NDLI: Compressed CSI acquisition in FDD massive MIMO with partial support information

Content Provider	IEEE Xplore Digital Library
Author	Juei-Chin Shen Jun Zhang Alsusa, E. Letaief, K.B.
Copyright Year	2015
Description	Author affiliation: Dept. of ECE, Hong Kong Univ. of Sci. & Technol., Hong Kong, China (Juei-Chin Shen; Jun Zhang; Letaief, K.B.) \|\| Sch. of EEE, Univ. of Manchester, Manchester, UK (Alsusa, E.)
Abstract	Massive MIMO is a promising technique to provide unprecedented spectral efficiency. However, it has been well recognized that huge training overhead for obtaining channel side information (CSI) is a major handicap in frequency-division duplexing (FDD) massive MIMO. Several attempts have been made to reduce this training overhead by exploiting sparse structures of massive MIMO channels. So far, however, there has been little discussion about how to utilize partial support information of sparse channels to achieve further overhead reduction. This support information, which is a set of indexes of significant elements of a channel vector, actually can be acquired in advance. In this paper, we examine the required training overhead when partial support information is applied within a weighted $ℓ_{1}$ minimization framework and analytically show that a sharp estimate of this overhead size can be successfully obtained. Furthermore, we demonstrate that the accuracy of partial support information plays an important role in determining how much reduction can be achieved. Numerical results shall verify the main conclusions.
Starting Page	1459
Ending Page	1464
File Size	581912
Page Count	6
File Format	PDF
e-ISBN	9781467364324
DOI	10.1109/ICC.2015.7248529
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-08
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	MIMO Training Minimization Accuracy Estimation Channel estimation Geometry
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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