移动通信系统电波传播的信道模型与时变特性研究
|
摘要
无线信道中传输的电波信号会由于多种衰落机制的共同作用而引入失真、噪声和干扰。为了有效抑制无线通信系统性能的恶化,必须在收发端选择合理的调制、编码、均衡等信号处理技术。在进行抗衰落技术研发和设计的过程中,为了对算法和系统的性能进行评估,首先需要对无线信道的衰落特性进行深入的研究并建立起相应的数学模型。然而无线信道是一个时变的衰落信道,其易变性是目前电波传播理论研究领域的主要难点。为此,本论文以移动通信系统电波传播理论为背景,按照实际测量与理论分析相结合的研究路线,在信道模型与时变衰落特性两个方面开展了理论研究和工程实践,主要工作包含两个方面：
(1)宽带移动通信系统电波传播信道模型的仿真研究。工程实践中,信道模型的应用主要有两种：用于基带算法数值分析的计算机蒙特卡洛仿真和用于频带系统性能评估的信道模型仿真仪表。面向第一种仿真应用,本论文结合室内、外典型场景下的信道测量数据,分析比较了ITU-R以及WINNER所提出信道模型(即M.2135模型以及WINNER Ⅱ模型)对无线信道频率相关性以及多径相关性的仿真结果。随后,基于两种信道模型生成的信道冲激响应(Channel Impulse Responses, CIRs)分别开展LTE链路级仿真。通过对误比特率以及小区吞吐量仿真结果的统计比较,本文分析了ITU-R以及WINNER所提模型对仿真结果的影响,同时,基于对仿真结果置信区间以及模型参数收敛速度的统计分析,本文进一步探讨了上述两种模型对仿真效率的影响。基于上述分析结果,本文对仿真过程中的模型选择给出了建议。面向第二种仿真应用,本文提出了一种通用的信道模型仿真仪表评估方案。通过对信道模型仿真仪表内嵌信道模型程序生成的信道参数以及从仪表输出的衰落信号中提取的信道响应进行验证,该方案能够从软件程序和硬件实现两个方面来对信道模型仿真仪表进行校准和评估。
(2)移动通信系统电波传播的时变信道分析与估计。首先,本文简单概述了传统的多径束识别算法,在此基础上,针对时变信道中多径束动态变化的特性,提出了一种多径束的连续识别算法。该算法通过m阶马尔科夫随机过程来模拟无线信道参数的变化过程。利用该算法,本文从外场测量的时变信道冲激响应中提取了无线信道的时变传播特性,并对其变化过程进行统计建模。此外,本文基于优化的导频设计提出一种新的时变信道估计算法。该算法在有效地抑制由于子载波间干扰所引起的估计误差的同时,有效地避免了由于导频密度增加而导致的频谱效率下降。
本文的宽带信道模型评估方法以及信道模型仿真仪表验证方案为信道建模与仿真领域的研究工作提供了工程性的参考,相关分析结果对移动通信系统性能仿真而言也具有一定的借鉴意义。本文基于Markov随机过程提出的多径束连续识别算法可广泛应用于时变信道电波传播特性的分析中,同时,文章通过对时变信道估计算法的研究在未来高速移动通信系统接收机的设计领域也进行了积极的探索。
Due to the complicated fading mechanisms, signals may encounter severe distortion, noise and interference when they propagate in the radio channels. To suppress system performance deterioration, the appropriate signal processing techniques, such as modulation, coding, and equalization, should be employed at both transmit and receive ends. During the research and development of anti-fading techniques, the in-depth investigation and reliable model of the fading characteristics provide important foundation for the performance evaluation of algorithms and systems. However, due to the dynamic properties of wireless environments, the radio channels show time-varying characteristics, which is one of the biggest challenges in the radio propagation research. So, taking the radio signal propagation theory as background, this thesis dedicates to the investigation and research of the wireless channel models and time-varying properties for the mobile communication systems. Based on the channel measurements and theoretical analysis, some solid achievements have been obtained in this thesis. The main contributions of this thesis are as follows,
(1) The radio channel models of wideband mobile communication systems are evaluated and researched. Generally, the typical engineering applications of radio channel models include the Monte-Carlo simulation of baseband algorithms and channel model emulator designed for radio frequency (RF) system validation. Regarding the first engineering application, this thesis compares the frequency correlation and multipath correlation of the channel models proposed by the ITU-R and WINNER (i.e. the M.2135model and WINNER Ⅱ model), based on the indoor and outdoor channel measurement data. Moreover, these two models are used to generate channel impulse responses (CIRs) for the LTE link level simulation. According to the bit error ratio (BER) and cell throughput statistics, the effects of M.2135and WINNER Ⅱ on the simulation results are studied. Based on the analysis of confidence interval and convergence rate of the simulation results, the effects of M.2135and WINNER Ⅱ on the simulation efficiency are also investigated. This thesis also provides related suggestions for the model selection considering specific simulation requirements. Regarding the second engineering application, this thesis proposes a general validation scheme for the channel model emulator. By validating the channel parameters generated by the integrated model programs of Propsim-F8and CIRs extracted from the fading signals, the scheme verifies the reliability of channel emulators from the perspective of software and hardware.
