MIMO下行系统的自适应传输技术研究
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摘要
随着移动通信用户数的迅速增长和各种多媒体业务需求的日益增加,现有的频谱资源已经日趋紧张,而多天线(MIMO)技术可在不额外增加功率和带宽的条件下成倍增加传输速率和大幅改善链路质量,从而被认为是B3G或4G的关键技术之一。关于MIMO的点对点传输,目前已出现大量的研究工作,相对而言,MIMO多用户系统尤其是MIMO下行系统的研究最近几年才成为热点。由于宽带多媒体业务多为非对称业务,传输瓶颈多集中于下行链路。而且,自适应传输技术通过动态调整传输参数与传输方案以适配信道信息与业务信息,使系统在吞吐率与功率效率等性能上有显著的改善,因此研究MIMO下行系统的相关自适应传输技术具有重大意义。在此背景下,本论文在国家“十五”863计划重大课题“新一代蜂窝移动通信系统无线传输链路技术研究(2003AA12331005)”和国家自然科学基金重大项目“未来移动通信系统基础理论与技术研究”子课题“基于MIMO-OFDM系统的空中接口自适应技术研究(60496310)”以及“十一五”国家863项目“具有公平性与QoS保障的高效MIMO-OFDM传输技术研究”(2006AA01Z277)的资助下,以MIMO高斯广播信道的信息论结果为指导,研究了MIMO下行系统中自适应调制、多用户分集调度策略、实时业务和非实时业务同时传输以及部分信道信息下的自适应传输策略等问题。
论文首先分析和阐述了基于TDMA、迫零预编码和迫零污纸编码三种不同MIMO下行系统传输策略的自适应调制算法,TDMA指每个调度时隙仅传输信道质量最好的用户,后两者可支持多个用户的同时传输。目前自适应传输算法大多集中于点对点系统,本文将其推广到多天线下行系统。基于这三种传输策略的自适应调制算法均是以系统即时总速率最大为目标,在用户QoS(误比特率)与传输功率限制下,进行自适应的功率与比特分配。考虑自适应调制的比特整数限制,最优的比特分配算法复杂度与调制阶数呈指数关系,其复杂度相当可观,因此本文提出了具有线性复杂度但性能损失较小的次优算法。考虑非理想信道信息的影响,发射机仅知道误差信道信息及其二阶统计值,提出了基于信道误差模型的TDMA和迫零预编码的自适应调制算法,并给出了相应的低复杂度算法。仿真表明当信道信息不理想时,总速率下降严重,因此在这种情形下系统应选择以提高性能为目的的空时编码技术作为传输方案。
论文然后分析了MIMO下行系统多用户分集调度机制。尽管污纸编码可实现MIMO高斯广播信道的容量域,但其复杂度过高,因此本文主要关注基于多用户调度且可实现与污纸编码相同总速率缩放率的线性预编码的传输策略。当用户信道非同分布时,由于以总速率最大为目标的多用户调度缺乏公平性,提出了一种保障系统公平的多用户调度策略,物理层应用块对角化预编码将空间信道分解为并行无干扰子信道,链路层基于比例公平调度选择优化用户集,并进行自适应功率、速率分配,从而在有效提高总速率的同时保障公平性。由于最优算法复杂度与用户数呈指数关系,本文提出了复杂度为线性的贪婪算法,而且考虑功率注水分配的复杂性,给出了在高信噪比时基于用户权重分配功率的调度方式。然后,考虑用户数据的随机到达和排队,提出了一种保障系统稳定的跨层调度机制,使用户平均队列长度受限。其基本思想是物理层应用空分多址同时支持多个用户,利用迫零预编码消除用户间干扰,链路层根据用户信道信息和队列状态信息进行调度。当用户业务服从泊松分布时,相比其他调度方式,该机制在兼顾系统总速率的同时保障了系统的稳定,并给出了一种接近最优算法性能的贪婪算法。
随着无线宽带业务需求日益增加,要求系统能够支持多种不同业务,提出了一种新颖的MIMO下行系统支持多业务的传输策略。其基本思想是首先为实时用户分配资源,然后从非实时用户中选取最优用户集传输,从而在保障实时业务QoS的同时,最大化系统频谱效率。该方案首先对能否满足实时业务的目标速率作可行性分析,然后进行自适应功率、速率分配,并提出一种性能接近最优算法的贪婪算法。
以上内容大多假定基站具有各用户的理想信道信息,系统反馈开销很大。为降低信道信息反馈开销,Sharif等人提出了正交随机波束成型(ORBF)并证明ORBF与DPC编码具有相同总速率渐进缩放率。本文基于ORBF,提出了两种进一步改善性能的自适应传输策略。首先,由于系统用户少时,ORBF总速率与污纸编码差距很大,且其总速率随平均信噪比增长受限,因此提出了一种多用户多波束选择策略,并对波束间功率平均分配和自适应分配分别进行了分析。其基本思想是针对ORBF选定的用户集再次反馈部分信道信息,发射机基于此信息自适应选择优化波束集和用户集,从而有效克服了ORBF的缺点。并探究了发射端天线相关性对该策略的影响,表明随相关系数的增大,该策略与ORBF和白化ORBF相比性能改善越明显。然后,为了进一步降低ORBF反馈,提出了一种基于信干比门限的反馈策略,并给出了平均总速率与信干比门限的关系表达式。仿真表明,通过有效的选取信干比门限,可在几乎不降低总速率的同时,系统反馈极大降低。
With the development of growing number of mobile users and growing demands of diverse multimedia services, frequency resources have been becoming more important. Muliple-input multiple-out (MIMO), which is considered as one of key techniques for B3G/4G system, has been proven its protential to offer high sprectral efficiency as well as link reliability without additional power and frequency expenses. To date, research has focused on the single-user point-to-point scenario where the transmitter and receiver each have arrays. More recently, attention has shifted to multiuser MIMO system especially for MIMO downlink system. On one hand, broadband multimedia services are always asymmetric, and downlink link is considered to be the bottleneck of data transmission. On the other hand, adaptive technique could adptively adjust transmission scheme and transmission parameters to match the dynamic changes of wireless channel so as to improve the performance. Therefore, reaseach on adaptive transmission schemes for MIMO downlink system becomes highly important. Supported by the National High Technology Research and Development Program of China under Grant (No. 2003AA12331005 2006AA01Z277) and National Science Foundation of China under Grant No. 60496310, our research concentrates on several representative problems for MIMO downlink system based on MIMO Gaussian broadcast channel information theory. Our research is mainly focused on several aspects as follows: adaptive modulaton, multiuser scheduling, multitraffic transmission and adaptive transmission scheme under imperfect CSI.
Firstly, we explore adpaptive modulation (AM) schemes for MMO downlink system based on TDMA, zero-forcing (ZF) precoding and zero-forcing dirty-paper coding (ZF-DPC) respectively. For TDMA, only the user who has the highest rate is supported at one time. For the other two schemes, several users could be simultaneously transmitted. To date, reseach on adaptive modulation schemes are mainly focused on point-point system. In this paper, we investigate AM for MIMO downlink system. For all these different transmission schemes, adaptive power and bit allocations are conducted under the constraint of QoS (demand of bit error rate) and transmit power to maximize the immediate sum-rate. The optimal bit allocation acheme is extremely complex when the number of users becomes huge, due to the fact that the degree of complexity is exponential to the number of users. Therefore several low-complexity scehmes but with vanishing loses of sum-rate are proposed for different transmission schemes respectively, which have linear degree of compleixy with the number of users. Additionally, considering the effect of impectly CSI, AM based on Error Channel Model for TDMA and ZF precoding are proposed under the instantaneous error CSI and statistics of error CSI at the transmitter. And the low-complexity suboptimal approach is given too. It is shown that AM yields severely degraded sum-rate when the imperfectness of CSI is remarkable. Therefore, space-time coding with diversity gain should be the transmission scheme insteadly under such circumstances.
Secondely, multiuser diversity scheduling scheme for MIMO downlink system is analyzed. As the capacity-achieving dirty paper coding (DPC) approach is difficult to implement due to its high complexity, in this paper, we focus our attention on downlink linear precoding which has been shown asymptotically optimal in the sum-rate sense as DPC based on the multiuser diversity. Because scheduling scheme depending only on the CSI results in unfairness among users, we first propose a multiuser scheduling scheme with guaranteed fairness. Block diagonalization precoding is employed to decompose the broadcast channel into non-inferencing sub-channels in physical layer (PHY), and the optimal user set is chosen based on proportional fairness scheduling in link layer. System resources such as power and rate are adaptively allocated so as to guarantee fairness at the same time sum-rate is improved. Because the degree of complexity of the optimal scheduling scheme is exponential to the number of users, a low complexity scheme of linear relationship is proposed. And to reduce the complexity imposed by water-fill power allocation, a method of allocating user powers in direct proportion to user weights is proposed when average SNR is asymptotically high. Furthermore, considering the queue sytem with random packet arrivals, a cross-layer scheduling stategy with guaranteed stability for MIMO multiuser downlink system is proposed to keep average queue length finite. Spatial division multiple access (SDMA) is exploited to support several users’simultaneous transmission and ZF precoding is used to remove interference among users in PHY layer. Both user’s CSI and queue state information is considered in link layer. When user’s packets are generated according to independent Poisson arrival processes, simulation results show that this scheme could achieve large system sum-rate while guaranteeing the stability of the system compared with those only considering CSI in PHY layer. Meanwhile a low complexity but with vanishing losses greedy method is proposed.
One of distinct characteristics of next generation wireless system is its sustaining diversive multiuser services to cater for the increasing broadband demands. A novel multitraffic multiuser transmission scheme is proposed for MIMO downlink system. The key idea is that the power is firstly allocated to real-time (RT) users with high priority, then the optimal non RT (NRT) users is scheduled so as to maximize the system sum-rate at the same time guaranteeing RT user’s QoS. This scheme could be conducted in two steps: firstly, the feasibility of demands of target rates of RT users is examined; secondly, the resources such as power, rate and scheduling slot are adaptively allocated. And a suboptimal greedy scheduling scheme is proposed with vanishing losses to reduce the complexity of optimal one.
