Paper Info
Title
Beamspace channel estimation for millimeter-wave massive MIMO systems with lens antenna array
Abstract
By employing the lens antenna array, beamspace MIMO can utilize beam selection to reduce the number of required RF chains in mmWave massive MIMO systems without obvious performance loss. However, to achieve the capacity-approaching performance, beam selection requires the accurate information of beamspace channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. Specifically, we propose to decompose the total beamspace channel estimation problem into a series of sub-problems, each of which only considers one sparse channel component. For each channel component, we first reliably detect its support by utilizing the structural characteristics of mmWave beamspace channel. Then, the influence of this channel component is removed from the total beamspace channel estimation problem. After the supports of all channel components have been detected, the nonzero elements of the sparse beamspace channel can be estimated with low pilot overhead. Simulation results show that the proposed SD-based channel estimation outperforms conventional schemes and enjoys satisfying accuracy, even in the low SNR region.
Year
DOI
Venue
2016
10.1109/ICCChina.2016.7636854
2016 IEEE/CIC International Conference on Communications in China (ICCC)
Keywords
DocType
Volume
millimeter-wave massive MIMO system,lens antenna array,beam selection,RF chains,mmWave massive multiple-input multiple-output system,support detection-based channel estimation scheme,SD-based channel estimation scheme,total beamspace channel estimation problem,sparse channel component,low pilot overhead estimation
Conference
abs/1607.05130
ISBN
Citations 
PageRank 
978-1-5090-2144-4
12
0.66
References 
Authors
29
5
Name
Order
Citations
PageRank
Linglong Dai12713135.60
Xinyu Gao238717.68
Shuangfeng Han387534.37
Chih-Lin I42167211.25
Xiaodong Wang53958310.41