(2) The fading characteristics and channel estimation algorithms of time-varying channels are investigated in this thesis. The traditional multipath cluster identification algorithm is introduced, following that, a continuous identification algorithm, which uses the m-step Markov process to model the channel variation, is proposed for the multipath clusters in time-varying channels. Based on the continuous identification algorithm, this thesis investigates and models the time-varying fading properties of realistic radio channels obtained through measurement. Moreover, this thesis proposes a novel time-varying channel estimation algorithm with optimized pilot pattern. The proposed algorithm effectively reduces the estimation deviation induced by inter carrier interference and avoids the increase of pilot density and decrease of spectrum efficiency.
The wideband channel model evaluation method and channel emulator validation scheme proposed in this thesis provide practical references for channel modeling and simulation research, and the relevant analysis results are informative for mobile communication systems simulation. Moreover, the Markov-based cluster identification algorithm can be widely used in time-varying channel analysis, and the proposed time-varying channel estimation algorithm provides the foundation for the receiver design of high-speed mobile communication systems.
(1)宽带移动通信系统电波传播信道模型的仿真研究。工程实践中,信道模型的应用主要有两种：用于基带算法数值分析的计算机蒙特卡洛仿真和用于频带系统性能评估的信道模型仿真仪表。面向第一种仿真应用,本论文结合室内、外典型场景下的信道测量数据,分析比较了ITU-R以及WINNER所提出信道模型(即M.2135模型以及WINNER Ⅱ模型)对无线信道频率相关性以及多径相关性的仿真结果。随后,基于两种信道模型生成的信道冲激响应(Channel Impulse Responses, CIRs)分别开展LTE链路级仿真。通过对误比特率以及小区吞吐量仿真结果的统计比较,本文分析了ITU-R以及WINNER所提模型对仿真结果的影响,同时,基于对仿真结果置信区间以及模型参数收敛速度的统计分析,本文进一步探讨了上述两种模型对仿真效率的影响。基于上述分析结果,本文对仿真过程中的模型选择给出了建议。面向第二种仿真应用,本文提出了一种通用的信道模型仿真仪表评估方案。通过对信道模型仿真仪表内嵌信道模型程序生成的信道参数以及从仪表输出的衰落信号中提取的信道响应进行验证,该方案能够从软件程序和硬件实现两个方面来对信道模型仿真仪表进行校准和评估。
(2)移动通信系统电波传播的时变信道分析与估计。首先,本文简单概述了传统的多径束识别算法,在此基础上,针对时变信道中多径束动态变化的特性,提出了一种多径束的连续识别算法。该算法通过m阶马尔科夫随机过程来模拟无线信道参数的变化过程。利用该算法,本文从外场测量的时变信道冲激响应中提取了无线信道的时变传播特性,并对其变化过程进行统计建模。此外,本文基于优化的导频设计提出一种新的时变信道估计算法。该算法在有效地抑制由于子载波间干扰所引起的估计误差的同时,有效地避免了由于导频密度增加而导致的频谱效率下降。
本文的宽带信道模型评估方法以及信道模型仿真仪表验证方案为信道建模与仿真领域的研究工作提供了工程性的参考,相关分析结果对移动通信系统性能仿真而言也具有一定的借鉴意义。本文基于Markov随机过程提出的多径束连续识别算法可广泛应用于时变信道电波传播特性的分析中,同时,文章通过对时变信道估计算法的研究在未来高速移动通信系统接收机的设计领域也进行了积极的探索。
Due to the complicated fading mechanisms, signals may encounter severe distortion, noise and interference when they propagate in the radio channels. To suppress system performance deterioration, the appropriate signal processing techniques, such as modulation, coding, and equalization, should be employed at both transmit and receive ends. During the research and development of anti-fading techniques, the in-depth investigation and reliable model of the fading characteristics provide important foundation for the performance evaluation of algorithms and systems. However, due to the dynamic properties of wireless environments, the radio channels show time-varying characteristics, which is one of the biggest challenges in the radio propagation research. So, taking the radio signal propagation theory as background, this thesis dedicates to the investigation and research of the wireless channel models and time-varying properties for the mobile communication systems. Based on the channel measurements and theoretical analysis, some solid achievements have been obtained in this thesis. The main contributions of this thesis are as follows,
