摘要
为提高频谱效率,下一代蜂窝系统采用正交频分多址接入技术(OFDMA)技术,频率复用因子为1,小区内干扰可以忽略,干扰大部分来自于其他小区,即系统干扰主要为小区间干扰(ICI)。为了进一步提高系统吞吐量和小区边缘用户的性能,降低ICI成为一个重要的研究课题。协作多点传输／接收技术(CoMP)是第四代蜂窝移动通信系统中的关键技术之一。在下行链路中,多个小区协作表现为协作的基站通过对信道状态信息(CSI)和用户数据信息不同程度的共享,将传统意义上的干扰“转化”为有用信号,多个基站共同向用户传输数据或者协作波束成形进行干扰协调。
多小区协作是以协作基站间共享CSI和用户数据为条件的,会带来巨大的信令开销：反馈开销和无线回传开销。在设计预编码算法时,需要在算法所带来的性能增益和信令开销之间做出合适的折中。同时,要考虑实际系统中共享信息的获取,即用户向基站的反馈信道、基站之间交换信息的无线回传链路、信道互易性、信道估计误差等因素的局限性。所以,探索能降低CSI共享的分布式协作算法,研究有限反馈下的小区协作性能、规律有重要的现实意义。鉴于以上因素,论文围绕蜂窝系统中多小区协作预编码做了深入的研究,主要从以下几个方面展开：多小区联合传输预编码算法、分布式协作预编码算法、基于有限反馈的多小区协作预编码。
作为多小区协作的基础,论文详细介绍CoMP的分类,蜂窝小区的拓扑结构,多小区协作的信道模型。根据基站是否与中央处理单元相连,分为集中式协作和分布式协作；根据是否全部基站参与协作,分为完全协作和不完全协作；根据是否共享用户数据,分为协作多小区传输(基站联合传输)和协作单小区传输(基站协作波束成形／协作调度)。仿真中往往用到线形或者六边形的维纳模型来模拟蜂窝系统,单小区只需要考虑小规模衰落,多小区信道还要考虑大规模衰落,协作多小区传输和协作单小区传输的信道对应不同的表达式。
论文主要内容和贡献如下：
(1)将单小区中的预编码算法推广到多小区,并比较算法的性能。多个小区基站通过CSI和用户数据信息的共享,实现多基站协作传输,将传统意义上的干扰转化为对用户有用的信号,从而提高频谱效率,极大改善小区边缘用户的性能。块对角(BD)算法通过在干扰信道的零空间上设计合适的预编码向量,完全抵消干扰。将BD算法用于多小区联合传输,从空间维的角度剖析了其干扰抵消和宏分集增益的工作原理,并将其与迫零(ZF)算法,最小均方误差(MMSE)算法,时分多址(TDMA)以及单用户特征值波束成形算法比较,分析了各种算法的优劣。同时将协作BD算法与非协作BD算法比较。仿真结果表明BD算法优于上述预编码算法,协作传输优于非协作传输。
(2)提出了基于粒子群算法的线性预编码算法用于协作多小区传输。粒子群优化(PSO)理论比较简单,易于实现。另外,PSO有很快的收敛速度,并且,对搜索空间和梯度信息无特殊的要求。在多小区协作传输环境中,我们的问题常常转化为在总功率约束或者每基站功率约束下,最大化频谱效率或者系统容量。目标函数中,山于预编码向量的耦合,目标函数的凸性一般很难确定,并且,这往往是一个非凸的优化问题。采用PSO算法,各个用户独立在搜索空间搜索自身最优的预编码向量。介绍了粒子群算法的理论及流程,分析了其收敛性,推导协作多小区传输的粒子群优化算法的适应值,每个用户根据适应值函数搜索各自的预编码向量。仿真表明,该算法优于ZF和MMSE算法。
(3)无线回传开销和反馈开销过大是基站协作应用于实际蜂窝通信系统中的瓶颈之一。为了能够减少回传开销和预编码复杂度,同时还能进行干扰协调,提出基于信漏噪比(SLNR)的分布式协作多小区传输预编码算法。基于SLNR最大化的预编码设计仅仅需要本地的信道状态信息进行分布式编码,协作传输的基站不但有效减少了无线回传开销,同时由于共享用户的数据,还能得到基站协作带来的性能提高。将协作的分布式SLNR预编码策略与完全协作的迫零(ZF)算法和完全分布式的SLNR算法比较,由仿真可以看到该算法在开销和性能之间取得了良好的折中。
(4)提出有限反馈时基于干扰清零算法(ICIN)的协作单小区传输预编码算法。在ICIN算法的基础上,考虑信道RVQ量化带来的速率损耗,并得出使损率损耗最小化的自适应反馈比特分配方案闭合表达式。另外,分析表明每个用户的总反馈比特数可以按照一定规律变化从而保持恒定的速率损耗。仿真表明,该比特分配方法优于等比特分配。
(5)提出了基于SLNR最大化的分布式协作单小区传输预编码算法。该算法仅要求协作基站反馈干扰信道的CSI,不需要用户数据共享。SLNR在“自私”的特征值波束成形(EBF)和“无私”的ICIN两种算法间取得了良好的折中。为了有效的利用反馈资源,在随机量化信道的情况下,推导了SLNR算法下速率损耗的表达式,并且用穷举搜索找到了最小化速率损耗的最优解。仿真表明,比特应该根据期望信号和干扰信号的强度变化自适应的改变。
The next generation cellular system adopts Orthogonal Frequency Division Multiple Access (OFDMA) in the downlink (DL) channel to improve spectrum efficiency with full frequency reuse. The main interference is from adjacent cell, viz. inter-cell interference (ICI), and intra-cell interference can be ignored. To further improve system throughput and the cell-edge users' performance, reducing ICI is becoming a very important research topic. Coordinated Multi-Point Transmission/Reception (CoMP) is one of the key technologies of the4-th generation (4G) cellular mobile communication system. In the DL, by sharing information across BSs and designing downlink signals cooperatively, the traditional interference can be converted into useful signal, and neighboring base stations can cooperate to jointly transmit data or beamform to coordinate interference.
