A Low Complexity Antenna Selection Algorithm for Energy Efficiency in Massive MIMO Systems

2015 ◽  
Author(s):  
Tzu-Hao Tai ◽  
Wei-Ho Chung ◽  
Ta-Sung Lee
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Low Complexity ◽  
Selection Algorithm

scholarly journals Employing antenna selection to improve energy efficiency in massive MIMO systems

2017 ◽  
Vol 28 (12) ◽  
pp. e3212 ◽  
Author(s):  
Masoud Arash ◽  
Ehsan Yazdian ◽  
Mohammad Sadegh Fazel ◽  
Glauber Brante ◽  
Muhammad Ali Imran
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Improve Energy Efficiency

scholarly journals Energy Efficiency-Oriented Resource Allocation for Massive MIMO Systems with Separated Channel Estimation and Feedback

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 582
Author(s):  
Feng Hu ◽  
Kaiyue Wang ◽  
Shufeng Li ◽  
Libiao Jin
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Spatial Separation ◽  
Low Complexity ◽  
Maximization Problem

This paper proposes a dynamic resource allocation scheme to maximize the energy efficiency (EE) for Massive MIMO Systems. The imperfect channel estimation (CE) and feedback are explicitly considered in the EE maximization problem, which aim to optimize the power allocation, the antenna subset selection for transmission, and the pilot assignment. Assuming CE error to be bounded for the complex-constrained Cramer–Rao Bound (CRB), theoretical results show that the lower bound is directly proportional to its number of unconstrained parameters. Utilizing this perspective, a separated and bi-directional estimation is developed to achieve both low CRB and low complexity by exploiting channel and noise spatial separation. Exploiting global optimization procedure, the optimal resource allocation can be transformed into a standard convex optimization problem. This allows us to derive an efficient iterative algorithm for obtaining the optimal solution. Numerical results are provided to demonstrate that the outperformance of the proposed algorithms are superior to existing schemes.

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