The content described above mainly assumes that the transmitter has perfect CSI, which may be infeasible in pratical environment due to the fact of much feedback information. In order to reduce feedback information, orthonormal random beamforming (ORBF) proposed by Sharif achieves the optimal sum-rate capacity of DPC for a large number of users based on asymptotic analysis. In this paper, we propose two adaptive transmission schemes to further increase the system performance based on ORBF. Firstly, ORBF is quickly degrading with decreasing of number of users, and the system tends to be interference-limited with growing average SNR. To overcome the shortcomings of ORBF, a novel multiuser multibeam selection scheme (MBS) is investigated. The key idea of MBS is to feedback more CSI for the user set indentified by ORBF, based on which the transmitter adaptively selects the optimal user set and beam set. The equal power allocation and adaptive power allocation among users are respectively analyzed. Furthermore, the correlation among transmitter antennas is explored for MBS. Simulation results show that the performance improvement is remarkable especially for large correlation factor, when compared with ORBF and whitening ORBF. Secondly, a feedback scheme based on signal to interference plus noise ratios (SINR) theshhold is proposed to further reduce the feedback of ORBF. The relationship between SINR theshhold and sum-rate is given too. Simulation results show that the proposed scheme achieves greatly reducd feedback with almost the same sum-rate compared with ORBF by the proper SINR threshold.
论文首先分析和阐述了基于TDMA、迫零预编码和迫零污纸编码三种不同MIMO下行系统传输策略的自适应调制算法,TDMA指每个调度时隙仅传输信道质量最好的用户,后两者可支持多个用户的同时传输。目前自适应传输算法大多集中于点对点系统,本文将其推广到多天线下行系统。基于这三种传输策略的自适应调制算法均是以系统即时总速率最大为目标,在用户QoS(误比特率)与传输功率限制下,进行自适应的功率与比特分配。考虑自适应调制的比特整数限制,最优的比特分配算法复杂度与调制阶数呈指数关系,其复杂度相当可观,因此本文提出了具有线性复杂度但性能损失较小的次优算法。考虑非理想信道信息的影响,发射机仅知道误差信道信息及其二阶统计值,提出了基于信道误差模型的TDMA和迫零预编码的自适应调制算法,并给出了相应的低复杂度算法。仿真表明当信道信息不理想时,总速率下降严重,因此在这种情形下系统应选择以提高性能为目的的空时编码技术作为传输方案。
论文然后分析了MIMO下行系统多用户分集调度机制。尽管污纸编码可实现MIMO高斯广播信道的容量域,但其复杂度过高,因此本文主要关注基于多用户调度且可实现与污纸编码相同总速率缩放率的线性预编码的传输策略。当用户信道非同分布时,由于以总速率最大为目标的多用户调度缺乏公平性,提出了一种保障系统公平的多用户调度策略,物理层应用块对角化预编码将空间信道分解为并行无干扰子信道,链路层基于比例公平调度选择优化用户集,并进行自适应功率、速率分配,从而在有效提高总速率的同时保障公平性。由于最优算法复杂度与用户数呈指数关系,本文提出了复杂度为线性的贪婪算法,而且考虑功率注水分配的复杂性,给出了在高信噪比时基于用户权重分配功率的调度方式。然后,考虑用户数据的随机到达和排队,提出了一种保障系统稳定的跨层调度机制,使用户平均队列长度受限。其基本思想是物理层应用空分多址同时支持多个用户,利用迫零预编码消除用户间干扰,链路层根据用户信道信息和队列状态信息进行调度。当用户业务服从泊松分布时,相比其他调度方式,该机制在兼顾系统总速率的同时保障了系统的稳定,并给出了一种接近最优算法性能的贪婪算法。
随着无线宽带业务需求日益增加,要求系统能够支持多种不同业务,提出了一种新颖的MIMO下行系统支持多业务的传输策略。其基本思想是首先为实时用户分配资源,然后从非实时用户中选取最优用户集传输,从而在保障实时业务QoS的同时,最大化系统频谱效率。该方案首先对能否满足实时业务的目标速率作可行性分析,然后进行自适应功率、速率分配,并提出一种性能接近最优算法的贪婪算法。
以上内容大多假定基站具有各用户的理想信道信息,系统反馈开销很大。为降低信道信息反馈开销,Sharif等人提出了正交随机波束成型(ORBF)并证明ORBF与DPC编码具有相同总速率渐进缩放率。本文基于ORBF,提出了两种进一步改善性能的自适应传输策略。首先,由于系统用户少时,ORBF总速率与污纸编码差距很大,且其总速率随平均信噪比增长受限,因此提出了一种多用户多波束选择策略,并对波束间功率平均分配和自适应分配分别进行了分析。其基本思想是针对ORBF选定的用户集再次反馈部分信道信息,发射机基于此信息自适应选择优化波束集和用户集,从而有效克服了ORBF的缺点。并探究了发射端天线相关性对该策略的影响,表明随相关系数的增大,该策略与ORBF和白化ORBF相比性能改善越明显。然后,为了进一步降低ORBF反馈,提出了一种基于信干比门限的反馈策略,并给出了平均总速率与信干比门限的关系表达式。仿真表明,通过有效的选取信干比门限,可在几乎不降低总速率的同时,系统反馈极大降低。
With the development of growing number of mobile users and growing demands of diverse multimedia services, frequency resources have been becoming more important. Muliple-input multiple-out (MIMO), which is considered as one of key techniques for B3G/4G system, has been proven its protential to offer high sprectral efficiency as well as link reliability without additional power and frequency expenses. To date, research has focused on the single-user point-to-point scenario where the transmitter and receiver each have arrays. More recently, attention has shifted to multiuser MIMO system especially for MIMO downlink system. On one hand, broadband multimedia services are always asymmetric, and downlink link is considered to be the bottleneck of data transmission. On the other hand, adaptive technique could adptively adjust transmission scheme and transmission parameters to match the dynamic changes of wireless channel so as to improve the performance. Therefore, reaseach on adaptive transmission schemes for MIMO downlink system becomes highly important. Supported by the National High Technology Research and Development Program of China under Grant (No. 2003AA12331005 2006AA01Z277) and National Science Foundation of China under Grant No. 60496310, our research concentrates on several representative problems for MIMO downlink system based on MIMO Gaussian broadcast channel information theory. Our research is mainly focused on several aspects as follows: adaptive modulaton, multiuser scheduling, multitraffic transmission and adaptive transmission scheme under imperfect CSI.
Firstly, we explore adpaptive modulation (AM) schemes for MMO downlink system based on TDMA, zero-forcing (ZF) precoding and zero-forcing dirty-paper coding (ZF-DPC) respectively. For TDMA, only the user who has the highest rate is supported at one time. For the other two schemes, several users could be simultaneously transmitted. To date, reseach on adaptive modulation schemes are mainly focused on point-point system. In this paper, we investigate AM for MIMO downlink system. For all these different transmission schemes, adaptive power and bit allocations are conducted under the constraint of QoS (demand of bit error rate) and transmit power to maximize the immediate sum-rate. The optimal bit allocation acheme is extremely complex when the number of users becomes huge, due to the fact that the degree of complexity is exponential to the number of users. Therefore several low-complexity scehmes but with vanishing loses of sum-rate are proposed for different transmission schemes respectively, which have linear degree of compleixy with the number of users. Additionally, considering the effect of impectly CSI, AM based on Error Channel Model for TDMA and ZF precoding are proposed under the instantaneous error CSI and statistics of error CSI at the transmitter. And the low-complexity suboptimal approach is given too. It is shown that AM yields severely degraded sum-rate when the imperfectness of CSI is remarkable. Therefore, space-time coding with diversity gain should be the transmission scheme insteadly under such circumstances.
Secondely, multiuser diversity scheduling scheme for MIMO downlink system is analyzed. As the capacity-achieving dirty paper coding (DPC) approach is difficult to implement due to its high complexity, in this paper, we focus our attention on downlink linear precoding which has been shown asymptotically optimal in the sum-rate sense as DPC based on the multiuser diversity. Because scheduling scheme depending only on the CSI results in unfairness among users, we first propose a multiuser scheduling scheme with guaranteed fairness. Block diagonalization precoding is employed to decompose the broadcast channel into non-inferencing sub-channels in physical layer (PHY), and the optimal user set is chosen based on proportional fairness scheduling in link layer. System resources such as power and rate are adaptively allocated so as to guarantee fairness at the same time sum-rate is improved. Because the degree of complexity of the optimal scheduling scheme is exponential to the number of users, a low complexity scheme of linear relationship is proposed. And to reduce the complexity imposed by water-fill power allocation, a method of allocating user powers in direct proportion to user weights is proposed when average SNR is asymptotically high. Furthermore, considering the queue sytem with random packet arrivals, a cross-layer scheduling stategy with guaranteed stability for MIMO multiuser downlink system is proposed to keep average queue length finite. Spatial division multiple access (SDMA) is exploited to support several users’simultaneous transmission and ZF precoding is used to remove interference among users in PHY layer. Both user’s CSI and queue state information is considered in link layer. When user’s packets are generated according to independent Poisson arrival processes, simulation results show that this scheme could achieve large system sum-rate while guaranteeing the stability of the system compared with those only considering CSI in PHY layer. Meanwhile a low complexity but with vanishing losses greedy method is proposed.
One of distinct characteristics of next generation wireless system is its sustaining diversive multiuser services to cater for the increasing broadband demands. A novel multitraffic multiuser transmission scheme is proposed for MIMO downlink system. The key idea is that the power is firstly allocated to real-time (RT) users with high priority, then the optimal non RT (NRT) users is scheduled so as to maximize the system sum-rate at the same time guaranteeing RT user’s QoS. This scheme could be conducted in two steps: firstly, the feasibility of demands of target rates of RT users is examined; secondly, the resources such as power, rate and scheduling slot are adaptively allocated. And a suboptimal greedy scheduling scheme is proposed with vanishing losses to reduce the complexity of optimal one.
The content described above mainly assumes that the transmitter has perfect CSI, which may be infeasible in pratical environment due to the fact of much feedback information. In order to reduce feedback information, orthonormal random beamforming (ORBF) proposed by Sharif achieves the optimal sum-rate capacity of DPC for a large number of users based on asymptotic analysis. In this paper, we propose two adaptive transmission schemes to further increase the system performance based on ORBF. Firstly, ORBF is quickly degrading with decreasing of number of users, and the system tends to be interference-limited with growing average SNR. To overcome the shortcomings of ORBF, a novel multiuser multibeam selection scheme (MBS) is investigated. The key idea of MBS is to feedback more CSI for the user set indentified by ORBF, based on which the transmitter adaptively selects the optimal user set and beam set. The equal power allocation and adaptive power allocation among users are respectively analyzed. Furthermore, the correlation among transmitter antennas is explored for MBS. Simulation results show that the performance improvement is remarkable especially for large correlation factor, when compared with ORBF and whitening ORBF. Secondly, a feedback scheme based on signal to interference plus noise ratios (SINR) theshhold is proposed to further reduce the feedback of ORBF. The relationship between SINR theshhold and sum-rate is given too. Simulation results show that the proposed scheme achieves greatly reducd feedback with almost the same sum-rate compared with ORBF by the proper SINR threshold.
引文
[1] Yu X.-H., Chen G., Chen M., et al. The FuTURE Project in China. IEEE Communications Magazine, 2005, 43(1): 70~75.
