Network MIMO With Linear Zero-Forcing Beamforming: Large System Analysis, Impact of Channel Estimation, and Reduced-Complexity Scheduling
Network MIMO With Linear Zero-Forcing Beamforming: Large System Analysis, Impact of Channel Estimation, and Reduced-Complexity Scheduling
复制标题
DOI：
10.1109/tit.2011.2178230
复制
发表时间：
2010-12
影响因子：
2.5
通讯作者：
Hoon Huh;A. Tulino;G. Caire
中科院分区：
文献类型：
--
作者：
Hoon Huh;A. Tulino;G. Caire
我们考虑了具有多天线基站和单天线用户终端的多小区系统的下行链路、任意基站合作集群、距离相关的传播路径损耗以及一般的“公平性”要求。同一合作集群中的基站采用线性零强迫波束形成的联合传输，受总功率或每个基站功率约束。集群间的干扰在用户终端被当作噪声处理。在大系统极限下，每个基站的用户数量和天线数量都以给定的比例趋于无穷大，得到了系统频谱效率的解析表达式。特别是对于每基站功率约束，我们在随机矩阵理论中得到了新的结果，得到了结构化非i.i.d的Moore-Penrose伪逆的子矩阵的平方Frobenius范数。信道矩阵由合作聚类、用户分布和路径损耗系数产生。将分析扩展到通过显式下行信道训练和上行反馈获得的发射机非理想信道状态信息的情况。具体来说，我们的结果阐明了大量合作天线的好处与估计高维信道矢量的成本之间的权衡。此外，我们的分析还导致了一种新的简化的下行调度方案，该方案根据期望的公平性准则，根据从大系统结果中获得的概率来预选用户。所提出的方案执行接近最优（有限维）机会用户选择，同时需要更少的通道状态反馈，因为只有一小部分预选用户必须反馈他们的通道状态信息。
We consider the downlink of a multicell system with multiantenna base stations and single-antenna user terminals, arbitrary base station cooperation clusters, distance-dependent propagation pathloss, and general “fairness” requirements. Base stations in the same cooperation cluster employ joint transmission with linear zero-forcing beamforming, subject to sum or per-base station power constraints. Intercluster interference is treated as noise at the user terminals. Analytic expressions for the system spectral efficiency are found in the large-system limit where both the numbers of users and antennas per base station tend to infinity with a given ratio. In particular, for the per-base station power constraint, we find new results in random matrix theory, yielding the squared Frobenius norm of submatrices of the Moore-Penrose pseudo-inverse for the structured non-i.i.d. channel matrix resulting from the cooperation cluster, user distribution, and path-loss coefficients. The analysis is extended to the case of nonideal Channel State Information at the Transmitters obtained through explicit downlink channel training and uplink feedback. Specifically, our results illuminate the trade-off between the benefit of a larger number of cooperating antennas and the cost of estimating higher-dimensional channel vectors. Furthermore, our analysis leads to a new simplified downlink scheduling scheme that preselects the users according to probabilities obtained from the large-system results, depending on the desired fairness criterion. The proposed scheme performs close to the optimal (finite-dimensional) opportunistic user selection while requiring significantly less channel state feedback, since only a small fraction of preselected users must feed back their channel state information.