Title
Energy Efficient Power Allocation for Downlink NOMA Heterogeneous Networks With Imperfect CSI.
Abstract
Non-orthogonal multiple access (NOMA) is a promising emerging technology that can significantly improve the utilization of spectrum and system capacity in heterogeneous wireless networks. Power allocation plays a key role in the successful deployment of NOMA. In the most prior power allocation schemes, the perfect channel state information (CSI) is assumed to be known which is difficult to obtain in a realistic environment. In this paper, we propose a power allocation scheme to maximize energy efficiency for downlink NOMA heterogeneous networks based on imperfect CSI. The system model for imperfect CSI is built, in which the optimization problem is a probabilistic non-convex problem with the constraint of outage probability. To solve the optimization problem, the probabilistic problem is transformed to a non-probabilistic problem through relaxation. The power allocation for each small cell is achieved via bisection search algorithm based on gradient value, where the trend of energy efficiency as a function of the power of the small cell is analyzed. The sequential convex programming is adapted to transform the non-convex problem to a convex problem. The closed-form solutions of power allocation factors are derived by the Lagrangian multiplier method. The simulation results show the superiority and efficiency of the proposed scheme compared with the traditional algorithms.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2906780
IEEE ACCESS
Keywords
Field
DocType
Heterogeneous networks,non-orthogonal multiple access,power allocation,imperfect channel state information,energy efficiency
Wireless network,Search algorithm,Efficient energy use,Computer science,Probabilistic logic,Heterogeneous network,Optimization problem,Convex optimization,Distributed computing,Channel state information
Journal
Volume
ISSN
Citations 
7
2169-3536
1
PageRank 
References 
Authors
0.35
0
5
Name
Order
Citations
PageRank
Xin Song11515.82
Li Dong212.04
Jingpu Wang311.02
Lei Qin412.37
Xiuwei Han510.68