(1) The radio channel models of wideband mobile communication systems are evaluated and researched. Generally, the typical engineering applications of radio channel models include the Monte-Carlo simulation of baseband algorithms and channel model emulator designed for radio frequency (RF) system validation. Regarding the first engineering application, this thesis compares the frequency correlation and multipath correlation of the channel models proposed by the ITU-R and WINNER (i.e. the M.2135model and WINNER Ⅱ model), based on the indoor and outdoor channel measurement data. Moreover, these two models are used to generate channel impulse responses (CIRs) for the LTE link level simulation. According to the bit error ratio (BER) and cell throughput statistics, the effects of M.2135and WINNER Ⅱ on the simulation results are studied. Based on the analysis of confidence interval and convergence rate of the simulation results, the effects of M.2135and WINNER Ⅱ on the simulation efficiency are also investigated. This thesis also provides related suggestions for the model selection considering specific simulation requirements. Regarding the second engineering application, this thesis proposes a general validation scheme for the channel model emulator. By validating the channel parameters generated by the integrated model programs of Propsim-F8and CIRs extracted from the fading signals, the scheme verifies the reliability of channel emulators from the perspective of software and hardware.
(2) The fading characteristics and channel estimation algorithms of time-varying channels are investigated in this thesis. The traditional multipath cluster identification algorithm is introduced, following that, a continuous identification algorithm, which uses the m-step Markov process to model the channel variation, is proposed for the multipath clusters in time-varying channels. Based on the continuous identification algorithm, this thesis investigates and models the time-varying fading properties of realistic radio channels obtained through measurement. Moreover, this thesis proposes a novel time-varying channel estimation algorithm with optimized pilot pattern. The proposed algorithm effectively reduces the estimation deviation induced by inter carrier interference and avoids the increase of pilot density and decrease of spectrum efficiency.
The wideband channel model evaluation method and channel emulator validation scheme proposed in this thesis provide practical references for channel modeling and simulation research, and the relevant analysis results are informative for mobile communication systems simulation. Moreover, the Markov-based cluster identification algorithm can be widely used in time-varying channel analysis, and the proposed time-varying channel estimation algorithm provides the foundation for the receiver design of high-speed mobile communication systems.
引文
[1]IEEE P802.11p-2010:Part 11, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications:Amendment 6:Wireless Access in Vehicular Environments, Jul.15,2010.
[2]M. Hata, "Empirical formulas for propagation loss in land mobile radio service," IEEE Trans. Veh. Technol., vol.29,1980,317-325.
[3]W. C. Y. Lee, Mobile Communications Engineering, New York:McGraw-Hill,1982.
[4]R. H. Clarke, "A statistical theory of mobile-radio reception," Bell Syst. Tech. J.1968, 47:975-1000.
[5]W. C. Jakes, Microwave Mobile Communications, New York:Wiley,1974.
[6]R. R. Kurth, D. L. Snyder, and E. V. Hoversten, "Detection and estimation theory," MIT, Research Lab of Electronics, No.93,1969,177-205.
[7]J. P. Kermoal, et al., "A stochastic MIMO radio channel model with experimental validation," IEEE J. Sel. Areas Coramun, vol.20,2002,1211-1226.
