Experimental Evaluation of Low Complexity User Selection Schemes for Multi-User MIMO Systems

2015 ◽  
Vol E98.A (2) ◽  
pp. 606-610 ◽  
Author(s):  
Satoshi NISHINO ◽  
Hidekazu MURATA
Keyword(s):  
Experimental Evaluation ◽  
Mimo Systems ◽  
Low Complexity ◽  
User Selection

Low complexity user selection algorithms for multiuser MIMO systems with block diagonalization

2006 ◽  
Vol 54 (9) ◽  
pp. 3658-3663 ◽  
Author(s):  
Zukang Shen ◽  
Runhua Chen ◽  
J.G. Andrews ◽  
R.W. Heath ◽  
B.L. Evans
Keyword(s):  
Mimo Systems ◽  
Low Complexity ◽  
User Selection ◽  
Block Diagonalization ◽  
Multiuser Mimo ◽  
Selection Algorithms ◽  
Multiuser Mimo Systems

scholarly journals Low-Complexity Downlink User Selection for Massive MIMO Systems

2017 ◽  
Vol 11 (2) ◽  
pp. 1072-1083 ◽  
Author(s):  
Haijing Liu ◽  
Hui Gao ◽  
Shaoshi Yang ◽  
Tiejun Lv
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity ◽  
User Selection ◽  
Selection For

scholarly journals Low‐complexity user selection method in 3D MU‐MIMO systems

2018 ◽  
Vol 54 (20) ◽  
pp. 1176-1178 ◽  
Author(s):  
Mengshi Hu ◽  
Yongyu Chang ◽  
Yi Xiong ◽  
Tianyi Zeng
Keyword(s):  
Mimo Systems ◽  
Low Complexity ◽  
Selection Method ◽  
User Selection

Vector Quantization Multi-User Scheduling in Broadcast Channel

2012 ◽  
Vol 461 ◽  
pp. 53-57
Author(s):  
Juan Hang ◽  
Feng She
Keyword(s):  
Vector Quantization ◽  
Mimo Systems ◽  
Scheduling Algorithm ◽  
Low Complexity ◽  
Mimo Channel ◽  
User Selection ◽  
User Scheduling ◽  
Channel State ◽  
Lower Complexity ◽  
Sum Rate

This paper deals with the research on user scheduling algorithm in downlink MIMO systems with limited feedback of channel state information (CSI). In this paper, we use vector quantization to simplify the procedure of user selection. By quantizing MIMO channel into several un-overlapped channel regions, we can get semi-orthogonal region sets with predefined threshold. Then, based on the semi-orthogonal region sets, we present a low complexity user scheduling algorithm. Simulation results show that the achieved sum rate by the presented algorithm can achieve nearly optimal sum rate while with much lower complexity than that of the existing algorithms

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