Multi-cell cooperation is on the condition that the related BSs share CSI and user data, which brings huge signal overhead involving feedback overhead and backhaul overhead. When precoding algorithm is applied, tradeoff has to be made between the performance gain and the signal overhead. At the same time, how the shared information is available in practice has to be taken into account, for instance, the dedicated feedback channels which feedback the CSI from users to BSs, the backhaul channel with which the coordinated BSs exchange information, the channel duality and the channel estimation error, etc. So it is of great practical significance to explore distributed strategy to reduce the CSI sharing and the performance of coordinated precoding with limited feedback. With such a variety of forementioned factors, the dissertation makes an intensive study of multi-cell coordinated precoding in cellular system. The main content and contribution of this thesis can be summarized as:
As the basis of CoMP technology, the thesis present in detail the classifying of CoMP from different point of view, the topology of the cellular system and the channel model of multicell coordination. It is classified by centralized coordination and distributed coordination according to if all base station are connected with a central processing unit, by full coordination and partial coordination depending on if all the BS cooperate with data-transmitting, by coordinated multi-cell transmission (base station coordinated transmission) and coordinated single cell transmission (coordinated beamforming/coordinated scheduling) relying on if the user data is shared between coordinated base stations. In simulation, the linear or hexagonal wyner model is often used to model the cellular system, and both small-scale fading and large-scale fading are to be considered. The channel model of coordinated multi-cell transmission is different from that of coordinated single cell transmission.
(1) The precoding algorithms used in single cell are extended to multi-cell system and compared. Base station (BS) in different cells share in different extent both channel state information and data signals of their respective users to convert conventional interference to useful signals, which increases spectrum efficiency and greatly improves the quality of service of the users in cell edge region. In this paper, Block Diagnalization (BD) algorithm which designs proper precoding vectors in the null space of interfering channels in order to completely mitigate interference is applied in multicell joint transmission and compared with other algorithms, such as zero forcing algorithm, minimum mean square error algorithm, time division multiple access and single-user eigenbeamforming. The principles of interference mitigation and macrodiversity gain of BD are analyzed from the view of spatial dimension and the performance corresponding to each algorithm is discussed. Simulation results show that BD outperforms the algorithms aforementioned. Meanwhile, coordinated BD is superior to uncoordinated BD.
