Paper Info
Title
Learning the Structured Sparsity: 3D Massive MIMO Channel Estimation and Adaptive Spatial Interpolation
Abstract
This paper addresses the channel estimation problem for three-dimensional (3-D) massive multiple-input multiple-output (MIMO) systems, where the base station (BS) is equipped with a two-dimensional uniform planar array (UPA) to serve a number of user equipments (UEs). To implement with low hardware complexity, the number of available radio-frequency (RF) chains at BS is constrained to be much smaller than the number of antennas. The theoretical analysis of sparse property for 3-D massive MIMO channel reveals that there exists two kinds of sparse structures in beam-domain channel vector, namely the common sparsity structure among sub-arrays of UPA and block sparsity structure per sub-array. Based on this property, a novel structured sparse Bayesian learning framework is proposed to estimate the channel between BS and UE reliably. Moreover, an adaptive spatial interpolation scheme is proposed to further reduce the number of required RF chains at BS while maintaining the estimation performance. The simulation results show that the proposed scheme provides stable estimation performance for a variety of scenarios with different numbers of RF chains, transmit signal-to-noise ratios, Rician factors, and angular spreads, and outperforms the reference schemes significantly.
Year
DOI
Venue
2019
10.1109/TVT.2019.2909392
IEEE Transactions on Vehicular Technology
Keywords
Field
DocType
Channel estimation,Radio frequency,MIMO communication,Antenna arrays,Training,Three-dimensional displays
Base station,Planar array,Bayesian inference,Multivariate interpolation,Computer science,Communication channel,Algorithm,MIMO,Electronic engineering,Radio frequency,Rician fading
Journal
Volume
Issue
ISSN
68
11
0018-9545
Citations 
PageRank 
References 
1
0.35
0
Authors
4
Name
Order
Citations
PageRank
Yurong Wang1558.68
aijun liu26118.26
Xiaochen Xia319019.24
Kui Xu46111.07