铁路环境下基于LTE的分布式MIMO无线通信系统研究
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摘要
为了满足铁路运输向高速化、信息化、智能化方向发展的需求,铁路通信技术也需要向数字化、无线移动化、综合业务化及宽带化方向发展。目前我国铁路系统正在使用基于2G(2rd Generation)技术的GSM-R(GSM for Railway)通信平台,但GSM-R通信系统仅能支持速率为9.6kb／s的话音传输,无法满足未来对高速数据传输的需求。此外,铁路环境中的无线信道有其特殊性,具有多径环境简单、直射波占主导位置、多普勒频移较大等特点。
LTE(Long Term Evolution)是常被人们称作“3.9G”的五个无线传输标准中的一个,其性能优势主要包括：更低的比特开销、提供更好的服务、更加灵活的带宽分配策略、更简单的网络结构和频谱利用率的提升。MIMO(Multiple Input Multiple Output)做为LTE的关键技术之一,可以有效地利用信道的随机衰落和多径传播,在不额外增加功率和带宽条件下成倍增加传输速率和大幅度改善链路质量。因此研究MIMO技术在铁路通信中的应用对寻找下一代铁路无线通信技术有重要意义。
本文首先研究了MIMO信道建模,从模型分类、天线间空间相关矩阵计算方法、参数设置等方面,对基于Kronekcer积的空时相关统计特性建模(KBSM, Kronekcer-based Spatial Model)方法进行详细的阐述,并进行了仿真。给出了信道时间色散特性、频率相关性、空间相关性以及多普勒功率谱等信道特性,论证了KBSM建模的合理性。其次,本文参照LTE标准内容,介绍了LTE下行信道的帧结构和下行物理信道处理过程,并对下行物理信道处理过程进行仿真。然后还讨论了三种MIMO检测技术：迫零算法,最小均方误差算法和串行干扰消除算法,对它们的理论进行了推导,并进行了仿真比较,仿真结果表明将串行干扰消除算法与ZF(Zero Forcing)和MMSE(Minimum Mean Square Error)算法相结合,可以有效的提高系统的误码率性能,且算法复杂度较低、耗时较少、易于实现。
本文主要贡献是基于列车车身较长的特点,讨论了一种适用于列车的分布式MIMO系统。与一般讨论较多的发射端天线为分布式的系统不同,列车的分布式MIMO系统采用发射端单MIMO组,而在列车上分布多个MIMO天线组。根据此分布式MIMO的特点研究了两种基于范数的方法进行MIMO天线组选择并对其进行了仿真。仿真结果表明,采用分布式MIMO系统可以大幅度提升误码率性能,对信道容量也有一定的提升,且算法简单易于实现。
In order to meet the high-speed, informationization and intelligent demands of railway transport, railway communication technologies also need to move towards the direction to provide digital, wireless mobile and broadband integrated services. Currently, while the GSM-R (GSM for Railway) communications platform has been utilized in our country, it can only support voice services of data rate 9.6kb/s and can not meet the future demands of railway transport for high-rate data transmissions. In addition, the wireless channel in railway environment possesses its particularity including simple multipath environment, direct wave in dominant position, larger Doppler frequency shift and so on.
LTE (Long Term Evolution)is one of five major wireless standards referred to as "3.9G" the performance advantages of which involves lower bit-cost, better service quality, more flexible bandwidth allocations, simpler network architecture and higher spectral efficiency. As one of the key technologies of LTE, MIMO (Multiple Input Multiple Output) can effectively utilize the random channel fading and multipath propagation to increase the transmission rate and improve the link performance without additional power and bandwidth consumption. Therefore, researches on the application of MIMO technology in railway communications is important for searching next generation railway wireless communication system.
