An Energy-Saving Algorithm With Joint User Association, Clustering, and On/Off Strategies in Dense Heterogeneous Networks
| Autor: | Qianbin Chen, Lun Tang, Weili Wang, Yang Wang |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
General Computer Science Computer science Distributed computing 02 engineering and technology 0203 mechanical engineering 0202 electrical engineering electronic engineering information engineering energy-saving General Materials Science Cluster analysis user association Integer programming General Engineering 020206 networking & telecommunications 020302 automobile design & engineering Energy consumption Load balancing (computing) Dense HetNets Data stream clustering Algorithm design lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:TK1-9971 Algorithm switching on/off Heterogeneous network clustering Efficient energy use |
| Zdroj: | IEEE Access, Vol 5, Pp 12988-13000 (2017) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2017.2723680 |
| Popis: | Green networks, which is put forward for the environmental and economic benefits, has received much attention recently because of the vast energy cost in wireless cellular networks. To reduce the energy consumption and simultaneously guarantee the service performance of the dense heterogeneous networks, this paper proposes an energy-saving algorithm with joint user association, clustering, and ON/OFF strategies. First, for the user association subproblem, an optimal association policy, which is related to load balancing and energy efficiency, is designed for the new arriving user equipment (UE) and re-associated UE. Second, based on the locations and load of the base stations (BSs), the clustering subproblem is modeled as an integer linear programming, and the near-optimal clustering results are obtained by using the semi-definite programming. Finally, an intra-cluster ON/OFF strategy for the switching ON/OFF subproblem is designed in which the chosen BSs to be switched OFF are decided by their load effect to other BSs in the clusters. The simulation results demonstrate that, compared with the traditional approaches, the clustering-based energy-saving algorithm can reduce the average network cost by 25.2%–66.7% for different network load conditions. |
| Databáze: | OpenAIRE |
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