京邮电大学学报. 2006, 29(3), 53~56
97张贤达.矩阵分析与应用.北京:清华大学出版社, 2004: 244~245
98 L. Rugini, P. Banelli. Banded Equalizers FOR MIMO-OFDM in FastTime-Vaying Channels, Eusipco2006 , 2006, http://www.eurasip.org/Proceedings/Eusipco/Eusipco2006/papers/1568982916.pdf
99 ASTM E2213, Standard Specification for Telecommunications and InformationExchange Between Roadside and Vehicle Systems–5GHz Band DedicatedShort Range Communications (DSRC) Medium Access Control (MAC) andPhysical Layer (PHY) Specifications, http://www.astm.org, February2007.
100B. B. Wang, I. Sen, D. W. Matolak. Performance Evaluation of 802.16e inVehicle to Vehicle Channels. VTC 2007-Fall, Baltimore, MD, United states,2007, Piscataway, NJ, United States, IEEE:1406~1410
101Y. R. Zheng and C. Xiao. Simulation models with correct statistical propertiesfor rayleigh fading channels. IEEE Transactions on Communications. 2003,51(6):920~928
102D. F. Xu, L. X. Yang. Channel Estimation for OFDM Using SuperimposedTraining. First IEEE and IFIP International Conference in Central Asia onInternet, Bishkek, Kyrgyzstan, 2005, Inst. of Elec. and Elec. Eng. ComputerSociety:1~5
103N. Chen, G. T. Zhou. What is the price paid for superimposed training inOFDM. IEEE International Conference on Acoustics, Speech, and SignalProcessing, Montreal, Que, Canada ,2004, IEEE:421~424
104A. Jayalath, C. Tellambura. Reducing the peak-to-average power ratio of anOFDM signal through bit or symbol interleaving. Electronics Letters, 2000,36(13):1161~1163
105M. Breiling. SLM peak-power reduction without explicit side information.IEEE Comm. Letters, 2000, 5(6): 239~241
106G. T. Zhou and N. Chen. Superimposed training for doubly selective channels.IEEE Workshop on Statistical Signal Processing, 2003, IEEE: 1652~1655
107A. G. Orozco-Lugo, M. M. Lara, and D. C. McLernon. Channel estimationusing implicit training. IEEE Transactions on Signal Processing. 2004,52(1):240–254
108N. Chen, G. T. Zhou. Superimposed Training for OFDM: A Peak-to-AveragePower Ratio Analysis. IEEE Transactions on Signal Processing. 2006,
54(6):2277~2287
109L. D. Wang, D. M. Lim. A Pilot Embedded Scheme for Doubly-SelectiveMIMO-OFDM Channel Estimation. Asia-Pacific Conference onCommunications, Busan, Korea, 2006, Piscataway, NJ, United States, IEEE:1~5
110X Meng, J. K. Tugnait. Doubly-selective MIMO channel estimation usingsuperimposed training. Sensor Array and Multichannel Signal ProcessingWorkshop, Barcelona, Spain , 2004, IEEE Computer Society: 407~411
111W. M. Younis, A. H. Sayed. Interference suppression of asynchronousmulti-user space-time block coded transmissions. IEEE InternationalConference on Acoustics, Speech and Signal Processing, Montreal, Que,Canada, 2004, IEEE: 789~792
112C. Windpassinger, R. F. H. Fischer, J. B. Huber. Precoding in multiantenna andmultiuser communications. IEEE Transactions on Wireless Communications.2004, (4): 1305~1316
113P. Viswanath and D. Tse. Sum capacity of the vector Gaussian broadcastchannel and downlink-uplink duality. IEEE Transactions on InformationTheory. 2003, 49(8): 1912~1921
114Q. H. Spencer, L. Swindlehurst, M. Haardt. Zero-forcing methods for downlinkspatial multiplexingin multiuser MIMO channels. IEEE Transactions on SignalProcessing. 2004, 2(2): 461~471
115E. Telatar. Capacity of multi-antenna gaussian channels. AT&T-Bell Labs,Internal Tech. Memo, 1995:87~290
116G. J. Foschini. Layered space-time architecture for wireless communication ina fading environ-ment when using multiple antennas. Bell Labs Tech. J. 1996,1(2):41~59
117J. P. Kermoal, L. Schumacher, P. E. Mogensen. Experimental investigation ofcorrelation properties of MIMO radio channels for indoor picocell scenario.IEEE Vehicular TechnologyConfere, Boston, MA, USA, 2000, Piscataway, NJ,United States, IEEE:14–21
118P. Kyritsi, D. C. Cox. Correlation properties of MIMO radio channels forindoor scenarios. Conference Record of the Asilomar Conference on Signals,Systems and Computers, Pacific Grove, CA, United states, 2001, IEEEComputer Society: 994~998
119A. J. Paulraj, R. U. Nabar, and D. Gore. Introduction to space-Time wirelesscommunications. Introd. Space-Time Wireless Commun, 2003, CambridgeUniversityPress : 1~270
120C. N. Chuah, J. M. Kahn, D. N. C. Tse. Capacity Scaling in MIMO WirelessSystems under Correlated Fading. IEEE Trans. Inform. Theory, 2002, 48(3):637–650
121D. Shiu, G. J. Foschini, M. Gans, J. Kahn. Fading Correlation and Its Effect onthe Capacity of Multielement Antenna Systems. IEEE Transactions onCommunications. 2000, 48(3):502–513
122A. M. Sayeed. Deconstructing multi-antenna fading channels. IEEETransactions on Signal Processing. 2002, 50(10):2563~2579
123K. Liu, V. Raghavan, A. M. Sayeed. Capacity Scaling and Spectral Efficiencyin Wideband Correlated MIMO Channels. IEEE Trans. Inform. Theory, 2003,49(10):2504~2526
124V. Veeravalli, Y. Liang, A. M. Sayeed. Correlated MIMO Rayleigh FadingChannels: Capacity, Optimal Signaling and Asymptotics. IEEE Trans. Inform.Theory, 2005. 51 (6):2058~2072
125史荣昌.矩阵分析.北京理工大学出版社, 1996:142~166
126S. Serbetli, Y. Yener. MMSE transmitter design for correlated MIMO systemswith imperfect channel estimates: ower allocation trade-offs. IEEETransactions on wireless communications. 2006, 5(8): 2295~2304.