[2] Zhang P., Tao X. F., Zhang J. H., et al. A vision from the future: beyond 3G TDD. IEEE Communications Magazine, 2005, 43(1): 38~44.
[3] Liu G. Y., Zhang J. H., Zhang P., et al. Evolution map from TD-SCDMA to FuTURE B3G TDD. IEEE Communications Magazine, 2006, 44(3): 54~61.
[4] Hui S. Y., Yeung K. H. Challenges in the migration to 4G mobile systems. IEEE Communications Magazine, 2003, 41(12): 54~59.
[5] Cheng Y., Jiang H., Zhuang W. H., et al. Efficient resource allocation for China's 3G/4G wireless networks. IEEE Communications Magazine, 2005, 43(1): 76~83.
[6]尤肖虎. FuTURE B3G研究开发及关键技术进展.移动通信, 2006, 6: 18~22.
[7] Paulraj A. J., Gore D. A., Nabar R. U., et al. An overview of MIMO communications - a key to gigabit wireless. Proceedings of the IEEE, 2004, 92(2): 198~218.
[8] Wolniansky P., Foschini G., Golgen G. V-Blast: an architecture for realizing very high data rates over the rich-scattering wireless channel. in: Conference Proceedings of the International Symposium on Signals, Systems and Electronics: 1998. 295~300.
[9] Telatar I. Capacity of multi-antenna Gaussian channel. European Transactions on Telecommunications, 1999, 10(6): 585~595.
[10] Foschini G., Gans M. On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas, Wireless Personal Communications, 1998, 6: 311~335.
[11] Raleigh G. G., Cioffi J. M. Spatio-temporal coding for wireless communication. IEEE Transactions on Communications, 1998, 46(3): 357~366.
[12] Foschini G. Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell Labs Tech. J, 1996: 41~59.
[13] Wubben D., Rinas J., Bohnke R. Efficient Algorithm for Detecting Layered Space-Time Codes. in: ITG Conference on Source and Channel Coding: 2002. 399~405.
[14] Da-Shan S., Kahn M. Layered space-time codes for wireless communications using multiple transmit antennas. in: IEEE International Conference on Communications:vol. 1, 1999. 436~440.
[15] Hwang K. J., Lee K. B. Transmit power allocation with small feedback overhead for a multiple antenna system. in: IEEE 56th Vehicular Technology Conference: vol. 4, 2002. 2158~2162.
[16] Nam S. H., Shin O.-S., Lee K. B. Transmit power allocation for a modified V-BLAST system. IEEE Transactions on Communications, 2004, 52(7): 1074~1079.
[17] Stefanov A., Duman T. M. Turbo-coded modulation for systems with transmit and receive antenna diversity over block fading channels: system model, decoding approaches, and practical considerations. IEEE Journal on Selected Areas in Communications, 2001, 19(5): 958~968.
[18] Lek S. Turbo space-time processing to improve wireless channel capacity. IEEE Transactions on Communications, 2000, 48(8): 1347~1359.
[19] Xin Y., Liu Z. Q., Giannakis G. B. High-rate layered space-time transmissions based on constellation-rotation. in: IEEE Wireless Communications and Networking Conference: vol. 1, 2002. 471~476.
[20] Palomar D. P., Cioffi J. M., Lagunas M. A. Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization. IEEE Transactions on Signal Processing, 2003, 51(9): 2381~2401.
[21] Ding Y., Davidson T. N., Luo Z.-Q., et al. Minimum BER block precoders for zero-forcing equalization. IEEE Transactions on Signal Processing, 2003, 51(9): 2410~2423.
[22] Love D. J., Heath R. W., Jr. Multimode precoding for MIMO wireless systems. IEEE Transactions on Signal Processing, 2005, 53(10): 3674~3687.
[23] Palomar D. P., Barbarossa S. Designing MIMO communication systems: constellation choice and linear transceiver design. IEEE Transactions on Signal Processing, 2005, 53(10): 3804~3818.
[24] Wittneben A. A new bandwidth efficient transmit antenna modulation diversity scheme for linear digital modulation. in: IEEE International Conference on Communications: vol. 3, 1993. 1630~1634.
[25] Tarokh V., Seshadri N., Calderbank A. R. Space-time codes for high data rate wireless communication: performance criterion and code construction. IEEE Transactions on Information Theory, 1998, 44(2): 744~765.
[26] Alamouti S. M. A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications, 1998, 16(8): 1451~1458.
[27] Tarokh V., Jafarkhani H., Calderbank A. R. Space-time block codes from orthogonal designs. IEEE Transactions on Information Theory, 1999, 45(5): 1456~1467.
[28] Yi G., Ben Letaief K. Concatenated space-time block coding with trellis coded modulation in fading channels. IEEE Transactions on Wireless Communications, 2002, 1(4): 580~590.
[29] Hassibi B., Hochwald B. M. How much training is needed in multiple-antenna wireless links? IEEE Transactions on Information Theory, 2003, 49(4): 951~963.
[30] Zheng L. Z., Tse D. N. C. Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels. IEEE Transactions on Information Theory, 2003, 49(5): 1073~1096.
[31] Goldsmith A., Jafar S. A., Jindal N., et al. Capacity limits of MIMO channels. IEEE Journal on Selected Areas in Communications, 2003, 21(5): 684~702.
[32] Boyd S., Vandenberghe L. Convex Optimization. Cambridge University Press, 2004.
[33] Wei Y., Wonjong R., Boyd S., et al. Iterative water-filling for Gaussian vector multiple-access channels. Information Theory, IEEE Transactions on, 2004, 50(1): 145~152.
[34] Cover T. M., Thomas J. A. Elements of Information Theory. New York: Wiley, 1991.
[35] Caire G., Shamai S. On the achievable throughput of a multiantenna Gaussian broadcast channel. IEEE Transactions on Information Theory, 2003, 49(7): 1691~1706.
[36] Costa M. Writing on dirty paper. IEEE Transactions on Information Theory, 1983, 29(3): 439~441.
[37] Wei Y., Cioffi J. M. Sum capacity of Gaussian vector broadcast channels. IEEE Transactions on Information Theory, 2004, 50(9): 1875~1892.
[38] Vishwanath S., Jindal N., Goldsmith A. Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels. IEEE Transactions on Information Theory, 2003, 49(10): 2658~2668.
[39] Viswanath P., Tse D. N. C. Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality. IEEE Transactions on Information Theory, 2003, 49(8): 1912~1921.
[40] Weingarten H., Steinberg Y., Shamai S. S. The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel. IEEE Transactions onInformation Theory, 2006, 52(9): 3936~3964.
[41] Schubert M., Boche H. Solution of the multiuser downlink beamforming problem with individual SINR constraints. IEEE Transactions on Vehicular Technology, 2004, 53(1): 18~28.
[42] Hassibi B., Marzetta T. L. Multiple-antennas and isotropically random unitary inputs: the received signal density in closed form. IEEE Transactions on Information Theory, 2002, 48(6): 1473~1484.
[43] Zheng L. Z., Tse D. N. C. Communication on the Grassmann manifold: a geometric approach to the noncoherent multiple-antenna channel. IEEE Transactions on Information Theory, 2002, 48(2): 359~383.
[44] Goldsmith A. J., Soon-Ghee C. Variable-rate variable-power MQAM for fading channels. IEEE Transactions on Communications, 1997, 45(10): 1218~1230.
[45] Chung S. T., Goldsmith A. J. Degrees of freedom in adaptive modulation: a unified view. IEEE Transactions on Communications, 2001, 49(9): 1561~1571.
[46] Zhou Z. D., Vucetic B., Dohler M., et al. MIMO systems with adaptive modulation. IEEE Transactions on Vehicular Technology, 2005, 54(5): 1828~1842.
[47] Chung S. T., Lozano A., Huang H. C. Approaching eigenmode BLAST channel capacity using V-BLAST with rate and power feedback. in: IEEE 54th Vehicular Technology Conference: vol. 2, 2001. 915~919.
[48] Zhang R., Cioffi J. M., Liang Y.-C. Approaching MIMO-OFDM Capacity with Per Antenna Power and Rate Feedback. in: IEEE International Conference on Communications: vol. 12, 2006. 5348~5353.
[49] Li L., Goldsmith A. J. Capacity and optimal resource allocation for fading broadcast channels .I. Ergodic capacity. IEEE Transactions on Information Theory, 2001, 47(3): 1083~1102.
[50] Ajib W., Haccoun D. An overview of scheduling algorithms in MIMO-based fourth-generation wireless systems. IEEE Network, 2005, 19(5): 43~48.
[51] Hochwald B. M., Marzetta T. L., Tarokh V. Multiple-antenna channel hardening and its implications for rate feedback and scheduling. IEEE Transactions on Information Theory, 2004, 50(9): 1893~1909.
[52] Gozali R., Buehrer R. M., Woerner B. D. The impact of multiuser diversity on space-time block coding. IEEE Communications Letters, 2003, 7(5): 213~215.
[53] Viswanath P., Tse D. N. C., Laroia R. Opportunistic beamforming using dumb antennas. IEEE Transactions on Information Theory, 2002, 48(6): 1277~1294.
[54] Larsson E. G. On the combination of spatial diversity and multiuser diversity. IEEECommunications Letters, 2004, 8(8): 517~519.
[55] Sharma N., Ozarow L. H. A study of opportunism for multiple-antenna systems. IEEE Transactions on Information Theory, 2005, 51(5): 1804~1814.
[56] Chung J., Hwang C.-S., Kim K., et al. A random beamforming technique in MIMO systems exploiting multiuser diversity. IEEE Journal on Selected Areas in Communications, 2003, 21(5): 848~855.
[57] Jindal N., Goldsmith A. Dirty-paper coding versus TDMA for MIMO Broadcast channels. IEEE Transactions on Information Theory, 2005, 51(5): 1783~1794.
[58] Sharif M., Hassibi B. A Comparison of Time-Sharing, DPC, and Beamforming for MIMO Broadcast Channels With Many Users. IEEE Transactions on Communications, 2007, 55(1): 11~15.
[59] Jindal N., Wonjong R., Vishwanath S., et al. Sum power iterative water-filling for multi-antenna Gaussian broadcast channels. IEEE Transactions on Information Theory, 2005, 51(4): 1570~1580.
[60] Wei Y. Sum-capacity computation for the Gaussian vector broadcast channel via dual decomposition. IEEE Transactions on Information Theory, 2006, 52(2): 754~759.
[61] Zamir R., Shamai S., Erez U. Nested linear/lattice codes for structured multiterminal binning. IEEE Transactions on Information Theory, 2002, 48(6): 1250~1276.
