An energy-efficient joint antenna and user selection algorithm for multi-user massive MIMO downlink

2018 ◽  
Vol 12 (3) ◽  
pp. 255-260 ◽  
Author(s):  
Maryam Olyaee ◽  
Mohsen Eslami ◽  
Javad Haghighat
Keyword(s):  
Energy Efficient ◽  
Massive Mimo ◽  
User Selection ◽  
Selection Algorithm ◽  
Mimo Downlink

Combined User and Antenna Selection in Massive MIMO Using Precoding Technique

2019 ◽  
Vol 9 (2) ◽  
pp. 214-223 ◽  
Author(s):  
Tasher Ali Sheikh ◽  
Joyatri Bora ◽  
Md. Anwar Hussain
Keyword(s):  
Massive Mimo ◽  
Antenna Selection ◽  
Orthogonality Condition ◽  
Channel Gain ◽  
User Selection ◽  
Selection Algorithm

Background and Objectives: We propose here joint semi-orthogonal user selection and antenna selection algorithm based on precoding scheme. Methods: The focus of this proposed algorithm is to increase the system sumrate and decrease the complexity. We select and schedule users from a large number of users based on semi-orthogonality condition among them. Here, we select only the maximum channel gain antennas to maximize the system sumrate. Subsequently, the user selection and antenna selection have been scheduled in an adequate manner in order to obtain maximum system sumrate. We calculate the system sumrate for two scenarios: firstly, by considering the interference and secondly without considering the interference. We achieve maximum system sumrate at MMSE and lowest at without precoding while considering the interference. However, when we do not consider the interference we obtain lowest sumrate at MMSE and maximum at without precoding. Results and Conclusion: Here, we apply the precoding scheme to increase the system sumrate and we obtain approximately 20% to 35% higher system sumrate compared to without precoding, when interference is considered. Thus, we achieve higher sumrate in our proposed algorithms compared to other existing work.

scholarly journals Effective user selection algorithm for quantized precoding in massive MIMO

2015 ◽  
Vol 2 (4) ◽  
pp. e3 ◽  
Author(s):  
Nayan fang ◽  
Jie Zeng ◽  
Xin Su ◽  
Yujun Kuang
Keyword(s):  
Massive Mimo ◽  
User Selection ◽  
Selection Algorithm

Two-Step User Selection Algorithm in Multi-User Massive MIMO with Hybrid Beamforming for 5G Evolution

2021 ◽  
Author(s):  
Nobuhide Nonaka ◽  
Satoshi Suyama ◽  
Tatsuki Okuyama ◽  
Kazushi Muraoka ◽  
Yukihiko Okumura
Keyword(s):  
Massive Mimo ◽  
User Selection ◽  
Selection Algorithm ◽  
Hybrid Beamforming

Energy-efficient beamforming for two-tier massive MIMO downlink

2015 ◽  
Vol 12 (10) ◽  
pp. 64-75 ◽  
Author(s):  
Guozhen Xu ◽  
An Liu ◽  
Wei Jiang ◽  
Haige Xiang ◽  
Wu Luo
Keyword(s):  
Energy Efficient ◽  
Massive Mimo ◽  
Mimo Downlink

scholarly journals Sum Rate Analysis for Massive MIMO Downlink With MRC Beamforming and User Selection

2021 ◽  
Vol 2113 (1) ◽  
pp. 012025
Author(s):  
Yiyang Wu ◽  
Chang Chang ◽  
Fei Xie ◽  
Dacheng Ju ◽  
Yilun Pan
Keyword(s):  
Channel Model ◽  
Base Station ◽  
Transmission Model ◽  
User Selection ◽  
Selection Algorithm ◽  
User Scheduling ◽  
Downlink System ◽  
Sum Rate ◽  
Simulation Results ◽  
Mimo Downlink

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.

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