(2) An optimal linear precoding scheme based on particle swarm optimization (PSO) is proposed for a multicell coorperation system. Particle swarm optimization (PSO) algorithm is simple in principle and easy to be realized. In addition, PSO, while with fast converge speed, has no specific requirements for the search space and the gradient information. In multi-cell coordinated transmission condition, we often aim to maximize the average spectrum efficiency under sum power constraint or per base power constraint. It finally converts to an optimization problem. Usually it is very difficult to validate the convexity of the objection function. In this dissertation, the PSO theory and its flow chart are presented and the convergence character is analyzed. Then the fitness value function of PSO adapting to multicell cooperation is derivated. With such a scheme, the optimal precoding vector could be easily searched for each user according to a simplified objective function. Simulation results show that the proposed scheme can obtain larger average spectrum efficiency and a better bit error rate (BER) performance than zero forcing (ZF) and minimum mean square error algorithm (MMSE).
(3) Feedback and wireless backhaul overhead of base station coordination is one of the bottlenecks threatening its acceptance into commercial networks. To reduce the backhaul overheads and precoding complexity while benefiting from base station cooperation, the distributed precoding strategy based on maximizing the signal to leakage plus noise ratio (SLNR) is proposed. It only needs the local channel state information and the user data, which efficiently reduces backhaul overheads while enjoys the gain of the base station coordinated transmission. The distributed precoding strategy based on SLNR is compared with both fully coordinated zero forcing algorithm and fully distributed SLNR algorithm. Simulation results show that the proposed strategy get a good tradeoff between overheads and performance.
(4) A precoding strategy of ICIN based on limited feedback is presented for a coordinated single cell transmission system. On the basis of inter-cell interference nulling (ICIN) at the transmitter sides of cooperative base stations, we propose an adaptive feedback bit allocation scheme to minimize the rate loss due to limited feedback. Furthermore, we investigate the scaling law of the total number of bits per user to maintain a constant rate loss. Simulation results show that the proposed feedback bits allocation strategy provides significant gain compared to equal bits allocation.
(5) To reduce backhaul overhead and feedback overhead, a distributed precoding method based on signal-to-leakage-and-noise-ratio maximization for coordinated single cell transmission is proposed, which only needs the sharing of channel state information of interference channel and not the user data. It gets a better performance compared with eigenbeaming and intercell interference nulling which are two extreme sides. And then, to fully exploit feedback, we derive the expression of sum rate loss and find the optimal solution to minimize it by brute-force research. Simulation results show that the performance can be further improved through adaptively allocating bits relying on the relative strength between the desired and interfering signals within the cell.
引文
[I]D. Tse and P.Viswanath. Fundamentals of Wireless Communication [M]. New York: Cambridge University Press,2005.
[2]啜刚,工文博,常永宇等.《移动通信原理与系统》[M].北京：北京邮电大学出版社,2005.
[3]Recommendation ITU-R M.0225. Guidelines for Evaluation of Radio Transmission Technologies for IMT-2000.
[4]H. Holma, A. Toskala著,陈泽强,周华,付景兴等译.《WCDMA技术与系统设计：第三代移动通信系统的无线接入》[M].第三版,北京：机械工业出版社,2005.
[5]3GPP2 C.R1002-0 v 1.0. CDMA2000 Evaluation Methodology.
[6]高泽华,赵国安,宁帆等.《宽带无线城域网——WiMAX技术与应用》[M].北京：人民邮电出版社,2008.
[7]王亚峰.《TD-SCDMA及其增强和演进技术》[M].北京：人民邮电出版社,2009.
[8]Huawei.3GPP TSG R1-082448.Carrier Aggregation in Advanced E-UTRA [S]. Warsaw, Poland, July 2008.
[9]J. P. Li, Z. H., Tan, C. Tao, et al. A New Spectrum Aggregation Algorithm for IMT-Advanced Based on Cognitive Science [C]//International Conference on Wireless Communications and Signal Processing (WCSP),2010:1-5.
[10]S. W. Peters, A. Y. Panah, K. T. Truong, et al. Relay Architectures for 3GPP LTE-Advanced [J]. EURASIP Journal on Wireless Communications and Networking.2009,1-14.
[11]V. Chandrasekhar, J. G. Andrews and A. Gatherer. Femtocell Networks:A Survey [J]. IEEE Communications Magazine.2008:59-67.
[12]D. Gesbert, S. Hanly, H. Huang, S. S. Shitz, et al. Multi-Cell MIMO Cooperative Networks: A New Look at Interference [J]. IEEE Journal on Selected Areas in Communications,2010, 28(9):1-29.
