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Towards Large Intelligent Surface (LIS)-Based Communications

delete2020-10-01
delete19
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OA
AI
J
Jide Yuan *
H
Hien Quoc Ngo
M
Michail Matthaiou
DOI:10.1109/TCOMM.2020.3009115delete
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摘要

摘要

En 中文
The concept of large intelligent surface (LIS)-based communication has recently raised research attention, in which a LIS is regarded as an antenna array whose entire surface area can be used for radio signal transmission and reception. To provide a fundamental understanding of LIS-based communication, this paper studies the uplink (UL) performance of LIS-based communication with matched filtering. We first investigate the new properties introduced by LIS. In particular, the array gain, spatial resolution, and the capability of interference suppression are theoretically presented and characterized. Then, we study two possible LIS system layouts in terms of UL, i.e., centralized LIS (C-LIS) and distributed LIS (D-LIS). Our analysis showcases that a centralized system has strong capability of interference suppression; in fact, interference can nearly be eliminated if the surface area is sufficient large or the frequency band is sufficient high. For D-LIS, we propose a series of resource allocation algorithms, including user association scheme, orientation control, and power control, to extend the coverage area of a distributed system. Simulation results show that the proposed algorithms significantly improve the system performance, and even more importantly, we observe that D-LIS outperforms C-LIS in microwave bands, while C-LIS is superior to D-LIS in mmWave bands. These observations serve as useful guidelines for practical LIS deployments.
Keyword:
Antenna arrays
Wireless communication
Channel estimation
Antenna radiation patterns
Surface waves
Propagation losses
Layout
Achievable spectral efficiency (SE)
large intelligent surface (LIS)
orientation control
power control
user association
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期刊

IEEE Transactions on Communications 封面图
IEEE Transactions on Communications
IF:
8.3
论文数:
1.2W
被引数:
3.6W

机构

Q
Queen's University Belfast
学者数:
1.6W
论文数: 1.7W
被引数: 2.5W
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