Paper Info
Title
Pattern-Shared Sparse Bayesian Learning for Channel Estimation in FDD Massive MIMO Systems
Abstract
Channel state information (CSI) is indispensable to fully unleash the great potentials of massive multiple-input multiple-output (MIMO) systems. In this paper, we propose a pattern-shared sparse Bayesian learning (PS-SBL) method for downlink CSI estimation in frequency division duplex (FDD) massive MIMO. To characterize the common sparsity shared by different antennas, we design a pattern-coupled Gaussian prior model. The coefficients in channel vector are divided into several groups of equal length. Each group is associated with one common hyperparameter such that the coefficients in the group bear the same sparsity. Furthermore, by exploiting the expectation maximization (EM) procedure, we develop an iterative algorithm for Bayesian inference, where we treat the channel coefficients as hidden variables and the hyperparameters as unknown parameters. As a result, the obtained posterior mean of channel vector is used as the channel estimate. Simulations show that the proposed PS-SBL method remarkably outperforms the counterparts, and can approach the performance bound enabled by the genie-aided least square (LS) method.
Year
DOI
Venue
2018
10.1109/WCSP.2018.8555899
2018 10th International Conference on Wireless Communications and Signal Processing (WCSP)
Keywords
Field
DocType
channel vector,channel estimate,PS-SBL method,channel estimation,FDD massive MIMO systems,channel state information,massive multiple-input multiple-output systems,pattern-shared sparse Bayesian learning method,downlink CSI estimation,frequency division duplex massive MIMO,common sparsity,pattern-coupled Gaussian prior model,common hyperparameter,Bayesian inference,channel coefficients,genie-aided least square method,expectation maximization procedure,iterative algorithm
Bayesian inference,Hyperparameter,Computer science,Iterative method,Expectation–maximization algorithm,MIMO,Algorithm,Communication channel,Real-time computing,Channel state information,Telecommunications link
Conference
ISSN
ISBN
Citations 
2325-3746
978-1-5386-6120-8
0
PageRank 
References 
Authors
0.34
17
3
Name
Order
Citations
PageRank
Xiantao Cheng1348.09
Jingjing Sun2163.44
Shaoqian Li32276195.05