[13]D. Gesbert, S. G. Kiani, A. Gjendemsjo, et al. Adaptation, Coordination, and Distributed Resource Allocation in Interference-Limited Wireless Networks [J]. Proceedings of the IEEE,2007,95(12):2393-2409.
[14]S. G. Kiani, G. E.(?)ien, D. Gesbert. Maximizing Multicell Capacity Using Distributed Power Allocation and Scheduling [C]//. WCNC,2007,1692-1696.
[15]S. G. Kiani and D. Gesbert. Optimal and Distributed Scheduling for Multicell Capacity Maximization [J]. IEEE Transactions on Wireless Communications,2008,7(1):288-297.
[16]L. Venturino, N. Prasad, and X. D. Wang. Coordinated Scheduling and Power Allocation in Downlink Multicell OFDMA Networks [J]. IEEE Transactions on Vehicular Technology, 2009,58(6):2835-2848.
[17]H. Skjevling, D. Gesbert, and A. Hjorungnes. Low-Complexity Distributed Multibase Transmission and Scheduling [J]. EURASIP Journal on Advances in Signal Processing, 2008,2008:1-9.
[18]M. K. Karakayali, G. J. Foschini, R. A. Valenzuela. Network Coordination for Spectrally Efficient Communications in Cellular Systems [J]. IEEE Wireless Communications,2006, 13 (4):56-61.
[19]M. K. Karakayali, G. J. Foschini, R. A. Valenzuela, et al. On the Maximum Common Rate Achievable in A Coordinated Network [C]//. IEEE International Conference on Communications (ICC 06),2006(9):4333-4338.
[20]H. Y. Zhang and H. Y. Dai. Cochannel Interference Mitigation and Cooperative Processing in Downlink Multicell Multiuser MIMO Networks [J]. EURASIP Journal on Wireless Communications and Networking,2004 (2):222-235.
[21]G. J. Foschini, K. Karakayali and R.A. Valenzuela. Coordinating Multiple Antenna Cellular Networks to Achieve Enormous Spectral Efficiency [J]. IEEE Proceedings on Communications,2006,153(4):548-555.
[22]Y. M. Huang, G. Zheng, M. Bengtsson, et al. Distributed Multicell Beamforming with Limited Intercell Coordination [J]. IEEE Transactions on Signal Processing,2011,59(2): 728-738.
[23]Y. Hadisusanto, L. Thiele and V. Jungnickel. Distributed Base Station Cooperation via Block-Diagonalization and Dual-Decomposition [C]//. IEEE International Conference on Global Telecommunications (GLOBECOM'2008),2008:1-5.
[24]W. W. L. Ho, T. Q. S. Quek, and Sumei Sun. Distributed Precoding for Network MIMO [C]//. IEEE International Conference on Communications (ICC'2010),2010,1-5.
[25]A. Tolli, H. Pennanen, and P. Komulainen. Decentralized Minimum Power Multi-Cell Beamforming with Limited Backhaul Signaling [J]. IEEE Transactions on Wireless Communications,2011,10(2):570-580.
[26]R. Zakhour and D. Gesbert. Coordination on the MI SO Interference Channel Using the Virtual SINR Framework [C]//. ITG Workshop Smart Antennas,2009.
[27]N. Hassanpour, J. E. Smee, J.L. Hou, et al. Distributed Beamforming Based on Signal-to-Caused-Interference Ratio [C]//. IEEE International Symposium on Spread Spectrum Techniques and Applications,2008,405-410.
[28]M. Sadek, A. Tarighat, and A. H. Sayed. A Leakage-Based Precoding Scheme for Downlink Multi-User MIMO Channels [J]. IEEE Transactions on Wireless Communications,2007, 6(5):1711-1721.
[29]L. Venturino, N. Prasad, and X. D. Wang. Coordinated Linear Beamforming in Downlink Multi-Cell Wireless Networks [J]. IEEE Transactions on Wireless Communications,2010, 9(4):1451-1461.
[30]H. Dahrouj and W. Yu. Coordinated Beamforming for the Multicell Multi-Antenna Wireless System [J]. IEEE Transactions on Wireless Communications,2010,9(5):1748-1759.