[8]M. Failli, et al., Digital Land Mobile Radio Communications-COST 207, Luxemburg, European Union,1989.
[9]ITU, Guidelines for Evaluation of Radio Transmission Technologies for IMT-2000, International Telecommunications Union, Tech. Report,1997.
[10]3GPP TR 25.996, Spatial Channel Model for MIMO Simulations (Release 8),3GPP Organizational Partners,2008.
[11]D. S. Baum, J. Salo, G. Del Galdo, et al., "An interim channel model for beyond-3G systems," in Proc. IEEE VTC'05, Stockholm, Sweden, May 2005.
[12]V. Erceg, L. Schumacher, P. Kyritsi, et al., IEEE 802.11-03/940r2, TGn Channel Models, Jan.2004.
[13]ITU Std. M.2135, Guidelines for Evaluation of Radio Interface Technologies for IMT-Advanced,2008.
[14]IST-2003-507581 WINNER D5.4 v.1.4, Final Report on Link Level and System Level Channel Models, Nov.2005.
[15]IST-4-027756 WINNER Ⅱ D 1.1.1 V1.l, WINNER Ⅱ Interim Channel Models, Nov. 2006.
[16]IST-4-027756 WINNER Ⅱ Dl.1.2 V1.0, WINNER Ⅱ Channel Models,2007.
[17]M. Narandzic, C. Schneider, R. Thoma, et al., "Comparison of SCM, SCME, and WINNER channel models," in Proc. IEEE Veh. Technol. Conf. (VTC2007-Spring), Dublin, Ireland, Apr.2007, pp.413-417.
[18]J. Medbo, M. Riback, and J. E. Berg, "Validation of 3GPP spatial channel model including winner widebande extension using measurements," in Proc. IEEE Veh. Technol. Conf. (VTC2006-Fall), Montreal, Canada, Sep.2006, pp.1-5.
[19]C. C. Chong, F. Watanabe, H. Inamura, et al., "Performance comparison of the 3GPP/3GPP2 SCM and ITU-R IMT-Advanced MIMO channel models," in Proc. IEEE 20th Int. Symp. Personal, Indoor and Mobile Radio Communications (PIMRC2009), Tokyo, Japan, Sept.2009, pp.13-16.
[20]P. Kyosti and T. Jamsa, "Complexity comparison of MIMO channel modeling methods," in Proc. IEEE Int. Symp. Wireless Communication Systems (ISWCS2007), Trondheim, Norway, Oct.2007, pp.219-223.
[21]Y. Zhang, J. H. Zhang, P. J. Smith, et al., "Reduced complexity models for IMT-Advanced evaluation," EURASIP Journal on Wireless Communications and Networking,2009.
[22]M. K. Steven,统计信号处理基础：估计与检测理论.北京：电子工业出版社,2006,129-150.
[23]B. H. Fleury, P. Jourdan, and A. Stucki, "High-resolution channel parameter estimation for MIMO applications using the SAGE algorithm," in Proc. International Zurich Seminar on Broadband Communications,2002, pp.30-1-30-9.
[24]L. Vuokko, P. Vainikainen, and J. Takada, "Clusters extracted from measured propagation channels in macrocellular environments," IEEE Trans. Antennas Propag., vol.53, no.12, pp.4089-4098,2005.
[25]K. Yu, Q. Li, and M. Ho, "Measurement investigation of tap and cluster angular spreads at 5.2 GHz," IEEE Trans. Antennas Propag., vol.53, no.7, pp.2156-2160,2005.
[26]C.-C. Chong, C.-M. Tan, D. I. Laurenson, et al., "A new statistical wideband spatio-temporal channel model for 5 GHz band WLAN systems," IEEE J. Sel. Areas Commun., vol.21, no.2, pp.139-150,2003.
[27]N. Czink, P. Cera, J. Salo, et al., "A framework for automatic clustering of parametric MIMO channel data including path powers," in Proc. VTC-2006 Fall Vehicular Technology Conf.2006 IEEE 64th,2006, pp.1-5.
[28]W. Dong, J. Zhang, X. Gao, et al., "Cluster identification and properties of outdoor wideband MIMO channel," in Proc. VTC-2007 Fall Vehicular Technology Conf.2007 IEEE 66th,2007, pp.829-833.
