@Article{cmc.2020.010048,
AUTHOR = {Yifei Wei, Yu Gong, Qiao Li, Mei Song, Xiaojun Wang},
TITLE = {Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {501--514},
URL = {http://www.techscience.com/cmc/v64n1/39154},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2020.010048}
}