Energy-Efficient User Association Strategy for Hyperdense Heterogeneous Networking in the Fifth Generation Systems
Author(s) -
Lei Li,
Hao Jin,
Zhipeng Yan,
Changqing Yang,
Yong Wu
Publication year - 2015
Publication title -
international journal of antennas and propagation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.282
H-Index - 37
eISSN - 1687-5877
pISSN - 1687-5869
DOI - 10.1155/2015/686783
Subject(s) - mathematical optimization , metric (unit) , computer science , maximization , minification , node (physics) , joule (programming language) , function (biology) , power (physics) , throughput , focus (optics) , optimization problem , association (psychology) , transformation (genetics) , efficient energy use , engineering , mathematics , wireless , electrical engineering , telecommunications , philosophy , operations management , structural engineering , optics , biology , epistemology , quantum mechanics , evolutionary biology , physics , chemistry , biochemistry , gene
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
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