This thesis discusses the channel modeling of MIMO system firstly. On the basis of discussions on the classification of channel models, computational methods of spatial correlation matrix as well as the parameter configuration, the thesis elaborates the method of Kronekcer-based Spatial Model (KBSM) in detail and provides a corresponding simulation. Moreover, certain characteristics such as the time dispersion, frequency correlation, spatial correlation as well as the Doppler power spectrum are also presented in this paper to demonstrate the feasibility of KBSM channel modeling. Secondly, taking the LTE standards as the reference, the thesis introduces the frame structure and the processing of downlink physical channels, based on which, the simulation of the downlink physical channel are provided. Further, three types of MIMO detection techniques, zero forcing(ZF), minimum mean square error (MMSE) and successive interference canceling(SIC), are discussed and compared through the simulation results, which show that combining SIC with ZF and MMSE can effectively improve the bit error rate performance with feasible the algorithm complexity and time-consuming.
This main contribution of this thesis is that, on the basis of the characteristic that the train body is relatively long, a distributed MIMO system is proposed. Different from the conventional distributed transmit antenna system, the proposed scheme employs single MIMO antenna group at the transmitter and multiple distributed MIMO groups at the receiver which are installed on the train. According to the characteristics of the proposed distributed MIMO system, two kinds of norm-based approaches are discussed to implement MIMO group selection at the receiver side. Simulation results show that distributed MIMO system can be used to enhance the BER performance and channel capacity significantly with a acceptable complexity and feasibility.
LTE(Long Term Evolution)是常被人们称作“3.9G”的五个无线传输标准中的一个,其性能优势主要包括：更低的比特开销、提供更好的服务、更加灵活的带宽分配策略、更简单的网络结构和频谱利用率的提升。MIMO(Multiple Input Multiple Output)做为LTE的关键技术之一,可以有效地利用信道的随机衰落和多径传播,在不额外增加功率和带宽条件下成倍增加传输速率和大幅度改善链路质量。因此研究MIMO技术在铁路通信中的应用对寻找下一代铁路无线通信技术有重要意义。
本文首先研究了MIMO信道建模,从模型分类、天线间空间相关矩阵计算方法、参数设置等方面,对基于Kronekcer积的空时相关统计特性建模(KBSM, Kronekcer-based Spatial Model)方法进行详细的阐述,并进行了仿真。给出了信道时间色散特性、频率相关性、空间相关性以及多普勒功率谱等信道特性,论证了KBSM建模的合理性。其次,本文参照LTE标准内容,介绍了LTE下行信道的帧结构和下行物理信道处理过程,并对下行物理信道处理过程进行仿真。然后还讨论了三种MIMO检测技术：迫零算法,最小均方误差算法和串行干扰消除算法,对它们的理论进行了推导,并进行了仿真比较,仿真结果表明将串行干扰消除算法与ZF(Zero Forcing)和MMSE(Minimum Mean Square Error)算法相结合,可以有效的提高系统的误码率性能,且算法复杂度较低、耗时较少、易于实现。
本文主要贡献是基于列车车身较长的特点,讨论了一种适用于列车的分布式MIMO系统。与一般讨论较多的发射端天线为分布式的系统不同,列车的分布式MIMO系统采用发射端单MIMO组,而在列车上分布多个MIMO天线组。根据此分布式MIMO的特点研究了两种基于范数的方法进行MIMO天线组选择并对其进行了仿真。仿真结果表明,采用分布式MIMO系统可以大幅度提升误码率性能,对信道容量也有一定的提升,且算法简单易于实现。
In order to meet the high-speed, informationization and intelligent demands of railway transport, railway communication technologies also need to move towards the direction to provide digital, wireless mobile and broadband integrated services. Currently, while the GSM-R (GSM for Railway) communications platform has been utilized in our country, it can only support voice services of data rate 9.6kb/s and can not meet the future demands of railway transport for high-rate data transmissions. In addition, the wireless channel in railway environment possesses its particularity including simple multipath environment, direct wave in dominant position, larger Doppler frequency shift and so on.