[62] Tu Z., Blum R. S. Multiuser diversity for a dirty paper approach. IEEE Communications Letters, 2003, 7(8): 370~372.
[63] Dimic G., Sidiropoulos N. D. On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm. IEEE Transactions on Signal Processing, 2005, 53(10): 3857~3868.
[64] Wong K.-K., Murch R. D., Letaief K. B. A joint-channel diagonalization for multiuser MIMO antenna systems. IEEE Transactions on Wireless Communications, 2003, 2(4): 773~786.
[65] Spencer Q. H., Swindlehurst A. L., Haardt M. Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels. IEEE Transactions on Signal Processing, 2004, 52(2): 461~471.
[66] Peel C. B., Hochwald B. M., Swindlehurst A. L. A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization. IEEE Transactions on Communications, 2005, 53(1): 195~202.
[67] Hochwald B. M., Peel C. B., Swindlehurst A. L. A vector-perturbation techniquefor near-capacity multiantenna multiuser communication-part II: perturbation. IEEE Transactions on Communications, 2005, 53(3): 537~544.
[68] Lai U. C., Murch R. D. A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach. Wireless Communications, IEEE Transactions on, 2004, 3(1): 20~24.
[69] Yoo T., Goldsmith A. On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming. IEEE Journal on Selected Areas in Communications, 2006, 24(3): 528~541.
[70] Shen Z. K., Chen R. H., Andrews J. G., et al. Sum Capacity of Multiuser MIMO Broadcast Channels with Block Diagonalization. in: IEEE International Symposium on Information Theory: 2006. 886~890.
[71] Love D. J., Heath R. W., Jr., Santipach W., et al. What is the value of limited feedback for MIMO channels? IEEE Communications Magazine, 2004, 42(10): 54~59.
[72] Sharif M., Hassibi B. On the capacity of MIMO broadcast channels with partial side information. IEEE Transactions on Information Theory, 2005, 51(2): 506~522.
[73] Jindal N. MIMO Broadcast Channels With Finite-Rate Feedback. IEEE Transactions on Information Theory, 2006, 52(11): 5045~5060.
[74] Bergmans P. Random coding theorem for broadcast channels with degraded components. IEEE Transactions on Information Theory, 1973, 19(2): 197~207.
[75] Jindal N., Wonjong R., Vishwanath S., et al. Sum power iterative water-filling for multi-antenna Gaussian broadcast channels. Information Theory, IEEE Transactions on, 2005, 51(4): 1570~1580.
[76] Chen R., Andrews J. G., Heath R. W., Jr. Transmit selection diversity for multiuser spatial multiplexing systems. in: IEEE Global Telecommunications Conference: vol. 4, 2004. 2625~2629.
[77] Chae C.-B., Mazzarese D., Jr R. W. H. Coordinated beamforming for multiuser MIMO sysems with limited feedforward. in: Asilomar Conf. on Sign., Syst. and Computers: 2006.
[78] Pan Z. G., Wong K.-K., Ng T.-S. Generalized multiuser orthogonal space-division multiplexing. IEEE Transactions on Wireless Communications, 2004, 3(6): 1969~1973.
[79] Tomlinson M. New automatic equalizer employing modulo arithmetic. IEEE Electron. Lett., 1971, 7(5): 138~139.
[80] Harashima H., Miyakawa H. Matched-Transmission Technique for Channels WithIntersymbol Interference. IEEE Transactions on Communications, 1972, 20(4): 774~780.
[81] Hunger R., Joham M., Utschick W. Extension of linear and nonlinear transmit filters for decentralized receivers. in: Proc. European Wireless Conf: 2005. 40~46.
[82] Kusume K., Joham M., Utschick W., et al. Efficient Tomlinson-Harashima precoding for spatial multiplexing on flat MIMO channel. in: IEEE International Conference on Communications: vol. 3, 2005. 2021~2025.
[83] Windpassinger C., Fischer R. F. H., Vencel T., et al. Precoding in multiantenna and multiuser communications. IEEE Transactions on Wireless Communications, 2004, 3(4): 1305~1316.
[84] Yoo T., Nihar J., Goldsmith A. Finite-Rate Feedback MIMO Broadcast Channels with a Large Number of Users. in: IEEE International Symposium on Information Theory: 2006. 1214~1218.
[85] Kountouris M., Francisco R. d., Gesbert D., et al. Efficient Metric for Scheduling in MIMO Broadcast Channels with Limited Feedback. in: IEEE International Conference on Acoustics, Speech, and Signal Processing: 2007.
[86] Huang K.-B., Heath R. W., Andrews J. G. Space Division Multiple Access with a Sum Feedback Rate Constraint. in: IEEE International Conference on Acoustics, Speech, and Signal Processing: 2007.
[87] Kountouris M., Gesbert D. Memory-based opportunistic multi-user beamforming. in: International Symposium on Information Theory Proceedings: 2005. 1426~1430.
[88] Hammarwall D., Bengtsson M., Ottersten B. Beamforming and user selection in SDMA systems utilizing channel statistics and instantaneous SNR feedback. in: IEEE International Conference on Acoustics, Speech, and Signal Processing: 2007.
[89] Kountouris M., Gesbert D. Robust multi-user opportunistic beamforming for sparse networks. in: IEEE 6th Workshop on Signal Processing Advances in Wireless Communications: 2005. 975~979.
[90] Lee H., Shin M., Lee C. An eigen-based MIMO multiuser scheduler with partial feedback information. IEEE Communications Letters, 2005, 9(4): 328~330.
[91] Bayesteh A., Khandani A. K. On the user selection for MIMO broadcast channels. in: Proceedings International Symposium on Information Theory: 2005. 2325~2329.
[92] Furuskar A., Mazur S., Muller F., et al. EDGE: enhanced data rates for GSM and TDMA/136 evolution. IEEE Personal Communications, 1999, 6(3): 56~66.
[93] Ghosh A., Jalloul L., Love B., et al. Air-interface for 1XTREME/1xEV-DV. in: IEEE VTS 53rd Vehicular Technology Conference: vol. 4, 2001. 2474~2478.
[94] 3GPP, Physical layer aspects for evolved UTRA, Technical Report TR 25.814 V7.1.0, 2006.
[95] Paris J. F., Goldsmith A. J. Adaptive Modulation for MIMO Multiplexing under Average BER Constraints and Imperfect CSI. in: IEEE International Conference on Communications: vol. 3, 2006. 1318~1325.
[96] Fan L. Y., He C., Wang Z. Y., et al. Transmit power and bit allocation for the MIMO system. in: IEEE Global Telecommunications Conference: vol. 5, 2005. 2881~2884.
[97] Zhou S. L., Giannakis G. B. Adaptive Modulation for multiantenna transmissions with channel mean feedback. IEEE Transactions on Wireless Communications, 2004, 3(5): 1626~1636.
[98] Zhou Z. D., Vucetic B., Zhou C., et al. Design of adaptive modulation in MIMO systems using outdated CSI. in: IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications: vol. 2, 2005. 1101~1105.
[99] Zhou Z., Vucetic B. Design of adaptive modulation using imperfect CSI in MIMO systems. Electronics Letters, 2004, 40(17): 1073~1075.
[100] Yoo T., Goldsmith A. Capacity and power allocation for fading MIMO channels with channel estimation error. IEEE Transactions on Information Theory, 2006, 52(5): 2203~2214.
[101] Wang Z. Y., He C., A H., . Robust AM-MIMO based on minimized transmission power. IEEE Communications Letters, 2006, 10(6): 432~434.
[102] Etkin R., Tse D. Degrees of freedom in underspread mimo fading channels. in: IEEE International Symposium on Information Theory: 2003. 323~323.
[103] Baltersee J., Fock G., Meyr H. Achievable rate of MIMO channels with data-aided channel estimation and perfect interleaving. IEEE Journal on Selected Areas in Communications, 2001, 19(12): 2358~2368.
[104]胡臻平,朱光喜,肖啸.多用户空间复用OFDM系统中的子载波分配.华中科技大学学报(自然科学版) 2006, 2.
[105] Tse D. N. Optimal power allocation over parallel Gaussian broadcast channels. in: IEEE International Symposium on Information Theory Proceedings: 1997. 27~27.
[106] Yoo T., Goldsmith A. Optimality of zero-forcing beamforming with multiuser diversity. in: IEEE International Conference on Communications: vol. 1, 2005. 542~546.
[107] Caire G. MIMO downlink joint processing and scheduling: a survey of classical and recent results. in: Proceedings of Workshop on Information Theory and its Applications. San Diego, CA: 2006.
[108] Stuber G. L., Barry J. R., McLaughlin S. W., et al. Broadband MIMO-OFDM wireless communications. Proceedings of the IEEE, 2004, 92(2): 271~294.
[109] Yang H. W. A road to future broadband wireless access: MIMO-OFDM-Based air interface. IEEE Communications Magazine, 2005, 43(1): 53~60.
[110] Wong C. Y., Cheng R. S., Lataief K. B., et al. Multiuser OFDM with adaptive subcarrier, bit, and power allocation. IEEE Journal on Selected Areas in Communications, 1999, 17(10): 1747~1758.
[111] Chemaly R., Letaief K. B., Zeghlache D. Adaptive resource allocation for multiuser MIMO/OFDM networks based on partial channel state information. in: IEEE Global Telecommunications Conference: vol. 6, 2005. 3922~3926.
[112] Pan C. K., Cai Y., Xu Y. Adaptive subcarrier and power allocation for multiuser MIMO-OFDM systems. in: IEEE International Conference on Communications: vol. 4, 2005. 2631~2635.
[113] Kobayashi M., Caire G., Gesbert D. Transmit Diversity Versus Opportunistic Beamforming in Data Packet Mobile Downlink Transmission. IEEE Transactions on Communications, 2007, 55(1): 151~157.
[114] Ding P. L., Love D. J., Zoltowski M. D. On the Sum Rate of Multi-Antenna Broadcast Channels with Channel Estimation Error. in: Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers: 2005. 1524~1528.
[115] Shen Z. K., Heath R. W., Jr., Andrews J. G., et al. Comparison of space-time water-filling and spatial water-filling for MIMO fading channels. in: IEEE Global Telecommunications Conference: vol. 1, 2004. 431~435.
[116] Rappaport T. Wireless communications principles and practice. Prentice Hall, 2001.
[117] Shakkottai S., Rappaport T. S., Karlsson P. C. Cross-layer design for wireless networks. IEEE Communications Magazine, 2003, 41(10): 74~80.
[118] Berry R. A., Yeh E. M. Cross-layer wireless resource allocation. IEEE Signal Processing Magazine, 2004, 21(5): 59~68.
[119] Liu Q. W., Zhou S. L., Giannakis G. B. Cross-Layer combining of adaptive Modulation and coding with truncated ARQ over wireless links. IEEE Transactions on Wireless Communications, 2004, 3(5): 1746~1755.
