上海交通大学学报(英文版) ›› 2017, Vol. 22 ›› Issue (6): 742-751.doi: 10.1007/s12204-017-1895-4
XU Yuemei1* (徐月梅), WANG Zihou2 (王子厚), LI Yang3 (李杨), CAI Lianqiao1 (蔡连侨)
出版日期:
2017-12-01
发布日期:
2017-12-03
通讯作者:
XU Yuemei (徐月梅)
E-mail:xuyuemei@bfsu.edu.cn
XU Yuemei1* (徐月梅), WANG Zihou2 (王子厚), LI Yang3 (李杨), CAI Lianqiao1 (蔡连侨)
Online:
2017-12-01
Published:
2017-12-03
Contact:
XU Yuemei (徐月梅)
E-mail:xuyuemei@bfsu.edu.cn
摘要: To reduce network access latency, network traffic volume and server load, caching capacity has been proposed as a component of evolved Node B (eNodeB) in the ratio access network (RAN). These eNodeB caches reduce transport energy consumption but lead to additional energy cost by equipping every eNodeB with caching capacity. Existing researches focus on how to minimize total energy consumption, but often ignore the trade-off between energy efficiency and end user quality of experience, which may lead to undesired network performance degradation. In this paper, for the first time, we build an energy model to formulate the problem of minimizing total energy consumption at eNodeB caches by taking a trade-off between energy efficiency and end user quality of experience. Through coordinating all the eNodeB caches in the same RAN, the proposed model can take a good balance between caching energy and transport energy consumption while also guarantee end user quality of experience. The experimental results demonstrate the effectiveness of the proposed model. Compared with the existing works, our proposal significantly reduces the energy consumption by approximately 17% while keeps superior end user quality of experience performance.
中图分类号:
XU Yuemei1* (徐月梅), WANG Zihou2 (王子厚), LI Yang3 (李杨), CAI Lianqiao1 (蔡连侨). Trade-off in Optimizing Energy Consumption and End User Quality of Experience in Radio Access Network[J]. 上海交通大学学报(英文版), 2017, 22(6): 742-751.
XU Yuemei1* (徐月梅), WANG Zihou2 (王子厚), LI Yang3 (李杨), CAI Lianqiao1 (蔡连侨). Trade-off in Optimizing Energy Consumption and End User Quality of Experience in Radio Access Network[J]. Journal of Shanghai Jiao Tong University (Science), 2017, 22(6): 742-751.
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