LTE (Long Term Evolution)is one of five major wireless standards referred to as "3.9G" the performance advantages of which involves lower bit-cost, better service quality, more flexible bandwidth allocations, simpler network architecture and higher spectral efficiency. As one of the key technologies of LTE, MIMO (Multiple Input Multiple Output) can effectively utilize the random channel fading and multipath propagation to increase the transmission rate and improve the link performance without additional power and bandwidth consumption. Therefore, researches on the application of MIMO technology in railway communications is important for searching next generation railway wireless communication system.
This thesis discusses the channel modeling of MIMO system firstly. On the basis of discussions on the classification of channel models, computational methods of spatial correlation matrix as well as the parameter configuration, the thesis elaborates the method of Kronekcer-based Spatial Model (KBSM) in detail and provides a corresponding simulation. Moreover, certain characteristics such as the time dispersion, frequency correlation, spatial correlation as well as the Doppler power spectrum are also presented in this paper to demonstrate the feasibility of KBSM channel modeling. Secondly, taking the LTE standards as the reference, the thesis introduces the frame structure and the processing of downlink physical channels, based on which, the simulation of the downlink physical channel are provided. Further, three types of MIMO detection techniques, zero forcing(ZF), minimum mean square error (MMSE) and successive interference canceling(SIC), are discussed and compared through the simulation results, which show that combining SIC with ZF and MMSE can effectively improve the bit error rate performance with feasible the algorithm complexity and time-consuming.
This main contribution of this thesis is that, on the basis of the characteristic that the train body is relatively long, a distributed MIMO system is proposed. Different from the conventional distributed transmit antenna system, the proposed scheme employs single MIMO antenna group at the transmitter and multiple distributed MIMO groups at the receiver which are installed on the train. According to the characteristics of the proposed distributed MIMO system, two kinds of norm-based approaches are discussed to implement MIMO group selection at the receiver side. Simulation results show that distributed MIMO system can be used to enhance the BER performance and channel capacity significantly with a acceptable complexity and feasibility.
引文
[1]Doganata Y.N, Tantawi A.N, Analysis of communication requirements for intelligent transportation systems:methodology and examples. Proc.IEEE 45th Vehicular Technology Conference, vol.2,pp.971-975,July 1995
[2]Umemoto M. An experimental DSRC multimode terminal using software defined radio technology, Proc.IEEE Radio and Wireless Conference (RAWCON),pp.165-168,Aug.2001
[3]李承恕,高速铁路无线通信系统与GSM-R.《铁路通信信号》,vol.35,no.7,pp.30-32,1999。
[4]A. J. Paulraj, R. U. Nadar, D.A. Gore. Introduction to space-time wireless communications, Cambridge University Press,2002
[5]S. Alamouti. Space block coding:a simple transmitter diversity technique for wireless communications. IEEE Journal on Selected Areas in Communicarions, October 1998,Vol.16:1451-1458
[6]3GPP TSG R1-01-1305. MIMO channel measurement and modeling results., Qualcomm, November,2001
[7]J.P.Kermoal, L.Schumacher, K.I. Pedersen, et al. A stochastic MIMO radio channel model with experimental validation. IEEE Journal on Selected Areas in Communications, August 2002, Vol.20, No.6:1211-1226
[8]J.W. Wallace, M.A. Jensen, A.L. Swindlehurst, et al. Experimental characterization of the MIMO wireless channel:Data acquisition and analysis. IEEE Transactions on Wireless Communications,2003, Vol.2, No.2:335-343
[9]A. F. Molisch. A generic model for MIMO wireless propagation channels in macro-and microcells. IEEE Transactions on Signal Processing, January 2004, Vol.52, No.1:61-71
[10]C. Oestges, V. Erceg, A. J. Paulraj. A physical scattering model for MIMO macrocellular broadband wireless channels, IEEE Journal on Selected Areas in Communications,2003, Vol.21, No.5:721-729
[11]Roh W, Paulraj A. MIMO channel capacity for the distributed antenna systems [C],IEEE VTC2002, Vancouver. Canada:IEEE Press,2002:706-709
[12]Kerpez K J. A radio access system with distributed antennas[J]. IEEE Trans Vehic Technol,1996,45:265-275
[13]谢益溪,无线电波传播：原理与应用[M],人民邮电出版社,2008.7
[14]TD-SCDMA系统培训手册——工程篇,中国移动通信集团公司,2008.6
[15]刘小强,朱刚,高速铁路环境中无线信道传输特性的探讨[J],铁路通信信号, 2007.4,54-56
[16]3GPP. Spatial Channel Model for Multiple Input Multiple Output (MIMO) Simulations (Release 8).3GPP TR 25.996 V8.0.0,2008.12
[17]3GPP. MIMO conference call summary.3GPP R1-02-0141, January 2002
[18]3GPP. A standardized set of MIMO radio propagation channels.3GPP TSG R-01-1179.