[120] Knopp R., Humblet P. A. Information capacity and power control in single-cellmultiuser communications. in: IEEE International Conference on Communications: vol. 1, 1995. 331~335.
[121] Jalali A., Padovani R., Pankaj R. Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system. in: IEEE 51st Vehicular Technology Conference Proceedings: vol. 3, 2000. 1854~1858.
[122] Holtzman J. M. Asymptotic analysis of proportional fair algorithm. in: IEEE 2th International Symposium on Personal Indoor and Mobile Radio Communications: vol. 2, 2001. 33~37.
[123] Lai U. C., Murch R. D. A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach. IEEE Transactions on Wireless Communications, 2004, 3(1): 20~24.
[124] Heath R. W., Jr., Airy M., Paulraj A. J. Multiuser diversity for MIMO wireless systems with linear receivers. in: Conference Record of the Thirty-Fifth Asilomar Conference on Signals, Systems and Computers: vol. 2, 2001. 1194~1199.
[125] Lau V. K. N. Proportional Fair Space-Time Scheduling for Wireless Communications. IEEE Transactions on Communications, 2005, 53(8): 1353~1360.
[126] Lau V. K. N. Optimal downlink space-time scheduling design with convex utility functions - multiple antenna systems with orthogonal transmit beamforming. in: IEEE International Conference on Communications: vol. 5, 2004. 3040~3045.
[127] Park T., Shin O.-S., Lee K. B. Proportional fair scheduling for wireless communication with multiple transmit and receive antennas. in: IEEE 58th Vehicular Technology Conference: vol. 3, 2003. 1573~1577.
[128] Aniba G., Aissa S. Cross-layer design for scheduling and antenna sharing in MIMO networks. in: IEEE Global Telecommunications Conference: vol. 6, 2005. 3185~3189.
[129] Kountouris M., Pandharipande A., Hojin K., et al. QoS-based user scheduling for multiuser MIMO systems. in: IEEE 61st Vehicular Technology Conference: vol. 1, 2005. 211~215.
[130] Primolevo G., Simeone O., Spagnolini U. Channel aware scheduling for broadcast MIMO systems with orthogonal linear precoding and fairness constraints. in: IEEE International Conference on Communications: vol. 4, 2005. 2749~2753.
[131] Bosisio R., Primolevo G., Simeone O., et al. Fair scheduling and orthogonal linear precoding/decoding in broadcast MIMO systems. in: IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications: vol. 2, 2005.832~836.
[132] Lee J., Jindal N. Dirty Paper Coding vs. Linear Precoding for MIMO Broadcast Channels. in: Asilomar Conf. on Sign., Syst. and Computers: 2006. 779~783.
[133] Lee J., Jindal N. High SNR Analysis for MIMO Broadcast Channels: Dirty Paper Coding vs. Linear Precoding. Submitted to IEEE Trans. Information Theory, Dec. 2006.
[134] Shamai S., Verdu S. The impact of frequency-flat fading on the spectral efficiency of CDMA. IEEE Transactions on Information Theory, 2001, 47(4): 1302~1327.
[135] Neely M. J., Modiano E., Rohrs C. E. Power allocation and routing in multibeam satellites with time-varying channels. IEEE/ACM Transactions on Networking, 2003, 11(1): 138~152.
[136] Tassiulas L., Ephremides A. Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control, 1992, 37(12): 1936~1948.
[137] McKeown N., Mekkittikul A., Anantharam V., et al. Achieving 100% throughput in an input-queued switch. IEEE Transactions on Communications, 1999, 47(8): 1260~1267.
[138] Yeh E. M., Cohen A. S. Throughput and delay optimal resource allocation in multiaccess fading channels. in: IEEE International Symposium on Information Theory Proceedings: 2003. 245~245.
[139] Tse D. N. C., Hanly S. V. Multiaccess fading channels. I. Polymatroid structure, optimal resource allocation and throughput capacities. IEEE Transactions on Information Theory, 1998, 44(7): 2796~2815.
[140] Seong K., Narasimhan R., Cioffi J. M. Queue Proportional Scheduling via Geometric Programming in Fading Broadcast Channels. IEEE Journal on Selected Areas in Communications, 2006, 24(8): 1593~1602.
[141] Song G., Li Y., Cimini L. J., Jr., et al. Joint channel-aware and queue-aware data scheduling in multiple shared wireless channels. in: IEEE Wireless Communications and Networking Conference: vol. 3, 2004. 1939~1944.
[142] Viswanathan H., Kumaran K. Rate scheduling in multiple antenna downlink wireless systems. IEEE Transactions on Communications, 2005, 53(4): 645~655.
[143] Swannack C., Uysal-Biyikoglu E., G.W.Wornell. Low complexity multiuser scheduling for maximizing throughput in the MIMO broadcast channel. in: Allerton Conf. Commun., Control, Comput, 2004.
[144] Wang C., Murch R. D. Optimal downlink multi-user MIMO cross-layer schedulingusing HOL packet waiting time. IEEE Transactions on Wireless Communications, 2006, 5(10): 2856~2862.
[145] Koutsopoulos I., Tsoukatos K. P., Aggelis K. Physical layer techniques and maximum throughput scheduling with antenna arrays. IEEE Communications Letters, 2006, 10(6): 465~467.
[146] Wong K.-K. Adaptive space-division-multiplexing and bit-and-power allocation in multiuser MIMO flat fading broadcast channels. in: IEEE 58th Vehicular Technology Conference: vol. 1, 2003. 447~451.
[147] Zhang K., Niu Z. Joint Transmit Rate, Power and Antennas Allocation for MIMO systems with Multimedia Traffic. IEICE Transaction on Communications, 2006, E89-B(6):1939~1942
[148] Zhang R., Cioffic J. M., Liang Y.-C. MIMO broadcasting with delay-constrained and no-delay-constrained services. in: IEEE International Conference on Communications: vol. 2, 2005. 783~787.
[149] Jafar S. A., Goldsmith A. J. Isotropic fading vector broadcast Channels:The scalar upper bound and loss in degrees of freedom. IEEE Transactions on Information Theory, 2005, 51(3): 848~857.
[150] Love D. J., Heath R. W., Jr., Strohmer T. Grassmannian beamforming for multiple-input multiple-output wireless systems. IEEE Transactions on Information Theory, 2003, 49(10): 2735~2747.
[151] Narula A., Lopez M. J., Trott M. D., et al. Efficient use of side information in multiple-antenna data transmission over fading channels. IEEE Journal on Selected Areas in Communications, 1998, 16(8): 1423~1436.
[152] Mukkavilli K. K., Sabharwal A., Erkip E., et al. On beamforming with finite rate feedback in multiple-antenna systems. IEEE Transactions on Information Theory, 2003, 49(10): 2562~2579.
[153] Zhou S., Wang Z., Giannakis G. B. Quantifying the power loss when transmit beamforming relies on finite-rate feedback. IEEE Transactions on Wireless Communications, 2005, 4(4): 1948~1957.
[154] Blum R. S. MIMO with limited feedback of channel state information. in: IEEE International Conference on Acoustics, Speech, and Signal Processing: vol. 4, 2003. 89~92.
[155] Lau V., Youjian L., Chen T. A. On the design of MIMO block-fading channels with feedback-link capacity constraint. IEEE Transactions on Communications, 2004, 52(1): 62~70.
[156] Santipach W., Honig M. L. Asymptotic performance of MIMO wireless channels with limited feedback. in: IEEE Military Communications Conference: vol. 1, 2003. 141~146.
[157] Xia P., Zhou S., Giannakis G. B. Multiantenna adaptive modulation with beamforming based on bandwidth-constrained feedback. IEEE Transactions on Communications, 2005, 53(3): 526~536.
[158] Love D. J., Heath R. W., Jr. Limited feedback precoding for spatial multiplexing systems. in: IEEE Global Telecommunications Conference: vol. 4, 2003. 1857~1861.
[159] Ding P., Love D. J., Zoltowski M. D. Multiple Antenna Broadcast Channels With Limited Feedback. in: IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings: vol. 4, 2006. 25~28.
[160] Zhang K., Niu Z. Random Beamforming with Multi-beam Selection for MIMO Broadcast Channels. in: IEEE International Conference on Communications: vol. 9, 2006. 4191~4195.
[161] Yoo T., Goldsmith A. Sum-rate optimal multi-antenna downlink beamforming strategy based on clique search. in: IEEE Global Telecommunications Conference: vol. 3, 2005. 1510~1514.
[162] Al-Naffouri T. Y., Masoud S., Babak H. How Much Does Transmit Correlation Affect the Sum-Rate of MIMO Downlink Channels? in: IEEE International Symposium on Information Theory: 2006. 1574~1578.
[163] Diaz J., Simeone O., Bar-Ness Y. Sum-Rate of MIMO Broadcast Channels with One Bit Feedback. in: IEEE International Symposium on Information Theory: 2006. 1944~1948.
[164] Fung C. H. F., Wei Y., Lim T. J. Precoding for the Multiantenna Downlink: Multiuser SNR Gap and Optimal User Ordering. IEEE Transactions on Communications, 2007, 55(1): 188~197.
[165] Zhou Q., Dai H. Y., Zhang H. Y. Joint Tomlinson-Harashima precoding and scheduling for multiuser MIMO with imperfect feedback. in: IEEE Wireless Communications and Networking Conference: vol. 3, 2006. 1233~1238.
[166] Jiang M. L., Hui D. S. W., Lau V. K. N., et al. Cross-layer Downlink Scheduling of Multi-user Multi-Antenna Systems with Heterogeneous Delay Constraints. in: IEEE International Symposium on Information Theory: 2006. 2894~2898.
[167] Swannack C., Wornell G. W., Uysal-Biyikoglu E. MIMO Broadcast Scheduling with Quantized Channel State Information. in: IEEE International Symposium onInformation Theory: 2006. 1788~1792.
[168] Gesbert D., Kountouris M. Resource allocation in multicell wireless networks: Some capacity scaling laws. in: IEEE 3rd Workshop on Resource Allocation in Wireless Networks (WiOpt - RAWNET 2007): 2007.
[169] Andrews J. G., Choi W., R. W. Heath J. Overcoming Interference in Multi-Antenna Cellular Networks. IEEE Communications Magazine, 2007.
[170] Ren T. M., La R. J. Downlink beamforming algorithms with inter-cell interference in cellular networks. IEEE Transactions on Wireless Communications, 2006, 5(10): 2814~2823.
[171] Shim S., Kwak J. S., Jr R. W. H., et al. Downlink MIMO Block Diagonalization in the Presence of Other-Cell Interference. in: IEEE Global Telecommunications Conf: 2007.
