Abstract
Massive multiple-input-multiple-output (MIMO) is a promising technique for providing unprecedented spectral efficiency. However, it has been well recognized that the excessive training overhead required for obtaining the channel side information is a major handicap in frequency-division duplexing (FDD) massive MIMO. Several attempts have been made to reduce this training overhead by exploiting the sparsity structures of massive MIMO channels. So far, however, there has been little discussion about how to exploit the partial support information of these channels to achieve further overhead reductions. Such information, which is a set of indices of the significant elements of a channel vector, can be acquired in advance and hence is an important option to explore. In this paper, we examine the impact on the required training overhead when this information is applied within a weighted l1 minimization framework, and analytically show that a sharp estimate of the reduced overhead size can be successfully obtained. Furthermore, we examine how the accuracy of the partial support information impacts the achievable overhead reduction. Numerical results for a wide range of sparsity and partial support information reliability levels are presented to quantify our findings and main conclusions.
| Original language | English |
|---|---|
| Article number | 7420744 |
| Pages (from-to) | 4145-4156 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Wireless Communications |
| Volume | 15 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Jun 2016 |
| Externally published | Yes |
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Keywords
- channel estimation
- compressed sensing
- FDD
- Massive MIMO
- partial support information
- phase transition
- pilot contamination
- weighted l minimization
ASJC Scopus subject areas
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
Compressed CSI Acquisition in FDD Massive MIMO : How Much Training is Needed? / Shen, Juei Chin; Zhang, Jun; Alsusa, Emad; Letaief, Khaled.
In: IEEE Transactions on Wireless Communications, Vol. 15, No. 6, 7420744, 01.06.2016, p. 4145-4156.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Compressed CSI Acquisition in FDD Massive MIMO
T2 - How Much Training is Needed?
AU - Shen, Juei Chin
AU - Zhang, Jun
AU - Alsusa, Emad
AU - Letaief, Khaled
PY - 2016/6/1
Y1 - 2016/6/1
N2 - Massive multiple-input-multiple-output (MIMO) is a promising technique for providing unprecedented spectral efficiency. However, it has been well recognized that the excessive training overhead required for obtaining the channel side information is a major handicap in frequency-division duplexing (FDD) massive MIMO. Several attempts have been made to reduce this training overhead by exploiting the sparsity structures of massive MIMO channels. So far, however, there has been little discussion about how to exploit the partial support information of these channels to achieve further overhead reductions. Such information, which is a set of indices of the significant elements of a channel vector, can be acquired in advance and hence is an important option to explore. In this paper, we examine the impact on the required training overhead when this information is applied within a weighted l1 minimization framework, and analytically show that a sharp estimate of the reduced overhead size can be successfully obtained. Furthermore, we examine how the accuracy of the partial support information impacts the achievable overhead reduction. Numerical results for a wide range of sparsity and partial support information reliability levels are presented to quantify our findings and main conclusions.
AB - Massive multiple-input-multiple-output (MIMO) is a promising technique for providing unprecedented spectral efficiency. However, it has been well recognized that the excessive training overhead required for obtaining the channel side information is a major handicap in frequency-division duplexing (FDD) massive MIMO. Several attempts have been made to reduce this training overhead by exploiting the sparsity structures of massive MIMO channels. So far, however, there has been little discussion about how to exploit the partial support information of these channels to achieve further overhead reductions. Such information, which is a set of indices of the significant elements of a channel vector, can be acquired in advance and hence is an important option to explore. In this paper, we examine the impact on the required training overhead when this information is applied within a weighted l1 minimization framework, and analytically show that a sharp estimate of the reduced overhead size can be successfully obtained. Furthermore, we examine how the accuracy of the partial support information impacts the achievable overhead reduction. Numerical results for a wide range of sparsity and partial support information reliability levels are presented to quantify our findings and main conclusions.
KW - channel estimation
KW - compressed sensing
KW - FDD
KW - Massive MIMO
KW - partial support information
KW - phase transition
KW - pilot contamination
KW - weighted l minimization
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UR - http://www.scopus.com/inward/citedby.url?scp=84976334291&partnerID=8YFLogxK
U2 - 10.1109/TWC.2016.2535310
DO - 10.1109/TWC.2016.2535310
M3 - Article
VL - 15
SP - 4145
EP - 4156
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
SN - 1536-1276
IS - 6
M1 - 7420744
ER -