Deep learning based user scheduling for massive MIMO downlink system

2021 ◽  
Vol 64 (8) ◽  
Author(s):  
Xiaoxiang Yu ◽  
Jiajia Guo ◽  
Xiao Li ◽  
Shi Jin
2020 ◽  
Vol 96 ◽  
pp. 203-211
Author(s):  
J. Roscia Jeya Shiney ◽  
Ganesan Indumathi ◽  
A. Allwyn Clarence Asis

2017 ◽  
Vol 16 (4) ◽  
pp. 2181-2194 ◽  
Author(s):  
Cheng Zhang ◽  
Yongming Huang ◽  
Yindi Jing ◽  
Shi Jin ◽  
Luxi Yang

2021 ◽  
Vol 2113 (1) ◽  
pp. 012025
Author(s):  
Yiyang Wu ◽  
Chang Chang ◽  
Fei Xie ◽  
Dacheng Ju ◽  
Yilun Pan

Abstract Average allocation of data rate to each user is inefficient since the resource a base station can allocate is limited. Thus, user selection and user scheduling need to be applied into multi-user massive multiple-input multiple-output (MIMO) downlink system. In this paper, we mainly focus on the methods of user selection. First, we establish a downlink system model including transmission model and channel model. Then, two user-rate based user selection algorithms via the signal-to-interference-plus-noise-ratio (SINR) are proposed, where the SINR is generated by MRC beamforming. Finally, simulation results are provided to compare the performance of two proposed algorithms and their fairness towards selected users. In the simulation results, location-based selection algorithm and random selection algorithm are jointly compared. The second proposed algorithm possesses the highest total sum-rate and is the optimal algorithms among the four algorithms.


2017 ◽  
Vol 67 (3) ◽  
pp. 387-399 ◽  
Author(s):  
M. Hasbullah Mazlan ◽  
Mehran Behjati ◽  
Rosdiadee Nordin ◽  
Mahamod Ismail

2020 ◽  
Vol 38 (9) ◽  
pp. 1980-1993
Author(s):  
Yu Han ◽  
Mengyuan Li ◽  
Shi Jin ◽  
Chao-Kai Wen ◽  
Xiaoli Ma

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