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Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks

Yifei Wei1, *, Yu Gong1, Qiao Li1, Mei Song1, *, Xiaojun Wang2

1 Beijing Key Laboratory of Work Safety Intelligent Monitoring, Beijing University of Posts and Telecommunications, Beijing, 100867, China.
2 School of Electronic Engineering, Dublin City University, Dublin, 9, Ireland.

* Corresponding Authors: Yifei Wei. Email: ; Mei Song. Email: .

Computers, Materials & Continua 2020, 64(1), 501-514.


In this paper, maximizing energy efficiency (EE) through radio resource allocation for renewable energy powered heterogeneous cellular networks (HetNet) with energy sharing, is investigated. Our goal is to maximize the network EE, conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources. We define the objective function as a sum weighted EE of all links in the HetNet. We formulate the resource allocation problem in terms of subcarrier assignment, power allocation and energy sharing, as a mixed combinatorial and non-convex optimization problem. We propose an energy efficient resource allocation scheme, including a centralized resource allocation algorithm for iterative subcarrier allocation and power allocation in which the power allocation problem is solved by analytically solving the Karush-Kuhn-Tucker (KKT) conditions of the problem and a water-filling problem thereafter and a low-complexity distributed resource allocation algorithm based on reinforcement learning (RL). Our numerical results show that both centralized and distributed algorithms converge with a few times of iterations. The numerical results also show that our proposed centralized and distributed resource allocation algorithms outperform the existing reference algorithms in terms of the network EE.


Cite This Article

Y. Wei, Y. Gong, Q. Li, M. Song and X. Wang, "Energy efficient resource allocation approach for renewable energy powered heterogeneous cellular networks," Computers, Materials & Continua, vol. 64, no.1, pp. 501–514, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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