[29]K.-H. Li, M. A. Ingram, and A. Van Nguyen, "Impact of clustering in statistical indoor propagation models on link capacity," IEEE Trans. Commun., vol.50, no.4, pp. 521-523,2002.
[30]Y. Mostofi, and D.C. Cox, "ICI mitigation for pilot-aided OFDM mobile systems," IEEE Trans. Wireless Commun., vol.4, no.2, pp.765-774, March 2005.
[31]H. Hijazi, E.P. Simon, M. Lienard, et al., "Channel estimation for MIMO-OFDM systems in fast time-varying environments," 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP), pp.1-6,3-5 March 2010.
[32]M. C. Necker; and G. L. Stuber, "Totally blind channel estimation for OFDM on fast varying mobile radio channels," IEEE Trans. Wireless Commun., vol.3, no.5, pp.1514-1525, Sept.2004.
[33]A. F. Molisch, Wireless Communications,2nd ed., John Wiley & Sons,2010.
[34]M. Nakagami, "The m-distribution-A general formula for intensity distribution of rapid fading," in Statistical Methods in Radio Wave Propagation, W. G. Hoffman, Ed. Oxford, U. K.:Pergamon,1960.
[35]L. M. Correia, Wireless Flexible Personalized Communications-COST 259, European Co-operation in Mobile Radio Research, Chichester, UK:John Wiley & Sons, Ltd., 2001.
[36]L. M. Correia, Towards Mobile Broadband Multimedia Networks-COST 273, Antennas and Propagation, vol.2, pp.569-572, April 2003.
[37]C. N. Chuah, J. M. Kahn, and D. Tse, "Capacity of multi-antenna array systems in indoor wireless environment," in Proc.1998 IEEE Global Telecommunications Conference (GLOBECOM 1998). Sydney, Australia, November 8-12,1998, pp.1894-1899.
[38]D. S. Shiu, G. J. Foschini, M. J. Gans, et al., "Fading correlation and its effect on the capacity of multielement antenna systems," IEEE Trans. Commun., vol.48,2000, pp. 502-513.
[39]D. Chizhik, F. Rashid-Farrokhi, J. Ling, et al., "Effect of antenna separation on the capacity of BLAST in correlated channels," IEEE Communications Letters,2000,4(11), pp.337-339.
[40]J. P. Kermoal, L. Schumacher, K. I. Pedersen, et al., "A stochastic MIMO radio channel model with experimental validation," IEEE J. Sel. Areas Commu.,2002,20(6):pp. 1211-1226.
[41]W. Weichselberger, M. Herdin, H. Ozcelik, et al., "A stochastic MIMO channel model with joint correlation of both link ends," IEEE Trans. Wireless Commun.,2006,5(1):pp. 90-100.
[42]N. Costa, and S.Haykin, "A novel wideband MIMO channel model and experimental validation," IEEE Trans. Antenn. Propag.,2008,56 (2):pp.550-562.
[43]A. Stucki, et al., "PropSound system specification document:Concept and specifications," Elektrobit AG, Switzerland, Tech. Rep.,2001.
[44]R. J. C. Bultitude, "Estimating frequency correlation functions from propagation measurements on fading radio channels:a critical review," IEEE J. Sel. Areas Commun., vol.20, no.6, pp.1133-1143, Aug.2002.
[45]A. F. Molisch, "Wideband and directional channel characterization," in Wireless Communications,2nd ed. Chichester, UK:Wiley,2011.
[46]黄程祥,基于电波传播测量的宽带多天线信道空域特性研究和容量评估[学位论文],北京,北京邮电大学,2010.
[47]黄晨,宽带多维MIMO信道分簇算法研究[学位论文],北京,北京邮电大学,2009.
[48]N. Czink, P. Cera, J. Salo, et al., "Automatic clustering of MIMO channel parameters using the multi-path component distance measure," in Proc. WPMC2005,2005, pp. 829-833.
[49]U. Maulik and S. Bandyopadhyay, "Performance evaluation of some clustering algorithms and validity indices," IEEE Trans. Pattern Anal. Mach. Intell., vol.24, no.12, pp.1650-1654,2002.
[50]J. Menhi, Stochastic Process,2nd ed. New Age International Press,2002.
