Covariance Matrix Estimation in Massive MIMO
Covariance Matrix Estimation in Massive MIMO
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DOI：
10.1109/lsp.2018.2827323
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发表时间：
2017-05
影响因子：
3.9
通讯作者：
David Neumann;M. Joham;W. Utschick
中科院分区：
文献类型：
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作者：
David Neumann;M. Joham;W. Utschick
上行链路训练阶段的干扰会显著降低大规模MIMO系统的性能。干扰的影响可以通过利用信道向量的二阶统计量来减少，例如，以获得信道的最小均方误差估计。在实际应用中，需要估计信道协方差矩阵。训练阶段的干扰也阻碍了协方差矩阵的估计。然而，协方差矩阵的相干间隔大于信道向量的相干间隔。这使得我们可以通过将导频序列适当地分配给连续信道相干间隔中的用户来推导出精确的协方差矩阵估计的方法。为了保持计算的复杂性，我们利用了协方差矩阵的共同结构。
Interference during the uplink training phase significantly deteriorates the performance of a massive MIMO system. The impact of the interference can be reduced by exploiting the second-order statistics of the channel vectors, e.g., to obtain the minimum mean squared error estimates of the channel. In practice, the channel covariance matrices have to be estimated. The estimation of the covariance matrices is also impeded by the interference during the training phase. However, the coherence interval of the covariance matrices is larger than that of the channel vectors. This allows us to derive methods for accurate covariance matrix estimation by the appropriate assignment of pilot sequences to the users in consecutive channel coherence intervals. To keep the computational complexity in check, we exploit common structure of the covariance matrices.