Real-Valued Sparse Bayesian Learning Approach for Massive MIMO Channel Estimation

被引：4
|
作者：
Zhou, Lei [1 ]
Cao, Zheng [1 ]
Dai, Jisheng [1 ]
机构：
[1] Jiangsu Univ, Dept Elect Engn, Zhenjiang 212013, Jiangsu, Peoples R China
来源：
IEEE WIRELESS COMMUNICATIONS LETTERS | 2020年 / 9卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Channel estimation; multiple-input multiple-output (MIMO); real-valued transformation; sparse Bayesian learning (SBL); off-grid refinement;
D O I：
10.1109/LWC.2019.2953265
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：311 / 315
页数：5
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