This letter describes a real-valued sparse Bayesian learning (SBL) approach for massive multiple-input multipleoutput (MIMO) downlink channel estimation. The main idea of the approach is to introduce a certain unitary transformation into pilots, so as to convert complex-valued channel recovery problems into real ones. Due to exploiting the real-valued structure of the data matrices, the new approach brings a significant decrease in computational complexity, as well as a good noise suppression. Simulation results demonstrate that the new method can reduce the computation load and improve the channel estimation performance simultaneously.

中文翻译：

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用于大规模 MIMO 信道估计的实值稀疏贝叶斯学习方法

这封信描述了一种用于大规模多输入多输出 (MIMO) 下行链路信道估计的实值稀疏贝叶斯学习 (SBL) 方法。该方法的主要思想是在导频中引入某种幺正变换，从而将复值信道恢复问题转化为实际问题。由于利用了数据矩阵的实值结构，新方法带来了计算复杂度的显着降低，以及良好的噪声抑制。仿真结果表明，新方法可以在降低计算量的同时提高信道估计性能。