To glean the benefits offered by massive multi-input multi-output (MIMO) systems, channel state information must be accurately acquired. Despite the high accuracy, the computational complexity of classical linear minimum mean squared error (MMSE) estimator becomes prohibitively high in the context of massive MIMO, while the other low-complexity methods degrade the estimation accuracy seriously. In this paper, we develop a novel rank-1 subspace channel estimator to approximate the maximum likelihood (ML) estimator, which outperforms the linear MMSE estimator, but incurs a surprisingly low computational complexity. Our method first acquires the highly accurate angle-of-arrival (AoA) information via a constructed space-embedding matrix and the rank-1 subspace method. Then, it adopts the post-reception beamforming to acquire the unbiased estimate of channel gains. Furthermore, a fast method is designed to implement our new estimator. Theoretical analysis shows that the extra gain achieved by our method over the linear MMSE estimator grows according to the rule of O($\log_{10}M$), while its computational complexity is linearly scalable to the number of antennas $M$. Numerical simulations also validate the theoretical results. Our new method substantially extends the accuracy-complexity region and constitutes a promising channel estimation solution to the emerging massive MIMO communications.

翻译：为了获取大规模多输入多输出（MIMO）系统带来的优势，必须精确获取信道状态信息。尽管经典线性最小均方误差（MMSE）估计器具有高精度，但在大规模MIMO背景下其计算复杂度变得过高，而其他低复杂度方法则严重降低估计精度。本文提出一种新颖的秩-1子空间信道估计器来逼近最大似然（ML）估计器，其性能优于线性MMSE估计器，且计算复杂度出奇地低。我们的方法首先通过构造的空间嵌入矩阵和秩-1子空间方法获取高精度的到达角（AoA）信息，随后采用后接收波束赋形获得信道增益的无偏估计。此外，我们设计了一种快速算法来实现新估计器。理论分析表明，相较于线性MMSE估计器，本方法取得的额外增益遵循O($\log_{10}M$)的规律增长，同时其计算复杂度与天线数$M$呈线性可扩展关系。数值仿真也验证了理论结果。我们的新方法显著拓展了精度-复杂度区域，为新兴的大规模MIMO通信提供了一种有前景的信道估计解决方案。