Compressed Sensing-Based Sparsity Adaptive Doubly Selective Channel Estimation for Massive MIMO Systems
Joint Authors
Zhang, Wenjie
Li, Hui
Kong, Weisi
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-20
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
By exploiting the sparsity of the channel in the delay and angle domains, compressed sensing (CS) algorithms can be used for channel estimation of massive multiple-input multiple-output (MIMO) systems to reduce pilot overhead.
Due to the Doppler frequency shift, however, the intercarrier interference (ICI) and the rapid change of the channel state result in the poor estimation effect of doubly selective (DS) channel.
In this paper, we propose the block sparsity adaptive matching pursuit (B-SAMP) algorithm to solve this problem.
Firstly, the complex exponential basis expansion model (CE-BEM) is used to convert numerous channel tap coefficients into BEM parameter vectors and then the sparsity adaptive channel estimation scheme based on compressed sensing is proposed.
Specifically, the ICI-free model is obtained by using the proposed equally placed pilot group scheme, and the B-SAMP algorithm is proposed by using the spatio-temporal common sparsity of the channel to complete the estimation of DS channel.
Finally, a linear smoothing method is used to reduce the error caused by CE-BEM, thereby further improving the accuracy of the estimation.
The simulation results show that the proposed method not only improves the estimation accuracy compared with the existing scheme but also requires fewer pilots.
American Psychological Association (APA)
Kong, Weisi& Li, Hui& Zhang, Wenjie. 2019. Compressed Sensing-Based Sparsity Adaptive Doubly Selective Channel Estimation for Massive MIMO Systems. Wireless Communications and Mobile Computing،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1212203
Modern Language Association (MLA)
Kong, Weisi…[et al.]. Compressed Sensing-Based Sparsity Adaptive Doubly Selective Channel Estimation for Massive MIMO Systems. Wireless Communications and Mobile Computing No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1212203
American Medical Association (AMA)
Kong, Weisi& Li, Hui& Zhang, Wenjie. Compressed Sensing-Based Sparsity Adaptive Doubly Selective Channel Estimation for Massive MIMO Systems. Wireless Communications and Mobile Computing. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1212203
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1212203