[19]顿玉成,杜栓义,SCM与SCME信道模型比较,电子科技,2008,40～42
[20]D. S. Baum, J. Salo, G Del Galdo, M. Milojevic, P. Kyosti, and J. Hansen, "An interim channel model for beyond-3G systems", in Proc. IEEE VTC'05, Stockholm, Sweden, May 2005.
[21]J. Kivinen, X. Zhao, and P. Vainikainen, "Empirical characterization of wideband indoor radio channel at 5.3 GHz,"IEEE Trans. Ant. Prop.,Aug.2001, pp.1192-1203.
[22]Klaus Lpedersen,A Stochastic Miltiple-Input-Multiple-output Radio Channel Model for Evaluation of Space-Time Coding Algorithms,IEEE Vehicular Technology Conference VTC'2000 Fall,2000,9:893-897
[23]许君一,矩阵Kronecker乘积及其应用,计算机辅助设计与图形学学报,2003年4月,377～388
[24]曹志浩,数值线性代数[M],上海：复旦大学出版社,1996,20-60。
[25]Schumacher L., Pedersen K. And Mogensen P., From Antenna Spacings to Theoretical Capacities-Guidelines for Simulating Spatial Correlation in MIMO Systems,submitted for publication in IEEE Transactions on Antenna and Propagation, December 2001.
[26]J.Salz, J.Winters, Effect of Fading Correlation on Adaptive Arrays in Digital Mobile Radio, IEEE Trans.on Vechcular Technology,1998,40(4):1049-1057
[27]F. Adachi, M. Feeny, A.Williamson, J.Patsons, Crosscorrelation between the envelopes of 900 MHz signals received at a mobile radio base station site, IEEE Proc. Pt. F, 1986,133(6):506-512
[28]K.I.Pedersen,P.E.Mogensen,B.H.Fleury, Spatial Channel Characteristics in Outdoor Environments and their Impact on BS Atnenna System Performance, IEEE Proc. Vehicular Technology Conference (VTE'98), Ottawa, Canada,1998:719-724
[29]Ratish J. Punnoose, Pavel V. Nikitin, and Daniel D. Stancil, Efficient Simulation of Ricean Fading within a Packet Simulator, Vehicular Technology Conference.2000. IEEE VTS-Fall VTC 2000.52nd:764-767
[30]3GPP TR 25.913, Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN), Mar.2006, V8.3.0
[31]3GPP TS 36.300, Evolved Universal Terrestrial Radio Access (E-UTRA); Overall description; Stage 2, Sep.2008, V8.6.0
[32]3GPP TS 36.201, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Layer-General Description, Nov.2007, V8.1.0
[33]3GPP TS 36.211, Evolved Universal Terrestrial Radio Access (E-UTRA);Physical Channels and Modulation; May.2009, V8.7.0
[34]ALAMOUTI S M. A simple transmit diversity technique for wireless communications [J]. IEEE Journal,1998,16 (8):1451-1458
[35]Karami E and Shiva M. MAP detection of coded MIMO-OFDM signals. The 9th Asia-Pacific Conference on Communications. Sep.2003,1:398-401
[36]Baldur S, Luo Z and Wong K M. Soft Quasi-Maximum-Likelihood Detection for Multiple-Antennas Wireless Channels. IEEE Transaction on Signal Processing.2003, 51(11):2710-2719.