[2] Zhang P., Tao X. F., Zhang J. H., et al. A vision from the future: beyond 3G TDD. IEEE Communications Magazine, 2005, 43(1): 38~44.
[3] Liu G. Y., Zhang J. H., Zhang P., et al. Evolution map from TD-SCDMA to FuTURE B3G TDD. IEEE Communications Magazine, 2006, 44(3): 54~61.
[4] Hui S. Y., Yeung K. H. Challenges in the migration to 4G mobile systems. IEEE Communications Magazine, 2003, 41(12): 54~59.
[5] Cheng Y., Jiang H., Zhuang W. H., et al. Efficient resource allocation for China's 3G/4G wireless networks. IEEE Communications Magazine, 2005, 43(1): 76~83.
[6]尤肖虎. FuTURE B3G研究开发及关键技术进展.移动通信, 2006, 6: 18~22.
[7] Paulraj A. J., Gore D. A., Nabar R. U., et al. An overview of MIMO communications - a key to gigabit wireless. Proceedings of the IEEE, 2004, 92(2): 198~218.
[8] Wolniansky P., Foschini G., Golgen G. V-Blast: an architecture for realizing very high data rates over the rich-scattering wireless channel. in: Conference Proceedings of the International Symposium on Signals, Systems and Electronics: 1998. 295~300.
[9] Telatar I. Capacity of multi-antenna Gaussian channel. European Transactions on Telecommunications, 1999, 10(6): 585~595.
[10] Foschini G., Gans M. On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas, Wireless Personal Communications, 1998, 6: 311~335.
[11] Raleigh G. G., Cioffi J. M. Spatio-temporal coding for wireless communication. IEEE Transactions on Communications, 1998, 46(3): 357~366.
[12] Foschini G. Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell Labs Tech. J, 1996: 41~59.
[13] Wubben D., Rinas J., Bohnke R. Efficient Algorithm for Detecting Layered Space-Time Codes. in: ITG Conference on Source and Channel Coding: 2002. 399~405.
[14] Da-Shan S., Kahn M. Layered space-time codes for wireless communications using multiple transmit antennas. in: IEEE International Conference on Communications:vol. 1, 1999. 436~440.
[15] Hwang K. J., Lee K. B. Transmit power allocation with small feedback overhead for a multiple antenna system. in: IEEE 56th Vehicular Technology Conference: vol. 4, 2002. 2158~2162.
[16] Nam S. H., Shin O.-S., Lee K. B. Transmit power allocation for a modified V-BLAST system. IEEE Transactions on Communications, 2004, 52(7): 1074~1079.
[17] Stefanov A., Duman T. M. Turbo-coded modulation for systems with transmit and receive antenna diversity over block fading channels: system model, decoding approaches, and practical considerations. IEEE Journal on Selected Areas in Communications, 2001, 19(5): 958~968.
[18] Lek S. Turbo space-time processing to improve wireless channel capacity. IEEE Transactions on Communications, 2000, 48(8): 1347~1359.
[19] Xin Y., Liu Z. Q., Giannakis G. B. High-rate layered space-time transmissions based on constellation-rotation. in: IEEE Wireless Communications and Networking Conference: vol. 1, 2002. 471~476.
[20] Palomar D. P., Cioffi J. M., Lagunas M. A. Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization. IEEE Transactions on Signal Processing, 2003, 51(9): 2381~2401.
[21] Ding Y., Davidson T. N., Luo Z.-Q., et al. Minimum BER block precoders for zero-forcing equalization. IEEE Transactions on Signal Processing, 2003, 51(9): 2410~2423.
[22] Love D. J., Heath R. W., Jr. Multimode precoding for MIMO wireless systems. IEEE Transactions on Signal Processing, 2005, 53(10): 3674~3687.
[23] Palomar D. P., Barbarossa S. Designing MIMO communication systems: constellation choice and linear transceiver design. IEEE Transactions on Signal Processing, 2005, 53(10): 3804~3818.
[24] Wittneben A. A new bandwidth efficient transmit antenna modulation diversity scheme for linear digital modulation. in: IEEE International Conference on Communications: vol. 3, 1993. 1630~1634.
[25] Tarokh V., Seshadri N., Calderbank A. R. Space-time codes for high data rate wireless communication: performance criterion and code construction. IEEE Transactions on Information Theory, 1998, 44(2): 744~765.
[26] Alamouti S. M. A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications, 1998, 16(8): 1451~1458.
[27] Tarokh V., Jafarkhani H., Calderbank A. R. Space-time block codes from orthogonal designs. IEEE Transactions on Information Theory, 1999, 45(5): 1456~1467.
[28] Yi G., Ben Letaief K. Concatenated space-time block coding with trellis coded modulation in fading channels. IEEE Transactions on Wireless Communications, 2002, 1(4): 580~590.
[29] Hassibi B., Hochwald B. M. How much training is needed in multiple-antenna wireless links? IEEE Transactions on Information Theory, 2003, 49(4): 951~963.
[30] Zheng L. Z., Tse D. N. C. Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels. IEEE Transactions on Information Theory, 2003, 49(5): 1073~1096.
[31] Goldsmith A., Jafar S. A., Jindal N., et al. Capacity limits of MIMO channels. IEEE Journal on Selected Areas in Communications, 2003, 21(5): 684~702.
[32] Boyd S., Vandenberghe L. Convex Optimization. Cambridge University Press, 2004.
[33] Wei Y., Wonjong R., Boyd S., et al. Iterative water-filling for Gaussian vector multiple-access channels. Information Theory, IEEE Transactions on, 2004, 50(1): 145~152.
[34] Cover T. M., Thomas J. A. Elements of Information Theory. New York: Wiley, 1991.
[35] Caire G., Shamai S. On the achievable throughput of a multiantenna Gaussian broadcast channel. IEEE Transactions on Information Theory, 2003, 49(7): 1691~1706.
[36] Costa M. Writing on dirty paper. IEEE Transactions on Information Theory, 1983, 29(3): 439~441.
[37] Wei Y., Cioffi J. M. Sum capacity of Gaussian vector broadcast channels. IEEE Transactions on Information Theory, 2004, 50(9): 1875~1892.
[38] Vishwanath S., Jindal N., Goldsmith A. Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels. IEEE Transactions on Information Theory, 2003, 49(10): 2658~2668.
[39] Viswanath P., Tse D. N. C. Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality. IEEE Transactions on Information Theory, 2003, 49(8): 1912~1921.
[40] Weingarten H., Steinberg Y., Shamai S. S. The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel. IEEE Transactions onInformation Theory, 2006, 52(9): 3936~3964.
[41] Schubert M., Boche H. Solution of the multiuser downlink beamforming problem with individual SINR constraints. IEEE Transactions on Vehicular Technology, 2004, 53(1): 18~28.
[42] Hassibi B., Marzetta T. L. Multiple-antennas and isotropically random unitary inputs: the received signal density in closed form. IEEE Transactions on Information Theory, 2002, 48(6): 1473~1484.
[43] Zheng L. Z., Tse D. N. C. Communication on the Grassmann manifold: a geometric approach to the noncoherent multiple-antenna channel. IEEE Transactions on Information Theory, 2002, 48(2): 359~383.
[44] Goldsmith A. J., Soon-Ghee C. Variable-rate variable-power MQAM for fading channels. IEEE Transactions on Communications, 1997, 45(10): 1218~1230.
[45] Chung S. T., Goldsmith A. J. Degrees of freedom in adaptive modulation: a unified view. IEEE Transactions on Communications, 2001, 49(9): 1561~1571.
[46] Zhou Z. D., Vucetic B., Dohler M., et al. MIMO systems with adaptive modulation. IEEE Transactions on Vehicular Technology, 2005, 54(5): 1828~1842.
[47] Chung S. T., Lozano A., Huang H. C. Approaching eigenmode BLAST channel capacity using V-BLAST with rate and power feedback. in: IEEE 54th Vehicular Technology Conference: vol. 2, 2001. 915~919.
[48] Zhang R., Cioffi J. M., Liang Y.-C. Approaching MIMO-OFDM Capacity with Per Antenna Power and Rate Feedback. in: IEEE International Conference on Communications: vol. 12, 2006. 5348~5353.
[49] Li L., Goldsmith A. J. Capacity and optimal resource allocation for fading broadcast channels .I. Ergodic capacity. IEEE Transactions on Information Theory, 2001, 47(3): 1083~1102.
[50] Ajib W., Haccoun D. An overview of scheduling algorithms in MIMO-based fourth-generation wireless systems. IEEE Network, 2005, 19(5): 43~48.
[51] Hochwald B. M., Marzetta T. L., Tarokh V. Multiple-antenna channel hardening and its implications for rate feedback and scheduling. IEEE Transactions on Information Theory, 2004, 50(9): 1893~1909.
[52] Gozali R., Buehrer R. M., Woerner B. D. The impact of multiuser diversity on space-time block coding. IEEE Communications Letters, 2003, 7(5): 213~215.
[53] Viswanath P., Tse D. N. C., Laroia R. Opportunistic beamforming using dumb antennas. IEEE Transactions on Information Theory, 2002, 48(6): 1277~1294.
[54] Larsson E. G. On the combination of spatial diversity and multiuser diversity. IEEECommunications Letters, 2004, 8(8): 517~519.
[55] Sharma N., Ozarow L. H. A study of opportunism for multiple-antenna systems. IEEE Transactions on Information Theory, 2005, 51(5): 1804~1814.
[56] Chung J., Hwang C.-S., Kim K., et al. A random beamforming technique in MIMO systems exploiting multiuser diversity. IEEE Journal on Selected Areas in Communications, 2003, 21(5): 848~855.
[57] Jindal N., Goldsmith A. Dirty-paper coding versus TDMA for MIMO Broadcast channels. IEEE Transactions on Information Theory, 2005, 51(5): 1783~1794.
[58] Sharif M., Hassibi B. A Comparison of Time-Sharing, DPC, and Beamforming for MIMO Broadcast Channels With Many Users. IEEE Transactions on Communications, 2007, 55(1): 11~15.
[59] Jindal N., Wonjong R., Vishwanath S., et al. Sum power iterative water-filling for multi-antenna Gaussian broadcast channels. IEEE Transactions on Information Theory, 2005, 51(4): 1570~1580.
[60] Wei Y. Sum-capacity computation for the Gaussian vector broadcast channel via dual decomposition. IEEE Transactions on Information Theory, 2006, 52(2): 754~759.
[61] Zamir R., Shamai S., Erez U. Nested linear/lattice codes for structured multiterminal binning. IEEE Transactions on Information Theory, 2002, 48(6): 1250~1276.
[62] Tu Z., Blum R. S. Multiuser diversity for a dirty paper approach. IEEE Communications Letters, 2003, 7(8): 370~372.
