Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning
Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning
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DOI：
10.1109/twc.2014.2365813
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发表时间：
2015-03-01
影响因子：
10.4
通讯作者：
Ting, Pangan
中科院分区：
文献类型：
--
作者：
Wen, Chao-Kai;Jin, Shi;Ting, Pangan
由于无法准确进行信道估计，导频污染对大规模多输入多输出 (MIMO) 天线系统的性能造成了根本限制。为了解决这个问题，我们建议仅估计目标小区中期望链路的信道参数，但估计来自相邻小区的干扰链路的信道参数。然而，所需的估计是一个未确定的系统。在本文中，我们表明，如果可以利用大规模 MIMO 系统的传播特性，就有可能获得信道参数的准确估计。我们的策略受到以下观察的启发：对于蜂窝网络，从用户设备到基站的信道仅由空间中的几个集群路径组成。通过非常大的天线阵列，可以在空间中极其尖锐的区域下观察到信号。因此，如果在波束域中观察信号（使用傅立叶变换），则信道近似稀疏，即信道矩阵仅包含大分量的一小部分，而其他分量接近于零。然后，该观察使得能够基于稀疏贝叶斯学习方法进行信道估计，其中可以使用少量观察来重建稀疏信道分量。结果表明，与传统的估计器相比，所提出的方法在存在导频污染的情况下在信道估计精度和可实现速率方面实现了更好的性能。
Pilot contamination posts a fundamental limit on the performance of massive multiple-input-multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. Our strategy is inspired by the observation that for a cellular network, the channel from user equipment to a base station is composed of only a few clustered paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.