[37]Horn R A and Johnson C R. Matrix Analysis. Cambridge, U.K.:Cambridge Univ. Press, chapter 4,1993.
[38]Wolniansky P W, Foschini G J, Golden G D, et al. V-BLAST:an architecture for realizing very high data rates over the rich-scattering wireless channel.1998 URSI International Symposium.29 Sept.-2 Oct.1998,295-300.
[39]Patel P and Holtzman J. Analysis of a simple successive interference cancellation scheme in a DS/CDMA system. IEEE J. Sel. Areas. Commun. June 1994,12(5): 796-807
[40]Kerpez K J. A radio access system with distributed antennas[J]. IEEE Trans Vehic Technol,1996,45:265-275
[41]Shidong Zhou, Ming Zhao, Xibin Xu, et al. Distributed wireless communication system: a new architecture for future public wireless access.IEEE Communications Magazine, Magazine, March 2000, Vol.41, Issue 3:108-113
[42]徐晓军,MIMO无线信道建模与仿真[D],电子科技大学硕士学位论文,2003
[43]A. Bletsas, A. Lippman, D. P. Reed, A simple distributed method for relay selection in cooperative diversity wireless networks, based on reciprocity and channel measurements, in IEEE 61st Vehicular Technology Conference,2005. VTC 2005-Spring.2005 IEEE, Stockholm, Sweden,2005, pp.1484-1488
[44]刘慎发,吴伟陵。适用于分布式MIMO系统的快速天线选择算法[J].北京邮电大学学报,2007,30(3)：50-53
[45]Gharavi-Alkhansari M, Greshman A. Fast antenna selection In MIMO systems [J]. IEEE Trans. Sig,2004,52(2):339-347
[46]李膺东,朱光喜。基于分布式MIMO的分层快速天线选择算法[J].微电子学与计算机,2009,26(6)：246-249
[47]薛桂芹,MIMO系统中天线选择算法的研究[D],东北大学硕士学位论文,2006
[48]吕进文,MIMO天线选择及其相关技术研究[D],北京交通大学硕士学位论文,2007
[49]马磊,MIMO天线选择及其相关技术研究[D],西安电子科技大学硕士学位论文,2006
[50]C.-N. Chuah, J. M. Kahn, and D. Tse, (1998)"Capacity of multi-antenna array systems in indoor wireless environment", in Proc. IEEE Globecom, vol.4,pp.1894-1899, Sydney, Australia
[51]G. J. Foschini, M. J. Gans, On limits of wireless communications in fading environment when using multiple antennas, Wireless Personal Communications,1998,6(3),311-335
[2]Umemoto M. An experimental DSRC multimode terminal using software defined radio technology, Proc.IEEE Radio and Wireless Conference (RAWCON),pp.165-168,Aug.2001
[3]李承恕,高速铁路无线通信系统与GSM-R.《铁路通信信号》,vol.35,no.7,pp.30-32,1999。
[4]A. J. Paulraj, R. U. Nadar, D.A. Gore. Introduction to space-time wireless communications, Cambridge University Press,2002
[5]S. Alamouti. Space block coding:a simple transmitter diversity technique for wireless communications. IEEE Journal on Selected Areas in Communicarions, October 1998,Vol.16:1451-1458
