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  4. A ZF-Based Precoding Scheme with Phase Noise Suppression for Massive MIMO Downlink Systems
 
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A ZF-Based Precoding Scheme with Phase Noise Suppression for Massive MIMO Downlink Systems

Journal
IEEE Transactions on Vehicular Technology
Journal Volume
67
Journal Issue
2
Pages
3071-3084
Date Issued
2018
Author(s)
Y.-F. Wang
Ju-Hong Lee
JU-HONG LEE  
李枝宏
DOI
10.1109/tvt.2017.2749598
URI
https://scholars.lib.ntu.edu.tw/handle/123456789/427993
Abstract
In wireless communications, massive multiple-input multiple-output (MIMO) systems in which the base station (BS) is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO is an attractive technology for next-generation wireless communication systems and for green communications. However, phase noise (PN) introduced by the impairment of oscillators can cause a severe performance loss in wireless communication systems. Solving the PN problem for massive MIMO systems in both the downlink and uplink is challenging as it is a multivariate joint PN estimation and data detection problem. The optimal solution is difficult to derive and may lead to high complexity. In this paper, we propose a zero-forcing (ZF) based precoding scheme with PN suppression to solve the PN problem for massive MIMO downlink systems. By using the ideal PN free output signal-to-noise ratio (SNR) of the ZF precoding as a performance upper bound, analytical and numerical results show that when the required SNR is satisfied, the output SNR of the proposed scheme can approach the upper bound, except for very large PN variances. Moreover, the complexity of the proposed scheme is low on both the BS and the user sides, and the BS does not need any feedback information from the users. © 1967-2012 IEEE.
Subjects
Detection; downlink; estimation; massive MIMO; phase noise; precoding
SDGs

[SDGs]SDG7

Other Subjects
Channel estimation; Communication channels (information theory); Energy efficiency; Error detection; Estimation; MIMO systems; Mobile telecommunication systems; Phase noise; Problem solving; Signal processing; Wireless telecommunication systems; downlink; Massive multiple-input- multiple-output system (MIMO); Next generation wireless systems; Output signal-to-noise ratios; Phase noise suppression; Precoding; Wireless communication system; Wireless communications; Signal to noise ratio
Type
journal article

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