[31]E. Bjornson, R. Zakhour, D. Gesbert, et al. Cooperative Multicell Precoding:Rate Region Characterization and Distributed Strategies with Instantaneous and Statistical CSI [J]. IEEE Transactions on Signal Processing,2010,58(8):4298-4310.
[32]B. O. Lee, H. W. Je, I. Sohn, et al. Interference-Aware Decentralized Precoding for Multicell MIMO TDD Systems [C]//. IEEE International Conference on Global Telecommunications (GLOBECOM'2008),2008,1-5.
[33]J. Zhang and J. G. Andrews. Adaptive Spatial Intercell Interference Cancellation in Multicell Wireless Networks [J]. IEEE Journal on Selected Areas in Communications,2010,28(9): 1455-1467.
[34]J. Zhang, M. Kountouris, J. G. Andrews, et al. Multi-Mode Transmission for the MIMO Broadcast Channel with Imperfect Channel State Information [J]. IEEE Transactions on Communications,2011,59(3):803-814.
[35]S. Zhou, J. Gong, and Z. S. Niu. Distributed Adaptation of Quantized Feedback for Downlink Network MIMO Systems [J]. IEEE Transactions on Wireless Communications, 2011,10(1):61-67.
[36]R. Bhagavatula and R. W. Heath, Jr.. Adaptive Limited Feedback for Sum-Rate Maximizing Beamforming in Cooperative Multicell Systems [J]. IEEE Transactions on Signal Processing, 2011,59(2):800-811.
[37]A. Papadogiannis, H. J. Bang, D. Gesbert, et al, Efficient Selective Feedback Design for Multicell Cooperative Networks [J]. IEEE Transactions on Vehicular Technology,2011, 60(1):196-205.
[38]N. Lee and W. Shin. Adaptive Feedback Scheme on K-Cell MISO Interfering Broadcast Channel with Limited Feedback [J]. IEEE Transactions on Wireless Communications,2011, 10(2):401-406.
[39]L. J. Liu, J.Z. (Charlie) Zhang, J.C. Yu, et al. Intercell Interference Coordination through Limited Feedback [J]. International Journal of Digital Multimedia Broadcasting.2010,2010: 1-7.
[40]N. Jindal. MIMO Broadcast Channels with Finite-Rate Feedback [J]. IEEE Transactions on Information Theory,2006,52(11):5045-5060.
[41]D. J. Love, R. W. Heath, Jr., V. K. N. Lau, et al. An Overview of Limited Feedback in Wireless Communication systems [J]. IEEE Journal on Selected Areas in Communications, 2008,26(8):1341-1365.
[42]P. Marsch and G. Fettweis. On Multicell Cooperative Transmission in Backhaul-Constrained Cellular Systems [J]. Ann. Telecommun.63(2008):253-269.
[43]O. Simeone,O. Somekh, H. V. Poor, et al. Downlink Multicell Processing with Limited-Backhaul Capacity [J]. EURASIP Journal on Advances in Signal Processing,2009, (2009):1-10.
[44]A. Tajer, N. Prasad, and X. D. Wang. Robust Linear Precoder Design for Multi-Cell Downlink Transmission [J]. IEEE Transactions on Signal Processing,2011,59(1):235-251.
[45]D. W. H. Cai, T. Q. S. Quek and C. W. Tan. Max-min Weighted SIR for MIMO Downlink System:Optimality and Algorithms [C]//. ISIT,2010,2118-2122.
[46]H. Y. Zhang, N. B. Mehta, A. F. Molisch, et al. Asynchronous Interference Mitigation in Cooperative Base Station Systems [J]. IEEE Transactions on Wireless Communications, 2008,7(1):155-165.
[47]S. Catreux, P. F. Driessen and L. J. Greenstein. Simulation Results for An Interference-Limited Multiple-Input Multiple-Output Cellular System [J]. IEEE Communication Letter,2000,4 (11):334-336.
[48]S. Catreux, P. F. Driessen and L. J. Greenstein. Attainable Throughput of An Interference-Limited Multiple-Input Multiple-Output (MIMO) Cellular System [J]. IEEE Transactions on Communications,2001,49(8):1307-1311.
[49]R. S. Blum. MIMO Capacity with Interference [J]. IEEE Journal on Selected Areas in Communications,2003,21(5):793-801.
[50]3GPP.TR 25.814 V7.1.0.