[63] Dimic G., Sidiropoulos N. D. On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm. IEEE Transactions on Signal Processing, 2005, 53(10): 3857~3868.
[64] Wong K.-K., Murch R. D., Letaief K. B. A joint-channel diagonalization for multiuser MIMO antenna systems. IEEE Transactions on Wireless Communications, 2003, 2(4): 773~786.
[65] Spencer Q. H., Swindlehurst A. L., Haardt M. Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels. IEEE Transactions on Signal Processing, 2004, 52(2): 461~471.
[66] Peel C. B., Hochwald B. M., Swindlehurst A. L. A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization. IEEE Transactions on Communications, 2005, 53(1): 195~202.
[67] Hochwald B. M., Peel C. B., Swindlehurst A. L. A vector-perturbation techniquefor near-capacity multiantenna multiuser communication-part II: perturbation. IEEE Transactions on Communications, 2005, 53(3): 537~544.
[68] Lai U. C., Murch R. D. A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach. Wireless Communications, IEEE Transactions on, 2004, 3(1): 20~24.
[69] Yoo T., Goldsmith A. On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming. IEEE Journal on Selected Areas in Communications, 2006, 24(3): 528~541.
[70] Shen Z. K., Chen R. H., Andrews J. G., et al. Sum Capacity of Multiuser MIMO Broadcast Channels with Block Diagonalization. in: IEEE International Symposium on Information Theory: 2006. 886~890.
[71] Love D. J., Heath R. W., Jr., Santipach W., et al. What is the value of limited feedback for MIMO channels? IEEE Communications Magazine, 2004, 42(10): 54~59.
[72] Sharif M., Hassibi B. On the capacity of MIMO broadcast channels with partial side information. IEEE Transactions on Information Theory, 2005, 51(2): 506~522.
[73] Jindal N. MIMO Broadcast Channels With Finite-Rate Feedback. IEEE Transactions on Information Theory, 2006, 52(11): 5045~5060.
[74] Bergmans P. Random coding theorem for broadcast channels with degraded components. IEEE Transactions on Information Theory, 1973, 19(2): 197~207.
[75] Jindal N., Wonjong R., Vishwanath S., et al. Sum power iterative water-filling for multi-antenna Gaussian broadcast channels. Information Theory, IEEE Transactions on, 2005, 51(4): 1570~1580.
[76] Chen R., Andrews J. G., Heath R. W., Jr. Transmit selection diversity for multiuser spatial multiplexing systems. in: IEEE Global Telecommunications Conference: vol. 4, 2004. 2625~2629.
[77] Chae C.-B., Mazzarese D., Jr R. W. H. Coordinated beamforming for multiuser MIMO sysems with limited feedforward. in: Asilomar Conf. on Sign., Syst. and Computers: 2006.
[78] Pan Z. G., Wong K.-K., Ng T.-S. Generalized multiuser orthogonal space-division multiplexing. IEEE Transactions on Wireless Communications, 2004, 3(6): 1969~1973.
[79] Tomlinson M. New automatic equalizer employing modulo arithmetic. IEEE Electron. Lett., 1971, 7(5): 138~139.
[80] Harashima H., Miyakawa H. Matched-Transmission Technique for Channels WithIntersymbol Interference. IEEE Transactions on Communications, 1972, 20(4): 774~780.
[81] Hunger R., Joham M., Utschick W. Extension of linear and nonlinear transmit filters for decentralized receivers. in: Proc. European Wireless Conf: 2005. 40~46.
[82] Kusume K., Joham M., Utschick W., et al. Efficient Tomlinson-Harashima precoding for spatial multiplexing on flat MIMO channel. in: IEEE International Conference on Communications: vol. 3, 2005. 2021~2025.
[83] Windpassinger C., Fischer R. F. H., Vencel T., et al. Precoding in multiantenna and multiuser communications. IEEE Transactions on Wireless Communications, 2004, 3(4): 1305~1316.
[84] Yoo T., Nihar J., Goldsmith A. Finite-Rate Feedback MIMO Broadcast Channels with a Large Number of Users. in: IEEE International Symposium on Information Theory: 2006. 1214~1218.
[85] Kountouris M., Francisco R. d., Gesbert D., et al. Efficient Metric for Scheduling in MIMO Broadcast Channels with Limited Feedback. in: IEEE International Conference on Acoustics, Speech, and Signal Processing: 2007.
[86] Huang K.-B., Heath R. W., Andrews J. G. Space Division Multiple Access with a Sum Feedback Rate Constraint. in: IEEE International Conference on Acoustics, Speech, and Signal Processing: 2007.
[87] Kountouris M., Gesbert D. Memory-based opportunistic multi-user beamforming. in: International Symposium on Information Theory Proceedings: 2005. 1426~1430.
[88] Hammarwall D., Bengtsson M., Ottersten B. Beamforming and user selection in SDMA systems utilizing channel statistics and instantaneous SNR feedback. in: IEEE International Conference on Acoustics, Speech, and Signal Processing: 2007.
[89] Kountouris M., Gesbert D. Robust multi-user opportunistic beamforming for sparse networks. in: IEEE 6th Workshop on Signal Processing Advances in Wireless Communications: 2005. 975~979.
[90] Lee H., Shin M., Lee C. An eigen-based MIMO multiuser scheduler with partial feedback information. IEEE Communications Letters, 2005, 9(4): 328~330.
[91] Bayesteh A., Khandani A. K. On the user selection for MIMO broadcast channels. in: Proceedings International Symposium on Information Theory: 2005. 2325~2329.
[92] Furuskar A., Mazur S., Muller F., et al. EDGE: enhanced data rates for GSM and TDMA/136 evolution. IEEE Personal Communications, 1999, 6(3): 56~66.
[93] Ghosh A., Jalloul L., Love B., et al. Air-interface for 1XTREME/1xEV-DV. in: IEEE VTS 53rd Vehicular Technology Conference: vol. 4, 2001. 2474~2478.
[94] 3GPP, Physical layer aspects for evolved UTRA, Technical Report TR 25.814 V7.1.0, 2006.
[95] Paris J. F., Goldsmith A. J. Adaptive Modulation for MIMO Multiplexing under Average BER Constraints and Imperfect CSI. in: IEEE International Conference on Communications: vol. 3, 2006. 1318~1325.
[96] Fan L. Y., He C., Wang Z. Y., et al. Transmit power and bit allocation for the MIMO system. in: IEEE Global Telecommunications Conference: vol. 5, 2005. 2881~2884.
[97] Zhou S. L., Giannakis G. B. Adaptive Modulation for multiantenna transmissions with channel mean feedback. IEEE Transactions on Wireless Communications, 2004, 3(5): 1626~1636.
[98] Zhou Z. D., Vucetic B., Zhou C., et al. Design of adaptive modulation in MIMO systems using outdated CSI. in: IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications: vol. 2, 2005. 1101~1105.
[99] Zhou Z., Vucetic B. Design of adaptive modulation using imperfect CSI in MIMO systems. Electronics Letters, 2004, 40(17): 1073~1075.
[100] Yoo T., Goldsmith A. Capacity and power allocation for fading MIMO channels with channel estimation error. IEEE Transactions on Information Theory, 2006, 52(5): 2203~2214.
[101] Wang Z. Y., He C., A H., . Robust AM-MIMO based on minimized transmission power. IEEE Communications Letters, 2006, 10(6): 432~434.
[102] Etkin R., Tse D. Degrees of freedom in underspread mimo fading channels. in: IEEE International Symposium on Information Theory: 2003. 323~323.
[103] Baltersee J., Fock G., Meyr H. Achievable rate of MIMO channels with data-aided channel estimation and perfect interleaving. IEEE Journal on Selected Areas in Communications, 2001, 19(12): 2358~2368.
[104]胡臻平,朱光喜,肖啸.多用户空间复用OFDM系统中的子载波分配.华中科技大学学报(自然科学版) 2006, 2.
[105] Tse D. N. Optimal power allocation over parallel Gaussian broadcast channels. in: IEEE International Symposium on Information Theory Proceedings: 1997. 27~27.
[106] Yoo T., Goldsmith A. Optimality of zero-forcing beamforming with multiuser diversity. in: IEEE International Conference on Communications: vol. 1, 2005. 542~546.
[107] Caire G. MIMO downlink joint processing and scheduling: a survey of classical and recent results. in: Proceedings of Workshop on Information Theory and its Applications. San Diego, CA: 2006.
[108] Stuber G. L., Barry J. R., McLaughlin S. W., et al. Broadband MIMO-OFDM wireless communications. Proceedings of the IEEE, 2004, 92(2): 271~294.
[109] Yang H. W. A road to future broadband wireless access: MIMO-OFDM-Based air interface. IEEE Communications Magazine, 2005, 43(1): 53~60.
[110] Wong C. Y., Cheng R. S., Lataief K. B., et al. Multiuser OFDM with adaptive subcarrier, bit, and power allocation. IEEE Journal on Selected Areas in Communications, 1999, 17(10): 1747~1758.
[111] Chemaly R., Letaief K. B., Zeghlache D. Adaptive resource allocation for multiuser MIMO/OFDM networks based on partial channel state information. in: IEEE Global Telecommunications Conference: vol. 6, 2005. 3922~3926.
[112] Pan C. K., Cai Y., Xu Y. Adaptive subcarrier and power allocation for multiuser MIMO-OFDM systems. in: IEEE International Conference on Communications: vol. 4, 2005. 2631~2635.
[113] Kobayashi M., Caire G., Gesbert D. Transmit Diversity Versus Opportunistic Beamforming in Data Packet Mobile Downlink Transmission. IEEE Transactions on Communications, 2007, 55(1): 151~157.
[114] Ding P. L., Love D. J., Zoltowski M. D. On the Sum Rate of Multi-Antenna Broadcast Channels with Channel Estimation Error. in: Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers: 2005. 1524~1528.
[115] Shen Z. K., Heath R. W., Jr., Andrews J. G., et al. Comparison of space-time water-filling and spatial water-filling for MIMO fading channels. in: IEEE Global Telecommunications Conference: vol. 1, 2004. 431~435.
[116] Rappaport T. Wireless communications principles and practice. Prentice Hall, 2001.
[117] Shakkottai S., Rappaport T. S., Karlsson P. C. Cross-layer design for wireless networks. IEEE Communications Magazine, 2003, 41(10): 74~80.
[118] Berry R. A., Yeh E. M. Cross-layer wireless resource allocation. IEEE Signal Processing Magazine, 2004, 21(5): 59~68.
[119] Liu Q. W., Zhou S. L., Giannakis G. B. Cross-Layer combining of adaptive Modulation and coding with truncated ARQ over wireless links. IEEE Transactions on Wireless Communications, 2004, 3(5): 1746~1755.
