Massive MIMO Channel Estimation over the mmWave Systems through Parameters Learning
Massive MIMO Channel Estimation over the mmWave Systems through Parameters Learning
复制标题
通过参数学习对毫米波系统进行大规模 MIMO 信道估计
DOI：
10.1109/lcomm.2019.2897995
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Victor C.M.Leung
中科院分区：
文献类型：
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作者：
Weidong Shao;Shun Zhang;Xiushe Zhang;Jianpeng Ma;Nan Zhao;Victor C.M.Leung
在这封信中，我们建立了一个离网信道模型来描述基于离散傅立叶变换(DFT)的大规模多输入多输出(MIMO)毫米波(MmWave)信道估计中的空间样本失配。然后，将离网毫米波海量MIMO信道估计分解为模型参数学习和虚拟信道估计。首先提出了一种基于期望最大化(EM)的稀疏贝叶斯学习框架，用于学习含有未知噪声的模型参数，如偏差参数和空间特征。利用学习到的模型参数，我们采用线性最小均方误差方法以较小的导频开销估计出瞬时虚信道。最后，通过数值仿真验证了该方法的有效性。
In this letter, we formulate an off-grid channel model to characterize spatial sample mismatching in the discrete Fourier transform (DFT) based massive multiple-input-multiple-output (MIMO) channel estimation over the millimeter-wave (mmWave) band. Then, we decompose the off-grid mmWave massive MIMO channel estimation into the learning of model parameters and virtual channel estimation. Specifically, an expectation maximization (EM) based sparse Bayesian learning framework is first developed to learn the model parameters, such as bias parameters and spatial signatures, with unknown noise. With the learned model parameters, we resort to the linear minimum mean square error method to estimate the instantaneous virtual channel with less pilot overhead. Finally, we corroborate the validity of the proposed method through numerical simulations.