MUBFP: Multiuser Beamforming and Partitioning for Sum Capacity Maximization in MIMO Systems
MUBFP: Multiuser Beamforming and Partitioning for Sum Capacity Maximization in MIMO Systems
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DOI：
10.1109/tvt.2016.2536698
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发表时间：
2017
影响因子：
6.8
通讯作者：
B. Hu;Cunqing Hua;Cailian Chen;Xiaoli Ma;X. Guan
中科院分区：
文献类型：
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作者：
B. Hu;Cunqing Hua;Cailian Chen;Xiaoli Ma;X. Guan
多用户波束形成（MUBF）方案作为一种次优的线性预编码技术，在多输入多输出（MIMO）系统中实现了系统容量的好处，引起了极大的关注。由于同信道用户之间的相互干扰，最大化的系统容量和公平的服务提供给不同的用户的目标应该很好地平衡在MUBF方案的设计。为此，我们提出了一个框架的联合多用户波束成形和分区（MUBFP）的设计问题，它试图将用户划分成一组不相交的组，其中每组用户使用的MUBF计划。通过联合优化分组数目、用户划分和波束形成器设计，可以在不牺牲用户公平性的情况下最大化平均和容量。提出了一种分解方法，将该问题解耦为用户划分和波束形成器设计子问题与给定的组数，这可以分别使用凝聚层次聚类（AHC）算法和顺序参数凸逼近（SPCA）方法来解决。将这两个子问题的算法结合起来，提出了一种求解解耦MUBFP问题最优分组数的迭代算法，并提出了一种次优算法，以减少搜索次数，同时具有良好的逼近性能.仿真结果表明，所提出的方案的有效性，在收敛性，平均和容量，和复杂性。
The multiuser beamforming (MUBF) scheme has attracted tremendous attention as a suboptimal linear precoding technique to realize the benefits of system capacity in multiple-input–multiple-output (MIMO) systems. Due to mutual interference between cochannel users, the objective of maximizing the system capacity and fair service provisioning to different users should be well balanced in the design of the MUBF scheme. To this end, we propose a framework for the joint multiuser beamforming and partitioning (MUBFP) design problem, which attempts to partition users into a set of disjoint groups, where each group of users is served using the MUBF scheme. By jointly optimizing the grouping number, the user partitioning, and the beamformer design, the average sum capacity can be maximized without sacrificing user fairness. A decomposition approach is presented to decouple this problem into the user partitioning and beamformer design subproblems with the given number of groups, which can be solved using the agglomerative hierarchical clustering (AHC) algorithm and sequential parametric convex approximation (SPCA) approach, respectively. An iterative algorithm is proposed to search the optimal grouping number for the decoupled MUBFP problem by incorporating the algorithms of these two subproblems, and a suboptimal algorithm is developed to reduce the searching times with good approximation performance. Simulation results are provided to show the effectiveness of the proposed schemes in terms of convergence, average sum capacity, and complexity.