[51]J. Wang, "Ergodic for multiple nonhomogeneous markov chains and application," M.S. thesis, Inst. Sci., Jiangsu Univ., Zhenjiang, China, Dec.2006.
[52]N. Czink, C. Mecklenbrauker, and G. Del Galdo, "A novel automatic cluster tracking algorithm," in Proc. IEEE 17th Int. Personal, Indoor and Mobile Radio Communications Symp.,2006, pp.1-5.
[53]M. Ozdemir and H. Arslan, "Channel estimation for wireless OFDM systems," IEEE Surveys Tutorials Commun., vol.9, no.2, pp.18-48,2007.
[54]P. Strobach, "Low-rank adaptive filters," IEEE Trans. Signal Process., vol.44, no.12, pp.2932-2947, Dec.1996.
[55]X. Doukopoulos and GMoustakides, "Fast and stable subspace tracking," IEEE Trans. Signal Process., vol.56, no.4, pp.1452-1465, Apr.2008.
[56]E. De Carvalho and D. Slock, "Cramer-rao bounds for semi-blind, blind and training sequence based channel estimation," in Proc. IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications, pp.129-132, Apr.1997.
[57]IST-2000-30148,I METRA Project,2000.
[58]H. Hijazi, E.P. Simon, M. Lienard, et al., "Channel estimation for MIMO-OFDM systems in fast time-varying environments," in Proc.4th International Symposium on Communications, Control and Signal Processing (ISCCSP 2010), pp.1-6, March 2010.
[59]P. Schniter, "Low-complexity equalization of OFDM in doubly selective channels," IEEE Trans. Signal Process., vol.52, no.4, pp.1002-1011, Apr.2004.
[2]M. Hata, "Empirical formulas for propagation loss in land mobile radio service," IEEE Trans. Veh. Technol., vol.29,1980,317-325.
[3]W. C. Y. Lee, Mobile Communications Engineering, New York:McGraw-Hill,1982.
[4]R. H. Clarke, "A statistical theory of mobile-radio reception," Bell Syst. Tech. J.1968, 47:975-1000.
[5]W. C. Jakes, Microwave Mobile Communications, New York:Wiley,1974.
[6]R. R. Kurth, D. L. Snyder, and E. V. Hoversten, "Detection and estimation theory," MIT, Research Lab of Electronics, No.93,1969,177-205.
[7]J. P. Kermoal, et al., "A stochastic MIMO radio channel model with experimental validation," IEEE J. Sel. Areas Coramun, vol.20,2002,1211-1226.
[8]M. Failli, et al., Digital Land Mobile Radio Communications-COST 207, Luxemburg, European Union,1989.
[9]ITU, Guidelines for Evaluation of Radio Transmission Technologies for IMT-2000, International Telecommunications Union, Tech. Report,1997.
[10]3GPP TR 25.996, Spatial Channel Model for MIMO Simulations (Release 8),3GPP Organizational Partners,2008.
[11]D. S. Baum, J. Salo, G. Del Galdo, et al., "An interim channel model for beyond-3G systems," in Proc. IEEE VTC'05, Stockholm, Sweden, May 2005.
[12]V. Erceg, L. Schumacher, P. Kyritsi, et al., IEEE 802.11-03/940r2, TGn Channel Models, Jan.2004.
[13]ITU Std. M.2135, Guidelines for Evaluation of Radio Interface Technologies for IMT-Advanced,2008.
[14]IST-2003-507581 WINNER D5.4 v.1.4, Final Report on Link Level and System Level Channel Models, Nov.2005.
[15]IST-4-027756 WINNER Ⅱ D 1.1.1 V1.l, WINNER Ⅱ Interim Channel Models, Nov. 2006.
[16]IST-4-027756 WINNER Ⅱ Dl.1.2 V1.0, WINNER Ⅱ Channel Models,2007.
[17]M. Narandzic, C. Schneider, R. Thoma, et al., "Comparison of SCM, SCME, and WINNER channel models," in Proc. IEEE Veh. Technol. Conf. (VTC2007-Spring), Dublin, Ireland, Apr.2007, pp.413-417.
