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International Journal of Antennas and Propagation
Volume 2015, Article ID 686783, 9 pages
Research Article

Energy-Efficient User Association Strategy for Hyperdense Heterogeneous Networking in the Fifth Generation Systems

1The Key Laboratory of Universal Wireless Communications for Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
2Huawei Technologies Co., Ltd., Beijing 100085, China

Received 18 April 2015; Accepted 3 June 2015

Academic Editor: Lei Yang

Copyright © 2015 Lei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Redesigning user association strategies to improve energy efficiency (EE) has been viewed as one of the promising shifting paradigms for the fifth generation (5G) cellular networks. In this paper, we investigate how to optimize users’ association to enhance EE for hyper dense heterogeneous networking in the 5G cellular networks, where the low-power node (LPN) much outnumbers the high-power node (HPN). To characterize that densely deployed LPNs would undertake a majority of high-rate services, while HPNs mainly support coverage; the EE metric is defined as average weighted EE of access nodes with the unit of bit per joule. Then, the EE optimization objective function is formulated and proved to be nonconvex. Two mathematical transformation techniques are presented to solve the nonconvex problem. In the first case, the original problem is reformulated as an equivalent problem involving the maximization of a biconcave function. In the second case, it is equivalent to a concave minimization problem. We focus on the solution of the biconcave framework, and, by exploiting the biconcave structure, a novel iterative algorithm based on dual theory is proposed, where a partially optimal solution can be achieved. Simulation results have verified the effectiveness of the proposed algorithm.