[6]3GPP TSG R1-01-1305. MIMO channel measurement and modeling results., Qualcomm, November,2001
[7]J.P.Kermoal, L.Schumacher, K.I. Pedersen, et al. A stochastic MIMO radio channel model with experimental validation. IEEE Journal on Selected Areas in Communications, August 2002, Vol.20, No.6:1211-1226
[8]J.W. Wallace, M.A. Jensen, A.L. Swindlehurst, et al. Experimental characterization of the MIMO wireless channel:Data acquisition and analysis. IEEE Transactions on Wireless Communications,2003, Vol.2, No.2:335-343
[9]A. F. Molisch. A generic model for MIMO wireless propagation channels in macro-and microcells. IEEE Transactions on Signal Processing, January 2004, Vol.52, No.1:61-71
[10]C. Oestges, V. Erceg, A. J. Paulraj. A physical scattering model for MIMO macrocellular broadband wireless channels, IEEE Journal on Selected Areas in Communications,2003, Vol.21, No.5:721-729
[11]Roh W, Paulraj A. MIMO channel capacity for the distributed antenna systems [C],IEEE VTC2002, Vancouver. Canada:IEEE Press,2002:706-709
[12]Kerpez K J. A radio access system with distributed antennas[J]. IEEE Trans Vehic Technol,1996,45:265-275
[13]谢益溪,无线电波传播：原理与应用[M],人民邮电出版社,2008.7
[14]TD-SCDMA系统培训手册——工程篇,中国移动通信集团公司,2008.6
[15]刘小强,朱刚,高速铁路环境中无线信道传输特性的探讨[J],铁路通信信号, 2007.4,54-56
[16]3GPP. Spatial Channel Model for Multiple Input Multiple Output (MIMO) Simulations (Release 8).3GPP TR 25.996 V8.0.0,2008.12
[17]3GPP. MIMO conference call summary.3GPP R1-02-0141, January 2002
[18]3GPP. A standardized set of MIMO radio propagation channels.3GPP TSG R-01-1179.
[19]顿玉成,杜栓义,SCM与SCME信道模型比较,电子科技,2008,40～42
[20]D. S. Baum, J. Salo, G Del Galdo, M. Milojevic, P. Kyosti, and J. Hansen, "An interim channel model for beyond-3G systems", in Proc. IEEE VTC'05, Stockholm, Sweden, May 2005.
[21]J. Kivinen, X. Zhao, and P. Vainikainen, "Empirical characterization of wideband indoor radio channel at 5.3 GHz,"IEEE Trans. Ant. Prop.,Aug.2001, pp.1192-1203.
[22]Klaus Lpedersen,A Stochastic Miltiple-Input-Multiple-output Radio Channel Model for Evaluation of Space-Time Coding Algorithms,IEEE Vehicular Technology Conference VTC'2000 Fall,2000,9:893-897
[23]许君一,矩阵Kronecker乘积及其应用,计算机辅助设计与图形学学报,2003年4月,377～388
[24]曹志浩,数值线性代数[M],上海：复旦大学出版社,1996,20-60。
[25]Schumacher L., Pedersen K. And Mogensen P., From Antenna Spacings to Theoretical Capacities-Guidelines for Simulating Spatial Correlation in MIMO Systems,submitted for publication in IEEE Transactions on Antenna and Propagation, December 2001.