[51]S. Geirhofer and O. Oyman. Cooperative Fractional Frequency Reuse Based on Partial Connectivity Among Clients [C]//. IEEE GLOBECOM proceedings,2008,1-5.
[52]X. N. Fan, S. Chen and X.D. Zhang. An Inter-Cell Interference Coordination Technique Based on Users'Ratio and Multi-Level Frequency Allocations[C]//. WiCom,2007,799-802.
[53]X. H. Mao, A. Maaref and K. H. Teo. Adaptive Soft Frequency Reuse for Inter-cell Interference Coordination in SC-FDMA Based 3GPP LTE Uplinks[C]//. IEEE GLOBECOM,2008,1-6.
[54]张瑞,宋荣方.基于协作多点的干扰协调技术[J].《南京邮电大学学报(自然科学版)》.2010,30(3)：46-52.
[55]R. Zhang and R. F. Song. On Network MIMO:Base Station Coordination. Journal of Nanjing University of Posts and Telecommunications (Natural Science) [J].2010,30(1): 88-96.
[56]S. Shamai (Shitz), O. Simeone, O. Somekh, et al. Joint Multi-Cell Processing for Downlink Channels with Limited-Capacity Backhaul [C]//. Information Theory and Applications Workshop,2008,345-349.
[57]R. Bendlin, Y.F. Huang, M. T. Ivrlac, et al. Fast Distributed Multi-Cell Scheduling with Delayed Limited-Capacity Backhaul Links [C]//. IEEE International Conference on Communications(ICC'09),2009,3935-3939.
[58]M. K. Karakayali. Network Coordination for Spectrally Efficient Communications in Wireless Networks [D]. Graduate School-New Brunswick Rutgers, The State University of New Jersey,2007.
[59]A. Papadogiannis, E. Hardouin and D. Gesbert. A Framework for Decentralising Multi-Cell Cooperative Processing on the Downlink [C]//. IEEE GLOBECOM Workshops,2008,1-5.
[60]J. Zhang, R. H. Chen, J. G. Andrews, et al. Networked MIMO with Clustered Linear Precoding [J]. IEEE Transactions on Wireless Communications,2009,8(4):1910-1921.
[61]S.Y. Shi, M. Schubert, N. Vucic, et al. MMSE Optimization with Per-Base-Station Power Constraints for Network MIMO Systems[C]//. ICC proceedings,2008,4106-4110.
[62]L. Mailaender. Indoor Network MIMO Performance with Regularized Zero-Forcing Transmission[C]//. ISSSTA,2008,129-132.
[63]S. Jing, D. N. C. Tse, J. B. Soriaga, et al. Multi-Cell Downlink Capacity with Coordinated Processing [J]. EURASIP Journal on Wireless Communications and Networking,2008, 1-19.
[64]A. Alexiou and F. Boccardi. Coordination and Cooperation for Next Generation Wireless Systems:Overhead Signaling Requirements and Cross Layer Considerations[C]//. IEEE International Conference on Acoustics, Speech and Signal Processing,2009,3613-3616.
[65]O. Somekh, B. M. Zaidel and S. Shamai. Sum Rate Characterization of Joint Multiple Cell-Site Processing [J]. IEEE Transactions on Information Theory,2007,53 (12): 4473-4497.
[66]W. Choi and J. G. Andrews. The Capacity Gain from Intercell Scheduling in Multi-Antenna Systems [J]. IEEE Transactions on Wireless Communications,2008,7(2):714-725.
[67]W. Choi and J. G. Andrews. The Capacity Gain from Base Station Cooperative Scheduling in a MIMO DPC Cellular System [C]//. ISIT 2006, Seattle, USA,2006:1224-1228.
[68]S. G. Kiani and D. Gesbert. Optimal and Distributed Scheduling for Multicell Capacity Maximization [J]. IEEE Transactions on Wireless Communications,2008,7(1):288-297.
[69]P. Svedman, S. K. Wilson, L. J. Cimini, et al. Opportunistic Beamforming and Scheduling for OFDMA Systems. IEEE Transactions on Communications [J].2007,55(5):941-952.
[70]S Shamai and B M Zaidel. Enhancing the Cellular Downlink Capacity via Co-Processing at the Transmitting End. Proceedings of the 53rd IEEE Vehicular Technology Conference (VTC'01),2001(3):1745-1749.
