Robust User Scheduling with COST 2100 Channel Model for Massive MIMO Networks

Robust User Scheduling with COST 2100 Channel Model for Massive MIMO Networks
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DOI：
10.1049/iet-map.2017.0332
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发表时间：
2018-04
期刊：
ArXiv
影响因子：
--
通讯作者：
M. Bashar;A. Burr;K. Cumanan
M. Bashar;A. Burr;K. Cumanan
中科院分区：
其他
文献类型：
--
作者：
M. Bashar;A. Burr;K. Cumanan

文献摘要

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本文考虑了一种大规模多输入多输出（MIMO）网络，其中具有大量天线的基站（BS）与较少数量的用户进行通信。信号采用频分双工（FDD）模式传输。研究了大规模 MIMO 系统上行链路中减少信道估计开销的用户调度问题。我们考虑 COST 2100 渠道模型。在本文中，我们首先提出了一种新的用户选择算法，该算法基于服务区域的几何形状和集群位置的知识，而无需基站处的完整信道状态信息（CSI）。然后，我们证明基于几何的随机通道模型 (GSCM) 中的相关性源自该区域的常见集群。此外，利用封闭式 Cramer-Rao 下界（CRLB），对所提出的方案对聚类位置误差的鲁棒性进行了分析。通过分析容量上限表明，容量强烈依赖于集群在 GSCM 中的位置以及用户在系统中的位置。仿真结果表明，尽管BS接收器不需要所有用户的信道信息，但通过所提出的基于几何的用户调度（GUS）算法，系统的总速率仅略小于著名的贪婪权重团（GWC）方案\cite{SUSGoldsmithGlobcom，ITC09_Userselection_GWC}。 {最后，通过仿真结果验证了所提出算法对聚类定位的鲁棒性。
This paper considers a Massive multiple-input multiple-output (MIMO) network, where the base station (BS) with a large number of antennas communicates with a smaller number of users. The signals are transmitted using frequency division duplex (FDD) mode. The problem of user scheduling with reduced overhead of channel estimation in the uplink of Massive MIMO systems has been investigated. We consider the COST 2100 channel model. In this paper, we first propose a new user selection algorithm based on knowledge of the geometry of the service area and of location of clusters, without having full channel state information (CSI) at the BS. We then show that the correlation in geometry-based stochastic channel models (GSCMs) arises from the common clusters in the area. In addition, exploiting the closed-form Cramer-Rao lower bounds (CRLB)s, the analysis for the robustness of the proposed scheme to cluster position errors is presented. It is shown by analysing the capacity upper-bound that the capacity strongly depends on the position of clusters in the GSCMs and users in the system. Simulation results show that although the BS receiver does not require the channel information of all users, by the proposed geometry-based user scheduling (GUS) algorithm the sum-rate of the system is only slightly less than the well-known greedy weight clique (GWC) scheme \cite{SUSGoldsmithGlobcom,ITC09_Userselection_GWC}. {Finally, the robustness of the proposed algorithm to cluster localization is verified by the simulation results.