[26]J.Salz, J.Winters, Effect of Fading Correlation on Adaptive Arrays in Digital Mobile Radio, IEEE Trans.on Vechcular Technology,1998,40(4):1049-1057
[27]F. Adachi, M. Feeny, A.Williamson, J.Patsons, Crosscorrelation between the envelopes of 900 MHz signals received at a mobile radio base station site, IEEE Proc. Pt. F, 1986,133(6):506-512
[28]K.I.Pedersen,P.E.Mogensen,B.H.Fleury, Spatial Channel Characteristics in Outdoor Environments and their Impact on BS Atnenna System Performance, IEEE Proc. Vehicular Technology Conference (VTE'98), Ottawa, Canada,1998:719-724
[29]Ratish J. Punnoose, Pavel V. Nikitin, and Daniel D. Stancil, Efficient Simulation of Ricean Fading within a Packet Simulator, Vehicular Technology Conference.2000. IEEE VTS-Fall VTC 2000.52nd:764-767
[30]3GPP TR 25.913, Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN), Mar.2006, V8.3.0
[31]3GPP TS 36.300, Evolved Universal Terrestrial Radio Access (E-UTRA); Overall description; Stage 2, Sep.2008, V8.6.0
[32]3GPP TS 36.201, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Layer-General Description, Nov.2007, V8.1.0
[33]3GPP TS 36.211, Evolved Universal Terrestrial Radio Access (E-UTRA);Physical Channels and Modulation; May.2009, V8.7.0
[34]ALAMOUTI S M. A simple transmit diversity technique for wireless communications [J]. IEEE Journal,1998,16 (8):1451-1458
[35]Karami E and Shiva M. MAP detection of coded MIMO-OFDM signals. The 9th Asia-Pacific Conference on Communications. Sep.2003,1:398-401
[36]Baldur S, Luo Z and Wong K M. Soft Quasi-Maximum-Likelihood Detection for Multiple-Antennas Wireless Channels. IEEE Transaction on Signal Processing.2003, 51(11):2710-2719.
[37]Horn R A and Johnson C R. Matrix Analysis. Cambridge, U.K.:Cambridge Univ. Press, chapter 4,1993.
[38]Wolniansky P W, Foschini G J, Golden G D, et al. V-BLAST:an architecture for realizing very high data rates over the rich-scattering wireless channel.1998 URSI International Symposium.29 Sept.-2 Oct.1998,295-300.
[39]Patel P and Holtzman J. Analysis of a simple successive interference cancellation scheme in a DS/CDMA system. IEEE J. Sel. Areas. Commun. June 1994,12(5): 796-807
[40]Kerpez K J. A radio access system with distributed antennas[J]. IEEE Trans Vehic Technol,1996,45:265-275
[41]Shidong Zhou, Ming Zhao, Xibin Xu, et al. Distributed wireless communication system: a new architecture for future public wireless access.IEEE Communications Magazine, Magazine, March 2000, Vol.41, Issue 3:108-113
[42]徐晓军,MIMO无线信道建模与仿真[D],电子科技大学硕士学位论文,2003
[43]A. Bletsas, A. Lippman, D. P. Reed, A simple distributed method for relay selection in cooperative diversity wireless networks, based on reciprocity and channel measurements, in IEEE 61st Vehicular Technology Conference,2005. VTC 2005-Spring.2005 IEEE, Stockholm, Sweden,2005, pp.1484-1488
[44]刘慎发,吴伟陵。适用于分布式MIMO系统的快速天线选择算法[J].北京邮电大学学报,2007,30(3)：50-53
[45]Gharavi-Alkhansari M, Greshman A. Fast antenna selection In MIMO systems [J]. IEEE Trans. Sig,2004,52(2):339-347
[46]李膺东,朱光喜。基于分布式MIMO的分层快速天线选择算法[J].微电子学与计算机,2009,26(6)：246-249
[47]薛桂芹,MIMO系统中天线选择算法的研究[D],东北大学硕士学位论文,2006
[48]吕进文,MIMO天线选择及其相关技术研究[D],北京交通大学硕士学位论文,2007
[49]马磊,MIMO天线选择及其相关技术研究[D],西安电子科技大学硕士学位论文,2006
[50]C.-N. Chuah, J. M. Kahn, and D. Tse, (1998)"Capacity of multi-antenna array systems in indoor wireless environment", in Proc. IEEE Globecom, vol.4,pp.1894-1899, Sydney, Australia
[51]G. J. Foschini, M. J. Gans, On limits of wireless communications in fading environment when using multiple antennas, Wireless Personal Communications,1998,6(3),311-335