[120] Knopp R., Humblet P. A. Information capacity and power control in single-cellmultiuser communications. in: IEEE International Conference on Communications: vol. 1, 1995. 331~335.
[121] Jalali A., Padovani R., Pankaj R. Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system. in: IEEE 51st Vehicular Technology Conference Proceedings: vol. 3, 2000. 1854~1858.
[122] Holtzman J. M. Asymptotic analysis of proportional fair algorithm. in: IEEE 2th International Symposium on Personal Indoor and Mobile Radio Communications: vol. 2, 2001. 33~37.
[123] Lai U. C., Murch R. D. A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach. IEEE Transactions on Wireless Communications, 2004, 3(1): 20~24.
[124] Heath R. W., Jr., Airy M., Paulraj A. J. Multiuser diversity for MIMO wireless systems with linear receivers. in: Conference Record of the Thirty-Fifth Asilomar Conference on Signals, Systems and Computers: vol. 2, 2001. 1194~1199.
[125] Lau V. K. N. Proportional Fair Space-Time Scheduling for Wireless Communications. IEEE Transactions on Communications, 2005, 53(8): 1353~1360.
[126] Lau V. K. N. Optimal downlink space-time scheduling design with convex utility functions - multiple antenna systems with orthogonal transmit beamforming. in: IEEE International Conference on Communications: vol. 5, 2004. 3040~3045.
[127] Park T., Shin O.-S., Lee K. B. Proportional fair scheduling for wireless communication with multiple transmit and receive antennas. in: IEEE 58th Vehicular Technology Conference: vol. 3, 2003. 1573~1577.
[128] Aniba G., Aissa S. Cross-layer design for scheduling and antenna sharing in MIMO networks. in: IEEE Global Telecommunications Conference: vol. 6, 2005. 3185~3189.
[129] Kountouris M., Pandharipande A., Hojin K., et al. QoS-based user scheduling for multiuser MIMO systems. in: IEEE 61st Vehicular Technology Conference: vol. 1, 2005. 211~215.
[130] Primolevo G., Simeone O., Spagnolini U. Channel aware scheduling for broadcast MIMO systems with orthogonal linear precoding and fairness constraints. in: IEEE International Conference on Communications: vol. 4, 2005. 2749~2753.
[131] Bosisio R., Primolevo G., Simeone O., et al. Fair scheduling and orthogonal linear precoding/decoding in broadcast MIMO systems. in: IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications: vol. 2, 2005.832~836.
[132] Lee J., Jindal N. Dirty Paper Coding vs. Linear Precoding for MIMO Broadcast Channels. in: Asilomar Conf. on Sign., Syst. and Computers: 2006. 779~783.
[133] Lee J., Jindal N. High SNR Analysis for MIMO Broadcast Channels: Dirty Paper Coding vs. Linear Precoding. Submitted to IEEE Trans. Information Theory, Dec. 2006.
[134] Shamai S., Verdu S. The impact of frequency-flat fading on the spectral efficiency of CDMA. IEEE Transactions on Information Theory, 2001, 47(4): 1302~1327.
[135] Neely M. J., Modiano E., Rohrs C. E. Power allocation and routing in multibeam satellites with time-varying channels. IEEE/ACM Transactions on Networking, 2003, 11(1): 138~152.
[136] Tassiulas L., Ephremides A. Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control, 1992, 37(12): 1936~1948.
[137] McKeown N., Mekkittikul A., Anantharam V., et al. Achieving 100% throughput in an input-queued switch. IEEE Transactions on Communications, 1999, 47(8): 1260~1267.
[138] Yeh E. M., Cohen A. S. Throughput and delay optimal resource allocation in multiaccess fading channels. in: IEEE International Symposium on Information Theory Proceedings: 2003. 245~245.
[139] Tse D. N. C., Hanly S. V. Multiaccess fading channels. I. Polymatroid structure, optimal resource allocation and throughput capacities. IEEE Transactions on Information Theory, 1998, 44(7): 2796~2815.
[140] Seong K., Narasimhan R., Cioffi J. M. Queue Proportional Scheduling via Geometric Programming in Fading Broadcast Channels. IEEE Journal on Selected Areas in Communications, 2006, 24(8): 1593~1602.
[141] Song G., Li Y., Cimini L. J., Jr., et al. Joint channel-aware and queue-aware data scheduling in multiple shared wireless channels. in: IEEE Wireless Communications and Networking Conference: vol. 3, 2004. 1939~1944.
[142] Viswanathan H., Kumaran K. Rate scheduling in multiple antenna downlink wireless systems. IEEE Transactions on Communications, 2005, 53(4): 645~655.
[143] Swannack C., Uysal-Biyikoglu E., G.W.Wornell. Low complexity multiuser scheduling for maximizing throughput in the MIMO broadcast channel. in: Allerton Conf. Commun., Control, Comput, 2004.
[144] Wang C., Murch R. D. Optimal downlink multi-user MIMO cross-layer schedulingusing HOL packet waiting time. IEEE Transactions on Wireless Communications, 2006, 5(10): 2856~2862.
[145] Koutsopoulos I., Tsoukatos K. P., Aggelis K. Physical layer techniques and maximum throughput scheduling with antenna arrays. IEEE Communications Letters, 2006, 10(6): 465~467.
[146] Wong K.-K. Adaptive space-division-multiplexing and bit-and-power allocation in multiuser MIMO flat fading broadcast channels. in: IEEE 58th Vehicular Technology Conference: vol. 1, 2003. 447~451.
[147] Zhang K., Niu Z. Joint Transmit Rate, Power and Antennas Allocation for MIMO systems with Multimedia Traffic. IEICE Transaction on Communications, 2006, E89-B(6):1939~1942
[148] Zhang R., Cioffic J. M., Liang Y.-C. MIMO broadcasting with delay-constrained and no-delay-constrained services. in: IEEE International Conference on Communications: vol. 2, 2005. 783~787.
[149] Jafar S. A., Goldsmith A. J. Isotropic fading vector broadcast Channels:The scalar upper bound and loss in degrees of freedom. IEEE Transactions on Information Theory, 2005, 51(3): 848~857.
[150] Love D. J., Heath R. W., Jr., Strohmer T. Grassmannian beamforming for multiple-input multiple-output wireless systems. IEEE Transactions on Information Theory, 2003, 49(10): 2735~2747.
[151] Narula A., Lopez M. J., Trott M. D., et al. Efficient use of side information in multiple-antenna data transmission over fading channels. IEEE Journal on Selected Areas in Communications, 1998, 16(8): 1423~1436.
[152] Mukkavilli K. K., Sabharwal A., Erkip E., et al. On beamforming with finite rate feedback in multiple-antenna systems. IEEE Transactions on Information Theory, 2003, 49(10): 2562~2579.
[153] Zhou S., Wang Z., Giannakis G. B. Quantifying the power loss when transmit beamforming relies on finite-rate feedback. IEEE Transactions on Wireless Communications, 2005, 4(4): 1948~1957.
[154] Blum R. S. MIMO with limited feedback of channel state information. in: IEEE International Conference on Acoustics, Speech, and Signal Processing: vol. 4, 2003. 89~92.
[155] Lau V., Youjian L., Chen T. A. On the design of MIMO block-fading channels with feedback-link capacity constraint. IEEE Transactions on Communications, 2004, 52(1): 62~70.
[156] Santipach W., Honig M. L. Asymptotic performance of MIMO wireless channels with limited feedback. in: IEEE Military Communications Conference: vol. 1, 2003. 141~146.
[157] Xia P., Zhou S., Giannakis G. B. Multiantenna adaptive modulation with beamforming based on bandwidth-constrained feedback. IEEE Transactions on Communications, 2005, 53(3): 526~536.
[158] Love D. J., Heath R. W., Jr. Limited feedback precoding for spatial multiplexing systems. in: IEEE Global Telecommunications Conference: vol. 4, 2003. 1857~1861.
[159] Ding P., Love D. J., Zoltowski M. D. Multiple Antenna Broadcast Channels With Limited Feedback. in: IEEE International Conference on Acoustics, Speech and Signal Processing Proceedings: vol. 4, 2006. 25~28.
[160] Zhang K., Niu Z. Random Beamforming with Multi-beam Selection for MIMO Broadcast Channels. in: IEEE International Conference on Communications: vol. 9, 2006. 4191~4195.
[161] Yoo T., Goldsmith A. Sum-rate optimal multi-antenna downlink beamforming strategy based on clique search. in: IEEE Global Telecommunications Conference: vol. 3, 2005. 1510~1514.
[162] Al-Naffouri T. Y., Masoud S., Babak H. How Much Does Transmit Correlation Affect the Sum-Rate of MIMO Downlink Channels? in: IEEE International Symposium on Information Theory: 2006. 1574~1578.
[163] Diaz J., Simeone O., Bar-Ness Y. Sum-Rate of MIMO Broadcast Channels with One Bit Feedback. in: IEEE International Symposium on Information Theory: 2006. 1944~1948.
[164] Fung C. H. F., Wei Y., Lim T. J. Precoding for the Multiantenna Downlink: Multiuser SNR Gap and Optimal User Ordering. IEEE Transactions on Communications, 2007, 55(1): 188~197.
[165] Zhou Q., Dai H. Y., Zhang H. Y. Joint Tomlinson-Harashima precoding and scheduling for multiuser MIMO with imperfect feedback. in: IEEE Wireless Communications and Networking Conference: vol. 3, 2006. 1233~1238.
[166] Jiang M. L., Hui D. S. W., Lau V. K. N., et al. Cross-layer Downlink Scheduling of Multi-user Multi-Antenna Systems with Heterogeneous Delay Constraints. in: IEEE International Symposium on Information Theory: 2006. 2894~2898.
[167] Swannack C., Wornell G. W., Uysal-Biyikoglu E. MIMO Broadcast Scheduling with Quantized Channel State Information. in: IEEE International Symposium onInformation Theory: 2006. 1788~1792.
[168] Gesbert D., Kountouris M. Resource allocation in multicell wireless networks: Some capacity scaling laws. in: IEEE 3rd Workshop on Resource Allocation in Wireless Networks (WiOpt - RAWNET 2007): 2007.
[169] Andrews J. G., Choi W., R. W. Heath J. Overcoming Interference in Multi-Antenna Cellular Networks. IEEE Communications Magazine, 2007.
[170] Ren T. M., La R. J. Downlink beamforming algorithms with inter-cell interference in cellular networks. IEEE Transactions on Wireless Communications, 2006, 5(10): 2814~2823.
[171] Shim S., Kwak J. S., Jr R. W. H., et al. Downlink MIMO Block Diagonalization in the Presence of Other-Cell Interference. in: IEEE Global Telecommunications Conf: 2007.
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