Pilot decontamination under imperfect power control

Jitendra Tugnait
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引用次数: 1

Abstract

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.
飞行员在不完善的动力控制下进行净化
在时分双工(TDD)多天线系统中,信道状态信息(CSI)可以通过反向训练来估计。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,我们考虑了一种基于子空间的半盲方法,其中我们有来自基站(BS)的各种用户(包括小区内和邻近小区)的训练数据和信息承载数据。现有的子空间方法假设来自相邻小区的干扰用户与小区内用户相比,在BS处总是处于明显较低的功率水平。在本文中,我们不做任何这样的假设。与现有的方法不同,BS估计所有用户的信道:小区内和重要的邻近小区用户,即在BS中具有可比功率水平的用户。我们既利用相关的子空间方法,也利用高阶统计量的盲源分离。通过仿真实例说明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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