[71]Z. K. Shen, R. H. Chen, J. G. Andrews, et al. Sum Capacity of Multiuser MIMO Broadcast Channels with Block Diagonalization [J]. IEEE Transactions on Wireless Communications, 2007,6(6):2040-2045.
[72]S. Shim, J. S. Kwak, R. W. Heath,Jr., et al. Block Diagonalization for Multi-User MIMO with Other-Cell Interference [J]. IEEE Transactions on Wireless Communications,2008, 7(7):2671-2681.
[73]Y. D. Chen, K. H. Teo, S. Kishore, et al. Inter-Cell Interference Management in WiMAX Downlinks by A Stackelberg Game between BSs [C]//. ICC Proceedings,2008,3442-3446.
[74]Y. J. Chang, Z. F. Tao, J.Y. Zhang, et al. A Graph-based Approach to Multi-Cell OFDMA Downlink Resource Allocation[C]//. Global Telecommunications Conference,2008,1-6.
[75]R. Y. Chang, Z. F. Tao, J.Y. Zhang, et al. Multicell OFDMA Downlink Resource Allocation Using a Graphic Framework [J]. IEEE Transactions on Vehicular Technology,2009,58(7): 3494-3507.
[76]M. C. Necker. A Graph-Based Scheme for Distributed Interference Coordination in Cellular OFDMA Networks[C]//. Vehicular Technology Conference,2008,713-718.
[77]张瑞,宋荣方.网络MIMO中的基站联合传输技术[J].《信号处理》.2011,27(3)：456-460.
[78]H. Y. Zhang, N. B. Mehta, A. F. Molisch, et al. On the Fundamentally Asynchronous Nature of Interference in Cooperative Base Station Systems [C]//. ICC Proceedings,2007, 6073-6078.
[79]B. L. Ng, J. Evans, S. Hanly, et al. Distributed Downlink Beamforming with Cooperative Base Stations [J]. IEEE Transactions on Information Theory,2008,54(12):5491-5499.
[80]A. Sanderovich, O. Somekh, H. V. Poor, et al. Uplink Macro Diversity of Limited Backhaul Cellular Network [J]. IEEE Transactions on Information Theory,2009,55(8):3457-3478.
[81]A. Sanderovich, O. Somekh, and S. Shamai. Uplink Macro Diversity with Limited Backhaul Capacity [C]//. IEEE International Symposium on Information Theory,2007,11-15.
[82]O. Somekh, O. Simeone, Y. Bar-Ness, et al. Distributed Multi-Cell Zero-Forcing Beamforming in Cellular Downlink Channels [C]//. IEEE Global Telecommunications Conference, San Francisco, USA,2006,1-6.
[83]Q. H. Spencer, A. L. Swindlehurst and M. Haardt. Zero-forcing Methods for Downlink Spatial Multiplexing in Multi-User MI MO Channels [J]. IEEE Transactions on Signal Processing,2004,52(2):461-471.
[84]S. Fang, L. H. Li and P. Zhang. Optimal Multi-User MIMO Linear Precoding Based on Particle Swarm Optimization [C]//. Proceeding of the International Conference on Communications,2008,3355-3359.
[85]R. Zhang and R. F. Song. A Linear Precoding Strategy Based on Particle Swarm Optimization in Multicell Cooperative Transmission [J]. Journal of Electronics (China). 2011,28(1):15-21.
[86]J. Kennedy and R. C. Eberhart. Particle Swarm Optimization [C]//. Proceeding of the IEEE International Conference on Neural Networks, Perth, Australia,1995,1942-1948.
[87]J. Robinson and Y. Rahmat-Samii. Particle Swarm Optimization in Electromagnetics [J]. IEEE Transactions on Antennas and Propagation,2004,52(2):397-407.
[88]蒋励菁.多蜂窝系统中用户调度技术研究[D].南京：南京邮电大学硕士学位论文,2011，32-36.
[89]Y. Q. Hei, X. H. Li, K. C. Yi, et al. Multiuser Scheduling in Downlink MIMO Systems Using Particle Swarm Optimization [C]//. IEEE Wireless Communication and Network Conference, Budapest, Hungary,2009,1-5.
[90]M. Mussetta, S. Selleri, P. Pirinoli, et al. Improved Particle Swarm Optimization Algorithms for Electromagnetic Optimization [J]. Journal of Intelligent and Fuzzy Systems,2008,19(1): 75-84.