[18]J. Medbo, M. Riback, and J. E. Berg, "Validation of 3GPP spatial channel model including winner widebande extension using measurements," in Proc. IEEE Veh. Technol. Conf. (VTC2006-Fall), Montreal, Canada, Sep.2006, pp.1-5.
[19]C. C. Chong, F. Watanabe, H. Inamura, et al., "Performance comparison of the 3GPP/3GPP2 SCM and ITU-R IMT-Advanced MIMO channel models," in Proc. IEEE 20th Int. Symp. Personal, Indoor and Mobile Radio Communications (PIMRC2009), Tokyo, Japan, Sept.2009, pp.13-16.
[20]P. Kyosti and T. Jamsa, "Complexity comparison of MIMO channel modeling methods," in Proc. IEEE Int. Symp. Wireless Communication Systems (ISWCS2007), Trondheim, Norway, Oct.2007, pp.219-223.
[21]Y. Zhang, J. H. Zhang, P. J. Smith, et al., "Reduced complexity models for IMT-Advanced evaluation," EURASIP Journal on Wireless Communications and Networking,2009.
[22]M. K. Steven,统计信号处理基础：估计与检测理论.北京：电子工业出版社,2006,129-150.
[23]B. H. Fleury, P. Jourdan, and A. Stucki, "High-resolution channel parameter estimation for MIMO applications using the SAGE algorithm," in Proc. International Zurich Seminar on Broadband Communications,2002, pp.30-1-30-9.
[24]L. Vuokko, P. Vainikainen, and J. Takada, "Clusters extracted from measured propagation channels in macrocellular environments," IEEE Trans. Antennas Propag., vol.53, no.12, pp.4089-4098,2005.
[25]K. Yu, Q. Li, and M. Ho, "Measurement investigation of tap and cluster angular spreads at 5.2 GHz," IEEE Trans. Antennas Propag., vol.53, no.7, pp.2156-2160,2005.
[26]C.-C. Chong, C.-M. Tan, D. I. Laurenson, et al., "A new statistical wideband spatio-temporal channel model for 5 GHz band WLAN systems," IEEE J. Sel. Areas Commun., vol.21, no.2, pp.139-150,2003.
[27]N. Czink, P. Cera, J. Salo, et al., "A framework for automatic clustering of parametric MIMO channel data including path powers," in Proc. VTC-2006 Fall Vehicular Technology Conf.2006 IEEE 64th,2006, pp.1-5.
[28]W. Dong, J. Zhang, X. Gao, et al., "Cluster identification and properties of outdoor wideband MIMO channel," in Proc. VTC-2007 Fall Vehicular Technology Conf.2007 IEEE 66th,2007, pp.829-833.
[29]K.-H. Li, M. A. Ingram, and A. Van Nguyen, "Impact of clustering in statistical indoor propagation models on link capacity," IEEE Trans. Commun., vol.50, no.4, pp. 521-523,2002.
[30]Y. Mostofi, and D.C. Cox, "ICI mitigation for pilot-aided OFDM mobile systems," IEEE Trans. Wireless Commun., vol.4, no.2, pp.765-774, March 2005.
[31]H. Hijazi, E.P. Simon, M. Lienard, et al., "Channel estimation for MIMO-OFDM systems in fast time-varying environments," 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP), pp.1-6,3-5 March 2010.
[32]M. C. Necker; and G. L. Stuber, "Totally blind channel estimation for OFDM on fast varying mobile radio channels," IEEE Trans. Wireless Commun., vol.3, no.5, pp.1514-1525, Sept.2004.
[33]A. F. Molisch, Wireless Communications,2nd ed., John Wiley & Sons,2010.
[34]M. Nakagami, "The m-distribution-A general formula for intensity distribution of rapid fading," in Statistical Methods in Radio Wave Propagation, W. G. Hoffman, Ed. Oxford, U. K.:Pergamon,1960.
[35]L. M. Correia, Wireless Flexible Personalized Communications-COST 259, European Co-operation in Mobile Radio Research, Chichester, UK:John Wiley & Sons, Ltd., 2001.
[36]L. M. Correia, Towards Mobile Broadband Multimedia Networks-COST 273, Antennas and Propagation, vol.2, pp.569-572, April 2003.
