Pilot decontamination under imperfect power control
Pilot decontamination under imperfect power control
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DOI：
10.1109/acssc.2017.8335513
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Jitendra Tugnait
中科院分区：
文献类型：
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作者：
Jitendra Tugnait
在时分双工（TDD）多天线系统中，可以使用反向训练来估计信道状态信息（CSI）。在多小区多用户大规模MIMO系统中，导频污染会降低CSI估计性能，并对大规模MIMO系统性能产生不利影响。在本文中，我们考虑一种基于子空间的半盲方法，在基站（BS）处我们拥有来自各个用户（本小区和相邻小区）的训练数据以及承载信息的数据。现有的子空间方法假设来自相邻小区的干扰用户在基站处的功率水平总是明显低于本小区用户。在本文中我们不做任何此类假设。与现有方法不同，基站估计所有用户的信道：本小区用户和重要的相邻小区用户，即那些在基站处具有可比功率水平的用户。我们既利用基于相关性的子空间方法，也利用基于高阶统计量的盲源分离方法。通过仿真示例对所提出的方法进行了说明。
In a time-division duplex (TDD) multiple antenna system the channel state information (CSI) can be estimated using reverse training. In multicell multiuser massive MIMO systems, pilot contamination degrades CSI estimation performance and adversely affects massive MIMO system performance. In this paper we consider a subspace-based semi-blind approach where we have training data as well as information bearing data from various users (both in-cell and neighboring cells) at the base station (BS). Existing subspace approaches assume that the interfering users from neighboring cells are always at distinctly lower power levels at the BS compared to the in-cell users. In this paper we do not make any such assumption. Unlike existing approaches, the BS estimates the channels of all users: in-cell and significant neighboring cell users, i.e., ones with comparable power levels at the BS. We exploit both subspace method using correlation as well as blind source separation using higher-order statistics. The proposed approach is illustrated via simulation examples.