[91]Y. Q. Hei, X. H. Li, K. C. Yi, et al. Multi-user MIMO Broadcast System Grouping Strategy Based on Particle Swarm Optimization [C]//. International Conference on Advanced Information Networking and Applications, Bradford, United Kingdom,2009,212-216.
[92]W. W. L. Ho, T. Q. S. Quek, and S. M. Sun. Distributed Precoding for Network MIMO [C]//. IEEE International Conference on Communications (ICC'2010),2010:1-5.
[93]R. Zakhour and D. Gesbert. Coordination on the MISO Interference Channel Using the Virtual SINR Framework [C]//. ITG Workshop on Smart Antennas,2009,1-7.
[94]N. Hassanpour, J. E. Smee, J. L. Hou, et al. Distributed Beamforming Based on Signal-to-Caused-Interference Ratio [C]//. IEEE International Symposium on Spread Spectrum Techniques and Applications,2008:405-410.
[95]R. Bhagavatula, R. W. Heath and B. Rao. Limited Feedback with Joint CSI Quantization for Multicell Cooperative Generalized Eigenvector Beamforming [C]//. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2010,2838-2841.
[96]R. Bhagavatula and R. W. Heath, Jr.. Adaptive Bit Partitioning for Multicell Intercell Interference Nulling with Delayed Limited Feedback [J]. IEEE Transactions on Signal Processing,2011,59(8):3824-3836.
[97]D. Samardzija and H. Huang. Determining Backhaul Bandwidth Requirements of Network MIMO [C]//. Proceeding on European Signal Processing Conference,2009,1494-1498.
[98]U. Jang, H. Son, J., Park, et al. CoMP-CSB for ICI Nulling with User Selection [J]. IEEE Transactions on Wireless Communications,2011,10(9):2982-2993.
[99]R. Zhang and R. F. Song. Adaptive Feedback Scheme on Multicell Multiuser MISO System [C]//.2011 International Conference on Wireless Communications and Signal Processing, 2011,1-6.
[100]W. Santipach, M. L. Honig. Signature Optimization for CDMA with Limited Feedback. IEEE Transactions on Information Theory,2005,51:3475-3492.
[101]D. J. Love, R. W. Heath, Jr. Grassmannian Beamforming for Multiple-Input Multiple-Output Wireless Systems [J]. IEEE Transactions on Information Theory,2003,49(10):2735-2745.
[102]Ch. K.Au-Yeung and D. J. Love. On the Performance of Random Vector Quantization Limited Feedback Beamforming in A MISO System [J]. IEEE Transactions on Wireless Communications,2007,6(2):458-462.
[103]J. Havil. Gamma:Exploring Euler's Constant [M]. Princeton, NJ:Princeton Univ. Press, 2003.
[104]X. H. Ge, K. Huang, C. X. Wang, et al. Capacity Analysis of a Multi-Cell Multi-Antenna Cooperative Cellular Network with Co-Channel Interference [J]. IEEE Transactions on Wireless Communications,2011,10(10):3298-3309.
[105]M. Yoon, M. Kim and C. Lee. Decentralized Precoding Algorithm with Weighted SLNR for Limitedly Coordinated Network [J]. IEEE Communications Letters,2012,16(3):1-3.
[106]D. Lee, H. Seo, B. Clerckx, et al. Coordinated Multipoint Transmission and Reception in LTE-Advanced:Deployment Scenarios and Operational Challenges [J]. IEEE Communications Magazine,2012,50(2):148-155.
[107]W. W. L. Ho, T. Q. S. Quek, S. M. Sun, et al. Decentralized Precoding for Multicell MIMO Downlink [J]. IEEE Transactions on Wireless Communications,2011,10(6):1798-1809.
[108]A. Tajer and X. D. Wang. Information Exchange Limits in Cooperative MIMO Networks [J]. IEEE Transactions on Signal Processing,2011,59(6):2927-2942.
[109]R. Zhang and L. Hanzo. Cooperative Downlink Multicell Preprocessing Relying on Reduced-Rate Back-Haul Data Exchange [J]. IEEE Transactions on Vehicular Technology, 2011,60(2):539-545.
[110]R. Zakhour and D. Gesbert. Optimized Data Sharing in Multicell MIMO with Finite Backhaul Capacity [J]. IEEE Transactions on Signal Processing,2011,59(12):6102-6111.