TY - JOUR
T1 - Proportional Fair Energy-Efficient Resource Allocation in Energy-Harvesting-Based Wireless Networks
AU - Chung, Byung Chang
AU - Lee, Kisong
AU - Cho, Dong Ho
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - In energy-limited networks, battery-powered nodes suffer from energy famine, which can reduce network lifetime and affect the robustness of networks. To alleviate an energy problem, it is possible to harvest energy from ambient radio frequency signals. In this paper, we consider a proportional fair energy efficiency, which jointly considers energy efficiency and fairness in energy-harvesting-based wireless networks. We formulate a nonconvex optimization problem for solving subchannel and power allocation in order to maximize proportional fair energy efficiency. Using nonlinear fractional programming, we transform the optimization problem into a tractable convex problem. We also derive the solution of the transformed problem and propose a resource allocation algorithm using an iterative method. In addition, we prove the convergence of the proposed algorithm in view of a suboptimal point. Through intensive simulations, we compare the performance of our proposed algorithm with those of conventional algorithms. It is shown that the proposed algorithm improves fairness considerably while maintaining energy efficiency, compared with conventional algorithms.
AB - In energy-limited networks, battery-powered nodes suffer from energy famine, which can reduce network lifetime and affect the robustness of networks. To alleviate an energy problem, it is possible to harvest energy from ambient radio frequency signals. In this paper, we consider a proportional fair energy efficiency, which jointly considers energy efficiency and fairness in energy-harvesting-based wireless networks. We formulate a nonconvex optimization problem for solving subchannel and power allocation in order to maximize proportional fair energy efficiency. Using nonlinear fractional programming, we transform the optimization problem into a tractable convex problem. We also derive the solution of the transformed problem and propose a resource allocation algorithm using an iterative method. In addition, we prove the convergence of the proposed algorithm in view of a suboptimal point. Through intensive simulations, we compare the performance of our proposed algorithm with those of conventional algorithms. It is shown that the proposed algorithm improves fairness considerably while maintaining energy efficiency, compared with conventional algorithms.
KW - energy efficiency
KW - Energy harvesting (EH)
KW - optimization
KW - proportional fairness
KW - resource allocation
KW - simultaneous wireless information and power transfer
UR - http://www.scopus.com/inward/record.url?scp=84988646826&partnerID=8YFLogxK
U2 - 10.1109/JSYST.2016.2606238
DO - 10.1109/JSYST.2016.2606238
M3 - Article
AN - SCOPUS:84988646826
SN - 1932-8184
VL - 12
SP - 2106
EP - 2116
JO - IEEE Systems Journal
JF - IEEE Systems Journal
IS - 3
M1 - 7572874
ER -