DocumentCode
2786625
Title
A Compressed Analog Feedback Strategy for Spatially Correlated Massive MIMO Systems
Author
Lee, Junho ; Lee, Seung-Hwan
Author_Institution
Mobile Commun. & Digital Broadcasting Eng., Univ. of Sci. & Technol. (UST), Daejeon, South Korea
fYear
2012
fDate
3-6 Sept. 2012
Firstpage
1
Lastpage
6
Abstract
In multiuser MIMO systems, the amount of the required feedback increases with the number of transmit antennas to improve the sum rate performance. This feedback overhead is critical when it comes to massive multiple antenna wireless systems. Therefore, we propose a compressed analog feedback scheme. The proposed compressed analog feedback strategy exploits natural correlation characteristics among large-scale multiple antennas in order to reduce feedback overhead. By using the sparse approximation and low dimensional random projection at each mobile station (MS), and convex relaxation based feedback decompression at a base station (BS), the proposed scheme outperforms the uncompressed analog feedback which wastes long time duration and energy, in the consideration of the feedback delay, requiring lower feedback overhead from each MS.
Keywords
MIMO systems; antenna arrays; approximation theory; broadcast channels; convex programming; feedback; multiuser channels; precoding; transmitting antennas; wireless channels; base station; compressed analog feedback strategy; convex relaxation based feedback decompression; feedback delay; feedback overhead; low dimensional random projection; mobile station; multiple antenna wireless systems; multiuser MIMO systems; sparse approximation; spatially correlated massive MIMO systems; sum rate performance; transmit antennas; Antenna feeds; Approximation methods; Channel estimation; Correlation; MIMO; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Vehicular Technology Conference (VTC Fall), 2012 IEEE
Conference_Location
Quebec City, QC
ISSN
1090-3038
Print_ISBN
978-1-4673-1880-8
Electronic_ISBN
1090-3038
Type
conf
DOI
10.1109/VTCFall.2012.6399253
Filename
6399253
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