Power Allocation Scheme for Energy Efficiency Maximization in Distributed Antenna System with Discrete-Rate Adaptive Modulation

2019 ◽  
Vol E102.B (8) ◽  
pp. 1705-1714
Author(s):  
Xiangbin YU ◽  
Xi WANG ◽  
Tao TENG ◽  
Qiyishu LI ◽  
Fei WANG
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Adaptive Modulation ◽  
Antenna System ◽  
Allocation Scheme ◽  
Distributed Antenna System ◽  
Distributed Antenna ◽  
Rate Adaptive

An iterative scheme for optimal and energy effective power allocation using multi-scale resource GOA-EM in distributed antenna system

2021 ◽  
pp. 1-12
Author(s):  
K. Sakthidasan Sankaran ◽  
Xiao-Zhi Gao
Keyword(s):  
Computational Complexity ◽  
Power Allocation ◽  
Clustering Algorithms ◽  
Antenna System ◽  
Optimization Approach ◽  
Distributed Antenna System ◽  
Multi Scale ◽  
Clustering Model ◽  
Distributed Antenna ◽  
Grasshopper Optimization Algorithm

Nowadays, numerous algorithms on power allocation have been proposed for maximizing the EE (Energy efficiency) and SE (Spectral efficiency) in the Distributed Antenna System (DAS). Moreover, the conservative techniques employed for power allocation seem to be problematic, due to their high computational complexity. The main objective of this paper focuses on optimizing the power allocation in order to enhance the EE and SE along with the improved antenna capacity using an effective optimization approach with the clustering model. To obtain the optimized power allocation and antenna capacity, Multi-scale resource Grasshopper Optimization Algorithm (Multi-scale resource GOA) scheme is proposed and employed. Furthermore, clustering is developed based on the Discriminative cluster-based Expectation maximization (DC-EM) clustering algorithms, which also helps to reduce the interference rate and computational complexity. The performance analysis is made under various scenarios and circumstances. The proposed system (DAS with GOA-EM) is assessed and compared with the existing approaches in terms of both the EE and SE, which demonstrates that its superiority.

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