[37]C. N. Chuah, J. M. Kahn, and D. Tse, "Capacity of multi-antenna array systems in indoor wireless environment," in Proc.1998 IEEE Global Telecommunications Conference (GLOBECOM 1998). Sydney, Australia, November 8-12,1998, pp.1894-1899.
[38]D. S. Shiu, G. J. Foschini, M. J. Gans, et al., "Fading correlation and its effect on the capacity of multielement antenna systems," IEEE Trans. Commun., vol.48,2000, pp. 502-513.
[39]D. Chizhik, F. Rashid-Farrokhi, J. Ling, et al., "Effect of antenna separation on the capacity of BLAST in correlated channels," IEEE Communications Letters,2000,4(11), pp.337-339.
[40]J. P. Kermoal, L. Schumacher, K. I. Pedersen, et al., "A stochastic MIMO radio channel model with experimental validation," IEEE J. Sel. Areas Commu.,2002,20(6):pp. 1211-1226.
[41]W. Weichselberger, M. Herdin, H. Ozcelik, et al., "A stochastic MIMO channel model with joint correlation of both link ends," IEEE Trans. Wireless Commun.,2006,5(1):pp. 90-100.
[42]N. Costa, and S.Haykin, "A novel wideband MIMO channel model and experimental validation," IEEE Trans. Antenn. Propag.,2008,56 (2):pp.550-562.
[43]A. Stucki, et al., "PropSound system specification document:Concept and specifications," Elektrobit AG, Switzerland, Tech. Rep.,2001.
[44]R. J. C. Bultitude, "Estimating frequency correlation functions from propagation measurements on fading radio channels:a critical review," IEEE J. Sel. Areas Commun., vol.20, no.6, pp.1133-1143, Aug.2002.
[45]A. F. Molisch, "Wideband and directional channel characterization," in Wireless Communications,2nd ed. Chichester, UK:Wiley,2011.
[46]黄程祥,基于电波传播测量的宽带多天线信道空域特性研究和容量评估[学位论文],北京,北京邮电大学,2010.
[47]黄晨,宽带多维MIMO信道分簇算法研究[学位论文],北京,北京邮电大学,2009.
[48]N. Czink, P. Cera, J. Salo, et al., "Automatic clustering of MIMO channel parameters using the multi-path component distance measure," in Proc. WPMC2005,2005, pp. 829-833.
[49]U. Maulik and S. Bandyopadhyay, "Performance evaluation of some clustering algorithms and validity indices," IEEE Trans. Pattern Anal. Mach. Intell., vol.24, no.12, pp.1650-1654,2002.
[50]J. Menhi, Stochastic Process,2nd ed. New Age International Press,2002.
[51]J. Wang, "Ergodic for multiple nonhomogeneous markov chains and application," M.S. thesis, Inst. Sci., Jiangsu Univ., Zhenjiang, China, Dec.2006.
[52]N. Czink, C. Mecklenbrauker, and G. Del Galdo, "A novel automatic cluster tracking algorithm," in Proc. IEEE 17th Int. Personal, Indoor and Mobile Radio Communications Symp.,2006, pp.1-5.
[53]M. Ozdemir and H. Arslan, "Channel estimation for wireless OFDM systems," IEEE Surveys Tutorials Commun., vol.9, no.2, pp.18-48,2007.
[54]P. Strobach, "Low-rank adaptive filters," IEEE Trans. Signal Process., vol.44, no.12, pp.2932-2947, Dec.1996.
[55]X. Doukopoulos and GMoustakides, "Fast and stable subspace tracking," IEEE Trans. Signal Process., vol.56, no.4, pp.1452-1465, Apr.2008.
[56]E. De Carvalho and D. Slock, "Cramer-rao bounds for semi-blind, blind and training sequence based channel estimation," in Proc. IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications, pp.129-132, Apr.1997.
[57]IST-2000-30148,I METRA Project,2000.
[58]H. Hijazi, E.P. Simon, M. Lienard, et al., "Channel estimation for MIMO-OFDM systems in fast time-varying environments," in Proc.4th International Symposium on Communications, Control and Signal Processing (ISCCSP 2010), pp.1-6, March 2010.
[59]P. Schniter, "Low-complexity equalization of OFDM in doubly selective channels," IEEE Trans. Signal Process., vol.52, no.4, pp.1002-1011, Apr.2004.