Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90244
Title: Spectral and energy efficiency analysis for SLNR precoding in massive MIMO systems with imperfect CSI
Authors: Tran, Tuong Xuan
Teh, Kah Chan
Keywords: Massive MIMO System
SLNR Precoding Scheme
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Tran, T. X., & Teh, K. C. (2018). Spectral and energy efficiency analysis for SLNR precoding in massive MIMO systems with imperfect CSI. IEEE Transactions on Wireless Communications, 17(6), 4017-4027. doi:10.1109/TWC.2018.2819184
Series/Report no.: IEEE Transactions on Wireless Communications
Abstract: We derive tractable bound expressions on achievable spectral efficiency for a multiple-input multiple-output (MIMO) system with a signal-to-leakage-plus-noise ratio precoding scheme (SLNR-PS) under the condition of imperfect channel state information. These bounds are tight and approach exact values when the number of base station (BS) antennas is large. A problem of energy efficiency (EE) maximization is investigated by using a practical power consumption model. The effects of the system parameters and quality of channel estimation, including the number of BS antennas, transmit power, and training length on the performance metrics, are explicitly analyzed. Following that, an alternating optimization algorithm is employed to obtain the optimum EE. It has been shown that the proposed SLNR-PS performs better than the matched-filtering and zero-forcing schemes and a deployment of massive MIMO with the optimal transmit power and training length can achieve high EE.
URI: https://hdl.handle.net/10356/90244
http://hdl.handle.net/10220/48472
ISSN: 1536-1276
DOI: 10.1109/TWC.2018.2819184
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TWC.2018.2819184
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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