Sparse Beamforming for an Ultradensely Distributed Antenna System With Interlaced Clustering
Sparse Beamforming for an Ultradensely Distributed Antenna System With Interlaced Clustering
复制标题
具有交错聚类的超密集分布式天线系统的稀疏波束形成
DOI：
10.1109/access.2019.2895410
复制
发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
You Xiaohu
中科院分区：
文献类型：
--
作者：
Xia Xinjiang;Zhang Yu;Li Jiamin;Zhu Pengcheng;Xin Yuanxue;Wang Dongming;You Xiaohu
最近，一种新的网络架构的分布式天线系统与交错集群已经提出，以减轻小区边缘的问题。在这种网络架构下，我们提出了一个更实际的实现超密集部署的远程天线单元（RAU）与大量的用户。此外，我们专注于用户选择（USC）和稀疏波束形成技术，以优化加权和速率（WSR）与回程和功率约束。首先，我们将每个集群模式（CP）分成几个自适应小区，其中RAU通过有限容量的回程链路连接到中央处理器。为了降低大量用户和RAU的计算复杂度，我们解决了原来的问题，两个步骤。在第一阶段，我们提出了一个有效的USC算法，以找到最大的用户子集，满足服务质量的要求。在第二阶段，我们采用基于CP的加权和最小均方误差算法优化WSR问题的前一阶段中选定的用户。此外，两个分解算法，称为原始分解和对偶分解，利用进一步降低计算复杂度。在此基础上，提出了一种低复杂度的交替优化稀疏波束形成方法。最后，仿真结果表明，所提出的算法可以实现一个显着的边缘用户速率的性能增益，而不会失去太多的性能增益。同时，回程信息交换大大减少，并且大约90%的RAU对于每个RAU消耗少于66.7%的回程。
Recently, a novel network architecture for a distributed antenna system with interlaced clustering has been proposed to mitigate the cell-edge problem. Under this network architecture, we propose a more practical implementation for ultradensely deployed remote antenna units (RAUs) with large numbers of users. Furthermore, we focus on the user selection (USC) and sparse beamforming technologies to optimize the weighted sum rate (WSR) with both backhaul and power constraints. First, we divide each cluster pattern (CP) into several adaptive cells, where RAUs are connected to a central processor via finite-capacity backhaul links. Aiming at reducing the computational complexity for large numbers of users and RAUs, we solve the original problem with two steps. In the first stage, we propose an efficient USC algorithm to find the largest user subset that satisfies the quality of service requirement. In the second stage, we adopt a CP-based weighted sum minimum mean square error algorithm to optimize the WSR problem for the selected users in the previous stage. Moreover, two decomposition algorithms, named primal decomposition and dual decomposition, are exploited to further reduce the computational complexity. Furthermore, based on the adaptive cells, we provide a low-complexity alternating optimization method for sparse beamforming. Finally, simulation results show that the proposed algorithms can achieve a significant performance gain on edge-user rates without losing much performance gain. At the same time, the backhaul information exchange is largely reduced, and approximately 90% of the RAUs consumes less than 66.7% of backhaul for each RAU.
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影响因子：
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作者：
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